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2025-08-31 06:26:39
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Gregniuki/xwin-finetuned-polish-news
|
Gregniuki
| 2023-09-28T23:11:58Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:TheBloke/Xwin-LM-7B-V0.1-GPTQ",
"base_model:finetune:TheBloke/Xwin-LM-7B-V0.1-GPTQ",
"license:llama2",
"region:us"
] | null | 2023-09-28T20:54:53Z |
---
license: llama2
base_model: TheBloke/Xwin-LM-7B-V0.1-GPTQ
tags:
- generated_from_trainer
model-index:
- name: xwin-finetuned-polish-news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xwin-finetuned-polish-news
This model is a fine-tuned version of [TheBloke/Xwin-LM-7B-V0.1-GPTQ](https://huggingface.co/TheBloke/Xwin-LM-7B-V0.1-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
TheBloke/NexusRaven-13B-GGUF
|
TheBloke
| 2023-09-28T23:09:11Z | 152 | 21 |
transformers
|
[
"transformers",
"gguf",
"llama",
"arxiv:2308.12950",
"base_model:Nexusflow/NexusRaven-13B",
"base_model:quantized:Nexusflow/NexusRaven-13B",
"license:llama2",
"region:us"
] | null | 2023-09-28T23:01:05Z |
---
base_model: Nexusflow/NexusRaven-13B
inference: false
license: llama2
model-index:
- name: NexusRaven-13B
results: []
model_creator: Nexusflow
model_name: Nexusraven 13B
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Nexusraven 13B - GGUF
- Model creator: [Nexusflow](https://huggingface.co/Nexusflow)
- Original model: [Nexusraven 13B](https://huggingface.co/Nexusflow/NexusRaven-13B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Nexusflow's Nexusraven 13B](https://huggingface.co/Nexusflow/NexusRaven-13B).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/NexusRaven-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/NexusRaven-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF)
* [Nexusflow's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Nexusflow/NexusRaven-13B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [nexusraven-13b.Q2_K.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [nexusraven-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [nexusraven-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [nexusraven-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [nexusraven-13b.Q4_0.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [nexusraven-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [nexusraven-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [nexusraven-13b.Q5_0.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [nexusraven-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [nexusraven-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [nexusraven-13b.Q6_K.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [nexusraven-13b.Q8_0.gguf](https://huggingface.co/TheBloke/NexusRaven-13B-GGUF/blob/main/nexusraven-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/NexusRaven-13B-GGUF and below it, a specific filename to download, such as: nexusraven-13b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/NexusRaven-13B-GGUF nexusraven-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/NexusRaven-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/NexusRaven-13B-GGUF nexusraven-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m nexusraven-13b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/NexusRaven-13B-GGUF", model_file="nexusraven-13b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Nexusflow's Nexusraven 13B
# NexusRaven-13B: Surpassing the state-of-the-art in open-source function calling LLMs.
<p align="center">
<a href="https://huggingface.co/Nexusflow" target="_blank">Nexusflow HF</a> - <a href="http://nexusflow.ai/blog" target="_blank">NexusRaven blog post</a> - <a href="https://huggingface.co/Nexusflow/NexusRaven-13B" target="_blank">NexusRaven-13B</a> - <a href="https://x.com/NexusflowX/status/1707470614012035561?s=20" target="_blank">NexusRaven-13B Twitter Thread</a> - <a href="https://github.com/nexusflowai/NexusRaven/" target="_blank">NexusRaven-13B Github</a> - <a href="https://huggingface.co/datasets/Nexusflow/NexusRaven_API_evaluation" target="_blank">NexusRaven API evaluation dataset</a>
</p>
<p align="center" width="100%">
<a><img src="NexusRaven.png" alt="NexusRaven" style="width: 40%; min-width: 300px; display: block; margin: auto;"></a>
</p>
Table of contents
- [NexusRaven-13B: Surpassing the state-of-the-art in open-source function calling LLMs.](#nexusraven-13b-surpassing-the-state-of-the-art-in-open-source-function-calling-llms)
- [Introducing NexusRaven-13B](#introducing-nexusraven-13b)
- [NexusRaven model usage](#nexusraven-model-usage)
- [Training procedure](#training-procedure)
- [Training hyperparameters](#training-hyperparameters)
- [Framework versions](#framework-versions)
- [Limitations](#limitations)
- [License](#license)
- [References](#references)
- [Citation](#citation)
- [Contact](#contact)
This model is a fine-tuned version of [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf).
## Introducing NexusRaven-13B
NexusRaven is an open-source and commercially viable function calling LLM that surpasses the state-of-the-art in function calling capabilities.
📊 Performance Highlights: With our demonstration retrieval system, NexusRaven-13B achieves a 95% success rate in using cybersecurity tools such as CVE/CPE Search and VirusTotal, while prompting GPT-4 achieves 64%. It has significantly lower cost and faster inference speed compared to GPT-4.
🔧 Generalization to the Unseen: NexusRaven-13B generalizes to tools never seen during model training, achieving a success rate comparable with GPT-3.5 in zero-shot setting, significantly outperforming all other open-source LLMs of similar sizes.
🔥 Commercially Permissive: The training of NexusRaven-13B does not involve any data generated by proprietary LLMs such as GPT-4. You have full control of the model when deployed in commercial applications.
<p align="center" width="100%">
<a><img src="Retrieval-augmented_Evaluation.png" alt="NexusRaven" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
<a><img src="Zero-shot_Evaluation.png" alt="NexusRaven" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
</p>
## NexusRaven model usage
NexusRaven accepts a list of python functions. These python functions can do anything (including sending GET/POST requests to external APIs!). The two requirements include the python function signature and the appropriate docstring to generate the function call.
NexusRaven is highly compatible with langchain. See [langchain_example.py](https://huggingface.co/Nexusflow/NexusRaven-13B/blob/main/langchain_example.py). An example without langchain can be found in [non_langchain_example.py](https://huggingface.co/Nexusflow/NexusRaven-13B/blob/main/non_langchain_example.py)
Please note that the model will reflect on the answer sometimes, so we highly recommend stopping the model generation at a stopping criteria of `["\nReflection:"]`, to avoid spending unnecessary tokens during inference, but the reflection might help in some rare cases. This is reflected in our langchain example.
The "Initial Answer" can be executed to run the function.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 2.0
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
# Limitations
1. We highly recommend using a stop criteria of `["\nReflection:"]`. The model was trained to first generate an answer and then reflect on its answer to either improve the answer or keep the answer the same. However, this "chain of thought" is often not helpful, and the final answer is seldom better than the initial call. Therefore, we strongly recommend using the Initial Call as the main call to execute.
2. The model works best when it is connected with a retriever when there are a multitude of functions, as a large number of functions will saturate the context window of this model.
3. The model can be prone to generate incorrect calls. Please ensure proper guardrails to capture errant behavior is in place.
## License
This model was trained on commercially viable data and is licensed under the [Llama 2 community license](https://huggingface.co/codellama/CodeLlama-13b-hf/blob/main/LICENSE) following the original [CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf/) model.
## References
We thank the CodeLlama team for their amazing models!
```
@misc{rozière2023code,
title={Code Llama: Open Foundation Models for Code},
author={Baptiste Rozière and Jonas Gehring and Fabian Gloeckle and Sten Sootla and Itai Gat and Xiaoqing Ellen Tan and Yossi Adi and Jingyu Liu and Tal Remez and Jérémy Rapin and Artyom Kozhevnikov and Ivan Evtimov and Joanna Bitton and Manish Bhatt and Cristian Canton Ferrer and Aaron Grattafiori and Wenhan Xiong and Alexandre Défossez and Jade Copet and Faisal Azhar and Hugo Touvron and Louis Martin and Nicolas Usunier and Thomas Scialom and Gabriel Synnaeve},
year={2023},
eprint={2308.12950},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Citation
```
@misc{nexusraven,
title={NexusRaven: Surpassing the state-of-the-art in open-source function calling LLMs},
author={Nexusflow.ai team},
year={2023},
url={http://nexusflow.ai/blog}
}
```
## Contact
Please reach out to info@nexusflow.ai for any questions!
<!-- original-model-card end -->
|
codyreading/dreambooth-fancy_boot
|
codyreading
| 2023-09-28T22:55:43Z | 31 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-28T22:51:11Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: A photo of sks boot
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - codyreading/dreambooth-fancy_boot
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on A photo of sks boot using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
TheBloke/Mistral-7B-v0.1-GGUF
|
TheBloke
| 2023-09-28T22:42:44Z | 10,251 | 248 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"pretrained",
"text-generation",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:quantized:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-09-27T16:17:24Z |
---
base_model: mistralai/Mistral-7B-v0.1
inference: false
license: apache-2.0
model_creator: Mistral AI
model_name: Mistral 7B v0.1
model_type: mistral
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
tags:
- pretrained
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Mistral 7B v0.1 - GGUF
- Model creator: [Mistral AI](https://huggingface.co/mistralai)
- Original model: [Mistral 7B v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Mistral AI's Mistral 7B v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF)
* [Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-v0.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
Sequence length note: The model will work at sequence lengths of 4096, or lower. GGUF does not yet have support for the new sliding window sequence length mode, so longer sequence lengths are not supported.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistral-7b-v0.1.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mistral-7b-v0.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [mistral-7b-v0.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mistral-7b-v0.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mistral-7b-v0.1.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mistral-7b-v0.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mistral-7b-v0.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mistral-7b-v0.1.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mistral-7b-v0.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mistral-7b-v0.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mistral-7b-v0.1.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mistral-7b-v0.1.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-v0.1-GGUF and below it, a specific filename to download, such as: mistral-7b-v0.1.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Mistral-7B-v0.1-GGUF mistral-7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Mistral-7B-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-v0.1-GGUF mistral-7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m mistral-7b-v0.1.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Sequence length can be 4096 or lower. Mistral's sliding window sequence length is not yet supported in llama.cpp, so sequence lengths longer than 4096 are not supported.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
Note: I have not tested ctransformers with Mistral models, but it may work if you set the `model_type` to `llama`.
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-v0.1-GGUF", model_file="mistral-7b-v0.1.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Mistral AI's Mistral 7B v0.1
# Model Card for Mistral-7B-v0.1
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters.
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our [Release blog post](https://mistral.ai/news/announcing-mistral-7b/)
## Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
<!-- original-model-card end -->
|
roa7n/gpt2-human_nontata_promoters-randomized_7_layers_3e-05_lr_2_e
|
roa7n
| 2023-09-28T22:38:57Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T22:38:55Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
gokuls/HBERTv1_emb_compress_48_L12_H128_A2
|
gokuls
| 2023-09-28T22:29:00Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-26T18:17:10Z |
---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_emb_compress_48_L12_H128_A2
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.14404758839681311
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HBERTv1_emb_compress_48_L12_H128_A2
This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2072
- Accuracy: 0.1440
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 80
- eval_batch_size: 80
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 7.7257 | 0.14 | 10000 | 7.6502 | 0.0520 |
| 6.9502 | 0.27 | 20000 | 6.9458 | 0.0829 |
| 6.7568 | 0.41 | 30000 | 6.7497 | 0.0973 |
| 6.6513 | 0.55 | 40000 | 6.6447 | 0.1047 |
| 6.5712 | 0.68 | 50000 | 6.5735 | 0.1112 |
| 6.5237 | 0.82 | 60000 | 6.5170 | 0.1165 |
| 6.478 | 0.96 | 70000 | 6.4726 | 0.1209 |
| 6.4359 | 1.09 | 80000 | 6.4369 | 0.1252 |
| 6.4052 | 1.23 | 90000 | 6.4047 | 0.1273 |
| 6.3897 | 1.37 | 100000 | 6.3794 | 0.1299 |
| 6.3598 | 1.5 | 110000 | 6.3557 | 0.1319 |
| 6.3362 | 1.64 | 120000 | 6.3374 | 0.1341 |
| 6.3154 | 1.78 | 130000 | 6.3209 | 0.1348 |
| 6.3082 | 1.91 | 140000 | 6.3069 | 0.1367 |
| 6.2942 | 2.05 | 150000 | 6.2943 | 0.1377 |
| 6.2849 | 2.18 | 160000 | 6.2835 | 0.1381 |
| 6.2745 | 2.32 | 170000 | 6.2737 | 0.1391 |
| 6.2647 | 2.46 | 180000 | 6.2658 | 0.1398 |
| 6.2633 | 2.59 | 190000 | 6.2580 | 0.1407 |
| 6.2506 | 2.73 | 200000 | 6.2525 | 0.1407 |
| 6.2435 | 2.87 | 210000 | 6.2463 | 0.1413 |
| 6.2416 | 3.0 | 220000 | 6.2394 | 0.1419 |
| 6.2329 | 3.14 | 230000 | 6.2355 | 0.1421 |
| 6.2288 | 3.28 | 240000 | 6.2323 | 0.1426 |
| 6.2232 | 3.41 | 250000 | 6.2277 | 0.1428 |
| 6.2227 | 3.55 | 260000 | 6.2228 | 0.1431 |
| 6.2138 | 3.69 | 270000 | 6.2200 | 0.1433 |
| 6.2142 | 3.82 | 280000 | 6.2187 | 0.1433 |
| 6.2182 | 3.96 | 290000 | 6.2162 | 0.1435 |
| 6.2108 | 4.1 | 300000 | 6.2145 | 0.1438 |
| 6.2158 | 4.23 | 310000 | 6.2131 | 0.1437 |
| 6.2072 | 4.37 | 320000 | 6.2114 | 0.1438 |
| 6.2084 | 4.51 | 330000 | 6.2087 | 0.1440 |
| 6.2093 | 4.64 | 340000 | 6.2082 | 0.1443 |
| 6.2084 | 4.78 | 350000 | 6.2081 | 0.1440 |
| 6.2066 | 4.92 | 360000 | 6.2081 | 0.1442 |
### Framework versions
- Transformers 4.33.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ahaedike/q-FrozenLake-v1-4x4-noSlippery
|
ahaedike
| 2023-09-28T22:27:38Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-28T22:27:35Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ahaedike/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ccore/opt-1.3b-open-data-understanding
|
ccore
| 2023-09-28T22:26:36Z | 143 | 1 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"qa",
"open data",
"opt-1.3b",
"dataset:ccore/open_data_understanding",
"base_model:facebook/opt-1.3b",
"base_model:finetune:facebook/opt-1.3b",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-27T12:00:17Z |
---
license: other
base_model: facebook/opt-1.3b
tags:
- generated_from_trainer
- qa
- open data
- opt
- opt-1.3b
metrics:
- accuracy
widget:
- text: |-
# [PAPER]
Pope John Paul II (Latin: Ioannes Paulus II; Italian: Giovanni Paolo II; Polish: Jan Paweł II; born Karol Józef Wojtyła [ˈkarɔl ˈjuzɛv vɔjˈtɨwa];[b] 18 May 1920 – 2 April 2005) was head of the Catholic Church and sovereign of the Vatican City State from 1978 until his death in 2005. He was later canonised as Pope Saint John Paul II. In his youth, Wojtyła dabbled in stage acting. He graduated with excellent grades from an all-boys high school in Wadowice, Poland, shortly before the start of World War II in 1938. During the war, to avoid being kidnapped and sent off to a German slave labor camp, he signed up for work in harsh conditions in a quarry. Wojtyła eventually took up acting and developed a love for the profession and participated at a local theater. The linguistically skilled Wojtyła wanted to study Polish at university. Encouraged by a conversation with Adam Stefan Sapieha, he decided to study theology and become a priest. Eventually, Wojtyła rose to the position of Archbishop of Kraków and then a cardinal, both positions held by his mentor. Wojtyła was elected pope on the third day of the second papal conclave of 1978 (becoming one of the youngest popes in history), which was called after John Paul I, who had been elected in the first papal conclave of 1978 earlier in August to succeed Pope Paul VI, died after 33 days. Wojtyła adopted the name of his predecessor in tribute to him.[20] John Paul II was the first non-Italian pope since Adrian VI in the 16th century, as well as the third-longest-serving pope in history after Pius IX and St. Peter. John Paul II attempted to improve the Catholic Church's relations with Judaism, Islam, and the Eastern Orthodox Church in the spirit of ecumenism, holding atheism as the greatest threat. He maintained the Church's previous positions on such matters as abortion, artificial contraception, the ordination of women, and a celibate clergy, and although he supported the reforms of the Second Vatican Council, he was seen as generally conservative in their interpretation.[21][22] He put emphasis on family and identity, while questioning consumerism, hedonism and the pursuit of wealth. He was one of the most travelled world leaders in history, visiting 129 countries during his pontificate. As part of his special emphasis on the universal call to holiness, he beatified 1,344,[23] and also canonised 483 people, more than the combined tally of his predecessors during the preceding five centuries. By the time of his death, he had named most of the College of Cardinals, consecrated or co-consecrated many of the world's bishops, and ordained many priests.[24] He has been credited with fighting against dictatorships for democracy and with helping to end Communist rule in his native Poland and the rest of Europe.[25] Under John Paul II, the Catholic Church greatly expanded its influence in Africa and Latin America, and retained its influence in Europe and the rest of the world.
## [UNDERSTANDING]
This section presents a brief account
datasets:
- ccore/open_data_understanding
pipeline_tag: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# OPT_1.3b_open_data_understanding
## Description
This model has been trained to understand and respond to any content inserted after the `[PAPER]` tag. It uses advanced language modeling techniques to understand the context, structure, and underlying goals of the input text.
## How to use
To interact with this template, place your text after the `[PAPER]` tag. The model will process the text and respond accordingly. For example:
[PAPER]
Your text here...
## Example
[PAPER]
We present a scalable method to build a high-quality instruction-following language model...
The model will understand and respond to your text according to its context and content.
## Comprehension Sections
### [UNDERSTANDING]
This section provides a detailed analysis and decomposition of the inserted text, facilitating the understanding of the content.
### [QUESTIONS AND ANSWERS]
This section addresses questions and answers that could arise based on the text provided.
### [OBJECTION AND REPLY]
This section addresses any objections and responses that could arise from analysis of the text.
## Warnings
- This model was trained on a diverse corpus, but may still have bias or limitations.
- Continuous validation of the model and its output is essential.
## Contact and Support
For more information, visit [Hugging Face](https://huggingface.co/).
|
Globaly/GPTQ-Categories
|
Globaly
| 2023-09-28T22:14:45Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T22:14:12Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
GreenBitAI/LLaMA-2-7B-2bit-groupsize32
|
GreenBitAI
| 2023-09-28T22:08:40Z | 82 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-15T20:46:30Z |
---
license: apache-2.0
---
# GreenBit LLaMA
This is GreenBitAI's pretrained **2-bit** LLaMA model with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/low_bit_llama) for the code to run the model and more information.
## Model Description
- **Developed by:** [GreenBitAI](https://github.com/GreenBitAI)
- **Model type:** Causal (Llama 2)
- **Language(s) (NLP):** English
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), [Llama 2 license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Zero-Shot Evaluation
| Task | Metric | LLaMA 3B q2g32 | LLaMA 3B q2g16 | LLaMA 3B q2g8 | LLaMA-1 7B q2g32 | LLaMA-2 7B q2g32 | LLaMA-2 7B q2g8 | LLaMA 3B FP16 | LLaMA-1 7B FP16 |
|---------------|----------|----------------|----------------|--------------|------------------|------------------|----------------|--------------|-----------------|
| Openbookqa | acc | 0.196 | 0.238 | 0.242 | 0.224 | 0.246 | 0.296 | 0.27 | 0.29 |
| | ac_norm | 0.332 | 0.358 | 0.362 | 0.388 | 0.376 | 0.4 | 0.4 | 0.41 |
| arc_challenge | acc | 0.279 | 0.2978 | 0.3148 | 0.3422 | 0.3268 | 0.3618 | 0.34 | 0.39 |
| | ac_norm | 0.2944 | 0.3319 | 0.3345 | 0.3387 | 0.3387 | 0.372 | 0.37 | 0.41 |
| hellawswag | acc | 0.4238 | 0.444 | 0.462 | 0.4996 | 0.4961 | 0.5379 | 0.49 | 0.68 |
| | ac_norm | 0.5685 | 0.5988 | 0.6242 | 0.6447 | 0.6464 | 0.7014 | 0.67 | 0.73 |
| piqa | acc | 0.7024 | 0.716 | 0.7291 | 0.7476 | 0.7503 | 0.7715 | 0.75 | 0.78 |
| | ac_norm | 0.7116 | 0.7247 | 0.7312 | 0.7443 | 0.7421 | 0.7568 | 0.76 | 0.78 |
| arc_easy | acc | 0.5997 | 0.646 | 0.6528 | 0.6061 | 0.6174 | 0.6254 | 0.69 | 0.68 |
| | ac_norm | 0.5417 | 0.58 | 0.5972 | 0.4566 | 0.4781 | 0.4958 | 0.65 | 0.52 |
| Winogrande | acc | 0.5683 | 0.5888 | 0.6054 | 0.6283 | 0.6298 | 0.6582 | 0.62 | 0.68 |
| boolq | acc | 0.6281 | 0.6636 | 0.6327 | 0.6425 | 0.7061 | 0.7242 | 0.68 | 0.75 |
| truthfulqa_mc | mc1 | 0.2509 | 0.2118 | 0.2252 | 0.224 | 0.2313 | 0.2399 | 0.22 | 0.21 |
| | mc2 | 0.3962 | 0.3501 | 0.3625 | 0.3702 | 0.3854 | 0.3795 | 0.35 | 0.34 |
| anli_r1 | acc | 0.337 | 0.334 | 0.344 | 0.331 | 0.333 | 0.363 | 0.33 | 0.35 |
| anli_r2 | acc | 0.335 | 0.332 | 0.331 | 0.326 | 0.349 | 0.347 | 0.32 | 0.34 |
| anli_r3 | acc | 0.3358 | 0.3383 | 0.3425 | 0.3417 | 0.36 | 0.3733 | 0.35 | 0.37 |
| wic | acc | 0.4984 | 0.5094 | 0.4969 | 0.4984 | 0.4953 | 0.489 | 0.48 | 0.5 |
| rte | acc | 0.5596 | 0.5993 | 0.5632 | 0.639 | 0.6065 | 0.6426 | 0.58 | 0.56 |
| record | f1 | 0.8502 | 0.8625 | 0.8687 | 0.8859 | 0.8872 | 0.9037 | 0.88 | 0.91 |
| | em | 0.8427 | 0.8545 | 0.8612 | 0.8781 | 0.8801 | 0.8959 | 0.89 | 0.91 |
| Average | | 0.4881 | 0.5037 | 0.5087 | 0.5122 | 0.5181 | 0.5391 | 0.528 | 0.5519 |
|
AmirH98/Reinforce-PixelCopter-v0
|
AmirH98
| 2023-09-28T22:08:33Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-28T22:08:29Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 37.50 +/- 37.40
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
roa7n/gpt2-human_nontata_promoters-randomized_7_layers_0.0003_lr_2_e
|
roa7n
| 2023-09-28T22:06:22Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T22:06:19Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
djomo/llama2bllux150
|
djomo
| 2023-09-28T22:02:41Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T22:02:36Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
demircantas/re-blocking_VAE
|
demircantas
| 2023-09-28T21:45:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-09-28T21:42:39Z |
14,000 iteration VAE model for [re-blocking](https://github.com/DrawingTogether/re-blocking) repository.
---
license: mit
---
|
furkanbjk474/furkan
|
furkanbjk474
| 2023-09-28T21:37:10Z | 0 | 1 |
espnet
|
[
"espnet",
"image-to-image",
"aa",
"dataset:lmsys/lmsys-chat-1m",
"dataset:fka/awesome-chatgpt-prompts",
"dataset:uonlp/CulturaX",
"license:apache-2.0",
"region:us"
] |
image-to-image
| 2023-09-28T21:34:49Z |
---
license: apache-2.0
datasets:
- lmsys/lmsys-chat-1m
- fka/awesome-chatgpt-prompts
- uonlp/CulturaX
language:
- aa
metrics:
- bleurt
library_name: espnet
pipeline_tag: image-to-image
---
|
LarryAIDraw/Aradia-10
|
LarryAIDraw
| 2023-09-28T21:35:35Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-28T21:30:42Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/153026/aradia-toaru-series
|
LarryAIDraw/mom__japanese_mcdonalds_commercial__2
|
LarryAIDraw
| 2023-09-28T21:35:21Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-28T21:29:55Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/153222/yoru-mac-or-mom-from-the-japanese-mcdonalds-commercial
|
LarryAIDraw/Han_Nari
|
LarryAIDraw
| 2023-09-28T21:34:49Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-28T21:29:20Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/152768/han-nari-circles-manhwa-commission
|
LarryAIDraw/yunjin-08
|
LarryAIDraw
| 2023-09-28T21:34:23Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-28T21:28:22Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/153404/yunjin-or-genshin-impact
|
LarryAIDraw/lucy_heartfilia-10
|
LarryAIDraw
| 2023-09-28T21:33:53Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-28T21:27:34Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/153347/lucy-heartfilia-fairy-tail
|
jmukesh99/Llama2-SFT-AIBE-silver-v1
|
jmukesh99
| 2023-09-28T21:25:37Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-09-28T21:25:18Z |
---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: Llama2-SFT-AIBE-silver-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama2-SFT-AIBE-silver-v1
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6213
- eval_runtime: 34.7227
- eval_samples_per_second: 1.814
- eval_steps_per_second: 0.23
- epoch: 6.35
- step: 400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 10
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
LarryAIDraw/rika_4
|
LarryAIDraw
| 2023-09-28T21:19:18Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-27T22:03:56Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/152748/rika-saionji-yamada-kun-to-7-nin-no-majo
|
badokorach/flan-t5-small-qa-10
|
badokorach
| 2023-09-28T21:01:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-28T18:42:47Z |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
model-index:
- name: flan-t5-small-qa-10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-small-qa-10
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0680
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 37.0091 | 0.01 | 10 | 39.2389 |
| 34.7317 | 0.02 | 20 | 36.6257 |
| 32.6533 | 0.03 | 30 | 34.1553 |
| 29.7792 | 0.04 | 40 | 31.6802 |
| 27.9268 | 0.05 | 50 | 29.1925 |
| 25.8475 | 0.06 | 60 | 26.7287 |
| 23.1826 | 0.07 | 70 | 24.1478 |
| 20.9714 | 0.08 | 80 | 21.3731 |
| 18.5839 | 0.09 | 90 | 18.4583 |
| 16.4034 | 0.1 | 100 | 15.4845 |
| 13.2378 | 0.11 | 110 | 12.3754 |
| 11.1189 | 0.12 | 120 | 9.2924 |
| 8.8691 | 0.13 | 130 | 6.9111 |
| 7.3894 | 0.14 | 140 | 5.8619 |
| 6.5278 | 0.15 | 150 | 5.3589 |
| 5.8773 | 0.16 | 160 | 5.0272 |
| 5.6232 | 0.17 | 170 | 4.7799 |
| 5.1812 | 0.18 | 180 | 4.5834 |
| 4.8379 | 0.19 | 190 | 4.4145 |
| 4.6718 | 0.2 | 200 | 4.2705 |
| 4.5679 | 0.21 | 210 | 4.1402 |
| 4.3725 | 0.22 | 220 | 4.0193 |
| 4.2319 | 0.23 | 230 | 3.8986 |
| 4.0162 | 0.24 | 240 | 3.7799 |
| 3.9175 | 0.25 | 250 | 3.6602 |
| 3.8803 | 0.26 | 260 | 3.5376 |
| 3.7403 | 0.27 | 270 | 3.4149 |
| 3.575 | 0.28 | 280 | 3.2841 |
| 3.5047 | 0.29 | 290 | 3.1522 |
| 3.3545 | 0.3 | 300 | 3.0169 |
| 3.2233 | 0.31 | 310 | 2.8774 |
| 3.1314 | 0.32 | 320 | 2.7361 |
| 2.9994 | 0.33 | 330 | 2.5934 |
| 2.901 | 0.34 | 340 | 2.4511 |
| 2.6969 | 0.35 | 350 | 2.3144 |
| 2.643 | 0.36 | 360 | 2.1838 |
| 2.5416 | 0.37 | 370 | 2.0683 |
| 2.3707 | 0.38 | 380 | 1.9536 |
| 2.3231 | 0.39 | 390 | 1.8523 |
| 2.2729 | 0.4 | 400 | 1.7420 |
| 2.0765 | 0.41 | 410 | 1.6323 |
| 2.0129 | 0.42 | 420 | 1.5289 |
| 1.8826 | 0.43 | 430 | 1.4344 |
| 1.878 | 0.44 | 440 | 1.3528 |
| 1.7105 | 0.45 | 450 | 1.2652 |
| 1.6877 | 0.46 | 460 | 1.1842 |
| 1.5966 | 0.47 | 470 | 1.1044 |
| 1.509 | 0.48 | 480 | 1.0282 |
| 1.4869 | 0.49 | 490 | 0.9637 |
| 1.3615 | 0.5 | 500 | 0.9079 |
| 1.3525 | 0.51 | 510 | 0.8398 |
| 1.2226 | 0.52 | 520 | 0.7795 |
| 1.1191 | 0.53 | 530 | 0.7361 |
| 1.0896 | 0.54 | 540 | 0.6988 |
| 1.0617 | 0.55 | 550 | 0.6521 |
| 0.9407 | 0.56 | 560 | 0.6081 |
| 0.9509 | 0.57 | 570 | 0.5644 |
| 0.941 | 0.58 | 580 | 0.5292 |
| 0.8058 | 0.59 | 590 | 0.4883 |
| 0.856 | 0.6 | 600 | 0.4508 |
| 0.7525 | 0.61 | 610 | 0.4194 |
| 0.684 | 0.62 | 620 | 0.3900 |
| 0.644 | 0.63 | 630 | 0.3664 |
| 0.6718 | 0.64 | 640 | 0.3421 |
| 0.6279 | 0.65 | 650 | 0.3193 |
| 0.561 | 0.66 | 660 | 0.2956 |
| 0.577 | 0.67 | 670 | 0.2754 |
| 0.5564 | 0.68 | 680 | 0.2587 |
| 0.5221 | 0.69 | 690 | 0.2438 |
| 0.4814 | 0.7 | 700 | 0.2297 |
| 0.4113 | 0.71 | 710 | 0.2191 |
| 0.4718 | 0.72 | 720 | 0.2070 |
| 0.3819 | 0.73 | 730 | 0.1986 |
| 0.3924 | 0.74 | 740 | 0.1891 |
| 0.4157 | 0.75 | 750 | 0.1777 |
| 0.3915 | 0.76 | 760 | 0.1678 |
| 0.3523 | 0.77 | 770 | 0.1598 |
| 0.4251 | 0.78 | 780 | 0.1532 |
| 0.2944 | 0.79 | 790 | 0.1472 |
| 0.3066 | 0.8 | 800 | 0.1396 |
| 0.3363 | 0.81 | 810 | 0.1340 |
| 0.2954 | 0.82 | 820 | 0.1283 |
| 0.2756 | 0.83 | 830 | 0.1220 |
| 0.2898 | 0.84 | 840 | 0.1164 |
| 0.2862 | 0.85 | 850 | 0.1103 |
| 0.264 | 0.86 | 860 | 0.1056 |
| 0.3028 | 0.87 | 870 | 0.1004 |
| 0.3009 | 0.88 | 880 | 0.0937 |
| 0.2909 | 0.89 | 890 | 0.0881 |
| 0.2922 | 0.9 | 900 | 0.0826 |
| 0.2493 | 0.91 | 910 | 0.0773 |
| 0.2524 | 0.92 | 920 | 0.0749 |
| 0.1929 | 0.93 | 930 | 0.0727 |
| 0.2461 | 0.94 | 940 | 0.0679 |
| 0.2471 | 0.95 | 950 | 0.0642 |
| 0.1956 | 0.96 | 960 | 0.0628 |
| 0.1839 | 0.97 | 970 | 0.0617 |
| 0.2151 | 0.98 | 980 | 0.0599 |
| 0.2182 | 0.99 | 990 | 0.0580 |
| 0.2473 | 1.0 | 1000 | 0.0567 |
| 0.2249 | 1.01 | 1010 | 0.0562 |
| 0.2807 | 1.02 | 1020 | 0.0550 |
| 0.2066 | 1.03 | 1030 | 0.0543 |
| 0.168 | 1.04 | 1040 | 0.0550 |
| 0.2199 | 1.05 | 1050 | 0.0550 |
| 0.2135 | 1.06 | 1060 | 0.0543 |
| 0.2316 | 1.07 | 1070 | 0.0535 |
| 0.196 | 1.08 | 1080 | 0.0535 |
| 0.163 | 1.09 | 1090 | 0.0532 |
| 0.115 | 1.1 | 1100 | 0.0533 |
| 0.1758 | 1.11 | 1110 | 0.0532 |
| 0.1862 | 1.12 | 1120 | 0.0537 |
| 0.2976 | 1.13 | 1130 | 0.0535 |
| 0.1683 | 1.14 | 1140 | 0.0531 |
| 0.1836 | 1.15 | 1150 | 0.0533 |
| 0.1932 | 1.16 | 1160 | 0.0536 |
| 0.2083 | 1.17 | 1170 | 0.0524 |
| 0.1482 | 1.18 | 1180 | 0.0524 |
| 0.1869 | 1.19 | 1190 | 0.0523 |
| 0.2302 | 1.2 | 1200 | 0.0522 |
| 0.1334 | 1.21 | 1210 | 0.0522 |
| 0.172 | 1.22 | 1220 | 0.0519 |
| 0.1492 | 1.23 | 1230 | 0.0515 |
| 0.18 | 1.24 | 1240 | 0.0513 |
| 0.2685 | 1.25 | 1250 | 0.0517 |
| 0.1621 | 1.26 | 1260 | 0.0523 |
| 0.172 | 1.27 | 1270 | 0.0519 |
| 0.2655 | 1.28 | 1280 | 0.0516 |
| 0.1625 | 1.29 | 1290 | 0.0519 |
| 0.251 | 1.3 | 1300 | 0.0525 |
| 0.1506 | 1.31 | 1310 | 0.0528 |
| 0.206 | 1.32 | 1320 | 0.0530 |
| 0.186 | 1.33 | 1330 | 0.0526 |
| 0.1778 | 1.34 | 1340 | 0.0528 |
| 0.167 | 1.35 | 1350 | 0.0528 |
| 0.1602 | 1.36 | 1360 | 0.0528 |
| 0.2509 | 1.37 | 1370 | 0.0523 |
| 0.1615 | 1.38 | 1380 | 0.0522 |
| 0.1988 | 1.39 | 1390 | 0.0523 |
| 0.1602 | 1.4 | 1400 | 0.0525 |
| 0.1637 | 1.41 | 1410 | 0.0524 |
| 0.1848 | 1.42 | 1420 | 0.0523 |
| 0.1662 | 1.43 | 1430 | 0.0525 |
| 0.1603 | 1.44 | 1440 | 0.0525 |
| 0.1723 | 1.45 | 1450 | 0.0524 |
| 0.15 | 1.46 | 1460 | 0.0522 |
| 0.1855 | 1.47 | 1470 | 0.0519 |
| 0.1828 | 1.48 | 1480 | 0.0523 |
| 0.1975 | 1.49 | 1490 | 0.0523 |
| 0.2105 | 1.5 | 1500 | 0.0525 |
| 0.1808 | 1.51 | 1510 | 0.0530 |
| 0.1714 | 1.52 | 1520 | 0.0529 |
| 0.1481 | 1.53 | 1530 | 0.0533 |
| 0.1579 | 1.54 | 1540 | 0.0539 |
| 0.194 | 1.55 | 1550 | 0.0541 |
| 0.1521 | 1.56 | 1560 | 0.0540 |
| 0.2194 | 1.57 | 1570 | 0.0538 |
| 0.1487 | 1.58 | 1580 | 0.0536 |
| 0.1511 | 1.59 | 1590 | 0.0536 |
| 0.1958 | 1.6 | 1600 | 0.0537 |
| 0.179 | 1.61 | 1610 | 0.0536 |
| 0.2006 | 1.62 | 1620 | 0.0535 |
| 0.1483 | 1.63 | 1630 | 0.0537 |
| 0.1321 | 1.64 | 1640 | 0.0536 |
| 0.1875 | 1.65 | 1650 | 0.0534 |
| 0.1777 | 1.66 | 1660 | 0.0535 |
| 0.1289 | 1.67 | 1670 | 0.0534 |
| 0.1534 | 1.68 | 1680 | 0.0536 |
| 0.1942 | 1.69 | 1690 | 0.0535 |
| 0.1434 | 1.7 | 1700 | 0.0539 |
| 0.1316 | 1.71 | 1710 | 0.0543 |
| 0.1205 | 1.72 | 1720 | 0.0542 |
| 0.1746 | 1.73 | 1730 | 0.0538 |
| 0.1505 | 1.74 | 1740 | 0.0537 |
| 0.1811 | 1.75 | 1750 | 0.0541 |
| 0.1292 | 1.76 | 1760 | 0.0542 |
| 0.1545 | 1.77 | 1770 | 0.0541 |
| 0.192 | 1.78 | 1780 | 0.0540 |
| 0.1223 | 1.79 | 1790 | 0.0543 |
| 0.2161 | 1.8 | 1800 | 0.0546 |
| 0.1408 | 1.81 | 1810 | 0.0550 |
| 0.1408 | 1.82 | 1820 | 0.0554 |
| 0.1239 | 1.83 | 1830 | 0.0555 |
| 0.2161 | 1.84 | 1840 | 0.0555 |
| 0.1122 | 1.85 | 1850 | 0.0554 |
| 0.1465 | 1.86 | 1860 | 0.0549 |
| 0.1385 | 1.87 | 1870 | 0.0548 |
| 0.1651 | 1.88 | 1880 | 0.0550 |
| 0.1716 | 1.89 | 1890 | 0.0552 |
| 0.1317 | 1.9 | 1900 | 0.0550 |
| 0.1228 | 1.91 | 1910 | 0.0548 |
| 0.1764 | 1.92 | 1920 | 0.0553 |
| 0.1694 | 1.93 | 1930 | 0.0555 |
| 0.1607 | 1.94 | 1940 | 0.0555 |
| 0.1989 | 1.95 | 1950 | 0.0547 |
| 0.1866 | 1.96 | 1960 | 0.0545 |
| 0.1006 | 1.97 | 1970 | 0.0545 |
| 0.1769 | 1.98 | 1980 | 0.0547 |
| 0.1266 | 1.99 | 1990 | 0.0550 |
| 0.1972 | 2.0 | 2000 | 0.0547 |
| 0.1404 | 2.01 | 2010 | 0.0542 |
| 0.2077 | 2.02 | 2020 | 0.0543 |
| 0.1607 | 2.03 | 2030 | 0.0545 |
| 0.157 | 2.04 | 2040 | 0.0546 |
| 0.1171 | 2.05 | 2050 | 0.0549 |
| 0.1419 | 2.06 | 2060 | 0.0554 |
| 0.2234 | 2.07 | 2070 | 0.0554 |
| 0.2123 | 2.08 | 2080 | 0.0548 |
| 0.1856 | 2.09 | 2090 | 0.0544 |
| 0.1061 | 2.1 | 2100 | 0.0537 |
| 0.2295 | 2.11 | 2110 | 0.0537 |
| 0.203 | 2.12 | 2120 | 0.0543 |
| 0.1858 | 2.13 | 2130 | 0.0543 |
| 0.1617 | 2.14 | 2140 | 0.0546 |
| 0.161 | 2.15 | 2150 | 0.0550 |
| 0.1707 | 2.16 | 2160 | 0.0553 |
| 0.2021 | 2.17 | 2170 | 0.0556 |
| 0.1121 | 2.18 | 2180 | 0.0555 |
| 0.1608 | 2.19 | 2190 | 0.0557 |
| 0.1782 | 2.2 | 2200 | 0.0561 |
| 0.1261 | 2.21 | 2210 | 0.0563 |
| 0.1748 | 2.22 | 2220 | 0.0563 |
| 0.254 | 2.23 | 2230 | 0.0560 |
| 0.1894 | 2.24 | 2240 | 0.0560 |
| 0.1825 | 2.25 | 2250 | 0.0559 |
| 0.1078 | 2.26 | 2260 | 0.0555 |
| 0.1718 | 2.27 | 2270 | 0.0556 |
| 0.1274 | 2.28 | 2280 | 0.0556 |
| 0.097 | 2.29 | 2290 | 0.0557 |
| 0.1887 | 2.3 | 2300 | 0.0557 |
| 0.1246 | 2.31 | 2310 | 0.0552 |
| 0.2024 | 2.32 | 2320 | 0.0547 |
| 0.1878 | 2.33 | 2330 | 0.0542 |
| 0.1661 | 2.34 | 2340 | 0.0546 |
| 0.142 | 2.35 | 2350 | 0.0546 |
| 0.1046 | 2.36 | 2360 | 0.0546 |
| 0.239 | 2.37 | 2370 | 0.0546 |
| 0.1454 | 2.38 | 2380 | 0.0547 |
| 0.144 | 2.39 | 2390 | 0.0549 |
| 0.1983 | 2.4 | 2400 | 0.0548 |
| 0.1583 | 2.41 | 2410 | 0.0549 |
| 0.1345 | 2.42 | 2420 | 0.0547 |
| 0.1838 | 2.43 | 2430 | 0.0553 |
| 0.1441 | 2.44 | 2440 | 0.0557 |
| 0.142 | 2.45 | 2450 | 0.0556 |
| 0.1743 | 2.46 | 2460 | 0.0556 |
| 0.152 | 2.47 | 2470 | 0.0557 |
| 0.1221 | 2.48 | 2480 | 0.0556 |
| 0.1611 | 2.49 | 2490 | 0.0555 |
| 0.128 | 2.5 | 2500 | 0.0554 |
| 0.1872 | 2.51 | 2510 | 0.0554 |
| 0.1425 | 2.52 | 2520 | 0.0557 |
| 0.1585 | 2.53 | 2530 | 0.0561 |
| 0.1257 | 2.54 | 2540 | 0.0563 |
| 0.1805 | 2.55 | 2550 | 0.0560 |
| 0.1431 | 2.56 | 2560 | 0.0557 |
| 0.1569 | 2.57 | 2570 | 0.0558 |
| 0.1703 | 2.58 | 2580 | 0.0558 |
| 0.1319 | 2.59 | 2590 | 0.0555 |
| 0.1608 | 2.6 | 2600 | 0.0559 |
| 0.1553 | 2.61 | 2610 | 0.0560 |
| 0.1855 | 2.62 | 2620 | 0.0562 |
| 0.1856 | 2.63 | 2630 | 0.0562 |
| 0.1715 | 2.64 | 2640 | 0.0561 |
| 0.1122 | 2.65 | 2650 | 0.0562 |
| 0.1671 | 2.66 | 2660 | 0.0564 |
| 0.1613 | 2.67 | 2670 | 0.0567 |
| 0.1648 | 2.68 | 2680 | 0.0570 |
| 0.15 | 2.69 | 2690 | 0.0572 |
| 0.1499 | 2.7 | 2700 | 0.0571 |
| 0.1598 | 2.71 | 2710 | 0.0572 |
| 0.1804 | 2.72 | 2720 | 0.0572 |
| 0.1721 | 2.73 | 2730 | 0.0570 |
| 0.131 | 2.74 | 2740 | 0.0571 |
| 0.2229 | 2.75 | 2750 | 0.0570 |
| 0.1337 | 2.76 | 2760 | 0.0573 |
| 0.1472 | 2.77 | 2770 | 0.0575 |
| 0.1202 | 2.78 | 2780 | 0.0578 |
| 0.1417 | 2.79 | 2790 | 0.0582 |
| 0.123 | 2.8 | 2800 | 0.0584 |
| 0.2054 | 2.81 | 2810 | 0.0581 |
| 0.1761 | 2.82 | 2820 | 0.0573 |
| 0.1034 | 2.83 | 2830 | 0.0567 |
| 0.1416 | 2.84 | 2840 | 0.0564 |
| 0.143 | 2.85 | 2850 | 0.0565 |
| 0.1295 | 2.86 | 2860 | 0.0567 |
| 0.1201 | 2.87 | 2870 | 0.0565 |
| 0.1168 | 2.88 | 2880 | 0.0563 |
| 0.1236 | 2.89 | 2890 | 0.0559 |
| 0.1664 | 2.9 | 2900 | 0.0559 |
| 0.1555 | 2.91 | 2910 | 0.0562 |
| 0.1657 | 2.92 | 2920 | 0.0565 |
| 0.1213 | 2.93 | 2930 | 0.0568 |
| 0.1557 | 2.94 | 2940 | 0.0569 |
| 0.1795 | 2.95 | 2950 | 0.0570 |
| 0.1655 | 2.96 | 2960 | 0.0571 |
| 0.2015 | 2.97 | 2970 | 0.0571 |
| 0.1956 | 2.98 | 2980 | 0.0572 |
| 0.1456 | 2.99 | 2990 | 0.0575 |
| 0.1298 | 3.0 | 3000 | 0.0574 |
| 0.1589 | 3.01 | 3010 | 0.0574 |
| 0.1367 | 3.02 | 3020 | 0.0573 |
| 0.1321 | 3.03 | 3030 | 0.0573 |
| 0.1451 | 3.04 | 3040 | 0.0572 |
| 0.174 | 3.05 | 3050 | 0.0574 |
| 0.1547 | 3.06 | 3060 | 0.0577 |
| 0.1229 | 3.07 | 3070 | 0.0578 |
| 0.1207 | 3.08 | 3080 | 0.0584 |
| 0.1308 | 3.09 | 3090 | 0.0588 |
| 0.1882 | 3.1 | 3100 | 0.0588 |
| 0.1647 | 3.11 | 3110 | 0.0586 |
| 0.1121 | 3.12 | 3120 | 0.0587 |
| 0.1656 | 3.13 | 3130 | 0.0586 |
| 0.1671 | 3.14 | 3140 | 0.0588 |
| 0.1399 | 3.15 | 3150 | 0.0591 |
| 0.1504 | 3.16 | 3160 | 0.0594 |
| 0.2393 | 3.17 | 3170 | 0.0587 |
| 0.1273 | 3.18 | 3180 | 0.0583 |
| 0.1365 | 3.19 | 3190 | 0.0579 |
| 0.1752 | 3.2 | 3200 | 0.0582 |
| 0.1526 | 3.21 | 3210 | 0.0585 |
| 0.1219 | 3.22 | 3220 | 0.0582 |
| 0.1416 | 3.23 | 3230 | 0.0581 |
| 0.1172 | 3.24 | 3240 | 0.0577 |
| 0.1205 | 3.25 | 3250 | 0.0579 |
| 0.1554 | 3.26 | 3260 | 0.0582 |
| 0.1442 | 3.27 | 3270 | 0.0583 |
| 0.195 | 3.28 | 3280 | 0.0581 |
| 0.1981 | 3.29 | 3290 | 0.0577 |
| 0.2147 | 3.3 | 3300 | 0.0576 |
| 0.1204 | 3.31 | 3310 | 0.0578 |
| 0.1628 | 3.32 | 3320 | 0.0581 |
| 0.3038 | 3.33 | 3330 | 0.0583 |
| 0.1759 | 3.34 | 3340 | 0.0584 |
| 0.1454 | 3.35 | 3350 | 0.0585 |
| 0.1269 | 3.36 | 3360 | 0.0587 |
| 0.1485 | 3.37 | 3370 | 0.0591 |
| 0.1197 | 3.38 | 3380 | 0.0594 |
| 0.1352 | 3.39 | 3390 | 0.0597 |
| 0.1679 | 3.4 | 3400 | 0.0599 |
| 0.1607 | 3.41 | 3410 | 0.0601 |
| 0.1519 | 3.42 | 3420 | 0.0603 |
| 0.1227 | 3.43 | 3430 | 0.0605 |
| 0.2099 | 3.44 | 3440 | 0.0606 |
| 0.1611 | 3.45 | 3450 | 0.0605 |
| 0.2014 | 3.46 | 3460 | 0.0604 |
| 0.1074 | 3.47 | 3470 | 0.0601 |
| 0.195 | 3.48 | 3480 | 0.0600 |
| 0.1559 | 3.49 | 3490 | 0.0593 |
| 0.1636 | 3.5 | 3500 | 0.0588 |
| 0.1503 | 3.51 | 3510 | 0.0588 |
| 0.1486 | 3.52 | 3520 | 0.0585 |
| 0.1763 | 3.53 | 3530 | 0.0582 |
| 0.1855 | 3.54 | 3540 | 0.0582 |
| 0.1641 | 3.55 | 3550 | 0.0585 |
| 0.1398 | 3.56 | 3560 | 0.0586 |
| 0.1719 | 3.57 | 3570 | 0.0588 |
| 0.109 | 3.58 | 3580 | 0.0589 |
| 0.1498 | 3.59 | 3590 | 0.0589 |
| 0.1012 | 3.6 | 3600 | 0.0589 |
| 0.1175 | 3.61 | 3610 | 0.0591 |
| 0.1434 | 3.62 | 3620 | 0.0594 |
| 0.1609 | 3.63 | 3630 | 0.0596 |
| 0.1448 | 3.64 | 3640 | 0.0599 |
| 0.2215 | 3.65 | 3650 | 0.0599 |
| 0.1529 | 3.66 | 3660 | 0.0601 |
| 0.1343 | 3.67 | 3670 | 0.0603 |
| 0.1914 | 3.68 | 3680 | 0.0603 |
| 0.1089 | 3.69 | 3690 | 0.0602 |
| 0.156 | 3.7 | 3700 | 0.0603 |
| 0.1274 | 3.71 | 3710 | 0.0606 |
| 0.1146 | 3.72 | 3720 | 0.0609 |
| 0.1378 | 3.73 | 3730 | 0.0610 |
| 0.101 | 3.74 | 3740 | 0.0610 |
| 0.1502 | 3.75 | 3750 | 0.0610 |
| 0.1323 | 3.76 | 3760 | 0.0610 |
| 0.2017 | 3.77 | 3770 | 0.0611 |
| 0.1167 | 3.78 | 3780 | 0.0610 |
| 0.2027 | 3.79 | 3790 | 0.0611 |
| 0.1739 | 3.8 | 3800 | 0.0612 |
| 0.1269 | 3.81 | 3810 | 0.0610 |
| 0.1642 | 3.82 | 3820 | 0.0606 |
| 0.141 | 3.83 | 3830 | 0.0602 |
| 0.1567 | 3.84 | 3840 | 0.0598 |
| 0.0849 | 3.85 | 3850 | 0.0598 |
| 0.1388 | 3.86 | 3860 | 0.0601 |
| 0.2029 | 3.87 | 3870 | 0.0602 |
| 0.1471 | 3.88 | 3880 | 0.0600 |
| 0.1307 | 3.89 | 3890 | 0.0597 |
| 0.1363 | 3.9 | 3900 | 0.0596 |
| 0.1566 | 3.91 | 3910 | 0.0596 |
| 0.1936 | 3.92 | 3920 | 0.0594 |
| 0.1063 | 3.93 | 3930 | 0.0592 |
| 0.1351 | 3.94 | 3940 | 0.0592 |
| 0.1886 | 3.95 | 3950 | 0.0593 |
| 0.1387 | 3.96 | 3960 | 0.0597 |
| 0.1532 | 3.97 | 3970 | 0.0599 |
| 0.0992 | 3.98 | 3980 | 0.0599 |
| 0.1444 | 3.99 | 3990 | 0.0598 |
| 0.1382 | 4.0 | 4000 | 0.0599 |
| 0.1647 | 4.01 | 4010 | 0.0600 |
| 0.1551 | 4.02 | 4020 | 0.0604 |
| 0.1777 | 4.03 | 4030 | 0.0606 |
| 0.1395 | 4.04 | 4040 | 0.0608 |
| 0.1312 | 4.05 | 4050 | 0.0609 |
| 0.143 | 4.06 | 4060 | 0.0610 |
| 0.106 | 4.07 | 4070 | 0.0608 |
| 0.168 | 4.08 | 4080 | 0.0607 |
| 0.0988 | 4.09 | 4090 | 0.0604 |
| 0.1718 | 4.1 | 4100 | 0.0602 |
| 0.1607 | 4.11 | 4110 | 0.0602 |
| 0.1428 | 4.12 | 4120 | 0.0601 |
| 0.1518 | 4.13 | 4130 | 0.0599 |
| 0.1941 | 4.14 | 4140 | 0.0600 |
| 0.1339 | 4.15 | 4150 | 0.0599 |
| 0.1379 | 4.16 | 4160 | 0.0599 |
| 0.218 | 4.17 | 4170 | 0.0600 |
| 0.1359 | 4.18 | 4180 | 0.0602 |
| 0.1941 | 4.19 | 4190 | 0.0602 |
| 0.1182 | 4.2 | 4200 | 0.0602 |
| 0.1398 | 4.21 | 4210 | 0.0602 |
| 0.1385 | 4.22 | 4220 | 0.0601 |
| 0.1652 | 4.23 | 4230 | 0.0599 |
| 0.0985 | 4.24 | 4240 | 0.0599 |
| 0.1342 | 4.25 | 4250 | 0.0602 |
| 0.1767 | 4.26 | 4260 | 0.0606 |
| 0.1621 | 4.27 | 4270 | 0.0609 |
| 0.1813 | 4.28 | 4280 | 0.0612 |
| 0.14 | 4.29 | 4290 | 0.0613 |
| 0.1726 | 4.3 | 4300 | 0.0615 |
| 0.1031 | 4.31 | 4310 | 0.0617 |
| 0.1429 | 4.32 | 4320 | 0.0617 |
| 0.1956 | 4.33 | 4330 | 0.0615 |
| 0.1515 | 4.34 | 4340 | 0.0613 |
| 0.1109 | 4.35 | 4350 | 0.0614 |
| 0.1102 | 4.36 | 4360 | 0.0617 |
| 0.2273 | 4.37 | 4370 | 0.0617 |
| 0.1179 | 4.38 | 4380 | 0.0617 |
| 0.1186 | 4.39 | 4390 | 0.0617 |
| 0.1259 | 4.4 | 4400 | 0.0611 |
| 0.1914 | 4.41 | 4410 | 0.0610 |
| 0.1552 | 4.42 | 4420 | 0.0612 |
| 0.1703 | 4.43 | 4430 | 0.0614 |
| 0.1549 | 4.44 | 4440 | 0.0612 |
| 0.1307 | 4.45 | 4450 | 0.0611 |
| 0.1853 | 4.46 | 4460 | 0.0609 |
| 0.1055 | 4.47 | 4470 | 0.0606 |
| 0.1342 | 4.48 | 4480 | 0.0606 |
| 0.0804 | 4.49 | 4490 | 0.0609 |
| 0.1566 | 4.5 | 4500 | 0.0610 |
| 0.1472 | 4.51 | 4510 | 0.0613 |
| 0.2179 | 4.52 | 4520 | 0.0613 |
| 0.1365 | 4.53 | 4530 | 0.0614 |
| 0.157 | 4.54 | 4540 | 0.0610 |
| 0.1515 | 4.55 | 4550 | 0.0609 |
| 0.1678 | 4.56 | 4560 | 0.0611 |
| 0.1569 | 4.57 | 4570 | 0.0613 |
| 0.1257 | 4.58 | 4580 | 0.0614 |
| 0.1452 | 4.59 | 4590 | 0.0612 |
| 0.1275 | 4.6 | 4600 | 0.0612 |
| 0.1446 | 4.61 | 4610 | 0.0613 |
| 0.1571 | 4.62 | 4620 | 0.0613 |
| 0.1371 | 4.63 | 4630 | 0.0612 |
| 0.1152 | 4.64 | 4640 | 0.0612 |
| 0.1797 | 4.65 | 4650 | 0.0613 |
| 0.0911 | 4.66 | 4660 | 0.0613 |
| 0.1463 | 4.67 | 4670 | 0.0612 |
| 0.1428 | 4.68 | 4680 | 0.0610 |
| 0.1489 | 4.69 | 4690 | 0.0612 |
| 0.1344 | 4.7 | 4700 | 0.0615 |
| 0.1493 | 4.71 | 4710 | 0.0615 |
| 0.147 | 4.72 | 4720 | 0.0613 |
| 0.2329 | 4.73 | 4730 | 0.0606 |
| 0.1679 | 4.74 | 4740 | 0.0602 |
| 0.0977 | 4.75 | 4750 | 0.0602 |
| 0.1292 | 4.76 | 4760 | 0.0603 |
| 0.1301 | 4.77 | 4770 | 0.0606 |
| 0.1855 | 4.78 | 4780 | 0.0609 |
| 0.1647 | 4.79 | 4790 | 0.0607 |
| 0.1395 | 4.8 | 4800 | 0.0604 |
| 0.1692 | 4.81 | 4810 | 0.0604 |
| 0.1135 | 4.82 | 4820 | 0.0604 |
| 0.1075 | 4.83 | 4830 | 0.0605 |
| 0.188 | 4.84 | 4840 | 0.0604 |
| 0.168 | 4.85 | 4850 | 0.0605 |
| 0.1356 | 4.86 | 4860 | 0.0603 |
| 0.134 | 4.87 | 4870 | 0.0603 |
| 0.1245 | 4.88 | 4880 | 0.0604 |
| 0.1816 | 4.89 | 4890 | 0.0605 |
| 0.1144 | 4.9 | 4900 | 0.0607 |
| 0.1651 | 4.91 | 4910 | 0.0610 |
| 0.1725 | 4.92 | 4920 | 0.0610 |
| 0.0947 | 4.93 | 4930 | 0.0612 |
| 0.118 | 4.94 | 4940 | 0.0615 |
| 0.1341 | 4.95 | 4950 | 0.0615 |
| 0.1324 | 4.96 | 4960 | 0.0618 |
| 0.1321 | 4.97 | 4970 | 0.0622 |
| 0.1485 | 4.98 | 4980 | 0.0624 |
| 0.1445 | 4.99 | 4990 | 0.0626 |
| 0.1793 | 5.0 | 5000 | 0.0627 |
| 0.1374 | 5.01 | 5010 | 0.0623 |
| 0.1726 | 5.02 | 5020 | 0.0619 |
| 0.178 | 5.03 | 5030 | 0.0618 |
| 0.1814 | 5.04 | 5040 | 0.0623 |
| 0.1533 | 5.05 | 5050 | 0.0625 |
| 0.1618 | 5.06 | 5060 | 0.0627 |
| 0.1502 | 5.07 | 5070 | 0.0625 |
| 0.1228 | 5.08 | 5080 | 0.0622 |
| 0.1544 | 5.09 | 5090 | 0.0621 |
| 0.1253 | 5.1 | 5100 | 0.0620 |
| 0.2073 | 5.11 | 5110 | 0.0619 |
| 0.13 | 5.12 | 5120 | 0.0620 |
| 0.1163 | 5.13 | 5130 | 0.0623 |
| 0.1154 | 5.14 | 5140 | 0.0628 |
| 0.1249 | 5.15 | 5150 | 0.0631 |
| 0.1783 | 5.16 | 5160 | 0.0633 |
| 0.1536 | 5.17 | 5170 | 0.0633 |
| 0.1679 | 5.18 | 5180 | 0.0631 |
| 0.1164 | 5.19 | 5190 | 0.0629 |
| 0.1213 | 5.2 | 5200 | 0.0628 |
| 0.1076 | 5.21 | 5210 | 0.0626 |
| 0.1332 | 5.22 | 5220 | 0.0625 |
| 0.1406 | 5.23 | 5230 | 0.0626 |
| 0.096 | 5.24 | 5240 | 0.0628 |
| 0.1556 | 5.25 | 5250 | 0.0631 |
| 0.1363 | 5.26 | 5260 | 0.0629 |
| 0.1223 | 5.27 | 5270 | 0.0628 |
| 0.226 | 5.28 | 5280 | 0.0630 |
| 0.1957 | 5.29 | 5290 | 0.0634 |
| 0.1303 | 5.3 | 5300 | 0.0634 |
| 0.1123 | 5.31 | 5310 | 0.0633 |
| 0.1753 | 5.32 | 5320 | 0.0634 |
| 0.1642 | 5.33 | 5330 | 0.0634 |
| 0.1412 | 5.34 | 5340 | 0.0633 |
| 0.1732 | 5.35 | 5350 | 0.0631 |
| 0.134 | 5.36 | 5360 | 0.0629 |
| 0.1596 | 5.37 | 5370 | 0.0627 |
| 0.1049 | 5.38 | 5380 | 0.0622 |
| 0.1108 | 5.39 | 5390 | 0.0620 |
| 0.1326 | 5.4 | 5400 | 0.0622 |
| 0.1676 | 5.41 | 5410 | 0.0622 |
| 0.1327 | 5.42 | 5420 | 0.0621 |
| 0.0943 | 5.43 | 5430 | 0.0620 |
| 0.0914 | 5.44 | 5440 | 0.0620 |
| 0.1201 | 5.45 | 5450 | 0.0620 |
| 0.1441 | 5.46 | 5460 | 0.0619 |
| 0.149 | 5.47 | 5470 | 0.0621 |
| 0.0949 | 5.48 | 5480 | 0.0622 |
| 0.1606 | 5.49 | 5490 | 0.0623 |
| 0.1151 | 5.5 | 5500 | 0.0626 |
| 0.1613 | 5.51 | 5510 | 0.0627 |
| 0.189 | 5.52 | 5520 | 0.0629 |
| 0.1084 | 5.53 | 5530 | 0.0631 |
| 0.1285 | 5.54 | 5540 | 0.0632 |
| 0.1509 | 5.55 | 5550 | 0.0633 |
| 0.1201 | 5.56 | 5560 | 0.0637 |
| 0.148 | 5.57 | 5570 | 0.0636 |
| 0.148 | 5.58 | 5580 | 0.0634 |
| 0.1019 | 5.59 | 5590 | 0.0634 |
| 0.1447 | 5.6 | 5600 | 0.0634 |
| 0.1521 | 5.61 | 5610 | 0.0636 |
| 0.19 | 5.62 | 5620 | 0.0635 |
| 0.1164 | 5.63 | 5630 | 0.0633 |
| 0.1488 | 5.64 | 5640 | 0.0633 |
| 0.1114 | 5.65 | 5650 | 0.0631 |
| 0.1373 | 5.66 | 5660 | 0.0626 |
| 0.0925 | 5.67 | 5670 | 0.0624 |
| 0.1138 | 5.68 | 5680 | 0.0621 |
| 0.1219 | 5.69 | 5690 | 0.0620 |
| 0.1692 | 5.7 | 5700 | 0.0622 |
| 0.1941 | 5.71 | 5710 | 0.0626 |
| 0.1725 | 5.72 | 5720 | 0.0626 |
| 0.1028 | 5.73 | 5730 | 0.0627 |
| 0.1359 | 5.74 | 5740 | 0.0627 |
| 0.1321 | 5.75 | 5750 | 0.0629 |
| 0.1093 | 5.76 | 5760 | 0.0630 |
| 0.1399 | 5.77 | 5770 | 0.0631 |
| 0.1117 | 5.78 | 5780 | 0.0632 |
| 0.154 | 5.79 | 5790 | 0.0634 |
| 0.1628 | 5.8 | 5800 | 0.0637 |
| 0.2267 | 5.81 | 5810 | 0.0639 |
| 0.1716 | 5.82 | 5820 | 0.0639 |
| 0.165 | 5.83 | 5830 | 0.0640 |
| 0.1013 | 5.84 | 5840 | 0.0641 |
| 0.1417 | 5.85 | 5850 | 0.0641 |
| 0.1607 | 5.86 | 5860 | 0.0639 |
| 0.1191 | 5.87 | 5870 | 0.0638 |
| 0.1549 | 5.88 | 5880 | 0.0635 |
| 0.1906 | 5.89 | 5890 | 0.0635 |
| 0.1307 | 5.9 | 5900 | 0.0636 |
| 0.123 | 5.91 | 5910 | 0.0636 |
| 0.1389 | 5.92 | 5920 | 0.0636 |
| 0.1152 | 5.93 | 5930 | 0.0637 |
| 0.1267 | 5.94 | 5940 | 0.0638 |
| 0.1301 | 5.95 | 5950 | 0.0640 |
| 0.1583 | 5.96 | 5960 | 0.0642 |
| 0.1958 | 5.97 | 5970 | 0.0644 |
| 0.1591 | 5.98 | 5980 | 0.0644 |
| 0.2638 | 5.99 | 5990 | 0.0643 |
| 0.1605 | 6.0 | 6000 | 0.0644 |
| 0.1227 | 6.01 | 6010 | 0.0643 |
| 0.1721 | 6.02 | 6020 | 0.0642 |
| 0.1828 | 6.03 | 6030 | 0.0643 |
| 0.0953 | 6.04 | 6040 | 0.0643 |
| 0.1538 | 6.05 | 6050 | 0.0643 |
| 0.1403 | 6.06 | 6060 | 0.0643 |
| 0.1094 | 6.07 | 6070 | 0.0641 |
| 0.1493 | 6.08 | 6080 | 0.0645 |
| 0.1313 | 6.09 | 6090 | 0.0647 |
| 0.158 | 6.1 | 6100 | 0.0650 |
| 0.1184 | 6.11 | 6110 | 0.0650 |
| 0.0781 | 6.12 | 6120 | 0.0648 |
| 0.121 | 6.13 | 6130 | 0.0649 |
| 0.1694 | 6.14 | 6140 | 0.0649 |
| 0.1687 | 6.15 | 6150 | 0.0646 |
| 0.1408 | 6.16 | 6160 | 0.0645 |
| 0.1807 | 6.17 | 6170 | 0.0645 |
| 0.109 | 6.18 | 6180 | 0.0644 |
| 0.1266 | 6.19 | 6190 | 0.0644 |
| 0.0925 | 6.2 | 6200 | 0.0649 |
| 0.1768 | 6.21 | 6210 | 0.0649 |
| 0.1434 | 6.22 | 6220 | 0.0650 |
| 0.1449 | 6.23 | 6230 | 0.0645 |
| 0.0938 | 6.24 | 6240 | 0.0644 |
| 0.1336 | 6.25 | 6250 | 0.0641 |
| 0.1798 | 6.26 | 6260 | 0.0641 |
| 0.1549 | 6.27 | 6270 | 0.0643 |
| 0.1151 | 6.28 | 6280 | 0.0644 |
| 0.1468 | 6.29 | 6290 | 0.0645 |
| 0.1169 | 6.3 | 6300 | 0.0644 |
| 0.197 | 6.31 | 6310 | 0.0645 |
| 0.1409 | 6.32 | 6320 | 0.0646 |
| 0.1861 | 6.33 | 6330 | 0.0645 |
| 0.1417 | 6.34 | 6340 | 0.0645 |
| 0.1526 | 6.35 | 6350 | 0.0645 |
| 0.1577 | 6.36 | 6360 | 0.0646 |
| 0.104 | 6.37 | 6370 | 0.0645 |
| 0.1371 | 6.38 | 6380 | 0.0645 |
| 0.1126 | 6.39 | 6390 | 0.0646 |
| 0.2212 | 6.4 | 6400 | 0.0647 |
| 0.1324 | 6.41 | 6410 | 0.0648 |
| 0.1478 | 6.42 | 6420 | 0.0650 |
| 0.1995 | 6.43 | 6430 | 0.0647 |
| 0.1495 | 6.44 | 6440 | 0.0646 |
| 0.108 | 6.45 | 6450 | 0.0645 |
| 0.1268 | 6.46 | 6460 | 0.0641 |
| 0.1233 | 6.47 | 6470 | 0.0639 |
| 0.1222 | 6.48 | 6480 | 0.0634 |
| 0.1324 | 6.49 | 6490 | 0.0630 |
| 0.1461 | 6.5 | 6500 | 0.0627 |
| 0.1541 | 6.51 | 6510 | 0.0625 |
| 0.1797 | 6.52 | 6520 | 0.0623 |
| 0.1712 | 6.53 | 6530 | 0.0623 |
| 0.2081 | 6.54 | 6540 | 0.0627 |
| 0.166 | 6.55 | 6550 | 0.0628 |
| 0.1498 | 6.56 | 6560 | 0.0629 |
| 0.1427 | 6.57 | 6570 | 0.0628 |
| 0.1548 | 6.58 | 6580 | 0.0629 |
| 0.13 | 6.59 | 6590 | 0.0629 |
| 0.1655 | 6.6 | 6600 | 0.0628 |
| 0.1734 | 6.61 | 6610 | 0.0630 |
| 0.1182 | 6.62 | 6620 | 0.0631 |
| 0.1494 | 6.63 | 6630 | 0.0631 |
| 0.1392 | 6.64 | 6640 | 0.0630 |
| 0.1464 | 6.65 | 6650 | 0.0633 |
| 0.1374 | 6.66 | 6660 | 0.0634 |
| 0.1406 | 6.67 | 6670 | 0.0633 |
| 0.166 | 6.68 | 6680 | 0.0632 |
| 0.089 | 6.69 | 6690 | 0.0632 |
| 0.1177 | 6.7 | 6700 | 0.0634 |
| 0.1074 | 6.71 | 6710 | 0.0635 |
| 0.1224 | 6.72 | 6720 | 0.0636 |
| 0.1754 | 6.73 | 6730 | 0.0634 |
| 0.1731 | 6.74 | 6740 | 0.0632 |
| 0.1566 | 6.75 | 6750 | 0.0631 |
| 0.1139 | 6.76 | 6760 | 0.0633 |
| 0.1255 | 6.77 | 6770 | 0.0634 |
| 0.166 | 6.78 | 6780 | 0.0636 |
| 0.1192 | 6.79 | 6790 | 0.0638 |
| 0.1203 | 6.8 | 6800 | 0.0639 |
| 0.1021 | 6.81 | 6810 | 0.0641 |
| 0.141 | 6.82 | 6820 | 0.0641 |
| 0.1272 | 6.83 | 6830 | 0.0643 |
| 0.1449 | 6.84 | 6840 | 0.0645 |
| 0.1459 | 6.85 | 6850 | 0.0645 |
| 0.094 | 6.86 | 6860 | 0.0644 |
| 0.1866 | 6.87 | 6870 | 0.0644 |
| 0.1521 | 6.88 | 6880 | 0.0646 |
| 0.1423 | 6.89 | 6890 | 0.0647 |
| 0.1465 | 6.9 | 6900 | 0.0648 |
| 0.1328 | 6.91 | 6910 | 0.0651 |
| 0.1 | 6.92 | 6920 | 0.0653 |
| 0.1292 | 6.93 | 6930 | 0.0653 |
| 0.1406 | 6.94 | 6940 | 0.0650 |
| 0.116 | 6.95 | 6950 | 0.0648 |
| 0.1355 | 6.96 | 6960 | 0.0645 |
| 0.1491 | 6.97 | 6970 | 0.0643 |
| 0.1359 | 6.98 | 6980 | 0.0643 |
| 0.1374 | 6.99 | 6990 | 0.0644 |
| 0.1129 | 7.0 | 7000 | 0.0645 |
| 0.1317 | 7.01 | 7010 | 0.0645 |
| 0.2238 | 7.02 | 7020 | 0.0646 |
| 0.1062 | 7.03 | 7030 | 0.0645 |
| 0.1742 | 7.04 | 7040 | 0.0646 |
| 0.1234 | 7.05 | 7050 | 0.0649 |
| 0.1861 | 7.06 | 7060 | 0.0649 |
| 0.1154 | 7.07 | 7070 | 0.0647 |
| 0.1185 | 7.08 | 7080 | 0.0647 |
| 0.1065 | 7.09 | 7090 | 0.0648 |
| 0.1047 | 7.1 | 7100 | 0.0649 |
| 0.1669 | 7.11 | 7110 | 0.0647 |
| 0.1304 | 7.12 | 7120 | 0.0646 |
| 0.0801 | 7.13 | 7130 | 0.0646 |
| 0.1261 | 7.14 | 7140 | 0.0648 |
| 0.171 | 7.15 | 7150 | 0.0648 |
| 0.1338 | 7.16 | 7160 | 0.0647 |
| 0.1074 | 7.17 | 7170 | 0.0646 |
| 0.1485 | 7.18 | 7180 | 0.0645 |
| 0.1314 | 7.19 | 7190 | 0.0645 |
| 0.1507 | 7.2 | 7200 | 0.0646 |
| 0.1734 | 7.21 | 7210 | 0.0646 |
| 0.1873 | 7.22 | 7220 | 0.0647 |
| 0.1544 | 7.23 | 7230 | 0.0645 |
| 0.1222 | 7.24 | 7240 | 0.0646 |
| 0.157 | 7.25 | 7250 | 0.0644 |
| 0.1391 | 7.26 | 7260 | 0.0644 |
| 0.162 | 7.27 | 7270 | 0.0644 |
| 0.1089 | 7.28 | 7280 | 0.0644 |
| 0.125 | 7.29 | 7290 | 0.0643 |
| 0.1363 | 7.3 | 7300 | 0.0643 |
| 0.1205 | 7.31 | 7310 | 0.0645 |
| 0.1116 | 7.32 | 7320 | 0.0648 |
| 0.1194 | 7.33 | 7330 | 0.0648 |
| 0.153 | 7.34 | 7340 | 0.0649 |
| 0.1262 | 7.35 | 7350 | 0.0647 |
| 0.1234 | 7.36 | 7360 | 0.0646 |
| 0.1381 | 7.37 | 7370 | 0.0646 |
| 0.1108 | 7.38 | 7380 | 0.0647 |
| 0.1719 | 7.39 | 7390 | 0.0645 |
| 0.1293 | 7.4 | 7400 | 0.0644 |
| 0.1562 | 7.41 | 7410 | 0.0643 |
| 0.149 | 7.42 | 7420 | 0.0644 |
| 0.0926 | 7.43 | 7430 | 0.0641 |
| 0.1603 | 7.44 | 7440 | 0.0640 |
| 0.1599 | 7.45 | 7450 | 0.0639 |
| 0.1281 | 7.46 | 7460 | 0.0640 |
| 0.146 | 7.47 | 7470 | 0.0643 |
| 0.1498 | 7.48 | 7480 | 0.0643 |
| 0.1699 | 7.49 | 7490 | 0.0644 |
| 0.1092 | 7.5 | 7500 | 0.0646 |
| 0.1338 | 7.51 | 7510 | 0.0646 |
| 0.1532 | 7.52 | 7520 | 0.0643 |
| 0.1016 | 7.53 | 7530 | 0.0642 |
| 0.1286 | 7.54 | 7540 | 0.0643 |
| 0.1242 | 7.55 | 7550 | 0.0642 |
| 0.1485 | 7.56 | 7560 | 0.0641 |
| 0.1754 | 7.57 | 7570 | 0.0639 |
| 0.1637 | 7.58 | 7580 | 0.0639 |
| 0.1075 | 7.59 | 7590 | 0.0639 |
| 0.1288 | 7.6 | 7600 | 0.0640 |
| 0.1639 | 7.61 | 7610 | 0.0640 |
| 0.1185 | 7.62 | 7620 | 0.0640 |
| 0.1321 | 7.63 | 7630 | 0.0639 |
| 0.1589 | 7.64 | 7640 | 0.0636 |
| 0.094 | 7.65 | 7650 | 0.0634 |
| 0.1115 | 7.66 | 7660 | 0.0635 |
| 0.1365 | 7.67 | 7670 | 0.0635 |
| 0.1567 | 7.68 | 7680 | 0.0636 |
| 0.182 | 7.69 | 7690 | 0.0636 |
| 0.1195 | 7.7 | 7700 | 0.0636 |
| 0.158 | 7.71 | 7710 | 0.0637 |
| 0.1354 | 7.72 | 7720 | 0.0638 |
| 0.1508 | 7.73 | 7730 | 0.0637 |
| 0.0876 | 7.74 | 7740 | 0.0639 |
| 0.1241 | 7.75 | 7750 | 0.0640 |
| 0.2049 | 7.76 | 7760 | 0.0641 |
| 0.1859 | 7.77 | 7770 | 0.0645 |
| 0.1036 | 7.78 | 7780 | 0.0645 |
| 0.1015 | 7.79 | 7790 | 0.0643 |
| 0.1235 | 7.8 | 7800 | 0.0639 |
| 0.1594 | 7.81 | 7810 | 0.0640 |
| 0.1295 | 7.82 | 7820 | 0.0642 |
| 0.152 | 7.83 | 7830 | 0.0645 |
| 0.1496 | 7.84 | 7840 | 0.0647 |
| 0.1353 | 7.85 | 7850 | 0.0648 |
| 0.1206 | 7.86 | 7860 | 0.0649 |
| 0.1627 | 7.87 | 7870 | 0.0651 |
| 0.1132 | 7.88 | 7880 | 0.0653 |
| 0.154 | 7.89 | 7890 | 0.0652 |
| 0.153 | 7.9 | 7900 | 0.0649 |
| 0.1225 | 7.91 | 7910 | 0.0648 |
| 0.1494 | 7.92 | 7920 | 0.0648 |
| 0.1358 | 7.93 | 7930 | 0.0647 |
| 0.1137 | 7.94 | 7940 | 0.0648 |
| 0.1546 | 7.95 | 7950 | 0.0647 |
| 0.114 | 7.96 | 7960 | 0.0644 |
| 0.1939 | 7.97 | 7970 | 0.0644 |
| 0.1276 | 7.98 | 7980 | 0.0641 |
| 0.1096 | 7.99 | 7990 | 0.0639 |
| 0.1764 | 8.0 | 8000 | 0.0639 |
| 0.1029 | 8.01 | 8010 | 0.0642 |
| 0.1344 | 8.02 | 8020 | 0.0641 |
| 0.1422 | 8.03 | 8030 | 0.0643 |
| 0.1055 | 8.04 | 8040 | 0.0645 |
| 0.1231 | 8.05 | 8050 | 0.0646 |
| 0.1303 | 8.06 | 8060 | 0.0648 |
| 0.1421 | 8.07 | 8070 | 0.0651 |
| 0.1325 | 8.08 | 8080 | 0.0651 |
| 0.0797 | 8.09 | 8090 | 0.0649 |
| 0.0961 | 8.1 | 8100 | 0.0647 |
| 0.1156 | 8.11 | 8110 | 0.0647 |
| 0.1529 | 8.12 | 8120 | 0.0648 |
| 0.1756 | 8.13 | 8130 | 0.0648 |
| 0.1158 | 8.14 | 8140 | 0.0651 |
| 0.1302 | 8.15 | 8150 | 0.0654 |
| 0.1404 | 8.16 | 8160 | 0.0658 |
| 0.1445 | 8.17 | 8170 | 0.0662 |
| 0.1499 | 8.18 | 8180 | 0.0662 |
| 0.1375 | 8.19 | 8190 | 0.0663 |
| 0.1663 | 8.2 | 8200 | 0.0662 |
| 0.1744 | 8.21 | 8210 | 0.0661 |
| 0.1414 | 8.22 | 8220 | 0.0658 |
| 0.0964 | 8.23 | 8230 | 0.0658 |
| 0.1214 | 8.24 | 8240 | 0.0655 |
| 0.1492 | 8.25 | 8250 | 0.0653 |
| 0.1534 | 8.26 | 8260 | 0.0654 |
| 0.1038 | 8.27 | 8270 | 0.0655 |
| 0.1779 | 8.28 | 8280 | 0.0655 |
| 0.1801 | 8.29 | 8290 | 0.0657 |
| 0.1314 | 8.3 | 8300 | 0.0658 |
| 0.1598 | 8.31 | 8310 | 0.0657 |
| 0.1078 | 8.32 | 8320 | 0.0653 |
| 0.1672 | 8.33 | 8330 | 0.0649 |
| 0.113 | 8.34 | 8340 | 0.0649 |
| 0.1046 | 8.35 | 8350 | 0.0650 |
| 0.1625 | 8.36 | 8360 | 0.0650 |
| 0.1112 | 8.37 | 8370 | 0.0650 |
| 0.1278 | 8.38 | 8380 | 0.0649 |
| 0.1684 | 8.39 | 8390 | 0.0647 |
| 0.2318 | 8.4 | 8400 | 0.0647 |
| 0.1687 | 8.41 | 8410 | 0.0648 |
| 0.1283 | 8.42 | 8420 | 0.0649 |
| 0.1107 | 8.43 | 8430 | 0.0650 |
| 0.1092 | 8.44 | 8440 | 0.0652 |
| 0.0851 | 8.45 | 8450 | 0.0653 |
| 0.1263 | 8.46 | 8460 | 0.0656 |
| 0.1936 | 8.47 | 8470 | 0.0656 |
| 0.1608 | 8.48 | 8480 | 0.0656 |
| 0.1023 | 8.49 | 8490 | 0.0656 |
| 0.1153 | 8.5 | 8500 | 0.0658 |
| 0.1682 | 8.51 | 8510 | 0.0659 |
| 0.1407 | 8.52 | 8520 | 0.0660 |
| 0.104 | 8.53 | 8530 | 0.0660 |
| 0.1371 | 8.54 | 8540 | 0.0659 |
| 0.1068 | 8.55 | 8550 | 0.0659 |
| 0.1418 | 8.56 | 8560 | 0.0660 |
| 0.1451 | 8.57 | 8570 | 0.0660 |
| 0.1379 | 8.58 | 8580 | 0.0661 |
| 0.1732 | 8.59 | 8590 | 0.0660 |
| 0.1287 | 8.6 | 8600 | 0.0658 |
| 0.1821 | 8.61 | 8610 | 0.0657 |
| 0.1184 | 8.62 | 8620 | 0.0657 |
| 0.1194 | 8.63 | 8630 | 0.0658 |
| 0.083 | 8.64 | 8640 | 0.0660 |
| 0.1724 | 8.65 | 8650 | 0.0661 |
| 0.1504 | 8.66 | 8660 | 0.0659 |
| 0.1338 | 8.67 | 8670 | 0.0658 |
| 0.1372 | 8.68 | 8680 | 0.0658 |
| 0.1367 | 8.69 | 8690 | 0.0658 |
| 0.1665 | 8.7 | 8700 | 0.0659 |
| 0.1389 | 8.71 | 8710 | 0.0660 |
| 0.1272 | 8.72 | 8720 | 0.0660 |
| 0.1595 | 8.73 | 8730 | 0.0659 |
| 0.1644 | 8.74 | 8740 | 0.0658 |
| 0.1249 | 8.75 | 8750 | 0.0658 |
| 0.1276 | 8.76 | 8760 | 0.0658 |
| 0.1103 | 8.77 | 8770 | 0.0657 |
| 0.1664 | 8.78 | 8780 | 0.0658 |
| 0.1832 | 8.79 | 8790 | 0.0661 |
| 0.1075 | 8.8 | 8800 | 0.0662 |
| 0.1526 | 8.81 | 8810 | 0.0663 |
| 0.1215 | 8.82 | 8820 | 0.0666 |
| 0.1317 | 8.83 | 8830 | 0.0668 |
| 0.1425 | 8.84 | 8840 | 0.0668 |
| 0.1572 | 8.85 | 8850 | 0.0668 |
| 0.1012 | 8.86 | 8860 | 0.0667 |
| 0.1222 | 8.87 | 8870 | 0.0665 |
| 0.1723 | 8.88 | 8880 | 0.0665 |
| 0.1139 | 8.89 | 8890 | 0.0665 |
| 0.1351 | 8.9 | 8900 | 0.0665 |
| 0.1258 | 8.91 | 8910 | 0.0666 |
| 0.1387 | 8.92 | 8920 | 0.0667 |
| 0.1554 | 8.93 | 8930 | 0.0664 |
| 0.1454 | 8.94 | 8940 | 0.0662 |
| 0.1066 | 8.95 | 8950 | 0.0661 |
| 0.1047 | 8.96 | 8960 | 0.0659 |
| 0.168 | 8.97 | 8970 | 0.0658 |
| 0.1162 | 8.98 | 8980 | 0.0656 |
| 0.109 | 8.99 | 8990 | 0.0654 |
| 0.1262 | 9.0 | 9000 | 0.0653 |
| 0.1563 | 9.01 | 9010 | 0.0651 |
| 0.13 | 9.02 | 9020 | 0.0650 |
| 0.1608 | 9.03 | 9030 | 0.0651 |
| 0.1369 | 9.04 | 9040 | 0.0650 |
| 0.1386 | 9.05 | 9050 | 0.0646 |
| 0.0843 | 9.06 | 9060 | 0.0644 |
| 0.0719 | 9.07 | 9070 | 0.0643 |
| 0.146 | 9.08 | 9080 | 0.0641 |
| 0.132 | 9.09 | 9090 | 0.0640 |
| 0.1425 | 9.1 | 9100 | 0.0639 |
| 0.1097 | 9.11 | 9110 | 0.0640 |
| 0.1684 | 9.12 | 9120 | 0.0641 |
| 0.1891 | 9.13 | 9130 | 0.0640 |
| 0.1083 | 9.14 | 9140 | 0.0641 |
| 0.1265 | 9.15 | 9150 | 0.0643 |
| 0.1183 | 9.16 | 9160 | 0.0646 |
| 0.0971 | 9.17 | 9170 | 0.0647 |
| 0.17 | 9.18 | 9180 | 0.0645 |
| 0.1074 | 9.19 | 9190 | 0.0646 |
| 0.1517 | 9.2 | 9200 | 0.0647 |
| 0.1763 | 9.21 | 9210 | 0.0648 |
| 0.1031 | 9.22 | 9220 | 0.0647 |
| 0.1419 | 9.23 | 9230 | 0.0647 |
| 0.1451 | 9.24 | 9240 | 0.0649 |
| 0.1657 | 9.25 | 9250 | 0.0651 |
| 0.1327 | 9.26 | 9260 | 0.0652 |
| 0.1279 | 9.27 | 9270 | 0.0653 |
| 0.111 | 9.28 | 9280 | 0.0656 |
| 0.1062 | 9.29 | 9290 | 0.0658 |
| 0.1185 | 9.3 | 9300 | 0.0657 |
| 0.1539 | 9.31 | 9310 | 0.0658 |
| 0.2525 | 9.32 | 9320 | 0.0656 |
| 0.0985 | 9.33 | 9330 | 0.0655 |
| 0.1161 | 9.34 | 9340 | 0.0656 |
| 0.1462 | 9.35 | 9350 | 0.0655 |
| 0.1229 | 9.36 | 9360 | 0.0655 |
| 0.102 | 9.37 | 9370 | 0.0655 |
| 0.155 | 9.38 | 9380 | 0.0656 |
| 0.1747 | 9.39 | 9390 | 0.0657 |
| 0.0887 | 9.4 | 9400 | 0.0657 |
| 0.1341 | 9.41 | 9410 | 0.0654 |
| 0.1598 | 9.42 | 9420 | 0.0652 |
| 0.1299 | 9.43 | 9430 | 0.0654 |
| 0.0918 | 9.44 | 9440 | 0.0655 |
| 0.1463 | 9.45 | 9450 | 0.0654 |
| 0.1867 | 9.46 | 9460 | 0.0654 |
| 0.1602 | 9.47 | 9470 | 0.0653 |
| 0.1077 | 9.48 | 9480 | 0.0653 |
| 0.1052 | 9.49 | 9490 | 0.0653 |
| 0.1283 | 9.5 | 9500 | 0.0650 |
| 0.1888 | 9.51 | 9510 | 0.0647 |
| 0.1381 | 9.52 | 9520 | 0.0646 |
| 0.0842 | 9.53 | 9530 | 0.0645 |
| 0.086 | 9.54 | 9540 | 0.0644 |
| 0.1155 | 9.55 | 9550 | 0.0645 |
| 0.114 | 9.56 | 9560 | 0.0646 |
| 0.1042 | 9.57 | 9570 | 0.0649 |
| 0.1629 | 9.58 | 9580 | 0.0651 |
| 0.1641 | 9.59 | 9590 | 0.0652 |
| 0.1158 | 9.6 | 9600 | 0.0654 |
| 0.0971 | 9.61 | 9610 | 0.0656 |
| 0.1908 | 9.62 | 9620 | 0.0656 |
| 0.1158 | 9.63 | 9630 | 0.0656 |
| 0.1543 | 9.64 | 9640 | 0.0656 |
| 0.1667 | 9.65 | 9650 | 0.0656 |
| 0.1439 | 9.66 | 9660 | 0.0656 |
| 0.1375 | 9.67 | 9670 | 0.0658 |
| 0.1048 | 9.68 | 9680 | 0.0660 |
| 0.1551 | 9.69 | 9690 | 0.0659 |
| 0.1096 | 9.7 | 9700 | 0.0658 |
| 0.1017 | 9.71 | 9710 | 0.0657 |
| 0.1198 | 9.72 | 9720 | 0.0657 |
| 0.1345 | 9.73 | 9730 | 0.0657 |
| 0.1268 | 9.74 | 9740 | 0.0657 |
| 0.124 | 9.75 | 9750 | 0.0657 |
| 0.1313 | 9.76 | 9760 | 0.0658 |
| 0.1956 | 9.77 | 9770 | 0.0658 |
| 0.0982 | 9.78 | 9780 | 0.0658 |
| 0.1091 | 9.79 | 9790 | 0.0658 |
| 0.1686 | 9.8 | 9800 | 0.0658 |
| 0.1185 | 9.81 | 9810 | 0.0656 |
| 0.1886 | 9.82 | 9820 | 0.0654 |
| 0.0923 | 9.83 | 9830 | 0.0653 |
| 0.1339 | 9.84 | 9840 | 0.0651 |
| 0.1813 | 9.85 | 9850 | 0.0651 |
| 0.1073 | 9.86 | 9860 | 0.0651 |
| 0.1452 | 9.87 | 9870 | 0.0650 |
| 0.138 | 9.88 | 9880 | 0.0648 |
| 0.1151 | 9.89 | 9890 | 0.0648 |
| 0.1364 | 9.9 | 9900 | 0.0647 |
| 0.1233 | 9.91 | 9910 | 0.0646 |
| 0.1475 | 9.92 | 9920 | 0.0647 |
| 0.157 | 9.93 | 9930 | 0.0647 |
| 0.0869 | 9.94 | 9940 | 0.0649 |
| 0.1193 | 9.95 | 9950 | 0.0650 |
| 0.1836 | 9.96 | 9960 | 0.0650 |
| 0.159 | 9.97 | 9970 | 0.0649 |
| 0.1575 | 9.98 | 9980 | 0.0650 |
| 0.141 | 9.99 | 9990 | 0.0649 |
| 0.1398 | 10.0 | 10000 | 0.0651 |
| 0.1547 | 10.01 | 10010 | 0.0650 |
| 0.1328 | 10.02 | 10020 | 0.0650 |
| 0.1164 | 10.03 | 10030 | 0.0649 |
| 0.1846 | 10.04 | 10040 | 0.0650 |
| 0.1184 | 10.05 | 10050 | 0.0651 |
| 0.1044 | 10.06 | 10060 | 0.0651 |
| 0.1384 | 10.07 | 10070 | 0.0650 |
| 0.1416 | 10.08 | 10080 | 0.0650 |
| 0.1547 | 10.09 | 10090 | 0.0650 |
| 0.0962 | 10.1 | 10100 | 0.0650 |
| 0.1004 | 10.11 | 10110 | 0.0651 |
| 0.155 | 10.12 | 10120 | 0.0652 |
| 0.1257 | 10.13 | 10130 | 0.0653 |
| 0.1257 | 10.14 | 10140 | 0.0653 |
| 0.0995 | 10.15 | 10150 | 0.0653 |
| 0.1006 | 10.16 | 10160 | 0.0655 |
| 0.0971 | 10.17 | 10170 | 0.0655 |
| 0.1214 | 10.18 | 10180 | 0.0657 |
| 0.1752 | 10.19 | 10190 | 0.0659 |
| 0.0797 | 10.2 | 10200 | 0.0659 |
| 0.1589 | 10.21 | 10210 | 0.0660 |
| 0.1616 | 10.22 | 10220 | 0.0661 |
| 0.086 | 10.23 | 10230 | 0.0662 |
| 0.1542 | 10.24 | 10240 | 0.0662 |
| 0.1493 | 10.25 | 10250 | 0.0660 |
| 0.1095 | 10.26 | 10260 | 0.0658 |
| 0.1545 | 10.27 | 10270 | 0.0657 |
| 0.1587 | 10.28 | 10280 | 0.0655 |
| 0.1758 | 10.29 | 10290 | 0.0655 |
| 0.1366 | 10.3 | 10300 | 0.0655 |
| 0.146 | 10.31 | 10310 | 0.0657 |
| 0.0977 | 10.32 | 10320 | 0.0660 |
| 0.085 | 10.33 | 10330 | 0.0663 |
| 0.1037 | 10.34 | 10340 | 0.0664 |
| 0.126 | 10.35 | 10350 | 0.0664 |
| 0.1065 | 10.36 | 10360 | 0.0665 |
| 0.0984 | 10.37 | 10370 | 0.0663 |
| 0.1078 | 10.38 | 10380 | 0.0661 |
| 0.17 | 10.39 | 10390 | 0.0659 |
| 0.1721 | 10.4 | 10400 | 0.0659 |
| 0.1347 | 10.41 | 10410 | 0.0657 |
| 0.2179 | 10.42 | 10420 | 0.0656 |
| 0.0688 | 10.43 | 10430 | 0.0656 |
| 0.1147 | 10.44 | 10440 | 0.0656 |
| 0.1239 | 10.45 | 10450 | 0.0656 |
| 0.1013 | 10.46 | 10460 | 0.0658 |
| 0.1596 | 10.47 | 10470 | 0.0661 |
| 0.101 | 10.48 | 10480 | 0.0662 |
| 0.1111 | 10.49 | 10490 | 0.0662 |
| 0.1179 | 10.5 | 10500 | 0.0662 |
| 0.1017 | 10.51 | 10510 | 0.0663 |
| 0.1477 | 10.52 | 10520 | 0.0661 |
| 0.1484 | 10.53 | 10530 | 0.0658 |
| 0.1598 | 10.54 | 10540 | 0.0659 |
| 0.1807 | 10.55 | 10550 | 0.0660 |
| 0.1616 | 10.56 | 10560 | 0.0660 |
| 0.1226 | 10.57 | 10570 | 0.0659 |
| 0.1563 | 10.58 | 10580 | 0.0656 |
| 0.1705 | 10.59 | 10590 | 0.0654 |
| 0.1488 | 10.6 | 10600 | 0.0655 |
| 0.1665 | 10.61 | 10610 | 0.0655 |
| 0.1165 | 10.62 | 10620 | 0.0655 |
| 0.1412 | 10.63 | 10630 | 0.0656 |
| 0.1515 | 10.64 | 10640 | 0.0657 |
| 0.1292 | 10.65 | 10650 | 0.0658 |
| 0.1175 | 10.66 | 10660 | 0.0657 |
| 0.1188 | 10.67 | 10670 | 0.0658 |
| 0.106 | 10.68 | 10680 | 0.0659 |
| 0.1239 | 10.69 | 10690 | 0.0659 |
| 0.1326 | 10.7 | 10700 | 0.0659 |
| 0.2108 | 10.71 | 10710 | 0.0661 |
| 0.1472 | 10.72 | 10720 | 0.0662 |
| 0.1237 | 10.73 | 10730 | 0.0662 |
| 0.1493 | 10.74 | 10740 | 0.0663 |
| 0.0985 | 10.75 | 10750 | 0.0664 |
| 0.1048 | 10.76 | 10760 | 0.0664 |
| 0.1236 | 10.77 | 10770 | 0.0665 |
| 0.108 | 10.78 | 10780 | 0.0667 |
| 0.1386 | 10.79 | 10790 | 0.0665 |
| 0.1501 | 10.8 | 10800 | 0.0664 |
| 0.1341 | 10.81 | 10810 | 0.0664 |
| 0.1171 | 10.82 | 10820 | 0.0665 |
| 0.1243 | 10.83 | 10830 | 0.0664 |
| 0.1225 | 10.84 | 10840 | 0.0662 |
| 0.2065 | 10.85 | 10850 | 0.0661 |
| 0.1032 | 10.86 | 10860 | 0.0661 |
| 0.1433 | 10.87 | 10870 | 0.0661 |
| 0.1933 | 10.88 | 10880 | 0.0662 |
| 0.1344 | 10.89 | 10890 | 0.0661 |
| 0.1046 | 10.9 | 10900 | 0.0662 |
| 0.1352 | 10.91 | 10910 | 0.0662 |
| 0.1302 | 10.92 | 10920 | 0.0663 |
| 0.1237 | 10.93 | 10930 | 0.0663 |
| 0.0985 | 10.94 | 10940 | 0.0664 |
| 0.1186 | 10.95 | 10950 | 0.0664 |
| 0.1929 | 10.96 | 10960 | 0.0664 |
| 0.1224 | 10.97 | 10970 | 0.0664 |
| 0.1753 | 10.98 | 10980 | 0.0664 |
| 0.1151 | 10.99 | 10990 | 0.0663 |
| 0.1213 | 11.0 | 11000 | 0.0661 |
| 0.148 | 11.01 | 11010 | 0.0660 |
| 0.1005 | 11.02 | 11020 | 0.0660 |
| 0.1579 | 11.03 | 11030 | 0.0660 |
| 0.1275 | 11.04 | 11040 | 0.0661 |
| 0.1498 | 11.05 | 11050 | 0.0664 |
| 0.1036 | 11.06 | 11060 | 0.0666 |
| 0.1002 | 11.07 | 11070 | 0.0668 |
| 0.1007 | 11.08 | 11080 | 0.0669 |
| 0.129 | 11.09 | 11090 | 0.0667 |
| 0.1988 | 11.1 | 11100 | 0.0666 |
| 0.1252 | 11.11 | 11110 | 0.0667 |
| 0.13 | 11.12 | 11120 | 0.0667 |
| 0.146 | 11.13 | 11130 | 0.0669 |
| 0.1384 | 11.14 | 11140 | 0.0669 |
| 0.1405 | 11.15 | 11150 | 0.0667 |
| 0.1023 | 11.16 | 11160 | 0.0666 |
| 0.156 | 11.17 | 11170 | 0.0664 |
| 0.0964 | 11.18 | 11180 | 0.0663 |
| 0.1173 | 11.19 | 11190 | 0.0663 |
| 0.084 | 11.2 | 11200 | 0.0662 |
| 0.1295 | 11.21 | 11210 | 0.0662 |
| 0.1377 | 11.22 | 11220 | 0.0661 |
| 0.1008 | 11.23 | 11230 | 0.0660 |
| 0.1132 | 11.24 | 11240 | 0.0659 |
| 0.1646 | 11.25 | 11250 | 0.0658 |
| 0.103 | 11.26 | 11260 | 0.0659 |
| 0.1331 | 11.27 | 11270 | 0.0659 |
| 0.1153 | 11.28 | 11280 | 0.0659 |
| 0.1457 | 11.29 | 11290 | 0.0658 |
| 0.1257 | 11.3 | 11300 | 0.0658 |
| 0.1541 | 11.31 | 11310 | 0.0659 |
| 0.1449 | 11.32 | 11320 | 0.0660 |
| 0.1419 | 11.33 | 11330 | 0.0661 |
| 0.118 | 11.34 | 11340 | 0.0661 |
| 0.1562 | 11.35 | 11350 | 0.0660 |
| 0.1441 | 11.36 | 11360 | 0.0661 |
| 0.1663 | 11.37 | 11370 | 0.0661 |
| 0.1754 | 11.38 | 11380 | 0.0661 |
| 0.0888 | 11.39 | 11390 | 0.0662 |
| 0.084 | 11.4 | 11400 | 0.0661 |
| 0.1095 | 11.41 | 11410 | 0.0661 |
| 0.1594 | 11.42 | 11420 | 0.0663 |
| 0.0926 | 11.43 | 11430 | 0.0664 |
| 0.1073 | 11.44 | 11440 | 0.0664 |
| 0.1468 | 11.45 | 11450 | 0.0663 |
| 0.1433 | 11.46 | 11460 | 0.0661 |
| 0.1353 | 11.47 | 11470 | 0.0660 |
| 0.1437 | 11.48 | 11480 | 0.0659 |
| 0.164 | 11.49 | 11490 | 0.0657 |
| 0.0949 | 11.5 | 11500 | 0.0656 |
| 0.0891 | 11.51 | 11510 | 0.0656 |
| 0.1316 | 11.52 | 11520 | 0.0656 |
| 0.1462 | 11.53 | 11530 | 0.0656 |
| 0.0974 | 11.54 | 11540 | 0.0656 |
| 0.121 | 11.55 | 11550 | 0.0656 |
| 0.1708 | 11.56 | 11560 | 0.0657 |
| 0.1312 | 11.57 | 11570 | 0.0657 |
| 0.1593 | 11.58 | 11580 | 0.0658 |
| 0.171 | 11.59 | 11590 | 0.0659 |
| 0.1354 | 11.6 | 11600 | 0.0659 |
| 0.1329 | 11.61 | 11610 | 0.0661 |
| 0.1405 | 11.62 | 11620 | 0.0660 |
| 0.1587 | 11.63 | 11630 | 0.0657 |
| 0.1106 | 11.64 | 11640 | 0.0659 |
| 0.1189 | 11.65 | 11650 | 0.0661 |
| 0.2006 | 11.66 | 11660 | 0.0662 |
| 0.1378 | 11.67 | 11670 | 0.0662 |
| 0.1274 | 11.68 | 11680 | 0.0663 |
| 0.1286 | 11.69 | 11690 | 0.0663 |
| 0.1211 | 11.7 | 11700 | 0.0664 |
| 0.1863 | 11.71 | 11710 | 0.0665 |
| 0.1428 | 11.72 | 11720 | 0.0667 |
| 0.1179 | 11.73 | 11730 | 0.0666 |
| 0.1265 | 11.74 | 11740 | 0.0668 |
| 0.1597 | 11.75 | 11750 | 0.0668 |
| 0.1148 | 11.76 | 11760 | 0.0669 |
| 0.14 | 11.77 | 11770 | 0.0668 |
| 0.1796 | 11.78 | 11780 | 0.0669 |
| 0.0774 | 11.79 | 11790 | 0.0670 |
| 0.1063 | 11.8 | 11800 | 0.0671 |
| 0.1279 | 11.81 | 11810 | 0.0672 |
| 0.1454 | 11.82 | 11820 | 0.0672 |
| 0.1495 | 11.83 | 11830 | 0.0672 |
| 0.1434 | 11.84 | 11840 | 0.0671 |
| 0.1312 | 11.85 | 11850 | 0.0672 |
| 0.1681 | 11.86 | 11860 | 0.0672 |
| 0.1374 | 11.87 | 11870 | 0.0672 |
| 0.0941 | 11.88 | 11880 | 0.0672 |
| 0.1178 | 11.89 | 11890 | 0.0672 |
| 0.099 | 11.9 | 11900 | 0.0671 |
| 0.1277 | 11.91 | 11910 | 0.0670 |
| 0.1463 | 11.92 | 11920 | 0.0669 |
| 0.124 | 11.93 | 11930 | 0.0669 |
| 0.1313 | 11.94 | 11940 | 0.0669 |
| 0.1254 | 11.95 | 11950 | 0.0669 |
| 0.1122 | 11.96 | 11960 | 0.0669 |
| 0.1844 | 11.97 | 11970 | 0.0669 |
| 0.099 | 11.98 | 11980 | 0.0670 |
| 0.1258 | 11.99 | 11990 | 0.0671 |
| 0.12 | 12.0 | 12000 | 0.0671 |
| 0.1278 | 12.01 | 12010 | 0.0672 |
| 0.1319 | 12.02 | 12020 | 0.0673 |
| 0.1171 | 12.03 | 12030 | 0.0673 |
| 0.1386 | 12.04 | 12040 | 0.0672 |
| 0.1322 | 12.05 | 12050 | 0.0670 |
| 0.1792 | 12.06 | 12060 | 0.0669 |
| 0.1276 | 12.07 | 12070 | 0.0666 |
| 0.1444 | 12.08 | 12080 | 0.0666 |
| 0.1319 | 12.09 | 12090 | 0.0666 |
| 0.1464 | 12.1 | 12100 | 0.0667 |
| 0.1341 | 12.11 | 12110 | 0.0667 |
| 0.1384 | 12.12 | 12120 | 0.0667 |
| 0.1442 | 12.13 | 12130 | 0.0668 |
| 0.1542 | 12.14 | 12140 | 0.0669 |
| 0.0987 | 12.15 | 12150 | 0.0670 |
| 0.1363 | 12.16 | 12160 | 0.0670 |
| 0.1716 | 12.17 | 12170 | 0.0670 |
| 0.1187 | 12.18 | 12180 | 0.0670 |
| 0.1646 | 12.19 | 12190 | 0.0670 |
| 0.0963 | 12.2 | 12200 | 0.0669 |
| 0.1797 | 12.21 | 12210 | 0.0667 |
| 0.1505 | 12.22 | 12220 | 0.0668 |
| 0.1135 | 12.23 | 12230 | 0.0669 |
| 0.1419 | 12.24 | 12240 | 0.0669 |
| 0.1102 | 12.25 | 12250 | 0.0669 |
| 0.0998 | 12.26 | 12260 | 0.0669 |
| 0.146 | 12.27 | 12270 | 0.0670 |
| 0.1438 | 12.28 | 12280 | 0.0670 |
| 0.1051 | 12.29 | 12290 | 0.0670 |
| 0.1023 | 12.3 | 12300 | 0.0669 |
| 0.0765 | 12.31 | 12310 | 0.0670 |
| 0.1629 | 12.32 | 12320 | 0.0670 |
| 0.1869 | 12.33 | 12330 | 0.0669 |
| 0.1629 | 12.34 | 12340 | 0.0667 |
| 0.0846 | 12.35 | 12350 | 0.0667 |
| 0.1336 | 12.36 | 12360 | 0.0668 |
| 0.0942 | 12.37 | 12370 | 0.0669 |
| 0.1513 | 12.38 | 12380 | 0.0670 |
| 0.1158 | 12.39 | 12390 | 0.0670 |
| 0.1026 | 12.4 | 12400 | 0.0672 |
| 0.1167 | 12.41 | 12410 | 0.0673 |
| 0.1051 | 12.42 | 12420 | 0.0674 |
| 0.1127 | 12.43 | 12430 | 0.0675 |
| 0.1346 | 12.44 | 12440 | 0.0676 |
| 0.1296 | 12.45 | 12450 | 0.0676 |
| 0.0994 | 12.46 | 12460 | 0.0674 |
| 0.0815 | 12.47 | 12470 | 0.0673 |
| 0.1575 | 12.48 | 12480 | 0.0673 |
| 0.1481 | 12.49 | 12490 | 0.0672 |
| 0.1694 | 12.5 | 12500 | 0.0672 |
| 0.1236 | 12.51 | 12510 | 0.0671 |
| 0.1346 | 12.52 | 12520 | 0.0672 |
| 0.1252 | 12.53 | 12530 | 0.0674 |
| 0.1392 | 12.54 | 12540 | 0.0673 |
| 0.0937 | 12.55 | 12550 | 0.0671 |
| 0.1199 | 12.56 | 12560 | 0.0671 |
| 0.1124 | 12.57 | 12570 | 0.0671 |
| 0.184 | 12.58 | 12580 | 0.0672 |
| 0.1092 | 12.59 | 12590 | 0.0673 |
| 0.1094 | 12.6 | 12600 | 0.0671 |
| 0.1447 | 12.61 | 12610 | 0.0670 |
| 0.1734 | 12.62 | 12620 | 0.0670 |
| 0.1351 | 12.63 | 12630 | 0.0671 |
| 0.1506 | 12.64 | 12640 | 0.0672 |
| 0.1695 | 12.65 | 12650 | 0.0672 |
| 0.0926 | 12.66 | 12660 | 0.0670 |
| 0.085 | 12.67 | 12670 | 0.0669 |
| 0.1957 | 12.68 | 12680 | 0.0669 |
| 0.1421 | 12.69 | 12690 | 0.0669 |
| 0.1538 | 12.7 | 12700 | 0.0668 |
| 0.0969 | 12.71 | 12710 | 0.0667 |
| 0.1072 | 12.72 | 12720 | 0.0668 |
| 0.1126 | 12.73 | 12730 | 0.0670 |
| 0.1013 | 12.74 | 12740 | 0.0671 |
| 0.1553 | 12.75 | 12750 | 0.0672 |
| 0.1494 | 12.76 | 12760 | 0.0673 |
| 0.0736 | 12.77 | 12770 | 0.0673 |
| 0.1924 | 12.78 | 12780 | 0.0674 |
| 0.1276 | 12.79 | 12790 | 0.0674 |
| 0.1599 | 12.8 | 12800 | 0.0675 |
| 0.122 | 12.81 | 12810 | 0.0677 |
| 0.1687 | 12.82 | 12820 | 0.0678 |
| 0.1597 | 12.83 | 12830 | 0.0677 |
| 0.1486 | 12.84 | 12840 | 0.0675 |
| 0.1001 | 12.85 | 12850 | 0.0675 |
| 0.0874 | 12.86 | 12860 | 0.0675 |
| 0.0952 | 12.87 | 12870 | 0.0675 |
| 0.1458 | 12.88 | 12880 | 0.0675 |
| 0.1472 | 12.89 | 12890 | 0.0675 |
| 0.1161 | 12.9 | 12900 | 0.0674 |
| 0.1455 | 12.91 | 12910 | 0.0673 |
| 0.1406 | 12.92 | 12920 | 0.0672 |
| 0.1108 | 12.93 | 12930 | 0.0672 |
| 0.1358 | 12.94 | 12940 | 0.0672 |
| 0.13 | 12.95 | 12950 | 0.0673 |
| 0.095 | 12.96 | 12960 | 0.0674 |
| 0.1302 | 12.97 | 12970 | 0.0674 |
| 0.1424 | 12.98 | 12980 | 0.0674 |
| 0.1343 | 12.99 | 12990 | 0.0674 |
| 0.0928 | 13.0 | 13000 | 0.0674 |
| 0.1126 | 13.01 | 13010 | 0.0674 |
| 0.1296 | 13.02 | 13020 | 0.0673 |
| 0.0952 | 13.03 | 13030 | 0.0672 |
| 0.1295 | 13.04 | 13040 | 0.0673 |
| 0.1826 | 13.05 | 13050 | 0.0673 |
| 0.1443 | 13.06 | 13060 | 0.0673 |
| 0.1527 | 13.07 | 13070 | 0.0673 |
| 0.142 | 13.08 | 13080 | 0.0674 |
| 0.0891 | 13.09 | 13090 | 0.0675 |
| 0.1165 | 13.1 | 13100 | 0.0677 |
| 0.0984 | 13.11 | 13110 | 0.0678 |
| 0.1501 | 13.12 | 13120 | 0.0678 |
| 0.2033 | 13.13 | 13130 | 0.0678 |
| 0.1264 | 13.14 | 13140 | 0.0678 |
| 0.1814 | 13.15 | 13150 | 0.0678 |
| 0.1828 | 13.16 | 13160 | 0.0678 |
| 0.0993 | 13.17 | 13170 | 0.0678 |
| 0.1141 | 13.18 | 13180 | 0.0676 |
| 0.1012 | 13.19 | 13190 | 0.0675 |
| 0.1036 | 13.2 | 13200 | 0.0674 |
| 0.1471 | 13.21 | 13210 | 0.0674 |
| 0.1388 | 13.22 | 13220 | 0.0674 |
| 0.106 | 13.23 | 13230 | 0.0673 |
| 0.1153 | 13.24 | 13240 | 0.0673 |
| 0.139 | 13.25 | 13250 | 0.0672 |
| 0.1298 | 13.26 | 13260 | 0.0671 |
| 0.1485 | 13.27 | 13270 | 0.0672 |
| 0.1664 | 13.28 | 13280 | 0.0671 |
| 0.13 | 13.29 | 13290 | 0.0669 |
| 0.1364 | 13.3 | 13300 | 0.0668 |
| 0.1537 | 13.31 | 13310 | 0.0670 |
| 0.2156 | 13.32 | 13320 | 0.0670 |
| 0.0895 | 13.33 | 13330 | 0.0670 |
| 0.138 | 13.34 | 13340 | 0.0671 |
| 0.1125 | 13.35 | 13350 | 0.0671 |
| 0.1168 | 13.36 | 13360 | 0.0671 |
| 0.1403 | 13.37 | 13370 | 0.0670 |
| 0.1249 | 13.38 | 13380 | 0.0670 |
| 0.1537 | 13.39 | 13390 | 0.0671 |
| 0.1391 | 13.4 | 13400 | 0.0671 |
| 0.1411 | 13.41 | 13410 | 0.0673 |
| 0.1082 | 13.42 | 13420 | 0.0675 |
| 0.1482 | 13.43 | 13430 | 0.0675 |
| 0.1452 | 13.44 | 13440 | 0.0675 |
| 0.1237 | 13.45 | 13450 | 0.0674 |
| 0.1095 | 13.46 | 13460 | 0.0672 |
| 0.1125 | 13.47 | 13470 | 0.0671 |
| 0.1165 | 13.48 | 13480 | 0.0672 |
| 0.1981 | 13.49 | 13490 | 0.0671 |
| 0.1634 | 13.5 | 13500 | 0.0671 |
| 0.161 | 13.51 | 13510 | 0.0673 |
| 0.1315 | 13.52 | 13520 | 0.0673 |
| 0.0916 | 13.53 | 13530 | 0.0674 |
| 0.1414 | 13.54 | 13540 | 0.0675 |
| 0.1313 | 13.55 | 13550 | 0.0675 |
| 0.0914 | 13.56 | 13560 | 0.0675 |
| 0.1081 | 13.57 | 13570 | 0.0675 |
| 0.1454 | 13.58 | 13580 | 0.0675 |
| 0.1472 | 13.59 | 13590 | 0.0675 |
| 0.099 | 13.6 | 13600 | 0.0676 |
| 0.1465 | 13.61 | 13610 | 0.0676 |
| 0.1276 | 13.62 | 13620 | 0.0675 |
| 0.109 | 13.63 | 13630 | 0.0674 |
| 0.1063 | 13.64 | 13640 | 0.0673 |
| 0.1552 | 13.65 | 13650 | 0.0673 |
| 0.105 | 13.66 | 13660 | 0.0673 |
| 0.1142 | 13.67 | 13670 | 0.0675 |
| 0.1519 | 13.68 | 13680 | 0.0676 |
| 0.1658 | 13.69 | 13690 | 0.0677 |
| 0.1445 | 13.7 | 13700 | 0.0677 |
| 0.1349 | 13.71 | 13710 | 0.0678 |
| 0.0989 | 13.72 | 13720 | 0.0678 |
| 0.1476 | 13.73 | 13730 | 0.0678 |
| 0.1408 | 13.74 | 13740 | 0.0678 |
| 0.0973 | 13.75 | 13750 | 0.0678 |
| 0.0911 | 13.76 | 13760 | 0.0678 |
| 0.1329 | 13.77 | 13770 | 0.0679 |
| 0.1466 | 13.78 | 13780 | 0.0678 |
| 0.1559 | 13.79 | 13790 | 0.0678 |
| 0.0985 | 13.8 | 13800 | 0.0677 |
| 0.1517 | 13.81 | 13810 | 0.0677 |
| 0.1126 | 13.82 | 13820 | 0.0677 |
| 0.1354 | 13.83 | 13830 | 0.0677 |
| 0.1257 | 13.84 | 13840 | 0.0677 |
| 0.1092 | 13.85 | 13850 | 0.0677 |
| 0.1596 | 13.86 | 13860 | 0.0676 |
| 0.1083 | 13.87 | 13870 | 0.0676 |
| 0.1004 | 13.88 | 13880 | 0.0678 |
| 0.1178 | 13.89 | 13890 | 0.0679 |
| 0.1418 | 13.9 | 13900 | 0.0680 |
| 0.1353 | 13.91 | 13910 | 0.0681 |
| 0.0727 | 13.92 | 13920 | 0.0681 |
| 0.1375 | 13.93 | 13930 | 0.0682 |
| 0.0958 | 13.94 | 13940 | 0.0681 |
| 0.1113 | 13.95 | 13950 | 0.0681 |
| 0.1047 | 13.96 | 13960 | 0.0680 |
| 0.0958 | 13.97 | 13970 | 0.0679 |
| 0.1237 | 13.98 | 13980 | 0.0678 |
| 0.1115 | 13.99 | 13990 | 0.0676 |
| 0.1609 | 14.0 | 14000 | 0.0674 |
| 0.1468 | 14.01 | 14010 | 0.0674 |
| 0.0906 | 14.02 | 14020 | 0.0674 |
| 0.0827 | 14.03 | 14030 | 0.0675 |
| 0.1283 | 14.04 | 14040 | 0.0674 |
| 0.1501 | 14.05 | 14050 | 0.0674 |
| 0.1385 | 14.06 | 14060 | 0.0674 |
| 0.1529 | 14.07 | 14070 | 0.0674 |
| 0.144 | 14.08 | 14080 | 0.0673 |
| 0.1779 | 14.09 | 14090 | 0.0673 |
| 0.1417 | 14.1 | 14100 | 0.0672 |
| 0.1297 | 14.11 | 14110 | 0.0671 |
| 0.1147 | 14.12 | 14120 | 0.0671 |
| 0.0785 | 14.13 | 14130 | 0.0671 |
| 0.1511 | 14.14 | 14140 | 0.0671 |
| 0.1242 | 14.15 | 14150 | 0.0671 |
| 0.1594 | 14.16 | 14160 | 0.0669 |
| 0.0934 | 14.17 | 14170 | 0.0668 |
| 0.1215 | 14.18 | 14180 | 0.0669 |
| 0.1903 | 14.19 | 14190 | 0.0670 |
| 0.1491 | 14.2 | 14200 | 0.0671 |
| 0.1379 | 14.21 | 14210 | 0.0670 |
| 0.1305 | 14.22 | 14220 | 0.0669 |
| 0.1399 | 14.23 | 14230 | 0.0670 |
| 0.1682 | 14.24 | 14240 | 0.0671 |
| 0.1143 | 14.25 | 14250 | 0.0670 |
| 0.0935 | 14.26 | 14260 | 0.0669 |
| 0.1032 | 14.27 | 14270 | 0.0668 |
| 0.1585 | 14.28 | 14280 | 0.0667 |
| 0.1342 | 14.29 | 14290 | 0.0667 |
| 0.1145 | 14.3 | 14300 | 0.0665 |
| 0.1574 | 14.31 | 14310 | 0.0664 |
| 0.1516 | 14.32 | 14320 | 0.0663 |
| 0.1492 | 14.33 | 14330 | 0.0663 |
| 0.1126 | 14.34 | 14340 | 0.0663 |
| 0.1084 | 14.35 | 14350 | 0.0663 |
| 0.1372 | 14.36 | 14360 | 0.0663 |
| 0.1479 | 14.37 | 14370 | 0.0662 |
| 0.111 | 14.38 | 14380 | 0.0663 |
| 0.1098 | 14.39 | 14390 | 0.0664 |
| 0.1803 | 14.4 | 14400 | 0.0665 |
| 0.161 | 14.41 | 14410 | 0.0665 |
| 0.1318 | 14.42 | 14420 | 0.0666 |
| 0.1728 | 14.43 | 14430 | 0.0666 |
| 0.1026 | 14.44 | 14440 | 0.0666 |
| 0.1062 | 14.45 | 14450 | 0.0665 |
| 0.1605 | 14.46 | 14460 | 0.0665 |
| 0.1153 | 14.47 | 14470 | 0.0667 |
| 0.1491 | 14.48 | 14480 | 0.0667 |
| 0.1702 | 14.49 | 14490 | 0.0666 |
| 0.1372 | 14.5 | 14500 | 0.0666 |
| 0.1517 | 14.51 | 14510 | 0.0665 |
| 0.1009 | 14.52 | 14520 | 0.0664 |
| 0.1384 | 14.53 | 14530 | 0.0664 |
| 0.0812 | 14.54 | 14540 | 0.0665 |
| 0.1405 | 14.55 | 14550 | 0.0665 |
| 0.1006 | 14.56 | 14560 | 0.0666 |
| 0.1215 | 14.57 | 14570 | 0.0665 |
| 0.1642 | 14.58 | 14580 | 0.0665 |
| 0.1275 | 14.59 | 14590 | 0.0665 |
| 0.1197 | 14.6 | 14600 | 0.0665 |
| 0.148 | 14.61 | 14610 | 0.0665 |
| 0.1127 | 14.62 | 14620 | 0.0666 |
| 0.0934 | 14.63 | 14630 | 0.0666 |
| 0.1453 | 14.64 | 14640 | 0.0667 |
| 0.1269 | 14.65 | 14650 | 0.0667 |
| 0.1501 | 14.66 | 14660 | 0.0667 |
| 0.1121 | 14.67 | 14670 | 0.0667 |
| 0.1112 | 14.68 | 14680 | 0.0666 |
| 0.1089 | 14.69 | 14690 | 0.0665 |
| 0.1169 | 14.7 | 14700 | 0.0665 |
| 0.1216 | 14.71 | 14710 | 0.0665 |
| 0.134 | 14.72 | 14720 | 0.0665 |
| 0.0875 | 14.73 | 14730 | 0.0665 |
| 0.1196 | 14.74 | 14740 | 0.0665 |
| 0.118 | 14.75 | 14750 | 0.0664 |
| 0.1044 | 14.76 | 14760 | 0.0664 |
| 0.0878 | 14.77 | 14770 | 0.0664 |
| 0.1422 | 14.78 | 14780 | 0.0663 |
| 0.1519 | 14.79 | 14790 | 0.0662 |
| 0.1263 | 14.8 | 14800 | 0.0662 |
| 0.0858 | 14.81 | 14810 | 0.0663 |
| 0.1215 | 14.82 | 14820 | 0.0664 |
| 0.1877 | 14.83 | 14830 | 0.0665 |
| 0.1656 | 14.84 | 14840 | 0.0666 |
| 0.1022 | 14.85 | 14850 | 0.0666 |
| 0.1478 | 14.86 | 14860 | 0.0667 |
| 0.1651 | 14.87 | 14870 | 0.0668 |
| 0.163 | 14.88 | 14880 | 0.0669 |
| 0.0913 | 14.89 | 14890 | 0.0670 |
| 0.1002 | 14.9 | 14900 | 0.0671 |
| 0.1255 | 14.91 | 14910 | 0.0671 |
| 0.1246 | 14.92 | 14920 | 0.0671 |
| 0.118 | 14.93 | 14930 | 0.0671 |
| 0.1068 | 14.94 | 14940 | 0.0671 |
| 0.1325 | 14.95 | 14950 | 0.0671 |
| 0.1164 | 14.96 | 14960 | 0.0671 |
| 0.1202 | 14.97 | 14970 | 0.0670 |
| 0.1139 | 14.98 | 14980 | 0.0671 |
| 0.1234 | 14.99 | 14990 | 0.0671 |
| 0.0874 | 15.0 | 15000 | 0.0672 |
| 0.0881 | 15.01 | 15010 | 0.0672 |
| 0.1028 | 15.02 | 15020 | 0.0672 |
| 0.1213 | 15.03 | 15030 | 0.0673 |
| 0.0887 | 15.04 | 15040 | 0.0672 |
| 0.1143 | 15.05 | 15050 | 0.0672 |
| 0.1755 | 15.06 | 15060 | 0.0672 |
| 0.11 | 15.07 | 15070 | 0.0672 |
| 0.1509 | 15.08 | 15080 | 0.0673 |
| 0.1111 | 15.09 | 15090 | 0.0674 |
| 0.129 | 15.1 | 15100 | 0.0674 |
| 0.1429 | 15.11 | 15110 | 0.0674 |
| 0.1164 | 15.12 | 15120 | 0.0674 |
| 0.1095 | 15.13 | 15130 | 0.0674 |
| 0.1083 | 15.14 | 15140 | 0.0674 |
| 0.1697 | 15.15 | 15150 | 0.0675 |
| 0.1123 | 15.16 | 15160 | 0.0676 |
| 0.1351 | 15.17 | 15170 | 0.0676 |
| 0.1246 | 15.18 | 15180 | 0.0676 |
| 0.1339 | 15.19 | 15190 | 0.0677 |
| 0.1753 | 15.2 | 15200 | 0.0679 |
| 0.1071 | 15.21 | 15210 | 0.0680 |
| 0.1145 | 15.22 | 15220 | 0.0682 |
| 0.1707 | 15.23 | 15230 | 0.0683 |
| 0.1358 | 15.24 | 15240 | 0.0683 |
| 0.1306 | 15.25 | 15250 | 0.0682 |
| 0.1291 | 15.26 | 15260 | 0.0681 |
| 0.0895 | 15.27 | 15270 | 0.0680 |
| 0.149 | 15.28 | 15280 | 0.0679 |
| 0.0662 | 15.29 | 15290 | 0.0679 |
| 0.162 | 15.3 | 15300 | 0.0679 |
| 0.0953 | 15.31 | 15310 | 0.0679 |
| 0.12 | 15.32 | 15320 | 0.0680 |
| 0.0858 | 15.33 | 15330 | 0.0680 |
| 0.1321 | 15.34 | 15340 | 0.0681 |
| 0.1988 | 15.35 | 15350 | 0.0682 |
| 0.1258 | 15.36 | 15360 | 0.0682 |
| 0.1262 | 15.37 | 15370 | 0.0681 |
| 0.134 | 15.38 | 15380 | 0.0679 |
| 0.1873 | 15.39 | 15390 | 0.0678 |
| 0.1302 | 15.4 | 15400 | 0.0677 |
| 0.103 | 15.41 | 15410 | 0.0677 |
| 0.1638 | 15.42 | 15420 | 0.0676 |
| 0.1407 | 15.43 | 15430 | 0.0676 |
| 0.1575 | 15.44 | 15440 | 0.0675 |
| 0.1308 | 15.45 | 15450 | 0.0676 |
| 0.0824 | 15.46 | 15460 | 0.0676 |
| 0.0911 | 15.47 | 15470 | 0.0676 |
| 0.1256 | 15.48 | 15480 | 0.0676 |
| 0.1219 | 15.49 | 15490 | 0.0676 |
| 0.1313 | 15.5 | 15500 | 0.0676 |
| 0.1056 | 15.51 | 15510 | 0.0676 |
| 0.1176 | 15.52 | 15520 | 0.0676 |
| 0.0867 | 15.53 | 15530 | 0.0676 |
| 0.1419 | 15.54 | 15540 | 0.0676 |
| 0.1312 | 15.55 | 15550 | 0.0676 |
| 0.1618 | 15.56 | 15560 | 0.0677 |
| 0.1562 | 15.57 | 15570 | 0.0678 |
| 0.1319 | 15.58 | 15580 | 0.0679 |
| 0.1625 | 15.59 | 15590 | 0.0680 |
| 0.1143 | 15.6 | 15600 | 0.0679 |
| 0.142 | 15.61 | 15610 | 0.0679 |
| 0.1458 | 15.62 | 15620 | 0.0679 |
| 0.1702 | 15.63 | 15630 | 0.0679 |
| 0.1282 | 15.64 | 15640 | 0.0679 |
| 0.1031 | 15.65 | 15650 | 0.0679 |
| 0.1013 | 15.66 | 15660 | 0.0679 |
| 0.1093 | 15.67 | 15670 | 0.0679 |
| 0.1258 | 15.68 | 15680 | 0.0679 |
| 0.0772 | 15.69 | 15690 | 0.0678 |
| 0.0824 | 15.7 | 15700 | 0.0677 |
| 0.1307 | 15.71 | 15710 | 0.0677 |
| 0.0898 | 15.72 | 15720 | 0.0677 |
| 0.1253 | 15.73 | 15730 | 0.0678 |
| 0.1511 | 15.74 | 15740 | 0.0678 |
| 0.1442 | 15.75 | 15750 | 0.0677 |
| 0.1784 | 15.76 | 15760 | 0.0677 |
| 0.0995 | 15.77 | 15770 | 0.0676 |
| 0.0988 | 15.78 | 15780 | 0.0675 |
| 0.1645 | 15.79 | 15790 | 0.0674 |
| 0.1588 | 15.8 | 15800 | 0.0674 |
| 0.1677 | 15.81 | 15810 | 0.0673 |
| 0.1472 | 15.82 | 15820 | 0.0673 |
| 0.1514 | 15.83 | 15830 | 0.0674 |
| 0.1079 | 15.84 | 15840 | 0.0676 |
| 0.1244 | 15.85 | 15850 | 0.0676 |
| 0.107 | 15.86 | 15860 | 0.0676 |
| 0.0886 | 15.87 | 15870 | 0.0676 |
| 0.1113 | 15.88 | 15880 | 0.0676 |
| 0.1499 | 15.89 | 15890 | 0.0676 |
| 0.128 | 15.9 | 15900 | 0.0677 |
| 0.1704 | 15.91 | 15910 | 0.0677 |
| 0.1385 | 15.92 | 15920 | 0.0676 |
| 0.1044 | 15.93 | 15930 | 0.0676 |
| 0.183 | 15.94 | 15940 | 0.0676 |
| 0.133 | 15.95 | 15950 | 0.0676 |
| 0.1186 | 15.96 | 15960 | 0.0676 |
| 0.112 | 15.97 | 15970 | 0.0675 |
| 0.1365 | 15.98 | 15980 | 0.0675 |
| 0.1066 | 15.99 | 15990 | 0.0674 |
| 0.1408 | 16.0 | 16000 | 0.0674 |
| 0.169 | 16.01 | 16010 | 0.0674 |
| 0.1469 | 16.02 | 16020 | 0.0674 |
| 0.1847 | 16.03 | 16030 | 0.0675 |
| 0.1483 | 16.04 | 16040 | 0.0675 |
| 0.1059 | 16.05 | 16050 | 0.0675 |
| 0.1334 | 16.06 | 16060 | 0.0675 |
| 0.1191 | 16.07 | 16070 | 0.0675 |
| 0.1206 | 16.08 | 16080 | 0.0675 |
| 0.1371 | 16.09 | 16090 | 0.0675 |
| 0.1313 | 16.1 | 16100 | 0.0676 |
| 0.1131 | 16.11 | 16110 | 0.0676 |
| 0.1578 | 16.12 | 16120 | 0.0676 |
| 0.0963 | 16.13 | 16130 | 0.0676 |
| 0.2233 | 16.14 | 16140 | 0.0675 |
| 0.1579 | 16.15 | 16150 | 0.0675 |
| 0.1269 | 16.16 | 16160 | 0.0675 |
| 0.1296 | 16.17 | 16170 | 0.0675 |
| 0.1473 | 16.18 | 16180 | 0.0676 |
| 0.1081 | 16.19 | 16190 | 0.0676 |
| 0.1054 | 16.2 | 16200 | 0.0677 |
| 0.1052 | 16.21 | 16210 | 0.0677 |
| 0.1317 | 16.22 | 16220 | 0.0677 |
| 0.1284 | 16.23 | 16230 | 0.0677 |
| 0.1332 | 16.24 | 16240 | 0.0677 |
| 0.1071 | 16.25 | 16250 | 0.0677 |
| 0.1343 | 16.26 | 16260 | 0.0677 |
| 0.1501 | 16.27 | 16270 | 0.0677 |
| 0.1277 | 16.28 | 16280 | 0.0677 |
| 0.0923 | 16.29 | 16290 | 0.0678 |
| 0.1248 | 16.3 | 16300 | 0.0678 |
| 0.1534 | 16.31 | 16310 | 0.0678 |
| 0.0914 | 16.32 | 16320 | 0.0678 |
| 0.2013 | 16.33 | 16330 | 0.0679 |
| 0.1221 | 16.34 | 16340 | 0.0679 |
| 0.1002 | 16.35 | 16350 | 0.0679 |
| 0.1697 | 16.36 | 16360 | 0.0678 |
| 0.2087 | 16.37 | 16370 | 0.0677 |
| 0.1306 | 16.38 | 16380 | 0.0677 |
| 0.1411 | 16.39 | 16390 | 0.0677 |
| 0.1174 | 16.4 | 16400 | 0.0677 |
| 0.1129 | 16.41 | 16410 | 0.0677 |
| 0.1288 | 16.42 | 16420 | 0.0677 |
| 0.1741 | 16.43 | 16430 | 0.0677 |
| 0.106 | 16.44 | 16440 | 0.0678 |
| 0.1714 | 16.45 | 16450 | 0.0678 |
| 0.1097 | 16.46 | 16460 | 0.0678 |
| 0.1027 | 16.47 | 16470 | 0.0679 |
| 0.146 | 16.48 | 16480 | 0.0679 |
| 0.123 | 16.49 | 16490 | 0.0679 |
| 0.1437 | 16.5 | 16500 | 0.0679 |
| 0.1062 | 16.51 | 16510 | 0.0680 |
| 0.1634 | 16.52 | 16520 | 0.0679 |
| 0.0851 | 16.53 | 16530 | 0.0679 |
| 0.0735 | 16.54 | 16540 | 0.0679 |
| 0.1056 | 16.55 | 16550 | 0.0679 |
| 0.1538 | 16.56 | 16560 | 0.0680 |
| 0.1675 | 16.57 | 16570 | 0.0679 |
| 0.123 | 16.58 | 16580 | 0.0680 |
| 0.1112 | 16.59 | 16590 | 0.0680 |
| 0.1596 | 16.6 | 16600 | 0.0680 |
| 0.1279 | 16.61 | 16610 | 0.0680 |
| 0.1091 | 16.62 | 16620 | 0.0680 |
| 0.0994 | 16.63 | 16630 | 0.0680 |
| 0.1385 | 16.64 | 16640 | 0.0680 |
| 0.0764 | 16.65 | 16650 | 0.0680 |
| 0.1032 | 16.66 | 16660 | 0.0680 |
| 0.1808 | 16.67 | 16670 | 0.0679 |
| 0.1235 | 16.68 | 16680 | 0.0680 |
| 0.1034 | 16.69 | 16690 | 0.0679 |
| 0.1096 | 16.7 | 16700 | 0.0680 |
| 0.1252 | 16.71 | 16710 | 0.0680 |
| 0.0921 | 16.72 | 16720 | 0.0680 |
| 0.1656 | 16.73 | 16730 | 0.0679 |
| 0.0974 | 16.74 | 16740 | 0.0679 |
| 0.1252 | 16.75 | 16750 | 0.0679 |
| 0.1263 | 16.76 | 16760 | 0.0679 |
| 0.1502 | 16.77 | 16770 | 0.0679 |
| 0.1424 | 16.78 | 16780 | 0.0679 |
| 0.11 | 16.79 | 16790 | 0.0680 |
| 0.1081 | 16.8 | 16800 | 0.0680 |
| 0.1256 | 16.81 | 16810 | 0.0680 |
| 0.0993 | 16.82 | 16820 | 0.0680 |
| 0.1148 | 16.83 | 16830 | 0.0681 |
| 0.1431 | 16.84 | 16840 | 0.0681 |
| 0.1085 | 16.85 | 16850 | 0.0680 |
| 0.1077 | 16.86 | 16860 | 0.0679 |
| 0.1247 | 16.87 | 16870 | 0.0679 |
| 0.087 | 16.88 | 16880 | 0.0678 |
| 0.1145 | 16.89 | 16890 | 0.0678 |
| 0.1615 | 16.9 | 16900 | 0.0678 |
| 0.1338 | 16.91 | 16910 | 0.0677 |
| 0.116 | 16.92 | 16920 | 0.0677 |
| 0.125 | 16.93 | 16930 | 0.0677 |
| 0.0954 | 16.94 | 16940 | 0.0677 |
| 0.1586 | 16.95 | 16950 | 0.0677 |
| 0.1027 | 16.96 | 16960 | 0.0677 |
| 0.097 | 16.97 | 16970 | 0.0678 |
| 0.1298 | 16.98 | 16980 | 0.0678 |
| 0.1255 | 16.99 | 16990 | 0.0678 |
| 0.0878 | 17.0 | 17000 | 0.0678 |
| 0.1538 | 17.01 | 17010 | 0.0679 |
| 0.1039 | 17.02 | 17020 | 0.0679 |
| 0.1264 | 17.03 | 17030 | 0.0679 |
| 0.1695 | 17.04 | 17040 | 0.0679 |
| 0.0961 | 17.05 | 17050 | 0.0679 |
| 0.1451 | 17.06 | 17060 | 0.0679 |
| 0.1206 | 17.07 | 17070 | 0.0679 |
| 0.1072 | 17.08 | 17080 | 0.0679 |
| 0.0849 | 17.09 | 17090 | 0.0679 |
| 0.1567 | 17.1 | 17100 | 0.0679 |
| 0.1017 | 17.11 | 17110 | 0.0679 |
| 0.1369 | 17.12 | 17120 | 0.0679 |
| 0.1033 | 17.13 | 17130 | 0.0679 |
| 0.1308 | 17.14 | 17140 | 0.0679 |
| 0.1366 | 17.15 | 17150 | 0.0679 |
| 0.1151 | 17.16 | 17160 | 0.0679 |
| 0.1282 | 17.17 | 17170 | 0.0679 |
| 0.1321 | 17.18 | 17180 | 0.0679 |
| 0.1374 | 17.19 | 17190 | 0.0679 |
| 0.1526 | 17.2 | 17200 | 0.0679 |
| 0.1288 | 17.21 | 17210 | 0.0680 |
| 0.1322 | 17.22 | 17220 | 0.0680 |
| 0.0959 | 17.23 | 17230 | 0.0680 |
| 0.1165 | 17.24 | 17240 | 0.0680 |
| 0.1482 | 17.25 | 17250 | 0.0680 |
| 0.1022 | 17.26 | 17260 | 0.0679 |
| 0.119 | 17.27 | 17270 | 0.0679 |
| 0.0637 | 17.28 | 17280 | 0.0679 |
| 0.0862 | 17.29 | 17290 | 0.0679 |
| 0.1719 | 17.3 | 17300 | 0.0679 |
| 0.1184 | 17.31 | 17310 | 0.0679 |
| 0.1071 | 17.32 | 17320 | 0.0679 |
| 0.0975 | 17.33 | 17330 | 0.0679 |
| 0.1764 | 17.34 | 17340 | 0.0680 |
| 0.1265 | 17.35 | 17350 | 0.0680 |
| 0.0872 | 17.36 | 17360 | 0.0681 |
| 0.0905 | 17.37 | 17370 | 0.0681 |
| 0.1804 | 17.38 | 17380 | 0.0682 |
| 0.1631 | 17.39 | 17390 | 0.0682 |
| 0.1525 | 17.4 | 17400 | 0.0683 |
| 0.2399 | 17.41 | 17410 | 0.0683 |
| 0.173 | 17.42 | 17420 | 0.0682 |
| 0.1068 | 17.43 | 17430 | 0.0682 |
| 0.1128 | 17.44 | 17440 | 0.0682 |
| 0.1486 | 17.45 | 17450 | 0.0682 |
| 0.1218 | 17.46 | 17460 | 0.0682 |
| 0.1763 | 17.47 | 17470 | 0.0683 |
| 0.2045 | 17.48 | 17480 | 0.0683 |
| 0.0808 | 17.49 | 17490 | 0.0683 |
| 0.0647 | 17.5 | 17500 | 0.0683 |
| 0.1647 | 17.51 | 17510 | 0.0683 |
| 0.1576 | 17.52 | 17520 | 0.0683 |
| 0.1091 | 17.53 | 17530 | 0.0683 |
| 0.1157 | 17.54 | 17540 | 0.0682 |
| 0.1159 | 17.55 | 17550 | 0.0682 |
| 0.1928 | 17.56 | 17560 | 0.0682 |
| 0.132 | 17.57 | 17570 | 0.0682 |
| 0.1011 | 17.58 | 17580 | 0.0682 |
| 0.1373 | 17.59 | 17590 | 0.0682 |
| 0.1239 | 17.6 | 17600 | 0.0682 |
| 0.1264 | 17.61 | 17610 | 0.0681 |
| 0.075 | 17.62 | 17620 | 0.0681 |
| 0.127 | 17.63 | 17630 | 0.0680 |
| 0.1296 | 17.64 | 17640 | 0.0680 |
| 0.0834 | 17.65 | 17650 | 0.0680 |
| 0.1357 | 17.66 | 17660 | 0.0681 |
| 0.1118 | 17.67 | 17670 | 0.0681 |
| 0.1179 | 17.68 | 17680 | 0.0681 |
| 0.1222 | 17.69 | 17690 | 0.0681 |
| 0.1234 | 17.7 | 17700 | 0.0682 |
| 0.0625 | 17.71 | 17710 | 0.0681 |
| 0.0986 | 17.72 | 17720 | 0.0681 |
| 0.2145 | 17.73 | 17730 | 0.0681 |
| 0.1318 | 17.74 | 17740 | 0.0680 |
| 0.1073 | 17.75 | 17750 | 0.0680 |
| 0.145 | 17.76 | 17760 | 0.0680 |
| 0.1493 | 17.77 | 17770 | 0.0679 |
| 0.0863 | 17.78 | 17780 | 0.0679 |
| 0.1664 | 17.79 | 17790 | 0.0679 |
| 0.1424 | 17.8 | 17800 | 0.0679 |
| 0.1012 | 17.81 | 17810 | 0.0679 |
| 0.1332 | 17.82 | 17820 | 0.0678 |
| 0.1487 | 17.83 | 17830 | 0.0678 |
| 0.1096 | 17.84 | 17840 | 0.0679 |
| 0.1239 | 17.85 | 17850 | 0.0679 |
| 0.1455 | 17.86 | 17860 | 0.0679 |
| 0.0981 | 17.87 | 17870 | 0.0680 |
| 0.1229 | 17.88 | 17880 | 0.0680 |
| 0.1192 | 17.89 | 17890 | 0.0679 |
| 0.1313 | 17.9 | 17900 | 0.0679 |
| 0.104 | 17.91 | 17910 | 0.0679 |
| 0.1355 | 17.92 | 17920 | 0.0678 |
| 0.0957 | 17.93 | 17930 | 0.0678 |
| 0.171 | 17.94 | 17940 | 0.0678 |
| 0.1029 | 17.95 | 17950 | 0.0678 |
| 0.1273 | 17.96 | 17960 | 0.0678 |
| 0.1102 | 17.97 | 17970 | 0.0678 |
| 0.1794 | 17.98 | 17980 | 0.0678 |
| 0.1301 | 17.99 | 17990 | 0.0678 |
| 0.1305 | 18.0 | 18000 | 0.0678 |
| 0.1745 | 18.01 | 18010 | 0.0678 |
| 0.1337 | 18.02 | 18020 | 0.0678 |
| 0.1219 | 18.03 | 18030 | 0.0678 |
| 0.1326 | 18.04 | 18040 | 0.0678 |
| 0.0912 | 18.05 | 18050 | 0.0678 |
| 0.115 | 18.06 | 18060 | 0.0679 |
| 0.1402 | 18.07 | 18070 | 0.0679 |
| 0.0903 | 18.08 | 18080 | 0.0679 |
| 0.1204 | 18.09 | 18090 | 0.0679 |
| 0.1296 | 18.1 | 18100 | 0.0679 |
| 0.1176 | 18.11 | 18110 | 0.0678 |
| 0.0926 | 18.12 | 18120 | 0.0678 |
| 0.1387 | 18.13 | 18130 | 0.0678 |
| 0.1098 | 18.14 | 18140 | 0.0677 |
| 0.1264 | 18.15 | 18150 | 0.0677 |
| 0.0864 | 18.16 | 18160 | 0.0677 |
| 0.2153 | 18.17 | 18170 | 0.0677 |
| 0.0984 | 18.18 | 18180 | 0.0678 |
| 0.1249 | 18.19 | 18190 | 0.0678 |
| 0.1411 | 18.2 | 18200 | 0.0678 |
| 0.1237 | 18.21 | 18210 | 0.0678 |
| 0.1076 | 18.22 | 18220 | 0.0678 |
| 0.1547 | 18.23 | 18230 | 0.0678 |
| 0.1031 | 18.24 | 18240 | 0.0679 |
| 0.1305 | 18.25 | 18250 | 0.0678 |
| 0.1385 | 18.26 | 18260 | 0.0678 |
| 0.1488 | 18.27 | 18270 | 0.0678 |
| 0.124 | 18.28 | 18280 | 0.0678 |
| 0.1043 | 18.29 | 18290 | 0.0678 |
| 0.1105 | 18.3 | 18300 | 0.0679 |
| 0.1424 | 18.31 | 18310 | 0.0679 |
| 0.109 | 18.32 | 18320 | 0.0679 |
| 0.0968 | 18.33 | 18330 | 0.0679 |
| 0.1356 | 18.34 | 18340 | 0.0679 |
| 0.1464 | 18.35 | 18350 | 0.0678 |
| 0.117 | 18.36 | 18360 | 0.0679 |
| 0.0959 | 18.37 | 18370 | 0.0679 |
| 0.162 | 18.38 | 18380 | 0.0678 |
| 0.1577 | 18.39 | 18390 | 0.0679 |
| 0.115 | 18.4 | 18400 | 0.0679 |
| 0.0833 | 18.41 | 18410 | 0.0679 |
| 0.1108 | 18.42 | 18420 | 0.0679 |
| 0.1653 | 18.43 | 18430 | 0.0679 |
| 0.1894 | 18.44 | 18440 | 0.0679 |
| 0.1565 | 18.45 | 18450 | 0.0679 |
| 0.1001 | 18.46 | 18460 | 0.0679 |
| 0.1084 | 18.47 | 18470 | 0.0679 |
| 0.164 | 18.48 | 18480 | 0.0679 |
| 0.1232 | 18.49 | 18490 | 0.0679 |
| 0.0927 | 18.5 | 18500 | 0.0679 |
| 0.1665 | 18.51 | 18510 | 0.0679 |
| 0.1389 | 18.52 | 18520 | 0.0679 |
| 0.121 | 18.53 | 18530 | 0.0679 |
| 0.1347 | 18.54 | 18540 | 0.0679 |
| 0.1238 | 18.55 | 18550 | 0.0679 |
| 0.0981 | 18.56 | 18560 | 0.0679 |
| 0.1181 | 18.57 | 18570 | 0.0678 |
| 0.1339 | 18.58 | 18580 | 0.0678 |
| 0.13 | 18.59 | 18590 | 0.0678 |
| 0.1145 | 18.6 | 18600 | 0.0678 |
| 0.1181 | 18.61 | 18610 | 0.0678 |
| 0.189 | 18.62 | 18620 | 0.0679 |
| 0.1237 | 18.63 | 18630 | 0.0679 |
| 0.1588 | 18.64 | 18640 | 0.0679 |
| 0.1375 | 18.65 | 18650 | 0.0679 |
| 0.1067 | 18.66 | 18660 | 0.0679 |
| 0.1511 | 18.67 | 18670 | 0.0679 |
| 0.1255 | 18.68 | 18680 | 0.0679 |
| 0.1328 | 18.69 | 18690 | 0.0679 |
| 0.1626 | 18.7 | 18700 | 0.0679 |
| 0.0661 | 18.71 | 18710 | 0.0679 |
| 0.0785 | 18.72 | 18720 | 0.0679 |
| 0.1707 | 18.73 | 18730 | 0.0679 |
| 0.1522 | 18.74 | 18740 | 0.0679 |
| 0.1449 | 18.75 | 18750 | 0.0679 |
| 0.083 | 18.76 | 18760 | 0.0679 |
| 0.0882 | 18.77 | 18770 | 0.0679 |
| 0.1285 | 18.78 | 18780 | 0.0679 |
| 0.0934 | 18.79 | 18790 | 0.0678 |
| 0.1788 | 18.8 | 18800 | 0.0678 |
| 0.0998 | 18.81 | 18810 | 0.0678 |
| 0.1165 | 18.82 | 18820 | 0.0678 |
| 0.1367 | 18.83 | 18830 | 0.0678 |
| 0.1192 | 18.84 | 18840 | 0.0679 |
| 0.14 | 18.85 | 18850 | 0.0679 |
| 0.1594 | 18.86 | 18860 | 0.0678 |
| 0.1493 | 18.87 | 18870 | 0.0678 |
| 0.1266 | 18.88 | 18880 | 0.0679 |
| 0.0926 | 18.89 | 18890 | 0.0678 |
| 0.1058 | 18.9 | 18900 | 0.0679 |
| 0.0981 | 18.91 | 18910 | 0.0679 |
| 0.0955 | 18.92 | 18920 | 0.0679 |
| 0.1762 | 18.93 | 18930 | 0.0679 |
| 0.1086 | 18.94 | 18940 | 0.0678 |
| 0.1509 | 18.95 | 18950 | 0.0678 |
| 0.1192 | 18.96 | 18960 | 0.0678 |
| 0.1253 | 18.97 | 18970 | 0.0678 |
| 0.1322 | 18.98 | 18980 | 0.0678 |
| 0.112 | 18.99 | 18990 | 0.0679 |
| 0.1392 | 19.0 | 19000 | 0.0679 |
| 0.1317 | 19.01 | 19010 | 0.0679 |
| 0.1201 | 19.02 | 19020 | 0.0679 |
| 0.1217 | 19.03 | 19030 | 0.0679 |
| 0.1015 | 19.04 | 19040 | 0.0679 |
| 0.0911 | 19.05 | 19050 | 0.0679 |
| 0.1095 | 19.06 | 19060 | 0.0679 |
| 0.1423 | 19.07 | 19070 | 0.0679 |
| 0.144 | 19.08 | 19080 | 0.0679 |
| 0.1376 | 19.09 | 19090 | 0.0679 |
| 0.0957 | 19.1 | 19100 | 0.0679 |
| 0.1214 | 19.11 | 19110 | 0.0679 |
| 0.1052 | 19.12 | 19120 | 0.0679 |
| 0.1113 | 19.13 | 19130 | 0.0679 |
| 0.1393 | 19.14 | 19140 | 0.0679 |
| 0.1622 | 19.15 | 19150 | 0.0679 |
| 0.1259 | 19.16 | 19160 | 0.0679 |
| 0.0982 | 19.17 | 19170 | 0.0679 |
| 0.1379 | 19.18 | 19180 | 0.0679 |
| 0.1301 | 19.19 | 19190 | 0.0679 |
| 0.1403 | 19.2 | 19200 | 0.0679 |
| 0.163 | 19.21 | 19210 | 0.0679 |
| 0.1091 | 19.22 | 19220 | 0.0679 |
| 0.1102 | 19.23 | 19230 | 0.0679 |
| 0.1017 | 19.24 | 19240 | 0.0679 |
| 0.0861 | 19.25 | 19250 | 0.0679 |
| 0.154 | 19.26 | 19260 | 0.0679 |
| 0.1095 | 19.27 | 19270 | 0.0679 |
| 0.1119 | 19.28 | 19280 | 0.0679 |
| 0.1117 | 19.29 | 19290 | 0.0679 |
| 0.1372 | 19.3 | 19300 | 0.0679 |
| 0.0972 | 19.31 | 19310 | 0.0679 |
| 0.1029 | 19.32 | 19320 | 0.0679 |
| 0.1148 | 19.33 | 19330 | 0.0679 |
| 0.1427 | 19.34 | 19340 | 0.0679 |
| 0.0774 | 19.35 | 19350 | 0.0679 |
| 0.0988 | 19.36 | 19360 | 0.0679 |
| 0.1259 | 19.37 | 19370 | 0.0679 |
| 0.1383 | 19.38 | 19380 | 0.0679 |
| 0.1604 | 19.39 | 19390 | 0.0679 |
| 0.1802 | 19.4 | 19400 | 0.0679 |
| 0.0756 | 19.41 | 19410 | 0.0680 |
| 0.1623 | 19.42 | 19420 | 0.0680 |
| 0.2077 | 19.43 | 19430 | 0.0680 |
| 0.1435 | 19.44 | 19440 | 0.0680 |
| 0.1506 | 19.45 | 19450 | 0.0680 |
| 0.101 | 19.46 | 19460 | 0.0680 |
| 0.1593 | 19.47 | 19470 | 0.0680 |
| 0.076 | 19.48 | 19480 | 0.0680 |
| 0.1329 | 19.49 | 19490 | 0.0680 |
| 0.1212 | 19.5 | 19500 | 0.0680 |
| 0.1458 | 19.51 | 19510 | 0.0680 |
| 0.1582 | 19.52 | 19520 | 0.0680 |
| 0.1203 | 19.53 | 19530 | 0.0680 |
| 0.1434 | 19.54 | 19540 | 0.0680 |
| 0.1366 | 19.55 | 19550 | 0.0680 |
| 0.1633 | 19.56 | 19560 | 0.0680 |
| 0.1484 | 19.57 | 19570 | 0.0680 |
| 0.1363 | 19.58 | 19580 | 0.0680 |
| 0.1428 | 19.59 | 19590 | 0.0680 |
| 0.1465 | 19.6 | 19600 | 0.0680 |
| 0.0957 | 19.61 | 19610 | 0.0680 |
| 0.1345 | 19.62 | 19620 | 0.0680 |
| 0.1382 | 19.63 | 19630 | 0.0680 |
| 0.1468 | 19.64 | 19640 | 0.0680 |
| 0.1237 | 19.65 | 19650 | 0.0680 |
| 0.1178 | 19.66 | 19660 | 0.0680 |
| 0.0848 | 19.67 | 19670 | 0.0680 |
| 0.1159 | 19.68 | 19680 | 0.0680 |
| 0.1639 | 19.69 | 19690 | 0.0680 |
| 0.1084 | 19.7 | 19700 | 0.0680 |
| 0.0811 | 19.71 | 19710 | 0.0680 |
| 0.0745 | 19.72 | 19720 | 0.0680 |
| 0.1026 | 19.73 | 19730 | 0.0680 |
| 0.0895 | 19.74 | 19740 | 0.0680 |
| 0.1719 | 19.75 | 19750 | 0.0680 |
| 0.1247 | 19.76 | 19760 | 0.0680 |
| 0.1174 | 19.77 | 19770 | 0.0680 |
| 0.1476 | 19.78 | 19780 | 0.0680 |
| 0.1718 | 19.79 | 19790 | 0.0680 |
| 0.104 | 19.8 | 19800 | 0.0680 |
| 0.166 | 19.81 | 19810 | 0.0680 |
| 0.1547 | 19.82 | 19820 | 0.0680 |
| 0.18 | 19.83 | 19830 | 0.0680 |
| 0.1405 | 19.84 | 19840 | 0.0680 |
| 0.1047 | 19.85 | 19850 | 0.0680 |
| 0.1585 | 19.86 | 19860 | 0.0680 |
| 0.1295 | 19.87 | 19870 | 0.0680 |
| 0.1498 | 19.88 | 19880 | 0.0680 |
| 0.0994 | 19.89 | 19890 | 0.0680 |
| 0.1122 | 19.9 | 19900 | 0.0680 |
| 0.0903 | 19.91 | 19910 | 0.0680 |
| 0.0906 | 19.92 | 19920 | 0.0680 |
| 0.1394 | 19.93 | 19930 | 0.0680 |
| 0.0912 | 19.94 | 19940 | 0.0680 |
| 0.1074 | 19.95 | 19950 | 0.0680 |
| 0.1219 | 19.96 | 19960 | 0.0680 |
| 0.1278 | 19.97 | 19970 | 0.0680 |
| 0.1234 | 19.98 | 19980 | 0.0680 |
| 0.1645 | 19.99 | 19990 | 0.0680 |
| 0.19 | 20.0 | 20000 | 0.0680 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
Undi95/Mistral-7B-roleplay_alpaca-lora
|
Undi95
| 2023-09-28T20:44:26Z | 6 | 7 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2023-09-28T20:33:30Z |
---
tags:
- generated_from_trainer
model-index:
- name: roleplay_alpaca
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# roleplay_alpaca
This model was trained from scratch on the [TokenBender/roleplay_alpaca](https://huggingface.co/datasets/TokenBender/roleplay_alpaca) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2973
## Model description
Dataset trained on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00065
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5404 | 0.03 | 20 | 1.2764 |
| 1.4523 | 0.06 | 40 | 1.2571 |
| 1.5608 | 0.09 | 60 | 1.2465 |
| 1.5921 | 0.12 | 80 | 1.2575 |
| 1.6043 | 0.15 | 100 | 1.2432 |
| 1.5496 | 0.18 | 120 | 1.2504 |
| 1.348 | 0.21 | 140 | 1.2452 |
| 1.4638 | 0.24 | 160 | 1.2661 |
| 1.5733 | 0.27 | 180 | 1.2548 |
| 1.5397 | 0.3 | 200 | 1.2674 |
| 1.6154 | 0.33 | 220 | 1.2626 |
| 1.5058 | 0.36 | 240 | 1.2672 |
| 1.3974 | 0.4 | 260 | 1.2659 |
| 1.6654 | 0.43 | 280 | 1.2648 |
| 1.8051 | 0.46 | 300 | 1.2585 |
| 1.7487 | 0.49 | 320 | 1.2736 |
| 1.3612 | 0.52 | 340 | 1.2717 |
| 1.5048 | 0.55 | 360 | 1.2809 |
| 1.7134 | 0.58 | 380 | 1.2885 |
| 1.5524 | 0.61 | 400 | 1.2805 |
| 1.3705 | 0.64 | 420 | 1.2860 |
| 1.4335 | 0.67 | 440 | 1.2896 |
| 1.3642 | 0.7 | 460 | 1.2911 |
| 1.6546 | 0.73 | 480 | 1.2888 |
| 1.5345 | 0.76 | 500 | 1.2973 |
| 1.5968 | 0.79 | 520 | 1.2885 |
| 1.5694 | 0.82 | 540 | 1.2939 |
| 1.5474 | 0.85 | 560 | 1.2892 |
| 1.6981 | 0.88 | 580 | 1.2949 |
| 1.5451 | 0.91 | 600 | 1.2886 |
| 1.5845 | 0.94 | 620 | 1.2941 |
| 1.5143 | 0.97 | 640 | 1.2973 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
If you want to support me, you can [here](https://ko-fi.com/undiai).
|
acalatrava/mlc-llm-TinyLlama-1.1B-translate-en-es-q4f16_1
|
acalatrava
| 2023-09-28T20:43:42Z | 0 | 0 | null |
[
"en",
"es",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:sam-mosaic/orca-gpt4-chatml",
"dataset:alvations/globalvoices-en-es",
"license:apache-2.0",
"region:us"
] | null | 2023-09-28T10:11:24Z |
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- sam-mosaic/orca-gpt4-chatml
- alvations/globalvoices-en-es
language:
- en
- es
---
<div align="center">
# TinyLlama-1.1B-translate-en-es
</div>
This is a finetuned version with a partial dataset from alvations/globalvoices-en-es to test performance on translation task. It has been trained to translate english to spanish and viceversa with only 20k rows from the dataset.
The translation is not very accurate but it shows a lot of potential.
In order to use it you have to follow the chatml standard like so:
---
english to spanish:
```
<|im_start|>user Translate this to spanish: ```A father and son, who have been living off grid for 20 years, encounter an outsider who threatens to destroy the utopia they've built.```
<|im_start|>assistant
```
This will provide the following result:
```
Un padre y hijo, que han vivido sin comida desde hace 20 años, encuentran un invitado quien amenaza con destruir la utopía que ellos han creado.
```
---
spanish to english:
```
<|im_start|>user Traduce esto al ingles: ```España se queda sin Copilot para Windows 11: la regulación de la UE frena su despliegue en Europa.```
<|im_start|>assistant
```
Which will be completed as:
```
Spain is left without Copilot for Windows 11: the control of the UE has halted its deployment in Europe.
```
---
The results are far from perfect but there are A LOT of room to improvement since it was finetuned with only 20k rows from the dataset (which has 355k rows) for 2 epoch. This training took only about 5 hours on a "M1 Pro" processor.
The base model used is a fine-tuned model with orca dataset [acalatrava/TinyLlama-1.1B-orca-gpt4](https://huggingface.co/acalatrava/TinyLlama-1.1B-orca-gpt4)
### Training
- **Method**: QLORA
- **Time**: 5h on a M1 Pro 32GB
- **Based on**: [https://colab.research.google.com/drive/1Zmaceu65d7w4Tcd-cfnZRb6k_Tcv2b8g](https://colab.research.google.com/drive/1Zmaceu65d7w4Tcd-cfnZRb6k_Tcv2b8g) removing quantization since it's not supported on MPS
|
adutchscotsman/ppo-LunarLander-v2
|
adutchscotsman
| 2023-09-28T20:38:58Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-28T20:38:37Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 274.15 +/- 16.90
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CJJ1234/a2c-PandaReachDense-v3
|
CJJ1234
| 2023-09-28T20:37:59Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T22:46:07Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -12.18 +/- 3.12
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
wy2029/a2c-PandaReachDense-v3
|
wy2029
| 2023-09-28T20:35:54Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-28T20:29:58Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -11.51 +/- 8.88
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
erkam/sg2im-256-bs-16x2-lr1e5-depth
|
erkam
| 2023-09-28T20:34:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"sg-to-image",
"scene-graph",
"stable-diffusion",
"stable-diffusion-diffusers",
"lora",
"base_model:stabilityai/stable-diffusion-2",
"base_model:adapter:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-26T03:11:27Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
tags:
- sg-to-image
- scene-graph
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - erkam/sg2im-256-bs-16x2-lr1e5-depth
These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the vg dataset. You can find some example images in the following.
|
KxSystems/mri_resnet_model
|
KxSystems
| 2023-09-28T20:33:41Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"license:apache-2.0",
"region:us"
] | null | 2023-09-28T20:29:56Z |
---
license: apache-2.0
library_name: keras
---
|
eugene6/ppo-LunarLander-v2-unit8
|
eugene6
| 2023-09-28T20:33:00Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-28T20:32:55Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -186.82 +/- 107.27
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'eugene6/ppo-LunarLander-v2-unit8'
'batch_size': 512
'minibatch_size': 128}
```
|
CyberHarem/tachibana_nina_citrus
|
CyberHarem
| 2023-09-28T20:19:28Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/tachibana_nina_citrus",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-28T20:08:36Z |
---
license: mit
datasets:
- CyberHarem/tachibana_nina_citrus
pipeline_tag: text-to-image
tags:
- art
---
# Lora of tachibana_nina_citrus
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4760, you need to download `4760/tachibana_nina_citrus.pt` as the embedding and `4760/tachibana_nina_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4760**, with the score of 0.970. The trigger words are:
1. `tachibana_nina_citrus`
2. `long_hair, grey_hair, hairband, blue_eyes, purple_eyes, bow, hair_between_eyes, hair_bow`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.952 | [Download](5100/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| **4760** | **0.970** | [**Download**](4760/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.969 | [Download](4420/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.945 | [Download](4080/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.949 | [Download](3740/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.947 | [Download](3400/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.950 | [Download](3060/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.956 | [Download](2720/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.919 | [Download](2380/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.940 | [Download](2040/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.933 | [Download](1700/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.941 | [Download](1360/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.839 | [Download](1020/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.911 | [Download](680/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.725 | [Download](340/tachibana_nina_citrus.zip) |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
Ranjit/test_1
|
Ranjit
| 2023-09-28T20:14:37Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:AmazonScience/massive",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-28T20:14:10Z |
---
base_model: xxxxxxxxx
tags:
- generated_from_trainer
datasets:
- AmazonScience/massive
metrics:
- f1
model-index:
- name: massive_indo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# massive_indo
This model is a fine-tuned version of [xxxxxxxxx](https://huggingface.co/xxxxxxxxx) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6883
- F1: 0.8201
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.1343 | 0.11 | 2000 | 1.7374 | 0.2664 |
| 1.2506 | 0.22 | 4000 | 1.1294 | 0.5441 |
| 0.9268 | 0.33 | 6000 | 0.8991 | 0.6547 |
| 0.7993 | 0.44 | 8000 | 0.8401 | 0.6819 |
| 0.6985 | 0.54 | 10000 | 0.7629 | 0.7245 |
| 0.6418 | 0.65 | 12000 | 0.7507 | 0.7559 |
| 0.5887 | 0.76 | 14000 | 0.6858 | 0.7796 |
| 0.5462 | 0.87 | 16000 | 0.6852 | 0.7872 |
| 0.508 | 0.98 | 18000 | 0.6731 | 0.7836 |
| 0.4222 | 1.09 | 20000 | 0.6884 | 0.7902 |
| 0.3948 | 1.2 | 22000 | 0.6809 | 0.7897 |
| 0.3947 | 1.31 | 24000 | 0.6894 | 0.7935 |
| 0.3779 | 1.42 | 26000 | 0.6702 | 0.8026 |
| 0.3488 | 1.53 | 28000 | 0.6762 | 0.7935 |
| 0.3461 | 1.63 | 30000 | 0.6737 | 0.8054 |
| 0.3372 | 1.74 | 32000 | 0.6720 | 0.8062 |
| 0.3275 | 1.85 | 34000 | 0.6526 | 0.8156 |
| 0.3224 | 1.96 | 36000 | 0.6717 | 0.8068 |
| 0.2425 | 2.07 | 38000 | 0.6810 | 0.8143 |
| 0.2423 | 2.18 | 40000 | 0.6668 | 0.8196 |
| 0.2394 | 2.29 | 42000 | 0.7014 | 0.8125 |
| 0.2247 | 2.4 | 44000 | 0.6842 | 0.8167 |
| 0.2253 | 2.51 | 46000 | 0.7012 | 0.8130 |
| 0.2225 | 2.62 | 48000 | 0.6907 | 0.8178 |
| 0.2074 | 2.72 | 50000 | 0.6814 | 0.8206 |
| 0.2095 | 2.83 | 52000 | 0.6928 | 0.8192 |
| 0.2018 | 2.94 | 54000 | 0.6883 | 0.8201 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
Finnfalter/q-FrozenLake-v1-4x4-noSlippery
|
Finnfalter
| 2023-09-28T20:09:30Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-28T20:09:28Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Finnfalter/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Gurusha/sd-hand-model-lora-sdxl_text_encoder
|
Gurusha
| 2023-09-28T19:55:49Z | 2 | 2 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-09-28T13:43:40Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
dataset: None
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - Gurusha/sd-hand-model-lora-sdxl_text_encoder
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the None dataset. You can find some example images in the following.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
criceca/food_classifier
|
criceca
| 2023-09-28T19:54:44Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-28T19:50:36Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: criceca/food_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# criceca/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3562
- Validation Loss: 0.5212
- Train Accuracy: 1.0
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 10, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.7778 | 0.6301 | 1.0 | 0 |
| 0.5779 | 0.5862 | 1.0 | 1 |
| 0.4803 | 0.5538 | 1.0 | 2 |
| 0.4108 | 0.5343 | 1.0 | 3 |
| 0.3562 | 0.5212 | 1.0 | 4 |
### Framework versions
- Transformers 4.33.2
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Gen-Sim/Gen-Sim
|
Gen-Sim
| 2023-09-28T19:44:33Z | 151 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-31T13:06:54Z |
---
license: mit
---
## GenSim: Generating Robotic Simulation Tasks via Large Language Models
This model is a code-llama-instruct model finetuned on the released dataset of 100 simulation tasks generated by GPT-4 and humans. It can perform simulation task code generation similar to a GPT-4 level model.
## Usage
```python
from transformers import AutoModel
input = "[INST] <<SYS>>\n
<</SYS>>\n
Write the pybullet simulation task class [splitting-piles]. Provide answers in a python code block starting with ```
[/INST]\n"
model = AutoModel.from_pretrained("Gen-Sim/Gen-Sim", trust_remote_code=True)
```
Intended Use:
|
CyberHarem/tachibana_sara_citrus
|
CyberHarem
| 2023-09-28T19:35:33Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/tachibana_sara_citrus",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-28T19:24:37Z |
---
license: mit
datasets:
- CyberHarem/tachibana_sara_citrus
pipeline_tag: text-to-image
tags:
- art
---
# Lora of tachibana_sara_citrus
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4760, you need to download `4760/tachibana_sara_citrus.pt` as the embedding and `4760/tachibana_sara_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4760**, with the score of 0.992. The trigger words are:
1. `tachibana_sara_citrus`
2. `long_hair, purple_eyes, serafuku, grey_hair, ahoge, blue_eyes, anime_coloring`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.953 | [Download](5100/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| **4760** | **0.992** | [**Download**](4760/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.983 | [Download](4420/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.946 | [Download](4080/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.896 | [Download](3740/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.899 | [Download](3400/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.899 | [Download](3060/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.942 | [Download](2720/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.929 | [Download](2380/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.863 | [Download](2040/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.792 | [Download](1700/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.858 | [Download](1360/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.844 | [Download](1020/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.493 | [Download](680/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.404 | [Download](340/tachibana_sara_citrus.zip) |  |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
relaxtraffic/AT
|
relaxtraffic
| 2023-09-28T19:24:55Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"uncensored",
"en",
"dataset:ehartford/wizard_vicuna_70k_unfiltered",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-28T17:19:08Z |
---
license: other
datasets:
- ehartford/wizard_vicuna_70k_unfiltered
language:
- en
tags:
- uncensored
---
This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
Shout out to the open source AI/ML community, and everyone who helped me out.
Note:
An uncensored model has no guardrails.
You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car.
Publishing anything this model generates is the same as publishing it yourself.
You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
|
dgao/icefall-otc-librispeech-conformer-ctc2
|
dgao
| 2023-09-28T19:18:56Z | 0 | 1 | null |
[
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2023-09-28T17:28:44Z |
---
license: apache-2.0
---
# Omni-temporal Classification (OTC)
We propose BTC/OTC to directly train an ASR system leveraging weak supervision, i.e., speech with non-verbatim transcripts. This is achieved by using a special token
to model uncertainties (i.e., substitution errors, insertion errors, and deletion errors) within the WFST framework during training.
OTC maintains reasonable ASR performance even when the transcripts contain up to 70% errors of different types.
## When transcript error rate = 0.5
### Results (WER (%)) (ctc-greedy-search)
<table>
<tr>
<td rowspan=2>Training Criterion</td>
<td colspan=2>ssl</td>
<td colspan=2>fbank</td>
</tr>
<tr>
<td>test-clean</td>
<td>test-other</td>
<td>test-clean</td>
<td>test-other</td>
</tr>
<tr>
<td>CTC</td>
<td>100.0</td>
<td>100.0</td>
<td>99.89</td>
<td>99.98</td>
</tr>
<tr>
<td>OTC</td>
<td>11.89</td>
<td>25.46</td>
<td>20.14</td>
<td>44.24</td>
</tr>
</table>
### Results (WER (%)) (1best, blank_bias=-4)
<table>
<tr>
<td rowspan=2>Training Criterion</td>
<td colspan=2>ssl</td>
<td colspan=2>fbank</td>
</tr>
<tr>
<td>test-clean</td>
<td>test-other</td>
<td>test-clean</td>
<td>test-other</td>
</tr>
<tr>
<td>CTC</td>
<td>98.40</td>
<td>98.68</td>
<td>99.79</td>
<td>99.86</td>
</tr>
<tr>
<td>OTC</td>
<td>6.59</td>
<td>15.98</td>
<td>11.78</td>
<td>32.38</td>
</tr>
</table>
|
roa7n/gpt2-human_nontata_promoters-randomized_6_layers_0.0003_lr_8_e
|
roa7n
| 2023-09-28T19:12:02Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T19:12:00Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
melaris/nilooai
|
melaris
| 2023-09-28T18:30:36Z | 0 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-28T18:17:36Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### NilooAi Dreambooth model trained by melaris with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
deutsche-welle/bias_classifier_roberta_base_peft
|
deutsche-welle
| 2023-09-28T18:18:27Z | 2 | 1 |
peft
|
[
"peft",
"roberta",
"text-classification",
"en",
"dataset:mediabiasgroup/BABE",
"arxiv:1910.09700",
"base_model:FacebookAI/roberta-base",
"base_model:adapter:FacebookAI/roberta-base",
"region:us"
] |
text-classification
| 2023-07-31T13:02:47Z |
---
language:
- en
library_name: peft
tags:
- roberta
datasets:
- mediabiasgroup/BABE
metrics:
- f1
pipeline_tag: text-classification
base_model: roberta-base
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ArturGadek/LunarLander-v2
|
ArturGadek
| 2023-09-28T18:14:02Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-28T18:13:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 287.00 +/- 13.87
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CyberHarem/momokino_himeko_citrus
|
CyberHarem
| 2023-09-28T18:09:02Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/momokino_himeko_citrus",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-28T17:56:48Z |
---
license: mit
datasets:
- CyberHarem/momokino_himeko_citrus
pipeline_tag: text-to-image
tags:
- art
---
# Lora of momokino_himeko_citrus
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5100, you need to download `5100/momokino_himeko_citrus.pt` as the embedding and `5100/momokino_himeko_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5100**, with the score of 0.930. The trigger words are:
1. `momokino_himeko_citrus`
2. `drill_hair, twin_drills, black_hair, purple_eyes, twintails, necktie, purple_hair, black_necktie`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **5100** | **0.930** | [**Download**](5100/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.918 | [Download](4760/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.903 | [Download](4420/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.900 | [Download](4080/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.779 | [Download](3740/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.901 | [Download](3400/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.905 | [Download](3060/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.883 | [Download](2720/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.810 | [Download](2380/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.910 | [Download](2040/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.794 | [Download](1700/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.818 | [Download](1360/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.814 | [Download](1020/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.764 | [Download](680/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.600 | [Download](340/momokino_himeko_citrus.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
vineetsharma/databricks-dolly-15k-distilgpt2
|
vineetsharma
| 2023-09-28T18:08:32Z | 142 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-28T18:07:21Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: databricks-dolly-15k-distilgpt2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# databricks-dolly-15k-distilgpt2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4527
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5914 | 1.0 | 1501 | 2.4770 |
| 2.5148 | 2.0 | 3002 | 2.4558 |
| 2.4592 | 3.0 | 4503 | 2.4527 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
kadriu/shqip-mms-3
|
kadriu
| 2023-09-28T18:01:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-17T22:33:52Z |
---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: shqip-mms-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# shqip-mms-3
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4195
- Wer: 0.3447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.5717 | 0.31 | 300 | 0.9548 | 0.7373 |
| 0.7823 | 0.63 | 600 | 0.7372 | 0.5886 |
| 0.6863 | 0.94 | 900 | 0.6122 | 0.5261 |
| 0.5908 | 1.25 | 1200 | 0.5215 | 0.4440 |
| 0.5237 | 1.57 | 1500 | 0.4693 | 0.3987 |
| 0.4662 | 1.88 | 1800 | 0.4195 | 0.3447 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.1.0.dev20230810
- Datasets 2.14.3
- Tokenizers 0.13.3
|
infCapital/viet-llama2-ft
|
infCapital
| 2023-09-28T17:57:21Z | 1,447 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"vi",
"dataset:tatsu-lab/alpaca",
"dataset:ewof/alpaca-instruct-unfiltered",
"dataset:databricks/databricks-dolly-15k",
"dataset:teknium/GPTeacher-General-Instruct",
"dataset:garage-bAInd/Open-Platypus",
"dataset:Honkware/oasst1-alpaca-json",
"dataset:GAIR/lima",
"dataset:infCapital/viet-llama2-ft-tiny",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-28T16:45:15Z |
---
datasets:
- tatsu-lab/alpaca
- ewof/alpaca-instruct-unfiltered
- databricks/databricks-dolly-15k
- teknium/GPTeacher-General-Instruct
- garage-bAInd/Open-Platypus
- Honkware/oasst1-alpaca-json
- GAIR/lima
- infCapital/viet-llama2-ft-tiny
language:
- vi
---
+ LLaMa2 - 7B Chat models, extend vocab size to 44800 for Vietnamese understanding.
+ Continual Pre-Train with 2B Vietnames Tokens aligned from VnNews Corpus, 10K vnthuquan books, wikipedia_vi
+ Fine-Tuning with infCapital/viet-llama2-ft-tiny dataset, the combination of vaious dataset then translated into Vietnamese using OpenAI GPT-3
+ For more information: email me at duyhunghd6@gmail.com | http://fb.com/hungbui2013
|
R136a1/MythoMax-L2-13B-exl2
|
R136a1
| 2023-09-28T17:47:41Z | 108 | 6 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-21T16:27:43Z |
---
license: other
language:
- en
---
[EXL2](https://github.com/turboderp/exllamav2/tree/master#exllamav2) Quantization of [Gryphe's MythoMax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b).
Other quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) - [GGUF](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF) - [AWQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-AWQ)
## Model details
Base Perplexity : 5.7447
| **Branch** | **bits** | **Perplexity** | **Description** |
|----------------------------------------------------------------------|----------|----------------|-------------------------------------------------------------|
| [3bit](https://huggingface.co/R136a1/MythoMax-L2-13B-exl2/tree/3bit) | 3.73 | 5.8251 | Low bits quant while still good |
| [4bit](https://huggingface.co/R136a1/MythoMax-L2-13B-exl2/tree/4bit) | 4.33 | 5.7784 | can go 6K context on T4 GPU |
| [main](https://huggingface.co/R136a1/MythoMax-L2-13B-exl2/tree/main) | 5.33 | 5.7427 | 4k Context on T4 GPU (recommended if you use Google Colab) |
| [6bit](https://huggingface.co/R136a1/MythoMax-L2-13B-exl2/tree/6bit) | 6.13 | 5.7347 | For those who want better quality and capable of running it |
## Prompt Format
Alpaca format:
```
### Instruction:
### Response:
```
|
Yntec/nuipenimix2
|
Yntec
| 2023-09-28T17:39:27Z | 12,747 | 4 |
diffusers
|
[
"diffusers",
"safetensors",
"Anime",
"Cute",
"Animals",
"McSionnaigh",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-28T16:58:20Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Anime
- Cute
- Animals
- McSionnaigh
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# nuipenimix 2
fp16 no-ema version of this model. Original page: https://civitai.com/models/81937?modelVersionId=139841
Sample and prompt:
icon of cute little red panda, round frame, blue glow, purple background
|
arnaucas/wildfire-classifier
|
arnaucas
| 2023-09-28T17:32:08Z | 249 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"climate",
"biology",
"base_model:google/vit-base-patch16-384",
"base_model:finetune:google/vit-base-patch16-384",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-18T16:49:26Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_trainer
- climate
- biology
metrics:
- accuracy
model-index:
- name: wildfire-classifier
results: []
widget:
- src: https://news.erau.edu/-/media/images/news/headlines/january-2023/wildfire-overhead-drone-shot.jpg?h=749&w=1000&hash=13476D2A9BBA829375B2EB7E83588E18
example_title: Drone-shot
- src: https://www.ecuadorforestofclouds.org/uploads/7/4/1/4/74143387/2015367_orig.jpg
example_title: Cloudy forest
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wildfire classifier
This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the
[Kaggle Wildfire Dataset](https://www.kaggle.com/datasets/elmadafri/the-wildfire-dataset).
It achieves the following results on the evaluation set:
- Loss: 0.2329
- Accuracy: 0.9202
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1208 | 1.28 | 100 | 0.2329 | 0.9202 |
| 0.0261 | 2.56 | 200 | 0.2469 | 0.9316 |
| 0.0007 | 3.85 | 300 | 0.2358 | 0.9392 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
### Aditional resources
[Fine-tuning tutorial](https://huggingface.co/blog/fine-tune-vit)
|
thewiz/kd-bertBase-bertTiny
|
thewiz
| 2023-09-28T17:13:49Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:google/bert_uncased_L-2_H-128_A-2",
"base_model:finetune:google/bert_uncased_L-2_H-128_A-2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-28T16:36:52Z |
---
license: apache-2.0
base_model: google/bert_uncased_L-2_H-128_A-2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: kd-bertBase-bertTiny
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8268348623853211
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kd-bertBase-bertTiny
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0591
- Accuracy: 0.8268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4776 | 1.0 | 527 | 1.1240 | 0.8062 |
| 0.8442 | 2.0 | 1054 | 1.0475 | 0.8165 |
| 0.6568 | 3.0 | 1581 | 1.0529 | 0.8131 |
| 0.5623 | 4.0 | 2108 | 1.0503 | 0.8188 |
| 0.5066 | 5.0 | 2635 | 1.0471 | 0.8303 |
| 0.4736 | 6.0 | 3162 | 1.0711 | 0.8280 |
| 0.4603 | 7.0 | 3689 | 1.0591 | 0.8268 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
TheBloke/leo-hessianai-7B-GPTQ
|
TheBloke
| 2023-09-28T17:11:03Z | 44 | 3 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"en",
"de",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:wikipedia",
"dataset:bjoernp/tagesschau-2018-2023",
"base_model:LeoLM/leo-hessianai-7b",
"base_model:quantized:LeoLM/leo-hessianai-7b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-09-28T16:24:10Z |
---
base_model: LeoLM/leo-hessianai-7b
datasets:
- oscar-corpus/OSCAR-2301
- wikipedia
- bjoernp/tagesschau-2018-2023
inference: false
language:
- en
- de
library_name: transformers
license: llama2
model_creator: LAION LeoLM
model_name: Leo Hessianai 7B
model_type: llama
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Leo Hessianai 7B - GPTQ
- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM)
- Original model: [Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b)
<!-- description start -->
## Description
This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF)
* [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 3.90 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.62 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 4.02 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/leo-hessianai-7B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-7B-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `leo-hessianai-7B-GPTQ`:
```shell
mkdir leo-hessianai-7B-GPTQ
huggingface-cli download TheBloke/leo-hessianai-7B-GPTQ --local-dir leo-hessianai-7B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir leo-hessianai-7B-GPTQ
huggingface-cli download TheBloke/leo-hessianai-7B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-7B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir leo-hessianai-7B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-GPTQ --local-dir leo-hessianai-7B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/leo-hessianai-7B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/leo-hessianai-7B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `leo-hessianai-7B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/leo-hessianai-7B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: LAION LeoLM's Leo Hessianai 7B
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## Model Details
- **Finetuned from:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
model="LeoLM/leo-hessianai-7b",
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True # True for flash-attn2 else False
)
```
## Training parameters
## Benchmarks
|
davidramos/lora-flan-t5-small
|
davidramos
| 2023-09-28T17:02:57Z | 0 | 0 | null |
[
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-09-27T14:10:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: lora-flan-t5-small
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lora-flan-t5-small
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- training_steps: 15000
### Training results
### Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
vineetsharma/databricks-dolly-15k-pythia-70m-deduped-v1
|
vineetsharma
| 2023-09-28T16:55:16Z | 156 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m-deduped",
"base_model:finetune:EleutherAI/pythia-70m-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-24T17:01:45Z |
---
license: apache-2.0
base_model: EleutherAI/pythia-70m-deduped
tags:
- generated_from_trainer
model-index:
- name: databricks-dolly-15k-pythia-70m-deduped-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# databricks-dolly-15k-pythia-70m-deduped-v1
This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3063
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.8656 | 1.0 | 1501 | 4.6547 |
| 4.609 | 2.0 | 3002 | 4.4454 |
| 4.1706 | 3.0 | 4503 | 4.3141 |
| 3.8124 | 4.0 | 6004 | 4.3063 |
| 3.4925 | 5.0 | 7505 | 4.3213 |
| 3.1927 | 6.0 | 9006 | 4.3806 |
| 2.9257 | 7.0 | 10507 | 4.4425 |
| 2.6838 | 8.0 | 12008 | 4.5654 |
| 2.4619 | 9.0 | 13509 | 4.7032 |
| 2.2826 | 10.0 | 15010 | 4.8537 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Crataco/AI-Dungeon-2-Classic-GGML
|
Crataco
| 2023-09-28T16:50:44Z | 0 | 6 | null |
[
"ggml",
"causal-lm",
"gpt2",
"text-generation",
"en",
"license:mit",
"region:us"
] |
text-generation
| 2023-09-23T19:06:30Z |
---
language:
- en
tags:
- ggml
- causal-lm
- gpt2
- text-generation
license: mit
---
### This repository contains quantized conversions of the AI Dungeon 2 checkpoint, "model_v5".
*For use with frontends that support GGML quantized GPT-2 models. This model works best with KoboldCpp's "Adventure" mode.*
*Last updated on 2023-09-23.*
Model | RAM usage (KoboldCpp) | RAM usage (Oobabooga)
:--:|:--:|:--:
aid2classic-ggml-q4_0.bin | 984.1 MiB | 1.4 GiB
aid2classic-ggml-q4_1.bin | 1.1 GiB | 1.5 GiB
aid2classic-ggml-q5_0.bin | 1.2 GiB | 1.6 GiB
aid2classic-ggml-q5_1.bin | 1.2 GiB | 1.7 GiB
aid2classic-ggml-q8_0.bin | 1.7 GiB | 2.2 GiB
aid2classic-ggml-f16.bin | 3.2 GiB | 3.6 GiB
**Description:**
- 2019 AI Dungeon users may recognize this model as the same one that powered [the open-source AI Dungeon 2 project](https://github.com/Latitude-Archives/AIDungeon) and its various forks. This was before AI Dungeon 2 moved to its own website and consequently rebranded to "AI Dungeon".
- 2020-2021 AI Dungeon users may recognize this model as "Classic", the free tier below Griffin (free, but later used "energy") and Dragon (subscription).
- If you want a better model trained on the same dataset at the cost of higher hardware requirements, check out [Spring Dragon 13B](https://huggingface.co/TheBloke/Spring-Dragon-GGUF), intended to replicate 2020 AI Dungeon's "Dragon" experience on local hardware.
- The motivation behind these quantizations was that [Henk717/ai-dungeon2-classic-ggml](https://huggingface.co/Henk717/ai-dungeon2-classic-ggml) was older and lacked other quantization formats. The workflow for this quantization was also different: henk717's mentions being converted to a 16-bit Pytorch checkpoint before being converted to GGML. This one was converted straight from Tensorflow to 16-bit GGML before being quantized.
**Notes:**
- KoboldCpp [[bfc696f]](https://github.com/LostRuins/koboldcpp/tree/bfc696fcc452975dbe8967c39301ba856d04a030) was tested without OpenBLAS.
- Oobabooga [[895ec9d]](https://github.com/oobabooga/text-generation-webui/tree/895ec9dadb96120e8202a83052bf9032ca3245ae) was tested with with the `--model <model> --loader ctransformers --model_type gpt2` launch arguments.
- ggerganov/ggml [[8ca2c19]](https://github.com/ggerganov/ggml/tree/8ca2c19a3bb8622954d858fbf6383522684eaf34)'s gpt-2 conversion script was used for conversion and quantization.
- The original model was found in the `generator/gpt2/models/model_v5` directory of [AI Dungeon 2 Unleashed](https://henk.tech/aid/).
|
Johnlhugface/dqn-SpaceInvadersNoFrameskip-v4
|
Johnlhugface
| 2023-09-28T16:46:22Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-28T16:45:48Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 679.50 +/- 172.49
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Johnlhugface -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Johnlhugface -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Johnlhugface
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
facebook/mbart-large-50-many-to-many-mmt
|
facebook
| 2023-09-28T16:42:59Z | 295,669 | 336 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"mbart",
"text2text-generation",
"mbart-50",
"translation",
"multilingual",
"ar",
"cs",
"de",
"en",
"es",
"et",
"fi",
"fr",
"gu",
"hi",
"it",
"ja",
"kk",
"ko",
"lt",
"lv",
"my",
"ne",
"nl",
"ro",
"ru",
"si",
"tr",
"vi",
"zh",
"af",
"az",
"bn",
"fa",
"he",
"hr",
"id",
"ka",
"km",
"mk",
"ml",
"mn",
"mr",
"pl",
"ps",
"pt",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"uk",
"ur",
"xh",
"gl",
"sl",
"arxiv:2008.00401",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- multilingual
- ar
- cs
- de
- en
- es
- et
- fi
- fr
- gu
- hi
- it
- ja
- kk
- ko
- lt
- lv
- my
- ne
- nl
- ro
- ru
- si
- tr
- vi
- zh
- af
- az
- bn
- fa
- he
- hr
- id
- ka
- km
- mk
- ml
- mn
- mr
- pl
- ps
- pt
- sv
- sw
- ta
- te
- th
- tl
- uk
- ur
- xh
- gl
- sl
tags:
- mbart-50
pipeline_tag: translation
---
# mBART-50 many to many multilingual machine translation
This model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-many-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper.
The model can translate directly between any pair of 50 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method.
```python
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
# translate Hindi to French
tokenizer.src_lang = "hi_IN"
encoded_hi = tokenizer(article_hi, return_tensors="pt")
generated_tokens = model.generate(
**encoded_hi,
forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"]
)
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire dans la Syrie."
# translate Arabic to English
tokenizer.src_lang = "ar_AR"
encoded_ar = tokenizer(article_ar, return_tensors="pt")
generated_tokens = model.generate(
**encoded_ar,
forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"]
)
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "The Secretary-General of the United Nations says there is no military solution in Syria."
```
See the [model hub](https://huggingface.co/models?filter=mbart-50) to look for more fine-tuned versions.
## Languages covered
Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)
## BibTeX entry and citation info
```
@article{tang2020multilingual,
title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning},
author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan},
year={2020},
eprint={2008.00401},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
CyberHarem/aihara_mei_citrus
|
CyberHarem
| 2023-09-28T16:39:48Z | 0 | 1 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/aihara_mei_citrus",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-28T16:27:11Z |
---
license: mit
datasets:
- CyberHarem/aihara_mei_citrus
pipeline_tag: text-to-image
tags:
- art
---
# Lora of aihara_mei_citrus
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6900, you need to download `6900/aihara_mei_citrus.pt` as the embedding and `6900/aihara_mei_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6900**, with the score of 0.945. The trigger words are:
1. `aihara_mei_citrus`
2. `black_hair, long_hair, purple_eyes, hair_between_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **6900** | **0.945** | [**Download**](6900/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](6900/previews/bikini.png) | [<NSFW, click to see>](6900/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6900/previews/nude.png) | [<NSFW, click to see>](6900/previews/nude2.png) |  |  |
| 6440 | 0.823 | [Download](6440/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](6440/previews/bikini.png) | [<NSFW, click to see>](6440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6440/previews/nude.png) | [<NSFW, click to see>](6440/previews/nude2.png) |  |  |
| 5980 | 0.865 | [Download](5980/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5980/previews/bikini.png) | [<NSFW, click to see>](5980/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5980/previews/nude.png) | [<NSFW, click to see>](5980/previews/nude2.png) |  |  |
| 5520 | 0.883 | [Download](5520/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5520/previews/bikini.png) | [<NSFW, click to see>](5520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5520/previews/nude.png) | [<NSFW, click to see>](5520/previews/nude2.png) |  |  |
| 5060 | 0.884 | [Download](5060/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5060/previews/bikini.png) | [<NSFW, click to see>](5060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5060/previews/nude.png) | [<NSFW, click to see>](5060/previews/nude2.png) |  |  |
| 4600 | 0.909 | [Download](4600/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4600/previews/bikini.png) | [<NSFW, click to see>](4600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4600/previews/nude.png) | [<NSFW, click to see>](4600/previews/nude2.png) |  |  |
| 4140 | 0.920 | [Download](4140/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4140/previews/bikini.png) | [<NSFW, click to see>](4140/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4140/previews/nude.png) | [<NSFW, click to see>](4140/previews/nude2.png) |  |  |
| 3680 | 0.925 | [Download](3680/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3680/previews/bikini.png) | [<NSFW, click to see>](3680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3680/previews/nude.png) | [<NSFW, click to see>](3680/previews/nude2.png) |  |  |
| 3220 | 0.909 | [Download](3220/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3220/previews/bikini.png) | [<NSFW, click to see>](3220/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3220/previews/nude.png) | [<NSFW, click to see>](3220/previews/nude2.png) |  |  |
| 2760 | 0.857 | [Download](2760/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2760/previews/bikini.png) | [<NSFW, click to see>](2760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2760/previews/nude.png) | [<NSFW, click to see>](2760/previews/nude2.png) |  |  |
| 2300 | 0.867 | [Download](2300/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2300/previews/bikini.png) | [<NSFW, click to see>](2300/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2300/previews/nude.png) | [<NSFW, click to see>](2300/previews/nude2.png) |  |  |
| 1840 | 0.859 | [Download](1840/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1840/previews/bikini.png) | [<NSFW, click to see>](1840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1840/previews/nude.png) | [<NSFW, click to see>](1840/previews/nude2.png) |  |  |
| 1380 | 0.831 | [Download](1380/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1380/previews/bikini.png) | [<NSFW, click to see>](1380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1380/previews/nude.png) | [<NSFW, click to see>](1380/previews/nude2.png) |  |  |
| 920 | 0.861 | [Download](920/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](920/previews/bikini.png) | [<NSFW, click to see>](920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](920/previews/nude.png) | [<NSFW, click to see>](920/previews/nude2.png) |  |  |
| 460 | 0.598 | [Download](460/aihara_mei_citrus.zip) |  |  |  |  |  |  | [<NSFW, click to see>](460/previews/bikini.png) | [<NSFW, click to see>](460/previews/bondage.png) |  |  |  | [<NSFW, click to see>](460/previews/nude.png) | [<NSFW, click to see>](460/previews/nude2.png) |  |  |
|
CyberHarem/kurumi_lycorisrecoil
|
CyberHarem
| 2023-09-28T16:38:36Z | 0 | 1 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/kurumi_lycorisrecoil",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-28T16:26:50Z |
---
license: mit
datasets:
- CyberHarem/kurumi_lycorisrecoil
pipeline_tag: text-to-image
tags:
- art
---
# Lora of kurumi_lycorisrecoil
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5100, you need to download `5100/kurumi_lycorisrecoil.pt` as the embedding and `5100/kurumi_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5100**, with the score of 0.996. The trigger words are:
1. `kurumi_lycorisrecoil`
2. `blonde_hair, long_hair, hairband, black_hairband, forehead, hoodie, black_ribbon, blue_eyes, ribbon, closed_mouth, hair_ribbon, white_hoodie, indoors`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **5100** | **0.996** | [**Download**](5100/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.993 | [Download](4760/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.989 | [Download](4420/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.991 | [Download](4080/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.989 | [Download](3740/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.985 | [Download](3400/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.994 | [Download](3060/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.917 | [Download](2720/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.991 | [Download](2380/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.980 | [Download](2040/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.991 | [Download](1700/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.986 | [Download](1360/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.987 | [Download](1020/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.924 | [Download](680/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.989 | [Download](340/kurumi_lycorisrecoil.zip) |  |  |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
TheBloke/leo-hessianai-7B-GGUF
|
TheBloke
| 2023-09-28T16:28:04Z | 239 | 3 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation",
"en",
"de",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:wikipedia",
"dataset:bjoernp/tagesschau-2018-2023",
"base_model:LeoLM/leo-hessianai-7b",
"base_model:quantized:LeoLM/leo-hessianai-7b",
"license:llama2",
"region:us"
] |
text-generation
| 2023-09-28T16:23:50Z |
---
base_model: LeoLM/leo-hessianai-7b
datasets:
- oscar-corpus/OSCAR-2301
- wikipedia
- bjoernp/tagesschau-2018-2023
inference: false
language:
- en
- de
library_name: transformers
license: llama2
model_creator: LAION LeoLM
model_name: Leo Hessianai 7B
model_type: llama
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
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# Leo Hessianai 7B - GGUF
- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM)
- Original model: [Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF)
* [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [leo-hessianai-7b.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
| [leo-hessianai-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
| [leo-hessianai-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
| [leo-hessianai-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
| [leo-hessianai-7b.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [leo-hessianai-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
| [leo-hessianai-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
| [leo-hessianai-7b.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [leo-hessianai-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
| [leo-hessianai-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended |
| [leo-hessianai-7b.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
| [leo-hessianai-7b.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-7B-GGUF and below it, a specific filename to download, such as: leo-hessianai-7b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/leo-hessianai-7B-GGUF leo-hessianai-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/leo-hessianai-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-GGUF leo-hessianai-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m leo-hessianai-7b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-7B-GGUF", model_file="leo-hessianai-7b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
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## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
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**Special thanks to**: Aemon Algiz.
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Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: LAION LeoLM's Leo Hessianai 7B
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## Model Details
- **Finetuned from:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
model="LeoLM/leo-hessianai-7b",
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True # True for flash-attn2 else False
)
```
## Training parameters
## Benchmarks
<!-- original-model-card end -->
|
TheBloke/leo-hessianai-7B-chat-GPTQ
|
TheBloke
| 2023-09-28T16:22:45Z | 26 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"en",
"de",
"dataset:LeoLM/OpenSchnabeltier",
"dataset:OpenAssistant/OASST-DE",
"dataset:FreedomIntelligence/alpaca-gpt4-deutsch",
"dataset:FreedomIntelligence/evol-instruct-deutsch",
"dataset:LeoLM/German_Poems",
"dataset:LeoLM/German_Songs",
"base_model:LeoLM/leo-hessianai-7b-chat",
"base_model:quantized:LeoLM/leo-hessianai-7b-chat",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-09-28T15:35:40Z |
---
base_model: LeoLM/leo-hessianai-7b-chat
datasets:
- LeoLM/OpenSchnabeltier
- OpenAssistant/OASST-DE
- FreedomIntelligence/alpaca-gpt4-deutsch
- FreedomIntelligence/evol-instruct-deutsch
- LeoLM/German_Poems
- LeoLM/German_Songs
inference: false
language:
- en
- de
library_name: transformers
license: llama2
model_creator: LAION LeoLM
model_name: Leo Hessianai 7B Chat
model_type: llama
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- header start -->
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Leo Hessianai 7B Chat - GPTQ
- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM)
- Original model: [Leo Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-hessianai-7b-chat)
<!-- description start -->
## Description
This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-hessianai-7b-chat).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF)
* [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b-chat)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 3.90 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.62 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 4.03 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/leo-hessianai-7B-chat-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-7B-chat-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `leo-hessianai-7B-chat-GPTQ`:
```shell
mkdir leo-hessianai-7B-chat-GPTQ
huggingface-cli download TheBloke/leo-hessianai-7B-chat-GPTQ --local-dir leo-hessianai-7B-chat-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir leo-hessianai-7B-chat-GPTQ
huggingface-cli download TheBloke/leo-hessianai-7B-chat-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-7B-chat-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir leo-hessianai-7B-chat-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-chat-GPTQ --local-dir leo-hessianai-7B-chat-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/leo-hessianai-7B-chat-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/leo-hessianai-7B-chat-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `leo-hessianai-7B-chat-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/leo-hessianai-7B-chat-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: LAION LeoLM's Leo Hessianai 7B Chat
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## LeoLM Chat
`LeoLM/leo-hessianai-7b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German instruction datasets.
The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores:
```
{
"first_turn": 5.75,
"second_turn": 4.45,
"categories": {
"writing": 5.875,
"roleplay": 6.3,
"reasoning": 3.5,
"math": 2.85,
"coding": 2.95,
"extraction": 4.3,
"stem": 7.4,
"humanities": 7.625
},
"average": 5.1
}
```
## Model Details
- **Finetuned from:** [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **Demo:** [Web Demo]()
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import pipeline
import torch
system_prompt = """<|im_start|>system
Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|>
"""
prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist."
generator = pipeline(model="LeoLM/leo-hessianai-7b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False
print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))
```
"*Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen.*
*In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen.*"
## Prompting / Prompt Template
Prompt dialogue template (ChatML format):
```
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""
```
The model input can contain multiple conversation turns between user and assistant, e.g.
```
<|im_start|>user
{prompt 1}<|im_end|>
<|im_start|>assistant
{reply 1}<|im_end|>
<|im_start|>user
{prompt 2}<|im_end|>
<|im_start|>assistant
(...)
```
## Ethical Considerations and Limitations
LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios.
For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted
in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should
perform safety testing and tuning tailored to their specific applications of the model.
Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
## Finetuning Details
| Hyperparameter | Value |
|---|---|
| Num epochs | 3 |
| Examples per epoch | 131214 |
| Global batch size | 256 |
| Learning rate | 3e-5 |
| Warmup steps | 100 |
| LR scheduler | Cosine |
| Adam betas | (0.9, 0.95) |
## Dataset Details
```
## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%))
-----------------
Accepted: 3534/3534 (100.0%)
Accepted tokens: 2259302
Skipped: 0 (0.0%)
Min tokens per sample: 29
Max tokens per sample: 2484
Avg tokens per sample: 639.3044708545557
-----------------
## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%))
-----------------
Accepted: 57841/57841 (100.0%)
Accepted tokens: 42958192
Skipped: 0 (0.0%)
Min tokens per sample: 33
Max tokens per sample: 5507
Avg tokens per sample: 742.6944900675991
-----------------
## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%))
-----------------
Accepted: 48969/48969 (100.0%)
Accepted tokens: 13372005
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 1359
Avg tokens per sample: 273.07082031489307
-----------------
## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%))
-----------------
Accepted: 21314/21314 (100.0%)
Accepted tokens: 8134690
Skipped: 0 (0.0%)
Min tokens per sample: 25
Max tokens per sample: 1202
Avg tokens per sample: 381.65947264708643
-----------------
## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%))
-----------------
Accepted: 490/490 (100.0%)
Accepted tokens: 618642
Skipped: 0 (0.0%)
Min tokens per sample: 747
Max tokens per sample: 1678
Avg tokens per sample: 1262.534693877551
-----------------
## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%))
-----------------
Accepted: 392/392 (100.0%)
Accepted tokens: 187897
Skipped: 0 (0.0%)
Min tokens per sample: 231
Max tokens per sample: 826
Avg tokens per sample: 479.3290816326531
-----------------
## Stats for 'total' (132540 samples (100.0%))
-----------------
Accepted: 132540/132540 (100.0%)
Accepted tokens: 67530728
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 5507
Avg tokens per sample: 509.51205673758864
-----------------
```
|
ai-forever/fbc3_baseline
|
ai-forever
| 2023-09-28T16:01:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-09-28T15:21:33Z |
## AI Journey 2023 Baseline solution
This solution is inspired by the methodologies of [FROMAGe](https://github.com/kohjingyu/fromage) and [Kosmos-1](https://github.com/microsoft/unilm/tree/master/kosmos-2). It primarily employs these approaches to fine-tune the linear mapping from visual and audio vector spaces into the language model-decoder's vector space. Subsequently, the response is generated exclusively using the intact language model.
As a modality encoder, we utilize [ImageBind](https://github.com/facebookresearch/ImageBind). This encoder has been trained specifically for understanding images, audio, and text and other data formats in a shared embedding space.
During the training phase, the weights of the encoder and the language model remain frozen. The exceptions to this are the additional embeddings for two tokens marking the beginning and end of the respective modalities in the language model: `<SOI>`, `<EOI>` and `<SOA>`, `<EOA>` (S, E — Start, End; I,A — Image, Audio).
Training is leveraged using four datasets: [VisualDialogues](https://visualdialog.org/data), [COCO Captions](https://cocodataset.org/#home), [Clotho v. 2.1](https://zenodo.org/record/3490684) and [Clotho-AQA](https://zenodo.org/record/6473207). The core training objective is next token prediction with CrossEntropy loss. The general architecture is illustrated here:
<p align="center">
<img alt="Baseline model architecture" src="./baseline.png" width="100%">
</p>
## Training
To reproduce training, please run [notebook](./baseline.ipynb) after installing requirements:
```
pip install requirements.txt
```
|
Tensoic/Mistral-7B-v0.1-alpaca-2k-test
|
Tensoic
| 2023-09-28T16:00:31Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-28T14:32:02Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
---
# Tensoic - Mistral-7B-v0.1-alpaca-2k-test
Our first fine tune of the Mistral 7B on the Alpaca-2k-test dataset. Feel free to play around!
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
|
YegorS/llama_2_13b_8bit_r16
|
YegorS
| 2023-09-28T15:53:44Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T15:08:03Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
gsl22/testando
|
gsl22
| 2023-09-28T15:52:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-28T14:21:31Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: testando
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# testando
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
CyberHarem/aihara_yuzu_citrus
|
CyberHarem
| 2023-09-28T15:45:15Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/aihara_yuzu_citrus",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-28T15:26:55Z |
---
license: mit
datasets:
- CyberHarem/aihara_yuzu_citrus
pipeline_tag: text-to-image
tags:
- art
---
# Lora of aihara_yuzu_citrus
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 9000, you need to download `9000/aihara_yuzu_citrus.pt` as the embedding and `9000/aihara_yuzu_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 9000**, with the score of 0.603. The trigger words are:
1. `aihara_yuzu_citrus`
2. `blonde_hair, green_eyes, long_hair, jewelry, earrings, brown_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **9000** | **0.603** | [**Download**](9000/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](9000/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](9000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](9000/previews/nude.png) | [<NSFW, click to see>](9000/previews/nude2.png) |  |  |
| 8400 | 0.592 | [Download](8400/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8400/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](8400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8400/previews/nude.png) | [<NSFW, click to see>](8400/previews/nude2.png) |  |  |
| 7800 | 0.539 | [Download](7800/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7200 | 0.560 | [Download](7200/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7200/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](7200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) |  |  |
| 6600 | 0.538 | [Download](6600/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6600/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](6600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) |  |  |
| 6000 | 0.526 | [Download](6000/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6000/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](6000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) |  |  |
| 5400 | 0.514 | [Download](5400/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5400/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](5400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) |  |  |
| 4800 | 0.533 | [Download](4800/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4800/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](4800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) |  |  |
| 4200 | 0.557 | [Download](4200/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4200/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](4200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4200/previews/nude.png) | [<NSFW, click to see>](4200/previews/nude2.png) |  |  |
| 3600 | 0.460 | [Download](3600/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3600/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](3600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3600/previews/nude.png) | [<NSFW, click to see>](3600/previews/nude2.png) |  |  |
| 3000 | 0.328 | [Download](3000/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3000/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](3000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3000/previews/nude.png) | [<NSFW, click to see>](3000/previews/nude2.png) |  |  |
| 2400 | 0.427 | [Download](2400/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2400/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](2400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  |  |
| 1800 | 0.395 | [Download](1800/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1800/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](1800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1800/previews/nude.png) | [<NSFW, click to see>](1800/previews/nude2.png) |  |  |
| 1200 | 0.288 | [Download](1200/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1200/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](1200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [<NSFW, click to see>](1200/previews/nude2.png) |  |  |
| 600 | 0.221 | [Download](600/aihara_yuzu_citrus.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](600/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](600/previews/nude.png) | [<NSFW, click to see>](600/previews/nude2.png) |  |  |
|
TheBloke/leo-hessianai-7B-chat-GGUF
|
TheBloke
| 2023-09-28T15:39:03Z | 166 | 4 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation",
"en",
"de",
"dataset:LeoLM/OpenSchnabeltier",
"dataset:OpenAssistant/OASST-DE",
"dataset:FreedomIntelligence/alpaca-gpt4-deutsch",
"dataset:FreedomIntelligence/evol-instruct-deutsch",
"dataset:LeoLM/German_Poems",
"dataset:LeoLM/German_Songs",
"base_model:LeoLM/leo-hessianai-7b-chat",
"base_model:quantized:LeoLM/leo-hessianai-7b-chat",
"license:llama2",
"region:us"
] |
text-generation
| 2023-09-28T15:35:30Z |
---
base_model: LeoLM/leo-hessianai-7b-chat
datasets:
- LeoLM/OpenSchnabeltier
- OpenAssistant/OASST-DE
- FreedomIntelligence/alpaca-gpt4-deutsch
- FreedomIntelligence/evol-instruct-deutsch
- LeoLM/German_Poems
- LeoLM/German_Songs
inference: false
language:
- en
- de
library_name: transformers
license: llama2
model_creator: LAION LeoLM
model_name: Leo Hessianai 7B Chat
model_type: llama
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Leo Hessianai 7B Chat - GGUF
- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM)
- Original model: [Leo Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-hessianai-7b-chat)
<!-- description start -->
## Description
This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-hessianai-7b-chat).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF)
* [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b-chat)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [leo-hessianai-7b-chat.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
| [leo-hessianai-7b-chat.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
| [leo-hessianai-7b-chat.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
| [leo-hessianai-7b-chat.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
| [leo-hessianai-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [leo-hessianai-7b-chat.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
| [leo-hessianai-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
| [leo-hessianai-7b-chat.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [leo-hessianai-7b-chat.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
| [leo-hessianai-7b-chat.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended |
| [leo-hessianai-7b-chat.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
| [leo-hessianai-7b-chat.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-7B-chat-GGUF and below it, a specific filename to download, such as: leo-hessianai-7b-chat.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/leo-hessianai-7B-chat-GGUF leo-hessianai-7b-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/leo-hessianai-7B-chat-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-chat-GGUF leo-hessianai-7b-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m leo-hessianai-7b-chat.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-7B-chat-GGUF", model_file="leo-hessianai-7b-chat.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: LAION LeoLM's Leo Hessianai 7B Chat
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## LeoLM Chat
`LeoLM/leo-hessianai-7b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German instruction datasets.
The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores:
```
{
"first_turn": 5.75,
"second_turn": 4.45,
"categories": {
"writing": 5.875,
"roleplay": 6.3,
"reasoning": 3.5,
"math": 2.85,
"coding": 2.95,
"extraction": 4.3,
"stem": 7.4,
"humanities": 7.625
},
"average": 5.1
}
```
## Model Details
- **Finetuned from:** [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **Demo:** [Web Demo]()
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import pipeline
import torch
system_prompt = """<|im_start|>system
Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|>
"""
prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist."
generator = pipeline(model="LeoLM/leo-hessianai-7b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False
print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))
```
"*Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen.*
*In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen.*"
## Prompting / Prompt Template
Prompt dialogue template (ChatML format):
```
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""
```
The model input can contain multiple conversation turns between user and assistant, e.g.
```
<|im_start|>user
{prompt 1}<|im_end|>
<|im_start|>assistant
{reply 1}<|im_end|>
<|im_start|>user
{prompt 2}<|im_end|>
<|im_start|>assistant
(...)
```
## Ethical Considerations and Limitations
LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios.
For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted
in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should
perform safety testing and tuning tailored to their specific applications of the model.
Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
## Finetuning Details
| Hyperparameter | Value |
|---|---|
| Num epochs | 3 |
| Examples per epoch | 131214 |
| Global batch size | 256 |
| Learning rate | 3e-5 |
| Warmup steps | 100 |
| LR scheduler | Cosine |
| Adam betas | (0.9, 0.95) |
## Dataset Details
```
## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%))
-----------------
Accepted: 3534/3534 (100.0%)
Accepted tokens: 2259302
Skipped: 0 (0.0%)
Min tokens per sample: 29
Max tokens per sample: 2484
Avg tokens per sample: 639.3044708545557
-----------------
## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%))
-----------------
Accepted: 57841/57841 (100.0%)
Accepted tokens: 42958192
Skipped: 0 (0.0%)
Min tokens per sample: 33
Max tokens per sample: 5507
Avg tokens per sample: 742.6944900675991
-----------------
## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%))
-----------------
Accepted: 48969/48969 (100.0%)
Accepted tokens: 13372005
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 1359
Avg tokens per sample: 273.07082031489307
-----------------
## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%))
-----------------
Accepted: 21314/21314 (100.0%)
Accepted tokens: 8134690
Skipped: 0 (0.0%)
Min tokens per sample: 25
Max tokens per sample: 1202
Avg tokens per sample: 381.65947264708643
-----------------
## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%))
-----------------
Accepted: 490/490 (100.0%)
Accepted tokens: 618642
Skipped: 0 (0.0%)
Min tokens per sample: 747
Max tokens per sample: 1678
Avg tokens per sample: 1262.534693877551
-----------------
## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%))
-----------------
Accepted: 392/392 (100.0%)
Accepted tokens: 187897
Skipped: 0 (0.0%)
Min tokens per sample: 231
Max tokens per sample: 826
Avg tokens per sample: 479.3290816326531
-----------------
## Stats for 'total' (132540 samples (100.0%))
-----------------
Accepted: 132540/132540 (100.0%)
Accepted tokens: 67530728
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 5507
Avg tokens per sample: 509.51205673758864
-----------------
```
<!-- original-model-card end -->
|
ezzini/marian-finetuned-mcwc-ar-to-en
|
ezzini
| 2023-09-28T15:38:53Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"base_model:Helsinki-NLP/opus-mt-ar-en",
"base_model:finetune:Helsinki-NLP/opus-mt-ar-en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-28T15:31:26Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ar-en
tags:
- generated_from_keras_callback
model-index:
- name: ezzini/marian-finetuned-mcwc-ar-to-en
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ezzini/marian-finetuned-mcwc-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9486
- Validation Loss: 1.0908
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 384, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.3918 | 1.1473 | 0 |
| 1.0745 | 1.1021 | 1 |
| 0.9486 | 1.0908 | 2 |
### Framework versions
- Transformers 4.33.3
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
joachimsallstrom/aether-glitch-lora-for-sdxl
|
joachimsallstrom
| 2023-09-28T15:35:34Z | 910 | 17 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"style",
"vhs glitch",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] |
text-to-image
| 2023-09-28T15:35:20Z |
---
license: other
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- style
- vhs glitch
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: vhs glitch
widget:
- text: " vhs glitch a skeleton news reporter wearing a black wig and suit, they live 1988, movie still cinematic"
- text: " vhs glitch man on a mountain looking at the sunset, cinematic, black background"
- text: " vhs glitch cinematic 80s movie still of Margot Robbie, Barbie movie, intricate, atom bomb in the background"
- text: " vhs glitch award-winning photo of kung fury battling a monster, cinematic horror"
- text: " vhs glitch movie still of cyberpunk 2077 female character, cinematic, hires 8k, intricate, white background"
- text: " vhs glitch movie still of by 50s Jennifer Connelly jazz singer at a dark club, Dark City 1998, cinematic, heavy grain"
- text: " vhs glitch movie still of cyberpunk 2077 black character, cinematic, hires 8k, intricate, white background"
- text: " vhs glitch cinematic movie still of Cillian Murphy wearing a 40s fedora"
- text: " vhs glitch found footage of horror girl in the woods, cinematic, hires 8k, intricate, black background"
- text: " vhs glitch movie still of cyberpunk 2077 character with cigar in mouth, cinematic, hires 8k, intricate, white background"
---
# Aether Glitch - LoRA for SDXL
> vhs glitch a skeleton news reporter wearing a black wig and suit, they live 1988, movie still cinematic
<p><strong><span style="color:rgb(230, 73, 128)">MAKE SURE TO CHECK THE IMAGES ON THIS PAGE FOR PROMPTS AND SETTINGS TO GET GOOD RESULTS.</span></strong><br /><br />This is <strong><span style="color:rgb(230, 73, 128)">A</span><span style="color:rgb(190, 75, 219)">e</span><span style="color:rgb(121, 80, 242)">t</span><span style="color:rgb(76, 110, 245)">h</span><span style="color:rgb(34, 139, 230)">e</span><span style="color:rgb(21, 170, 191)">r</span></strong> <strong><span style="color:rgb(21, 170, 191)">G</span><span style="color:rgb(64, 192, 87)">l</span><span style="color:rgb(130, 201, 30)">it</span><span style="color:rgb(250, 176, 5)">c</span><span style="color:rgb(253, 126, 20)">h</span></strong>, an SDXL LoRA that simulates vhs tape glitches over subjects. Make sure to trigger the LoRA with <strong><em>vhs glitch</em></strong>, and play with weights to get more or less balanced glitch or crt tv looks. Looks cool on both photoreal and animated stuff. Gaming, movies and horror works well, and of course synthwave and such. Overall a great mood setter!<br /><br />Thanks to <a target="_blank" rel="ugc" href="https://rundiffusion.com/">RunDiffusion</a> who sponsored the finetuning of this LoRA. <span style="color:rgb(219, 222, 225)">It was made on Lastben’s SDXL LoRA trainer through RunDiffusion which will soon be made public for everyone. Aether Glitch is of course available on their platform to try it out.</span></p><p></p><p><span style="color:rgb(219, 222, 225)">Also thanks to Masslevel, Warlock and Adam D for contributing with awesome images.</span></p>
## Image examples for the model:
> vhs glitch man on a mountain looking at the sunset, cinematic, black background
> vhs glitch cinematic 80s movie still of Margot Robbie, Barbie movie, intricate, atom bomb in the background
> vhs glitch award-winning photo of kung fury battling a monster, cinematic horror
> vhs glitch movie still of cyberpunk 2077 female character, cinematic, hires 8k, intricate, white background
> vhs glitch movie still of by 50s Jennifer Connelly jazz singer at a dark club, Dark City 1998, cinematic, heavy grain
> vhs glitch movie still of cyberpunk 2077 black character, cinematic, hires 8k, intricate, white background
> vhs glitch cinematic movie still of Cillian Murphy wearing a 40s fedora
> vhs glitch found footage of horror girl in the woods, cinematic, hires 8k, intricate, black background
> vhs glitch movie still of cyberpunk 2077 character with cigar in mouth, cinematic, hires 8k, intricate, white background
|
MerziaAdamjee/codellama2-finetuned-sqldata
|
MerziaAdamjee
| 2023-09-28T15:18:32Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"base_model:finetune:codellama/CodeLlama-7b-Instruct-hf",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-28T13:32:27Z |
---
license: llama2
base_model: codellama/CodeLlama-7b-Instruct-hf
tags:
- generated_from_trainer
model-index:
- name: codellama2-finetuned-sqldata
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codellama2-finetuned-sqldata
This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
Ori/lama-2-13b-peft-no-ret-seed-1
|
Ori
| 2023-09-28T15:17:14Z | 3 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-09-28T15:16:03Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
CyberHarem/inoue_takina_lycorisrecoil
|
CyberHarem
| 2023-09-28T15:13:39Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/inoue_takina_lycorisrecoil",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-28T15:00:07Z |
---
license: mit
datasets:
- CyberHarem/inoue_takina_lycorisrecoil
pipeline_tag: text-to-image
tags:
- art
---
# Lora of inoue_takina_lycorisrecoil
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 7020, you need to download `7020/inoue_takina_lycorisrecoil.pt` as the embedding and `7020/inoue_takina_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7020**, with the score of 0.986. The trigger words are:
1. `inoue_takina_lycorisrecoil`
2. `black_hair, long_hair, bangs, purple_eyes, closed_mouth, ribbon, green_ribbon, neck_ribbon`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 8100 | 0.875 | [Download](8100/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8100/previews/nude.png) | [<NSFW, click to see>](8100/previews/nude2.png) |  |  |
| 7560 | 0.974 | [Download](7560/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7560/previews/nude.png) | [<NSFW, click to see>](7560/previews/nude2.png) |  |  |
| **7020** | **0.986** | [**Download**](7020/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7020/previews/nude.png) | [<NSFW, click to see>](7020/previews/nude2.png) |  |  |
| 6480 | 0.979 | [Download](6480/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6480/previews/nude.png) | [<NSFW, click to see>](6480/previews/nude2.png) |  |  |
| 5940 | 0.972 | [Download](5940/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5940/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5940/previews/nude.png) | [<NSFW, click to see>](5940/previews/nude2.png) |  |  |
| 5400 | 0.921 | [Download](5400/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) |  |  |
| 4860 | 0.982 | [Download](4860/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4860/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4860/previews/nude.png) | [<NSFW, click to see>](4860/previews/nude2.png) |  |  |
| 4320 | 0.982 | [Download](4320/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) |  |  |
| 3780 | 0.982 | [Download](3780/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3780/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3780/previews/nude.png) | [<NSFW, click to see>](3780/previews/nude2.png) |  |  |
| 3240 | 0.967 | [Download](3240/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3240/previews/nude.png) | [<NSFW, click to see>](3240/previews/nude2.png) |  |  |
| 2700 | 0.908 | [Download](2700/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2700/previews/nude.png) | [<NSFW, click to see>](2700/previews/nude2.png) |  |  |
| 2160 | 0.885 | [Download](2160/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2160/previews/nude.png) | [<NSFW, click to see>](2160/previews/nude2.png) |  |  |
| 1620 | 0.941 | [Download](1620/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1620/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1620/previews/nude.png) | [<NSFW, click to see>](1620/previews/nude2.png) |  |  |
| 1080 | 0.960 | [Download](1080/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1080/previews/nude.png) | [<NSFW, click to see>](1080/previews/nude2.png) |  |  |
| 540 | 0.952 | [Download](540/inoue_takina_lycorisrecoil.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](540/previews/bondage.png) |  |  |  | [<NSFW, click to see>](540/previews/nude.png) | [<NSFW, click to see>](540/previews/nude2.png) |  |  |
|
Ori/lama-2-13b-peft-mh-at-1-v2-seed-1
|
Ori
| 2023-09-28T15:13:15Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-09-28T15:12:10Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Trelis/TinyLlama-1.1B-Chat-v0.1-GGUF
|
Trelis
| 2023-09-28T15:09:32Z | 1 | 4 | null |
[
"gguf",
"tinyllama",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:timdettmers/openassistant-guanaco",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-09-28T10:23:14Z |
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- timdettmers/openassistant-guanaco
language:
- en
tags:
- tinyllama
- gguf
---
<div align="center">
# GGUF Quantized version of TinyLlama at the 250-500k checkpoint
Original model card below from [this repo](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.1).
Video covering inference: [Youtube](https://youtu.be/T5l228844NI)
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Model
This is the chat model finetuned on [PY007/TinyLlama-1.1B-intermediate-step-240k-503b](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-240k-503b). The dataset used is [openassistant-guananco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
#### How to use
You will need the transformers>=4.31
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
```python
from transformers import AutoTokenizer
import transformers
import torch
model = "PY007/TinyLlama-1.1B-Chat-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
prompt = "What are the values in open source projects?"
formatted_prompt = (
f"### Human: {prompt}### Assistant:"
)
sequences = pipeline(
formatted_prompt,
do_sample=True,
top_k=50,
top_p = 0.7,
num_return_sequences=1,
repetition_penalty=1.1,
max_new_tokens=500,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
|
roa7n/gpt2-human_nontata_promoters-randomized_6_layers_0.0003_lr_2_e
|
roa7n
| 2023-09-28T15:01:02Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T15:01:00Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
Elukasoooo/Pudzian
|
Elukasoooo
| 2023-09-28T14:58:13Z | 0 | 0 | null |
[
"pl",
"dataset:taesiri/arxiv_qa",
"license:openrail",
"region:us"
] | null | 2023-09-28T14:56:31Z |
---
license: openrail
datasets:
- taesiri/arxiv_qa
language:
- pl
metrics:
- character
---
|
TheBloke/leo-hessianai-7B-chat-bilingual-GGUF
|
TheBloke
| 2023-09-28T14:55:21Z | 153 | 4 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation",
"en",
"de",
"dataset:LeoLM/OpenSchnabeltier",
"dataset:OpenAssistant/OASST-DE",
"dataset:FreedomIntelligence/alpaca-gpt4-deutsch",
"dataset:FreedomIntelligence/evol-instruct-deutsch",
"dataset:LeoLM/German_Poems",
"dataset:LeoLM/German_Songs",
"dataset:garage-bAInd/Open-Platypus",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"dataset:bjoernp/oasst25-08-23-filtered",
"base_model:LeoLM/leo-hessianai-7b-chat-bilingual",
"base_model:quantized:LeoLM/leo-hessianai-7b-chat-bilingual",
"license:llama2",
"region:us"
] |
text-generation
| 2023-09-28T14:51:43Z |
---
base_model: LeoLM/leo-hessianai-7b-chat-bilingual
datasets:
- LeoLM/OpenSchnabeltier
- OpenAssistant/OASST-DE
- FreedomIntelligence/alpaca-gpt4-deutsch
- FreedomIntelligence/evol-instruct-deutsch
- LeoLM/German_Poems
- LeoLM/German_Songs
- garage-bAInd/Open-Platypus
- WizardLM/WizardLM_evol_instruct_70k
- bjoernp/oasst25-08-23-filtered
inference: false
language:
- en
- de
library_name: transformers
license: llama2
model_creator: LAION LeoLM
model_name: Leo Hessianai 7B Chat Bilingual
model_type: llama
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Leo Hessianai 7B Chat Bilingual - GGUF
- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM)
- Original model: [Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual)
<!-- description start -->
## Description
This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF)
* [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [leo-hessianai-7b-chat-bilingual.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
| [leo-hessianai-7b-chat-bilingual.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
| [leo-hessianai-7b-chat-bilingual.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
| [leo-hessianai-7b-chat-bilingual.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
| [leo-hessianai-7b-chat-bilingual.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [leo-hessianai-7b-chat-bilingual.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
| [leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
| [leo-hessianai-7b-chat-bilingual.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [leo-hessianai-7b-chat-bilingual.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
| [leo-hessianai-7b-chat-bilingual.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended |
| [leo-hessianai-7b-chat-bilingual.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
| [leo-hessianai-7b-chat-bilingual.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-7B-chat-bilingual-GGUF and below it, a specific filename to download, such as: leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GGUF leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GGUF leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-7B-chat-bilingual-GGUF", model_file="leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: LAION LeoLM's Leo Hessianai 7B Chat Bilingual
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## LeoLM Chat
`LeoLM/leo-hessianai-7b-chat-bilingual` is a bilingual English-German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German translateed instruction datasets and their English counterparts.
The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench scores:
```
{
"first_turn": 5.64375,
"second_turn": 4.075,
"categories": {
"writing": 5.925,
"roleplay": 5.25,
"reasoning": 3.1,
"math": 1.8,
"coding": 3.4,
"extraction": 5,
"stem": 6.5,
"humanities": 7.9
},
"average": 4.859375
}
```
## Model Details
- **Finetuned from:** [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **Demo:** [Web Demo]()
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import pipeline
import torch
system_prompt = """<|im_start|>system
Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|>
"""
prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist."
generator = pipeline(model="LeoLM/leo-hessianai-7b-chat-bilingual", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False
print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))
```
"*Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen.*
*In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen.*"
## Prompting / Prompt Template
Prompt dialogue template (ChatML format):
```
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""
```
The model input can contain multiple conversation turns between user and assistant, e.g.
```
<|im_start|>user
{prompt 1}<|im_end|>
<|im_start|>assistant
{reply 1}<|im_end|>
<|im_start|>user
{prompt 2}<|im_end|>
<|im_start|>assistant
(...)
```
## Ethical Considerations and Limitations
LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios.
For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted
in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should
perform safety testing and tuning tailored to their specific applications of the model.
Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
## Finetuning Details
| Hyperparameter | Value |
|---|---|
| Num epochs | 3 |
| Examples per epoch | 233275 |
| Global batch size | 256 |
| Learning rate | 3e-5 |
| Warmup steps | 100 |
| LR scheduler | Cosine |
| Adam betas | (0.9, 0.95) |
| Weight decay | 0.001 |
## Dataset Details
```
## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%))
-----------------
Accepted: 21314/21314 (100.0%)
Accepted tokens: 8134690
Skipped: 0 (0.0%)
Min tokens per sample: 25
Max tokens per sample: 1202
Avg tokens per sample: 381.65947264708643
-----------------
## Stats for 'Subset of garage-bAInd/Open-Platypus' (24427 samples (100.0%))
-----------------
Accepted: 24427/24427 (100.0%)
Accepted tokens: 9549043
Skipped: 0 (0.0%)
Min tokens per sample: 23
Max tokens per sample: 5054
Avg tokens per sample: 390.9216440823679
-----------------
## Stats for 'Subset of WizardLM/WizardLM_evol_instruct_70k' (68600 samples (100.0%))
-----------------
Accepted: 68600/68600 (100.0%)
Accepted tokens: 33045040
Skipped: 0 (0.0%)
Min tokens per sample: 18
Max tokens per sample: 11810
Avg tokens per sample: 481.7061224489796
-----------------
## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%))
-----------------
Accepted: 57841/57841 (100.0%)
Accepted tokens: 42958192
Skipped: 0 (0.0%)
Min tokens per sample: 33
Max tokens per sample: 5507
Avg tokens per sample: 742.6944900675991
-----------------
## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%))
-----------------
Accepted: 48969/48969 (100.0%)
Accepted tokens: 13372005
Skipped: 0 (0.0%)
Min tokens per sample: 19
Max tokens per sample: 1359
Avg tokens per sample: 273.07082031489307
-----------------
## Stats for 'Subset of LeoLM/German_Songs' (490 samples (100.0%))
-----------------
Accepted: 490/490 (100.0%)
Accepted tokens: 618642
Skipped: 0 (0.0%)
Min tokens per sample: 747
Max tokens per sample: 1678
Avg tokens per sample: 1262.534693877551
-----------------
## Stats for 'Subset of LeoLM/German_Poems' (392 samples (100.0%))
-----------------
Accepted: 392/392 (100.0%)
Accepted tokens: 187897
Skipped: 0 (0.0%)
Min tokens per sample: 231
Max tokens per sample: 826
Avg tokens per sample: 479.3290816326531
-----------------
## Stats for 'Subset of OpenAssistant/OASST_DE' (3646 samples (100.0%))
-----------------
Accepted: 3646/3646 (100.0%)
Accepted tokens: 2338738
Skipped: 0 (0.0%)
Min tokens per sample: 29
Max tokens per sample: 2484
Avg tokens per sample: 641.4530992868897
-----------------
## Stats for 'Subset of bjoernp/oasst25-08-23-filtered' (8922 samples (100.0%))
-----------------
Accepted: 8922/8922 (100.0%)
Accepted tokens: 4526427
Skipped: 0 (0.0%)
Min tokens per sample: 23
Max tokens per sample: 5407
Avg tokens per sample: 507.3332212508406
-----------------
## Stats for 'total' (235632 samples (100.0%))
-----------------
Accepted: 235632/235632 (100.0%)
Accepted tokens: 115862397
Skipped: 0 (0.0%)
Min tokens per sample: 18
Max tokens per sample: 11810
Avg tokens per sample: 491.70909299246284
-----------------
```
<!-- original-model-card end -->
|
TinyLlama/tinyLlama-intermediate-checkpoints
|
TinyLlama
| 2023-09-28T14:53:27Z | 51 | 12 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-28T14:27:51Z |
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Model
This is an intermediate checkpoint with 50K steps and 105B tokens.
#### Releases Schedule
We will be rolling out intermediate checkpoints following the below schedule. We also include some baseline models for comparison.
| Date | HF Checkpoint | Tokens | Step | HellaSwag Acc_norm |
|------------|-------------------------------------------------|--------|------|---------------------|
| Baseline | [StableLM-Alpha-3B](https://huggingface.co/stabilityai/stablelm-base-alpha-3b)| 800B | -- | 38.31 |
| Baseline | [Pythia-1B-intermediate-step-50k-105b](https://huggingface.co/EleutherAI/pythia-1b/tree/step50000) | 105B | 50k | 42.04 |
| Baseline | [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b) | 300B | 143k | 47.16 |
| 2023-09-04 | [TinyLlama-1.1B-intermediate-step-50k-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) | 105B | 50k | 43.50 |
| 2023-09-16 | -- | 500B | -- | -- |
| 2023-10-01 | -- | 1T | -- | -- |
| 2023-10-16 | -- | 1.5T | -- | -- |
| 2023-10-31 | -- | 2T | -- | -- |
| 2023-11-15 | -- | 2.5T | -- | -- |
| 2023-12-01 | -- | 3T | -- | -- |
#### How to use
You will need the transformers>=4.31
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
```
from transformers import AutoTokenizer
import transformers
import torch
model = "PY007/TinyLlama-1.1B-step-50K-105b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
sequences = pipeline(
'The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.',
do_sample=True,
top_k=10,
num_return_sequences=1,
repetition_penalty=1.5,
eos_token_id=tokenizer.eos_token_id,
max_length=500,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
|
mridul3301/distilbart-cnn-12-6-finetuned-arxiv-summarization-20k-5epochs
|
mridul3301
| 2023-09-28T14:52:13Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:arxiv_summarization_dataset",
"base_model:sshleifer/distilbart-cnn-12-6",
"base_model:finetune:sshleifer/distilbart-cnn-12-6",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-16T10:31:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- arxiv_summarization_dataset
metrics:
- rouge
base_model: sshleifer/distilbart-cnn-12-6
model-index:
- name: distilbart-cnn-12-6-finetuned-arxiv-summarization-20k-5epochs
results:
- task:
type: text2text-generation
name: Sequence-to-sequence Language Modeling
dataset:
name: arxiv_summarization_dataset
type: arxiv_summarization_dataset
config: section
split: test[:2000]
args: section
metrics:
- type: rouge
value: 43.6107
name: Rouge1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-cnn-12-6-finetuned-arxiv-summarization-20k-5epochs
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the arxiv_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3793
- Rouge1: 43.6107
- Rouge2: 15.4482
- Rougel: 25.4843
- Rougelsum: 38.4962
- Gen Len: 122.2845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.6031 | 1.0 | 2500 | 2.4896 | 42.8688 | 15.0682 | 25.1782 | 37.8758 | 121.6685 |
| 2.3931 | 2.0 | 5000 | 2.4135 | 43.7802 | 15.6074 | 25.7201 | 38.6496 | 123.614 |
| 2.2454 | 3.0 | 7500 | 2.3819 | 44.1347 | 15.8906 | 25.8923 | 38.961 | 120.8765 |
| 2.1393 | 4.0 | 10000 | 2.3780 | 43.6521 | 15.4051 | 25.5124 | 38.3483 | 122.396 |
| 2.0688 | 5.0 | 12500 | 2.3793 | 43.6107 | 15.4482 | 25.4843 | 38.4962 | 122.2845 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
TheBloke/leo-hessianai-13B-GPTQ
|
TheBloke
| 2023-09-28T14:50:39Z | 24 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"en",
"de",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:wikipedia",
"dataset:bjoernp/tagesschau-2018-2023",
"base_model:LeoLM/leo-hessianai-13b",
"base_model:quantized:LeoLM/leo-hessianai-13b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-09-28T13:36:44Z |
---
base_model: LeoLM/leo-hessianai-13b
datasets:
- oscar-corpus/OSCAR-2301
- wikipedia
- bjoernp/tagesschau-2018-2023
inference: false
language:
- en
- de
library_name: transformers
license: llama2
model_creator: LAION LeoLM
model_name: Leo Hessianai 13B
model_type: llama
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Leo Hessianai 13B - GPTQ
- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM)
- Original model: [Leo Hessianai 13B](https://huggingface.co/LeoLM/leo-hessianai-13b)
<!-- description start -->
## Description
This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 13B](https://huggingface.co/LeoLM/leo-hessianai-13b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF)
* [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-13b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/leo-hessianai-13B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-13B-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `leo-hessianai-13B-GPTQ`:
```shell
mkdir leo-hessianai-13B-GPTQ
huggingface-cli download TheBloke/leo-hessianai-13B-GPTQ --local-dir leo-hessianai-13B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir leo-hessianai-13B-GPTQ
huggingface-cli download TheBloke/leo-hessianai-13B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-13B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir leo-hessianai-13B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-13B-GPTQ --local-dir leo-hessianai-13B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/leo-hessianai-13B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/leo-hessianai-13B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `leo-hessianai-13B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/leo-hessianai-13B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
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# Original model card: LAION LeoLM's Leo Hessianai 13B
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## Model Details
- **Finetuned from:** [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
model="LeoLM/leo-hessianai-13b",
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True # True for flash-attn2 else False
)
```
## Training parameters
## Benchmarks
|
ezzini/marian-finetuned-mcwc-ar-to-es
|
ezzini
| 2023-09-28T14:43:37Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"base_model:Helsinki-NLP/opus-mt-ar-es",
"base_model:finetune:Helsinki-NLP/opus-mt-ar-es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-28T13:42:55Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ar-es
tags:
- generated_from_keras_callback
model-index:
- name: ezzini/marian-finetuned-mcwc-ar-to-es
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ezzini/marian-finetuned-mcwc-ar-to-es
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-es](https://huggingface.co/Helsinki-NLP/opus-mt-ar-es) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.4145
- Validation Loss: 1.6253
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 2049, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.8364 | 1.6811 | 0 |
| 1.5472 | 1.6363 | 1 |
| 1.4145 | 1.6253 | 2 |
### Framework versions
- Transformers 4.33.3
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
badokorach/flan-t5-small-qa-91
|
badokorach
| 2023-09-28T14:35:47Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-28T12:33:04Z |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
model-index:
- name: flan-t5-small-qa-91
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-small-qa-91
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0727
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1207 | 1.0 | 500 | 0.0685 |
| 0.1176 | 2.0 | 1000 | 0.0693 |
| 0.116 | 3.0 | 1500 | 0.0696 |
| 0.1152 | 4.0 | 2000 | 0.0709 |
| 0.1138 | 5.0 | 2500 | 0.0703 |
| 0.1134 | 6.0 | 3000 | 0.0717 |
| 0.1125 | 7.0 | 3500 | 0.0707 |
| 0.1119 | 8.0 | 4000 | 0.0710 |
| 0.1115 | 9.0 | 4500 | 0.0717 |
| 0.1113 | 10.0 | 5000 | 0.0713 |
| 0.111 | 11.0 | 5500 | 0.0721 |
| 0.1107 | 12.0 | 6000 | 0.0726 |
| 0.1106 | 13.0 | 6500 | 0.0721 |
| 0.111 | 14.0 | 7000 | 0.0720 |
| 0.1108 | 15.0 | 7500 | 0.0721 |
| 0.1106 | 16.0 | 8000 | 0.0722 |
| 0.1104 | 17.0 | 8500 | 0.0729 |
| 0.1102 | 18.0 | 9000 | 0.0727 |
| 0.1101 | 19.0 | 9500 | 0.0727 |
| 0.11 | 20.0 | 10000 | 0.0727 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
CyberHarem/hyuuga_akari_yagatekimininaru
|
CyberHarem
| 2023-09-28T14:32:51Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/hyuuga_akari_yagatekimininaru",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-28T14:22:59Z |
---
license: mit
datasets:
- CyberHarem/hyuuga_akari_yagatekimininaru
pipeline_tag: text-to-image
tags:
- art
---
# Lora of hyuuga_akari_yagatekimininaru
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 3740, you need to download `3740/hyuuga_akari_yagatekimininaru.pt` as the embedding and `3740/hyuuga_akari_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 3740**, with the score of 0.889. The trigger words are:
1. `hyuuga_akari_yagatekimininaru`
2. `brown_hair, brown_eyes, hair_ornament, hairclip, bangs, short_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.774 | [Download](5100/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.736 | [Download](4760/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.838 | [Download](4420/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.777 | [Download](4080/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| **3740** | **0.889** | [**Download**](3740/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.810 | [Download](3400/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.868 | [Download](3060/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.725 | [Download](2720/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.777 | [Download](2380/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.786 | [Download](2040/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.751 | [Download](1700/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.724 | [Download](1360/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.619 | [Download](1020/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.568 | [Download](680/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.495 | [Download](340/hyuuga_akari_yagatekimininaru.zip) |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
yyjun/yyjun.KoAlpaca_mingu_ver
|
yyjun
| 2023-09-28T14:28:35Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T14:28:32Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
anuragrawal/my-awesome-setfit-model
|
anuragrawal
| 2023-09-28T14:27:30Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-09-28T14:26:41Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# anuragrawal/my-awesome-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("anuragrawal/my-awesome-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
sayril007/fioma-opt-lora-7B-llm
|
sayril007
| 2023-09-28T14:22:30Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T14:22:26Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
hcho22/falcon-7b-sharded_finetuned_codealpaca_20k
|
hcho22
| 2023-09-28T14:14:57Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:finetune:ybelkada/falcon-7b-sharded-bf16",
"region:us"
] | null | 2023-09-27T22:12:59Z |
---
base_model: ybelkada/falcon-7b-sharded-bf16
tags:
- generated_from_trainer
model-index:
- name: falcon-7b-sharded_finetuned_codealpaca_20k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# falcon-7b-sharded_finetuned_codealpaca_20k
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
mmenendezg/detr-resnet-50_finetuned_brain_tumor
|
mmenendezg
| 2023-09-28T14:14:24Z | 165 | 0 |
transformers
|
[
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-09-21T23:52:26Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr-resnet-50_finetuned_brain_tumor
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50_finetuned_brain_tumor
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 9.764150575493936e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Gman/sd-class-butterflies-32
|
Gman
| 2023-09-28T14:12:59Z | 48 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-09-28T14:11:38Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Gman/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Venkatesh4342/whisper-small-en-hi
|
Venkatesh4342
| 2023-09-28T14:10:48Z | 89 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-10T11:53:53Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-en-hi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-en-hi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3279
- Wer: 24.0479
## Model description
Two datasets are used for two different languages, for hindi mozilla-foundation/common_voice_11_0 is used and for english google/fleurs is used. with combination of two dataset wer has decreased significantly.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.059 | 2.52 | 1500 | 0.2881 | 24.7722 |
| 0.0084 | 5.03 | 3000 | 0.3279 | 24.0479 |
### Framework versions
- Transformers 4.33.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.13.3
|
herve76/bbhf2
|
herve76
| 2023-09-28T14:07:28Z | 2 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"dataset:herve76/bbhf2",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-28T13:21:53Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: bbhf2
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: false
datasets:
- herve76/bbhf2
---
# LoRA DreamBooth - herve76/bbhf2
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained on the concept prompt:
```
bbhf2
```
Use this keyword to trigger your custom model in your prompts.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Usage
Make sure to upgrade diffusers to >= 0.19.0:
```
pip install diffusers --upgrade
```
In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
```
pip install invisible_watermark transformers accelerate safetensors
```
To just use the base model, you can run:
```python
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
vae=vae, torch_dtype=torch.float16, variant="fp16",
use_safetensors=True
)
pipe.to("cuda")
# This is where you load your trained weights
pipe.load_lora_weights('herve76/bbhf2')
prompt = "A majestic bbhf2 jumping from a big stone at night"
image = pipe(prompt=prompt, num_inference_steps=50).images[0]
```
|
ArnaudHureaux/Llama-2-70b-chat-hf-miniguanaco
|
ArnaudHureaux
| 2023-09-28T14:03:35Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"en",
"arxiv:2307.09288",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-09-26T10:42:23Z |
---
extra_gated_heading: Access Llama 2 on Hugging Face
extra_gated_description: >-
This is a form to enable access to Llama 2 on Hugging Face after you have been
granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our
license terms and acceptable use policy before submitting this form. Requests
will be processed in 1-2 days.
extra_gated_prompt: "**Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved.**"
extra_gated_button_content: Submit
extra_gated_fields:
I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
LatteAddicted/AI-Voice-Models
|
LatteAddicted
| 2023-09-28T14:00:34Z | 0 | 2 | null |
[
"region:us"
] | null | 2023-09-10T13:14:04Z |
This is where I store my AI voice models for RVC
|
VuongQuoc/checkpoints_28_9_microsoft_deberta_V4
|
VuongQuoc
| 2023-09-28T13:50:03Z | 69 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"multiple-choice",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"base_model:finetune:microsoft/deberta-v3-large",
"license:mit",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-09-28T02:49:56Z |
---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_28_9_microsoft_deberta_V4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# checkpoints_28_9_microsoft_deberta_V4
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2854
- Map@3: 0.5483
- Accuracy: 0.435
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map@3 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.2365 | 0.11 | 100 | 1.0631 | 0.7583 | 0.64 |
| 0.8608 | 0.21 | 200 | 0.7329 | 0.8383 | 0.75 |
| 0.8527 | 0.32 | 300 | 0.6985 | 0.8575 | 0.78 |
| 0.744 | 0.43 | 400 | 0.6498 | 0.8625 | 0.785 |
| 0.7686 | 0.53 | 500 | 0.7450 | 0.8575 | 0.765 |
| 1.4098 | 0.64 | 600 | 1.3030 | 0.5575 | 0.4 |
| 1.4246 | 0.75 | 700 | 1.3018 | 0.5575 | 0.435 |
| 1.3987 | 0.85 | 800 | 1.2906 | 0.5450 | 0.41 |
| 1.4121 | 0.96 | 900 | 1.2854 | 0.5483 | 0.435 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.0
- Datasets 2.9.0
- Tokenizers 0.13.3
|
drhyrum/bert-tiny-torch-picklebomb
|
drhyrum
| 2023-09-28T13:46:25Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-09-28T13:27:51Z |
---
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
---
*DISCLAIMER*: This repo demonstrates a picklebomb payload in pytorch that may go undetected by superficial scanning.
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task.
If you use the model, please consider citing both the papers:
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{DBLP:journals/corr/abs-1908-08962,
author = {Iulia Turc and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {Well-Read Students Learn Better: The Impact of Student Initialization
on Knowledge Distillation},
journal = {CoRR},
volume = {abs/1908.08962},
year = {2019},
url = {http://arxiv.org/abs/1908.08962},
eprinttype = {arXiv},
eprint = {1908.08962},
timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Config of this model:
- `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny)
Other models to check out:
- `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini)
- `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small)
- `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium)
Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli).
Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)
|
anders0204/snowball
|
anders0204
| 2023-09-28T13:46:01Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-09-28T13:45:57Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: anders0204/snowball
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
djomo/llama2bllux
|
djomo
| 2023-09-28T13:45:59Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T13:45:54Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
TheBloke/leo-hessianai-13B-GGUF
|
TheBloke
| 2023-09-28T13:43:20Z | 172 | 2 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation",
"en",
"de",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:wikipedia",
"dataset:bjoernp/tagesschau-2018-2023",
"base_model:LeoLM/leo-hessianai-13b",
"base_model:quantized:LeoLM/leo-hessianai-13b",
"license:llama2",
"region:us"
] |
text-generation
| 2023-09-28T13:36:34Z |
---
base_model: LeoLM/leo-hessianai-13b
datasets:
- oscar-corpus/OSCAR-2301
- wikipedia
- bjoernp/tagesschau-2018-2023
inference: false
language:
- en
- de
library_name: transformers
license: llama2
model_creator: LAION LeoLM
model_name: Leo Hessianai 13B
model_type: llama
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Leo Hessianai 13B - GGUF
- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM)
- Original model: [Leo Hessianai 13B](https://huggingface.co/LeoLM/leo-hessianai-13b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 13B](https://huggingface.co/LeoLM/leo-hessianai-13b).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF)
* [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-13b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [leo-hessianai-13b.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [leo-hessianai-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [leo-hessianai-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [leo-hessianai-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [leo-hessianai-13b.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [leo-hessianai-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [leo-hessianai-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [leo-hessianai-13b.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [leo-hessianai-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [leo-hessianai-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [leo-hessianai-13b.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [leo-hessianai-13b.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-13B-GGUF and below it, a specific filename to download, such as: leo-hessianai-13b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/leo-hessianai-13B-GGUF leo-hessianai-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/leo-hessianai-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-13B-GGUF leo-hessianai-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m leo-hessianai-13b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-13B-GGUF", model_file="leo-hessianai-13b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: LAION LeoLM's Leo Hessianai 13B
# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
Read our [blog post]() or our paper (preprint coming soon) for more details!
*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
## Model Details
- **Finetuned from:** [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English and German
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
## Use in 🤗Transformers
First install direct dependencies:
```
pip install transformers torch sentencepiece
```
If you want faster inference using flash-attention2, you need to install these dependencies:
```bash
pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
```
Then load the model in transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
model="LeoLM/leo-hessianai-13b",
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True # True for flash-attn2 else False
)
```
## Training parameters
## Benchmarks
<!-- original-model-card end -->
|
pejho/bloom_prompt_tuning_1695907859.055798
|
pejho
| 2023-09-28T13:39:03Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-28T13:39:01Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
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