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hkivancoral/smids_5x_deit_tiny_rms_001_fold3
|
hkivancoral
| 2023-12-16T12:59:05Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T12:03:48Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7766666666666666
---
<!-- 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. -->
# smids_5x_deit_tiny_rms_001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8753
- Accuracy: 0.7767
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7906 | 1.0 | 375 | 0.9128 | 0.4983 |
| 0.7765 | 2.0 | 750 | 0.9232 | 0.4617 |
| 0.7977 | 3.0 | 1125 | 0.8743 | 0.5267 |
| 0.8093 | 4.0 | 1500 | 0.7926 | 0.5767 |
| 0.8508 | 5.0 | 1875 | 0.7894 | 0.5733 |
| 0.7532 | 6.0 | 2250 | 0.7991 | 0.6117 |
| 0.7584 | 7.0 | 2625 | 0.7566 | 0.625 |
| 0.7398 | 8.0 | 3000 | 0.7364 | 0.6083 |
| 0.7009 | 9.0 | 3375 | 0.7452 | 0.64 |
| 0.7014 | 10.0 | 3750 | 0.7192 | 0.6433 |
| 0.7226 | 11.0 | 4125 | 0.7119 | 0.6383 |
| 0.7293 | 12.0 | 4500 | 0.7180 | 0.6467 |
| 0.6344 | 13.0 | 4875 | 0.7612 | 0.6117 |
| 0.6251 | 14.0 | 5250 | 0.7810 | 0.66 |
| 0.6301 | 15.0 | 5625 | 0.6950 | 0.6733 |
| 0.6252 | 16.0 | 6000 | 0.7106 | 0.6767 |
| 0.688 | 17.0 | 6375 | 0.7082 | 0.6883 |
| 0.7261 | 18.0 | 6750 | 0.6859 | 0.6883 |
| 0.5633 | 19.0 | 7125 | 0.6734 | 0.7033 |
| 0.6092 | 20.0 | 7500 | 0.6580 | 0.7283 |
| 0.4728 | 21.0 | 7875 | 0.6793 | 0.7033 |
| 0.5681 | 22.0 | 8250 | 0.6598 | 0.7217 |
| 0.5951 | 23.0 | 8625 | 0.6134 | 0.7533 |
| 0.6592 | 24.0 | 9000 | 0.5954 | 0.7467 |
| 0.5215 | 25.0 | 9375 | 0.5847 | 0.74 |
| 0.5272 | 26.0 | 9750 | 0.6243 | 0.7017 |
| 0.5866 | 27.0 | 10125 | 0.6339 | 0.7233 |
| 0.5766 | 28.0 | 10500 | 0.5466 | 0.765 |
| 0.463 | 29.0 | 10875 | 0.5734 | 0.7583 |
| 0.5041 | 30.0 | 11250 | 0.5320 | 0.775 |
| 0.5133 | 31.0 | 11625 | 0.5507 | 0.7683 |
| 0.5402 | 32.0 | 12000 | 0.5711 | 0.7517 |
| 0.4526 | 33.0 | 12375 | 0.5736 | 0.7483 |
| 0.4724 | 34.0 | 12750 | 0.5009 | 0.79 |
| 0.3951 | 35.0 | 13125 | 0.5483 | 0.77 |
| 0.3876 | 36.0 | 13500 | 0.5689 | 0.755 |
| 0.3627 | 37.0 | 13875 | 0.5639 | 0.7733 |
| 0.4378 | 38.0 | 14250 | 0.5663 | 0.765 |
| 0.3725 | 39.0 | 14625 | 0.5574 | 0.7867 |
| 0.3444 | 40.0 | 15000 | 0.5740 | 0.7733 |
| 0.3158 | 41.0 | 15375 | 0.5671 | 0.7717 |
| 0.29 | 42.0 | 15750 | 0.6455 | 0.78 |
| 0.3784 | 43.0 | 16125 | 0.6093 | 0.785 |
| 0.318 | 44.0 | 16500 | 0.6835 | 0.7683 |
| 0.2949 | 45.0 | 16875 | 0.7092 | 0.7733 |
| 0.2996 | 46.0 | 17250 | 0.6699 | 0.7767 |
| 0.2938 | 47.0 | 17625 | 0.7545 | 0.7917 |
| 0.2248 | 48.0 | 18000 | 0.8050 | 0.775 |
| 0.2309 | 49.0 | 18375 | 0.8518 | 0.7767 |
| 0.1878 | 50.0 | 18750 | 0.8753 | 0.7767 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
TheBloke/Mistral-7B-AEZAKMI-v1-GGUF
|
TheBloke
| 2023-12-16T12:08:48Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"base_model:adamo1139/Mistral-7B-AEZAKMI-v1",
"license:other",
"text-generation-inference",
"region:us"
] | null | 2023-12-16T12:04:31Z |
---
base_model: adamo1139/Mistral-7B-AEZAKMI-v1
inference: false
license: other
license_link: LICENSE
license_name: other
model_creator: Adam
model_name: Mistral 7B Aezakmi v1
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 Aezakmi v1 - GGUF
- Model creator: [Adam](https://huggingface.co/adamo1139)
- Original model: [Mistral 7B Aezakmi v1](https://huggingface.co/adamo1139/Mistral-7B-AEZAKMI-v1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Adam's Mistral 7B Aezakmi v1](https://huggingface.co/adamo1139/Mistral-7B-AEZAKMI-v1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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 incomplete 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF)
* [Adam's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/adamo1139/Mistral-7B-AEZAKMI-v1)
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistral-7b-aezakmi-v1.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mistral-7b-aezakmi-v1.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.17 GB| 5.67 GB | very small, high quality loss |
| [mistral-7b-aezakmi-v1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mistral-7b-aezakmi-v1.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mistral-7b-aezakmi-v1.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.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-aezakmi-v1.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mistral-7b-aezakmi-v1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mistral-7b-aezakmi-v1.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mistral-7b-aezakmi-v1.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mistral-7b-aezakmi-v1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mistral-7b-aezakmi-v1.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mistral-7b-aezakmi-v1.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-AEZAKMI-v1-GGUF/blob/main/mistral-7b-aezakmi-v1.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-AEZAKMI-v1-GGUF and below it, a specific filename to download, such as: mistral-7b-aezakmi-v1.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-AEZAKMI-v1-GGUF mistral-7b-aezakmi-v1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Mistral-7B-AEZAKMI-v1-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-AEZAKMI-v1-GGUF mistral-7b-aezakmi-v1.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 35 -m mistral-7b-aezakmi-v1.Q4_K_M.gguf --color -c 32768 --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 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./mistral-7b-aezakmi-v1.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./mistral-7b-aezakmi-v1.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
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: Adam's Mistral 7B Aezakmi v1
Mistral 7B model fine-tuned on AEZAKMI v1 dataset that is derived from airoboros 2.2.1 and airoboros 2.2.
Finetuned with axolotl, using qlora and nf4 double quant, around 2 epochs, batch size 8, lr 0.00008, lr scheduler cosine. Scheduled training was 5 epochs, but loss seemed fine after 2 so I finished it quicker.
Training took around 10 hours on single RTX 3090 Ti.
Main feature of this model is that it's output is free of refusals and it feels somehow more natural.
Prompt format is standard chatml.
Don't expect it to be good at math, riddles or be crazy smart. My end goal with AEZAKMI is to create a cozy free chatbot.
Not sure what license it needs to have, given license of airoboros dataset. I'll leave it as other for now.
<!-- original-model-card end -->
|
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] |
alpcaferoglu/column-tag-bert-finetuned
|
alpcaferoglu
| 2023-12-16T12:09:04Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T12:09:04Z |
---
license: apache-2.0
---
|
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Ttonio/xlm-roberta-base-finetuned-panx-all
|
Ttonio
| 2023-12-16T12:23:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-16T12:09:51Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1728
- F1: 0.8516
## 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: 24
- eval_batch_size: 24
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3029 | 1.0 | 835 | 0.2081 | 0.8028 |
| 0.1583 | 2.0 | 1670 | 0.1715 | 0.8426 |
| 0.1036 | 3.0 | 2505 | 0.1728 | 0.8516 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
MarcW1g/ppo-LunarLander-v2
|
MarcW1g
| 2023-12-16T12:13:14Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T12:12:42Z |
---
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: 249.57 +/- 22.79
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
...
```
|
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damerajee/results
|
damerajee
| 2023-12-16T12:15:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T12:15:58Z |
Entry not found
|
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GHArt/RongHua_V4_xl_fp16
|
GHArt
| 2023-12-16T12:23:28Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-16T12:16:25Z |
Entry not found
|
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] |
virtsion/nilmformer_3apis_no_prompt_custom_tokenizer
|
virtsion
| 2023-12-16T13:46:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-16T12:22:05Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1
|
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] |
tuner23652/distilbert-base-uncased-finetuned-squad
|
tuner23652
| 2023-12-16T13:24:02Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-16T12:23:07Z |
Entry not found
|
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virtsion/nilmformer_2apis_no_prompt_custom_tokenizer
|
virtsion
| 2023-12-16T12:23:26Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-16T12:23:24Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1
|
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owanr/SBIC-roberta-base-intra-data-frequency-model_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T12:25:04Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T12:24:46Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-intra-data-frequency-model_annots_alpha0.5_whole_1e-05
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. -->
# SBIC-roberta-base-intra-data-frequency-model_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7712
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.7627 | 1.0 | 12516 | 1.7845 |
| 1.7825 | 2.0 | 25032 | 1.7712 |
| 1.6149 | 3.0 | 37548 | 1.7712 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Elinalinut/falcon-7b-instruct-ft-adapters
|
Elinalinut
| 2023-12-17T01:06:21Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"region:us"
] | null | 2023-12-16T12:26:07Z |
---
library_name: peft
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
---
# Model Card for Model ID
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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).
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### Framework versions
- PEFT 0.7.2.dev0
|
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] |
jlbaker361/mixed_multiplication_decimal
|
jlbaker361
| 2023-12-16T12:26:16Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | null | 2023-12-16T12:26:15Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
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## Training procedure
### Framework versions
- PEFT 0.6.2
|
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sksayril/distilgpt2-finetuned-hindi
|
sksayril
| 2023-12-16T13:20:22Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T12:26:43Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_keras_callback
model-index:
- name: sksayril/distilgpt2-finetuned-hindi
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. -->
# sksayril/distilgpt2-finetuned-hindi
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:
- Train Loss: 1.9012
- Validation Loss: 1.7582
- Epoch: 0
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.9012 | 1.7582 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
zelmously/distilbert-base-uncased-finetuned-emotion
|
zelmously
| 2023-12-16T23:43:58Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-16T12:32:08Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.9225834379847784
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2208
- Accuracy: 0.923
- F1: 0.9226
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8316 | 1.0 | 250 | 0.3162 | 0.91 | 0.9096 |
| 0.2521 | 2.0 | 500 | 0.2208 | 0.923 | 0.9226 |
### Framework versions
- Transformers 4.34.1
- Pytorch 1.13.1+cpu
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
GHArt/DuplicationXL_V1_xl_fp16
|
GHArt
| 2023-12-16T12:42:05Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-16T12:33:50Z |
Entry not found
|
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TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ
|
TheBloke
| 2023-12-16T12:51:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:ignos/LeoScorpius-GreenNode-Alpaca-7B-v1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-16T12:34:05Z |
---
base_model: ignos/LeoScorpius-GreenNode-Alpaca-7B-v1
inference: false
license: apache-2.0
model_creator: Ignos
model_name: Leoscorpius GreenNode Alpaca 7B v1
model_type: mistral
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# Leoscorpius GreenNode Alpaca 7B v1 - AWQ
- Model creator: [Ignos](https://huggingface.co/ignos)
- Original model: [Leoscorpius GreenNode Alpaca 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1)
<!-- description start -->
## Description
This repo contains AWQ model files for [Ignos's Leoscorpius GreenNode Alpaca 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF)
* [Ignos's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.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/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ`.
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: `LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Ignos's Leoscorpius GreenNode Alpaca 7B v1
# Model Card for Model ID
This model is a finetuning of other models based on mistralai/Mistral-7B-v0.1.
## Model Details
### Model Description
The model has been generated from the merging of the models [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO) and [GreenNode/GreenNodeLM-7B-v1olet](https://huggingface.co/GreenNode/GreenNodeLM-7B-v1olet) and a later finetuning with an Alpaca dataset [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca).
- **Developed by:** Ignos
- **Model type:** Mistral
- **License:** Apache-2.0
## Uses
Model created for the comparison of behaviors and metrics with respect to the base model, as well as the comparison with other models that using the same base have been finetuning on other different datasets.
## Bias, Risks, and Limitations
The same bias, risks and limitations from base models.
## Training Details
### Training Data
- [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca)
### Training Procedure
- Training with QLoRA approach and merging with base model.
### Results
- Huggingface evaluation pending
#### Summary
## Technical Specifications
### Model Architecture and Objective
- Models based on Mistral Architecture
### Compute Infrastructure
- Training on RunPod
#### Hardware
- 4 x Nvidia RTX 4090
- 64 vCPU 503 GB RAM
#### Software
- Mergekit (main)
- Axolotl 0.3.0
## 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
|
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-0.5172849893569946,
0.5481857657432556,
0.37428492307662964,
-1.0379120111465454,
-0.0949702337384224,
0.5179094076156616,
0.3608461022377014,
0.11477181315422058,
0.8083951473236084,
-0.06562752276659012,
0.34893229603767395,
-0.022902270779013634,
0.2937634289264679,
-0.009721329435706139,
0.03358979895710945,
-0.19531282782554626,
0.02664855681359768,
-0.2768653929233551,
-0.2103210687637329
] |
TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF
|
TheBloke
| 2023-12-16T12:38:32Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"base_model:ignos/LeoScorpius-GreenNode-Alpaca-7B-v1",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-16T12:34:05Z |
---
base_model: ignos/LeoScorpius-GreenNode-Alpaca-7B-v1
inference: false
license: apache-2.0
model_creator: Ignos
model_name: Leoscorpius GreenNode Alpaca 7B v1
model_type: mistral
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
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</div>
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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 -->
# Leoscorpius GreenNode Alpaca 7B v1 - GGUF
- Model creator: [Ignos](https://huggingface.co/ignos)
- Original model: [Leoscorpius GreenNode Alpaca 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Ignos's Leoscorpius GreenNode Alpaca 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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 incomplete 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF)
* [Ignos's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [leoscorpius-greennode-alpaca-7b-v1.Q2_K.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [leoscorpius-greennode-alpaca-7b-v1.Q3_K_S.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [leoscorpius-greennode-alpaca-7b-v1.Q3_K_M.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [leoscorpius-greennode-alpaca-7b-v1.Q3_K_L.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [leoscorpius-greennode-alpaca-7b-v1.Q4_0.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [leoscorpius-greennode-alpaca-7b-v1.Q4_K_S.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [leoscorpius-greennode-alpaca-7b-v1.Q4_K_M.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [leoscorpius-greennode-alpaca-7b-v1.Q5_0.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [leoscorpius-greennode-alpaca-7b-v1.Q5_K_S.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [leoscorpius-greennode-alpaca-7b-v1.Q5_K_M.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [leoscorpius-greennode-alpaca-7b-v1.Q6_K.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [leoscorpius-greennode-alpaca-7b-v1.Q8_0.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF/blob/main/leoscorpius-greennode-alpaca-7b-v1.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/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF and below it, a specific filename to download, such as: leoscorpius-greennode-alpaca-7b-v1.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/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF leoscorpius-greennode-alpaca-7b-v1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-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/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF leoscorpius-greennode-alpaca-7b-v1.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 35 -m leoscorpius-greennode-alpaca-7b-v1.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./leoscorpius-greennode-alpaca-7b-v1.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./leoscorpius-greennode-alpaca-7b-v1.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
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: Ignos's Leoscorpius GreenNode Alpaca 7B v1
# Model Card for Model ID
This model is a finetuning of other models based on mistralai/Mistral-7B-v0.1.
## Model Details
### Model Description
The model has been generated from the merging of the models [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO) and [GreenNode/GreenNodeLM-7B-v1olet](https://huggingface.co/GreenNode/GreenNodeLM-7B-v1olet) and a later finetuning with an Alpaca dataset [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca).
- **Developed by:** Ignos
- **Model type:** Mistral
- **License:** Apache-2.0
## Uses
Model created for the comparison of behaviors and metrics with respect to the base model, as well as the comparison with other models that using the same base have been finetuning on other different datasets.
## Bias, Risks, and Limitations
The same bias, risks and limitations from base models.
## Training Details
### Training Data
- [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca)
### Training Procedure
- Training with QLoRA approach and merging with base model.
### Results
- Huggingface evaluation pending
#### Summary
## Technical Specifications
### Model Architecture and Objective
- Models based on Mistral Architecture
### Compute Infrastructure
- Training on RunPod
#### Hardware
- 4 x Nvidia RTX 4090
- 64 vCPU 503 GB RAM
#### Software
- Mergekit (main)
- Axolotl 0.3.0
## 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
<!-- original-model-card end -->
|
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-0.7227159738540649,
-0.7024046778678894,
0.31529757380485535,
0.33366695046424866,
-0.4460103213787079,
-0.21347905695438385,
0.020772257819771767,
-0.7391717433929443,
0.5564786791801453,
0.13076315820217133,
-0.7520977258682251,
-0.6517010927200317,
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] |
TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ
|
TheBloke
| 2023-12-16T13:02:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:ignos/LeoScorpius-GreenNode-Alpaca-7B-v1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-16T12:34:06Z |
---
base_model: ignos/LeoScorpius-GreenNode-Alpaca-7B-v1
inference: false
license: apache-2.0
model_creator: Ignos
model_name: Leoscorpius GreenNode Alpaca 7B v1
model_type: mistral
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# Leoscorpius GreenNode Alpaca 7B v1 - GPTQ
- Model creator: [Ignos](https://huggingface.co/ignos)
- Original model: [Leoscorpius GreenNode Alpaca 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Ignos's Leoscorpius GreenNode Alpaca 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1).
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.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF)
* [Ignos's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients 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.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<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 and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.16 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/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.57 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/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.68 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/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.29 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/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-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 `LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ`:
```shell
mkdir LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ
huggingface-cli download TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ --local-dir LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ
huggingface-cli download TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir LeoScorpius-GreenNode-Alpaca-7B-v1-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 Hugging Face 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 LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ --local-dir LeoScorpius-GreenNode-Alpaca-7B-v1-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/LeoScorpius-GreenNode-Alpaca-7B-v1-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/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-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: `LeoScorpius-GreenNode-Alpaca-7B-v1-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-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### 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 --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-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 = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
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 Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Ignos's Leoscorpius GreenNode Alpaca 7B v1
# Model Card for Model ID
This model is a finetuning of other models based on mistralai/Mistral-7B-v0.1.
## Model Details
### Model Description
The model has been generated from the merging of the models [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO) and [GreenNode/GreenNodeLM-7B-v1olet](https://huggingface.co/GreenNode/GreenNodeLM-7B-v1olet) and a later finetuning with an Alpaca dataset [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca).
- **Developed by:** Ignos
- **Model type:** Mistral
- **License:** Apache-2.0
## Uses
Model created for the comparison of behaviors and metrics with respect to the base model, as well as the comparison with other models that using the same base have been finetuning on other different datasets.
## Bias, Risks, and Limitations
The same bias, risks and limitations from base models.
## Training Details
### Training Data
- [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca)
### Training Procedure
- Training with QLoRA approach and merging with base model.
### Results
- Huggingface evaluation pending
#### Summary
## Technical Specifications
### Model Architecture and Objective
- Models based on Mistral Architecture
### Compute Infrastructure
- Training on RunPod
#### Hardware
- 4 x Nvidia RTX 4090
- 64 vCPU 503 GB RAM
#### Software
- Mergekit (main)
- Axolotl 0.3.0
## 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
|
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ParazolGermany/ParazolGermany
|
ParazolGermany
| 2023-12-16T12:35:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T12:34:24Z |
Parazol: Tee, Bewertungen, Preis, Leistungen, Zutaten, Nebenwirkungen, Kosten, Apotheke! Germany
Parazol wurde speziell für die Gesundheit des Verdauungssystems entwickelt und trägt zur Verbesserung Ihres Verdauungssystems bei, einschließlich Überwucherung von Dünndarmbakterien, Parasiten, Leaky-Gut-Syndrom, Gewichtszunahme und anderen damit verbundenen Komplikationen.Parazol!
<a href="https://www.canvasclinic.com/ParazGerm">Kaufe jetzt!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website : <a href="https://www.canvasclinic.com/ParazGerm">www.parazol.com</a>
<p><a href="https://www.canvasclinic.com/ParazGerm"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhctugndF1ddxHIvnQuLZLUocB1on_w_geCU-IGcUBtZqK8o_ksD_9npWqtSMhL7Lp2EcGZ33oIw9ywtsb9dJ9CyEtk7_Rc6QPPaMP9czdBkEE2KAbFPh0lrGl_DBc_miuQUlkWHoSAJd6875aOWqWMsEWAT7rhUJSNbGxCOO7jw6-ChGJFeUDoRg_bAM4/w640-h244/Parazol%20Germany%201.png" alt="enter image description here"> </a></p>
➢Produktname – Parazol
➢Kategorie – Parasitenkapsel
➢Hauptvorteile – dieses Produkt fördert eine bessere Verdauungsfunktion
➢ Zusammensetzung – Natürliche organische Verbindung
➢ Nebenwirkungen – Nicht zutreffend
➢Endgültige Bewertung: – 4,8
➢ Verfügbarkeit – Online
➢Angebote und Rabatte; SPAREN SIE HEUTE! JETZT EINKAUFEN, UM SONDERANGEBOT zu kaufen!!!
Was ist Parazol?
Parazol ist eine außergewöhnliche tägliche Verdauungsformel, die sorgfältig entwickelt wurde, um gezielt schädliche Bakterien und andere Krankheitserreger zu bekämpfen und gleichzeitig das Darmmikrobiom zu stärken und die Integrität der Darmbarriere zu stärken. Diese außergewöhnliche Formel wurde mit dem wertvollen Experten für Darmgesundheit entwickelt.
<a href="https://www.canvasclinic.com/ParazGerm">Kaufe jetzt!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website : <a href="https://www.canvasclinic.com/ParazGerm">www.parazol.com</a>
<p><a href="https://www.canvasclinic.com/ParazGerm"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhctugndF1ddxHIvnQuLZLUocB1on_w_geCU-IGcUBtZqK8o_ksD_9npWqtSMhL7Lp2EcGZ33oIw9ywtsb9dJ9CyEtk7_Rc6QPPaMP9czdBkEE2KAbFPh0lrGl_DBc_miuQUlkWHoSAJd6875aOWqWMsEWAT7rhUJSNbGxCOO7jw6-ChGJFeUDoRg_bAM4/w640-h244/Parazol%20Germany%201.png" alt="enter image description here"> </a></p>
Parazol Parazol Tee Parazol Preis Parazol Bewertungen Parazol Zutaten Parazol Leistungen Parazol Nebenwirkungen Parazol Tee preis Parazol Tee Bewertungen Parazol Komposition Parazol Beschwerde Parazol Wo zu kaufen Parazol Wie benutzt man Parazol Kosten Parazol funktioniert Parazol Forum Parazol Original Parazol Apotheke
Wie funktioniert Parazol?
Parazol zeichnet sich unter den unzähligen Nahrungsergänzungsmitteln für die Darmgesundheit dadurch aus, dass es keine Probiotika enthält und sich auf die natürliche Verstärkung guter Bakterien konzentriert. Seine einzigartige Kombination aus Präbiotika, Kräutern und Antioxidantien zeichnet es aus. Gesundheit ist eine ähnliche Option, jedoch mit Probiotika und einigen Variationen der Inhaltsstoffe.
Wo kann man Parazol kaufen?
Parazol kann direkt auf der offiziellen Website erworben werden. Es ist in drei Paketoptionen erhältlich.
<a href="https://www.canvasclinic.com/ParazGerm">Kaufe jetzt!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website : <a href="https://www.canvasclinic.com/ParazGerm">www.parazol.com</a>
<p><a href="https://www.canvasclinic.com/ParazGerm"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhctugndF1ddxHIvnQuLZLUocB1on_w_geCU-IGcUBtZqK8o_ksD_9npWqtSMhL7Lp2EcGZ33oIw9ywtsb9dJ9CyEtk7_Rc6QPPaMP9czdBkEE2KAbFPh0lrGl_DBc_miuQUlkWHoSAJd6875aOWqWMsEWAT7rhUJSNbGxCOO7jw6-ChGJFeUDoRg_bAM4/w640-h244/Parazol%20Germany%201.png" alt="enter image description here"> </a></p>
Abschließende Gedanken: Parazol:
Wir haben unsere Bewertung abgeschlossen und es handelt sich um ein außergewöhnliches Nahrungsergänzungsmittel mit wissenschaftlich belegten Inhaltsstoffen. Was es auszeichnet, ist seine besondere Mischung aus Kräutern, Antioxidantien, Präbiotika und anderen Komponenten, die zusammenarbeiten, um nützliche Bakterien zu fördern, die Darmbarriere zu stärken und zu lindern Entzündungen und fördern die Verdauung. Bei regelmäßiger Einnahme kann der Tee zur Linderung von Verstopfung von Parazol And das Potenzial haben, bemerkenswerte Ergebnisse für Ihre Darmgesundheit zu erzielen und unangenehme Symptome wie Blähungen und Sodbrennen zu lindern.
Parazol Parazol Tee Parazol Preis Parazol Bewertungen Parazol Zutaten Parazol Leistungen Parazol Nebenwirkungen Parazol Tee preis Parazol Tee Bewertungen Parazol Komposition Parazol Beschwerde Parazol Wo zu kaufen Parazol Wie benutzt man Parazol Kosten Parazol funktioniert Parazol Forum Parazol Original Parazol Apotheke
https://www.canvasclinic.com/ParazGerm
https://sites.google.com/view/parazol-tee/home
https://healthtoned.blogspot.com/2023/12/parazol-germany.html
https://medium.com/@healthytalk24x7/parazol-germany-d4596939860e
https://medium.com/@healthytalk24x7/parazol-tee-bewertungen-preis-leistungen-zutaten-nebenwirkungen-kosten-apotheke-germany-6fef657c84ff
https://www.weddingwire.com/website/parazol-and-germany
https://infogram.com/parazol-germany-1hnq4107p7d7p23?live
https://softisenilspain.hashnode.dev/parazol-germany
https://sway.office.com/uGgZ4ADefsnZULXd
https://parazolgermany.company.site/
https://gamma.app/docs/Parazol-Germany-8k39leixuut5n5r?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/parazol-germany
https://groups.google.com/g/snshine/c/LDscPPLhki8
https://www.sunflower-cissp.com/glossary/cissp/7376/parazol-germany
https://nepal.tribe.so/post/parazol-germany-657d98286bcfd990984d9e13
https://community.thebatraanumerology.com/
https://community.thebatraanumerology.com/post/parazol-tee-bewertungen-preis-leistungen-zutaten-nebenwirkungen-kosten-apot--657d98946ea4b837f8a4b645
https://enkling.com/read-blog/9141
https://soundcloud.com/parazolgermany/parazol-germany?si=30e0d26006d64850bb2922803b5422f5&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing
https://www.deviantart.com/parazolgermany/art/Parazol-Germany-1002460864
|
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christinacdl/Mistral-PromptEncoder-Binary-Hate-Speech-Detection
|
christinacdl
| 2023-12-16T16:38:15Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T12:34:33Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Mistral-PromptEncoder-Binary-Hate-Speech-Detection
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. -->
# Mistral-PromptEncoder-Binary-Hate-Speech-Detection
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6826
- Micro F1: 0.6145
- Macro F1: 0.5106
- Accuracy: 0.6145
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0
|
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gdf2h1gfh2/Cr-Sali-Elected-as-Chair-of-Regional-Cities-Victoria-2b-updated
|
gdf2h1gfh2
| 2023-12-16T12:36:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T12:36:02Z |
Entry not found
|
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] |
KnutJaegersberg/phi2-platypus-adapter
|
KnutJaegersberg
| 2023-12-16T12:42:11Z | 0 | 1 | null |
[
"safetensors",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-16T12:37:08Z |
---
license: cc-by-nc-4.0
---
General Prompt Example:
```
### Instruction:
{instruction}
### Response:
```
|
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aminotran/test
|
aminotran
| 2023-12-16T13:04:59Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-12-16T12:43:26Z |
---
license: unknown
---
|
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Laurariend/AnimaleMaleEnhancementSouthAfricabuy
|
Laurariend
| 2023-12-16T12:47:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T12:46:27Z |
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] |
vincentclaes/llava-on-sagemaker
|
vincentclaes
| 2023-12-16T09:21:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T12:46:50Z |
# Llava On Sagemker
Deploy llava to sagemaker instantely.
Open the ./[llava.ipynb](llava.ipynb) notebook in Sagemaker Studio or in a Sagemaker Notebook and follow the instructions.
|
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Laurariend/AnimaleMaleEnhancementSouthAfricagetnow
|
Laurariend
| 2023-12-16T12:48:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T12:47:56Z |
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] |
stvhuang/rcr-run-b9bhsr7n-62291-master-0_20231213T152412-ep03
|
stvhuang
| 2023-12-16T12:49:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-16T12:48:57Z |
Entry not found
|
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] |
rehaidib/codeparrot
|
rehaidib
| 2023-12-17T20:50:59Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T12:49:10Z |
Entry not found
|
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avillia/bbb
|
avillia
| 2023-12-16T14:48:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T12:50:15Z |
hello
|
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] |
hkivancoral/smids_5x_beit_base_rms_0001_fold3
|
hkivancoral
| 2023-12-16T14:12:49Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T12:52:26Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_0001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.855
---
<!-- 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. -->
# smids_5x_beit_base_rms_0001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3488
- Accuracy: 0.855
## 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: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5467 | 1.0 | 375 | 0.5969 | 0.7483 |
| 0.4112 | 2.0 | 750 | 0.6522 | 0.7967 |
| 0.3586 | 3.0 | 1125 | 0.4558 | 0.83 |
| 0.3318 | 4.0 | 1500 | 0.3669 | 0.8567 |
| 0.318 | 5.0 | 1875 | 0.4227 | 0.8267 |
| 0.2611 | 6.0 | 2250 | 0.4142 | 0.8467 |
| 0.2866 | 7.0 | 2625 | 0.4534 | 0.83 |
| 0.2297 | 8.0 | 3000 | 0.4296 | 0.8517 |
| 0.1623 | 9.0 | 3375 | 0.5359 | 0.835 |
| 0.1313 | 10.0 | 3750 | 0.5677 | 0.8433 |
| 0.1856 | 11.0 | 4125 | 0.5198 | 0.8667 |
| 0.087 | 12.0 | 4500 | 0.6463 | 0.8417 |
| 0.0974 | 13.0 | 4875 | 0.5874 | 0.8417 |
| 0.0478 | 14.0 | 5250 | 0.7058 | 0.84 |
| 0.0326 | 15.0 | 5625 | 0.7427 | 0.8283 |
| 0.0198 | 16.0 | 6000 | 0.8945 | 0.84 |
| 0.0746 | 17.0 | 6375 | 0.8489 | 0.8333 |
| 0.1024 | 18.0 | 6750 | 0.7564 | 0.8383 |
| 0.0499 | 19.0 | 7125 | 0.8028 | 0.8483 |
| 0.0808 | 20.0 | 7500 | 1.0400 | 0.8267 |
| 0.0495 | 21.0 | 7875 | 1.0596 | 0.83 |
| 0.0441 | 22.0 | 8250 | 0.9512 | 0.85 |
| 0.0385 | 23.0 | 8625 | 0.8380 | 0.8483 |
| 0.0162 | 24.0 | 9000 | 1.0671 | 0.8517 |
| 0.0061 | 25.0 | 9375 | 0.8747 | 0.86 |
| 0.0284 | 26.0 | 9750 | 1.0398 | 0.815 |
| 0.0446 | 27.0 | 10125 | 0.9748 | 0.845 |
| 0.0208 | 28.0 | 10500 | 1.0700 | 0.8517 |
| 0.0357 | 29.0 | 10875 | 1.0579 | 0.845 |
| 0.0301 | 30.0 | 11250 | 0.9043 | 0.8583 |
| 0.0099 | 31.0 | 11625 | 0.9420 | 0.8533 |
| 0.0327 | 32.0 | 12000 | 1.0192 | 0.8467 |
| 0.0502 | 33.0 | 12375 | 0.8952 | 0.8517 |
| 0.0352 | 34.0 | 12750 | 0.9041 | 0.8667 |
| 0.0188 | 35.0 | 13125 | 1.2059 | 0.8433 |
| 0.0229 | 36.0 | 13500 | 1.2761 | 0.84 |
| 0.0123 | 37.0 | 13875 | 1.1077 | 0.8583 |
| 0.0002 | 38.0 | 14250 | 1.1468 | 0.85 |
| 0.0009 | 39.0 | 14625 | 1.1590 | 0.8617 |
| 0.0211 | 40.0 | 15000 | 1.3901 | 0.8683 |
| 0.001 | 41.0 | 15375 | 1.2933 | 0.8533 |
| 0.0077 | 42.0 | 15750 | 1.1576 | 0.8583 |
| 0.0369 | 43.0 | 16125 | 1.3070 | 0.8433 |
| 0.0132 | 44.0 | 16500 | 1.0120 | 0.8633 |
| 0.0003 | 45.0 | 16875 | 1.2641 | 0.8633 |
| 0.0001 | 46.0 | 17250 | 1.2268 | 0.8633 |
| 0.0004 | 47.0 | 17625 | 1.1854 | 0.8583 |
| 0.0001 | 48.0 | 18000 | 1.3326 | 0.865 |
| 0.0187 | 49.0 | 18375 | 1.3505 | 0.8567 |
| 0.0011 | 50.0 | 18750 | 1.3488 | 0.855 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_5x_beit_base_rms_00001_fold3
|
hkivancoral
| 2023-12-16T14:13:23Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T12:53:01Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_00001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9133333333333333
---
<!-- 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. -->
# smids_5x_beit_base_rms_00001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9185
- Accuracy: 0.9133
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2283 | 1.0 | 375 | 0.2339 | 0.9183 |
| 0.0993 | 2.0 | 750 | 0.2718 | 0.9117 |
| 0.0737 | 3.0 | 1125 | 0.3409 | 0.9133 |
| 0.0242 | 4.0 | 1500 | 0.3831 | 0.92 |
| 0.0583 | 5.0 | 1875 | 0.4502 | 0.915 |
| 0.0327 | 6.0 | 2250 | 0.4867 | 0.9183 |
| 0.0228 | 7.0 | 2625 | 0.5343 | 0.9333 |
| 0.0011 | 8.0 | 3000 | 0.6969 | 0.91 |
| 0.0064 | 9.0 | 3375 | 0.8807 | 0.89 |
| 0.0562 | 10.0 | 3750 | 0.8333 | 0.9117 |
| 0.0104 | 11.0 | 4125 | 0.6930 | 0.9083 |
| 0.0118 | 12.0 | 4500 | 0.8317 | 0.8933 |
| 0.0001 | 13.0 | 4875 | 0.8634 | 0.9 |
| 0.0154 | 14.0 | 5250 | 0.7424 | 0.9233 |
| 0.0231 | 15.0 | 5625 | 0.8048 | 0.9133 |
| 0.0 | 16.0 | 6000 | 0.8245 | 0.9083 |
| 0.0137 | 17.0 | 6375 | 0.7565 | 0.92 |
| 0.0069 | 18.0 | 6750 | 0.7751 | 0.9183 |
| 0.0115 | 19.0 | 7125 | 0.7824 | 0.9233 |
| 0.0051 | 20.0 | 7500 | 0.7691 | 0.9183 |
| 0.0 | 21.0 | 7875 | 0.8067 | 0.9117 |
| 0.0207 | 22.0 | 8250 | 0.7817 | 0.915 |
| 0.0053 | 23.0 | 8625 | 0.8276 | 0.9083 |
| 0.0001 | 24.0 | 9000 | 0.7978 | 0.9117 |
| 0.0169 | 25.0 | 9375 | 0.8806 | 0.9067 |
| 0.0007 | 26.0 | 9750 | 0.8278 | 0.9267 |
| 0.0038 | 27.0 | 10125 | 0.9428 | 0.9183 |
| 0.0 | 28.0 | 10500 | 0.8806 | 0.9167 |
| 0.0 | 29.0 | 10875 | 0.8180 | 0.91 |
| 0.0029 | 30.0 | 11250 | 0.9090 | 0.9117 |
| 0.0 | 31.0 | 11625 | 0.8537 | 0.9133 |
| 0.0002 | 32.0 | 12000 | 0.8596 | 0.915 |
| 0.0003 | 33.0 | 12375 | 0.8995 | 0.9183 |
| 0.0 | 34.0 | 12750 | 0.8853 | 0.925 |
| 0.0 | 35.0 | 13125 | 0.8638 | 0.9133 |
| 0.0 | 36.0 | 13500 | 0.8296 | 0.9167 |
| 0.0 | 37.0 | 13875 | 0.8113 | 0.9217 |
| 0.0 | 38.0 | 14250 | 0.8781 | 0.92 |
| 0.027 | 39.0 | 14625 | 0.8890 | 0.92 |
| 0.0038 | 40.0 | 15000 | 0.8330 | 0.925 |
| 0.0 | 41.0 | 15375 | 0.9306 | 0.9167 |
| 0.0 | 42.0 | 15750 | 0.8569 | 0.9183 |
| 0.0 | 43.0 | 16125 | 0.9060 | 0.9133 |
| 0.0 | 44.0 | 16500 | 0.8854 | 0.9167 |
| 0.0 | 45.0 | 16875 | 0.9021 | 0.91 |
| 0.0001 | 46.0 | 17250 | 0.9154 | 0.9133 |
| 0.0 | 47.0 | 17625 | 0.8802 | 0.915 |
| 0.0 | 48.0 | 18000 | 0.8999 | 0.915 |
| 0.0 | 49.0 | 18375 | 0.9100 | 0.9117 |
| 0.0 | 50.0 | 18750 | 0.9185 | 0.9133 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
grissi/q-FrozenLake-v1-4x4-noSlippery
|
grissi
| 2023-12-16T12:56:39Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T12:56:37Z |
---
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="grissi/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"])
```
|
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Prezily/distilgpt2-eli5
|
Prezily
| 2023-12-16T13:50:56Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T12:57:52Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-eli5
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. -->
# distilgpt2-eli5
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7459
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.9182 | 1.0 | 567 | 3.7666 |
| 3.8134 | 2.0 | 1134 | 3.7495 |
| 3.7733 | 3.0 | 1701 | 3.7459 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
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webestilo/lora-trained-xl
|
webestilo
| 2023-12-16T12:59:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T12:59:48Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_rms_001_fold4
|
hkivancoral
| 2023-12-16T13:54:50Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T12:59:53Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7766666666666666
---
<!-- 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. -->
# smids_5x_deit_tiny_rms_001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2524
- Accuracy: 0.7767
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.818 | 1.0 | 375 | 0.8019 | 0.5417 |
| 0.7912 | 2.0 | 750 | 0.8025 | 0.57 |
| 0.7276 | 3.0 | 1125 | 0.7672 | 0.6083 |
| 0.7922 | 4.0 | 1500 | 0.6983 | 0.6533 |
| 0.7335 | 5.0 | 1875 | 0.6685 | 0.6917 |
| 0.6959 | 6.0 | 2250 | 0.6471 | 0.7233 |
| 0.623 | 7.0 | 2625 | 0.6073 | 0.7233 |
| 0.6887 | 8.0 | 3000 | 0.6966 | 0.6667 |
| 0.6552 | 9.0 | 3375 | 0.5957 | 0.74 |
| 0.6126 | 10.0 | 3750 | 0.6205 | 0.7 |
| 0.5793 | 11.0 | 4125 | 0.5808 | 0.7567 |
| 0.6219 | 12.0 | 4500 | 0.5874 | 0.745 |
| 0.5436 | 13.0 | 4875 | 0.6140 | 0.7317 |
| 0.6012 | 14.0 | 5250 | 0.5834 | 0.7417 |
| 0.6043 | 15.0 | 5625 | 0.5539 | 0.75 |
| 0.5011 | 16.0 | 6000 | 0.5531 | 0.7383 |
| 0.5057 | 17.0 | 6375 | 0.5890 | 0.75 |
| 0.5517 | 18.0 | 6750 | 0.5510 | 0.7583 |
| 0.5553 | 19.0 | 7125 | 0.5435 | 0.76 |
| 0.5674 | 20.0 | 7500 | 0.4957 | 0.7933 |
| 0.4667 | 21.0 | 7875 | 0.5150 | 0.7867 |
| 0.4405 | 22.0 | 8250 | 0.5576 | 0.7867 |
| 0.4436 | 23.0 | 8625 | 0.4866 | 0.7967 |
| 0.454 | 24.0 | 9000 | 0.5354 | 0.775 |
| 0.4111 | 25.0 | 9375 | 0.5789 | 0.7717 |
| 0.4049 | 26.0 | 9750 | 0.5450 | 0.7817 |
| 0.397 | 27.0 | 10125 | 0.5808 | 0.7883 |
| 0.3436 | 28.0 | 10500 | 0.5933 | 0.7817 |
| 0.3249 | 29.0 | 10875 | 0.5969 | 0.7633 |
| 0.3897 | 30.0 | 11250 | 0.5739 | 0.7817 |
| 0.3938 | 31.0 | 11625 | 0.5794 | 0.7883 |
| 0.2714 | 32.0 | 12000 | 0.6582 | 0.775 |
| 0.2808 | 33.0 | 12375 | 0.6348 | 0.775 |
| 0.321 | 34.0 | 12750 | 0.7200 | 0.7567 |
| 0.2202 | 35.0 | 13125 | 0.6917 | 0.7817 |
| 0.1634 | 36.0 | 13500 | 0.7700 | 0.7733 |
| 0.3232 | 37.0 | 13875 | 0.7503 | 0.785 |
| 0.1845 | 38.0 | 14250 | 0.8724 | 0.7567 |
| 0.1357 | 39.0 | 14625 | 1.0521 | 0.7683 |
| 0.0994 | 40.0 | 15000 | 1.0716 | 0.77 |
| 0.0743 | 41.0 | 15375 | 1.1704 | 0.7717 |
| 0.1059 | 42.0 | 15750 | 1.2031 | 0.7783 |
| 0.0494 | 43.0 | 16125 | 1.3921 | 0.7633 |
| 0.0147 | 44.0 | 16500 | 1.5250 | 0.77 |
| 0.0663 | 45.0 | 16875 | 1.6538 | 0.7667 |
| 0.0618 | 46.0 | 17250 | 1.8210 | 0.765 |
| 0.0041 | 47.0 | 17625 | 1.9243 | 0.7617 |
| 0.0018 | 48.0 | 18000 | 2.1515 | 0.7717 |
| 0.0025 | 49.0 | 18375 | 2.2407 | 0.7683 |
| 0.0002 | 50.0 | 18750 | 2.2524 | 0.7767 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
LoneStriker/PiVoT-10.7B-Mistral-v0.2-RP-3.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T14:27:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:02:08Z |
---
license: cc-by-sa-4.0
language:
- en
pipeline_tag: text-generation
---
# PiVoT-10.7B-Mistral-v0.2-RP
# **Model Details**
### Description
PivoT is RP Finetuned model based on PiVoT-10.7B-Mistral-v0.2. Private RP dataset collected from ArcaLive Ai Chat Chan used.
This model has sequence length(Sliding Window) of **8192**
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
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giuliadc/mbart-large-cc25-mlsum-de-sport
|
giuliadc
| 2023-12-16T13:03:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:03:01Z |
Entry not found
|
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TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ
|
TheBloke
| 2023-12-16T13:31:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:ignos/LeoScorpius-GreenNode-Platypus-7B-v1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-16T13:03:10Z |
---
base_model: ignos/LeoScorpius-GreenNode-Platypus-7B-v1
inference: false
license: apache-2.0
model_creator: Ignos
model_name: Leoscorpius GreenNode Platypus 7B v1
model_type: mistral
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# Leoscorpius GreenNode Platypus 7B v1 - GPTQ
- Model creator: [Ignos](https://huggingface.co/ignos)
- Original model: [Leoscorpius GreenNode Platypus 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Platypus-7B-v1)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Ignos's Leoscorpius GreenNode Platypus 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Platypus-7B-v1).
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.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF)
* [Ignos's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ignos/LeoScorpius-GreenNode-Platypus-7B-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients 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.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<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 and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.16 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/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.57 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/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.68 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/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.29 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/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-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 `LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ`:
```shell
mkdir LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ
huggingface-cli download TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ --local-dir LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ
huggingface-cli download TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir LeoScorpius-GreenNode-Platypus-7B-v1-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 Hugging Face 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 LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ --local-dir LeoScorpius-GreenNode-Platypus-7B-v1-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/LeoScorpius-GreenNode-Platypus-7B-v1-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/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-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: `LeoScorpius-GreenNode-Platypus-7B-v1-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-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### 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 --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-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 = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
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 Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Ignos's Leoscorpius GreenNode Platypus 7B v1
# Model Card for Model ID
This model is a finetuning of other models based on mistralai/Mistral-7B-v0.1.
## Model Details
### Model Description
The model has been generated from the merging of the models [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO) and [GreenNode/GreenNodeLM-7B-v1olet](https://huggingface.co/GreenNode/GreenNodeLM-7B-v1olet) and a later finetuning with a Platypus dataset [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).
- **Developed by:** Ignos
- **Model type:** Mistral
- **License:** Apache-2.0
## Uses
The model aims to have good overall comparative results on HuggingFace metrics, improving reasoning.
## Bias, Risks, and Limitations
The same bias, risks and limitations from base models.
## Training Details
### Training Data
- [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
### Training Procedure
- Training with QLoRA approach and merging with base model.
### Results
- Huggingface evaluation pending
#### Summary
## Technical Specifications
### Model Architecture and Objective
- Models based on Mistral Architecture
### Compute Infrastructure
- Training on RunPod
#### Hardware
- 4 x Nvidia RTX 4090
- 64 vCPU 503 GB RAM
#### Software
- Mergekit (main)
- Axolotl 0.3.0
## 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
|
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] |
TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF
|
TheBloke
| 2023-12-16T13:07:35Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"base_model:ignos/LeoScorpius-GreenNode-Platypus-7B-v1",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-16T13:03:10Z |
---
base_model: ignos/LeoScorpius-GreenNode-Platypus-7B-v1
inference: false
license: apache-2.0
model_creator: Ignos
model_name: Leoscorpius GreenNode Platypus 7B v1
model_type: mistral
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# Leoscorpius GreenNode Platypus 7B v1 - GGUF
- Model creator: [Ignos](https://huggingface.co/ignos)
- Original model: [Leoscorpius GreenNode Platypus 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Platypus-7B-v1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Ignos's Leoscorpius GreenNode Platypus 7B v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Platypus-7B-v1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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 incomplete 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF)
* [Ignos's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ignos/LeoScorpius-GreenNode-Platypus-7B-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [leoscorpius-greennode-platypus-7b-v1.Q2_K.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [leoscorpius-greennode-platypus-7b-v1.Q3_K_S.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [leoscorpius-greennode-platypus-7b-v1.Q3_K_M.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [leoscorpius-greennode-platypus-7b-v1.Q3_K_L.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [leoscorpius-greennode-platypus-7b-v1.Q4_0.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [leoscorpius-greennode-platypus-7b-v1.Q4_K_S.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [leoscorpius-greennode-platypus-7b-v1.Q4_K_M.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [leoscorpius-greennode-platypus-7b-v1.Q5_0.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [leoscorpius-greennode-platypus-7b-v1.Q5_K_S.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [leoscorpius-greennode-platypus-7b-v1.Q5_K_M.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [leoscorpius-greennode-platypus-7b-v1.Q6_K.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [leoscorpius-greennode-platypus-7b-v1.Q8_0.gguf](https://huggingface.co/TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF/blob/main/leoscorpius-greennode-platypus-7b-v1.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/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF and below it, a specific filename to download, such as: leoscorpius-greennode-platypus-7b-v1.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/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF leoscorpius-greennode-platypus-7b-v1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/LeoScorpius-GreenNode-Platypus-7B-v1-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/LeoScorpius-GreenNode-Platypus-7B-v1-GGUF leoscorpius-greennode-platypus-7b-v1.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 35 -m leoscorpius-greennode-platypus-7b-v1.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./leoscorpius-greennode-platypus-7b-v1.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./leoscorpius-greennode-platypus-7b-v1.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
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: Ignos's Leoscorpius GreenNode Platypus 7B v1
# Model Card for Model ID
This model is a finetuning of other models based on mistralai/Mistral-7B-v0.1.
## Model Details
### Model Description
The model has been generated from the merging of the models [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO) and [GreenNode/GreenNodeLM-7B-v1olet](https://huggingface.co/GreenNode/GreenNodeLM-7B-v1olet) and a later finetuning with a Platypus dataset [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).
- **Developed by:** Ignos
- **Model type:** Mistral
- **License:** Apache-2.0
## Uses
The model aims to have good overall comparative results on HuggingFace metrics, improving reasoning.
## Bias, Risks, and Limitations
The same bias, risks and limitations from base models.
## Training Details
### Training Data
- [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
### Training Procedure
- Training with QLoRA approach and merging with base model.
### Results
- Huggingface evaluation pending
#### Summary
## Technical Specifications
### Model Architecture and Objective
- Models based on Mistral Architecture
### Compute Infrastructure
- Training on RunPod
#### Hardware
- 4 x Nvidia RTX 4090
- 64 vCPU 503 GB RAM
#### Software
- Mergekit (main)
- Axolotl 0.3.0
## 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
<!-- original-model-card end -->
|
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woywan/ddd
|
woywan
| 2023-12-16T13:03:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:03:21Z |
Entry not found
|
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alperiox/weapons-lora
|
alperiox
| 2023-12-16T13:03:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:03:30Z |
Entry not found
|
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AaronZhangQ/sdxl1.0_lora
|
AaronZhangQ
| 2023-12-17T09:10:35Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-12-16T13:05:34Z |
---
license: other
license_name: other
license_link: LICENSE
---
|
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baseten/mixtral-weights-only-quantized
|
baseten
| 2023-12-16T13:09:45Z | 0 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T13:05:35Z |
Entry not found
|
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Abhishek-1011/my_awesome_model
|
Abhishek-1011
| 2023-12-16T13:07:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:07:38Z |
Entry not found
|
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] |
dblasko/mirnet-low-light-img-enhancement
|
dblasko
| 2023-12-16T14:43:44Z | 0 | 0 | null |
[
"image-enhancement",
"computer-vision",
"image-to-image",
"dataset:geekyrakshit/LoL-Dataset",
"license:apache-2.0",
"has_space",
"region:us"
] |
image-to-image
| 2023-12-16T13:10:51Z |
---
license: apache-2.0
datasets:
- geekyrakshit/LoL-Dataset
pipeline_tag: image-to-image
tags:
- image-enhancement
- computer-vision
- image-to-image
---
# MIRNet low-light image enhancement
[](https://huggingface.co/spaces/dblasko/mirnet-low-light-img-enhancement)
MIRNet-based low-light image enhancer specialized on restoring dark images from events (concerts, parties, clubs...).
## Project source-code and further documentation
Documentation about pre-training, fine-tuning, model architecture, usage and all source code used for building and inference can be found in the [GitHub repository of the project](https://github.com/dblasko/low-light-event-img-enhancer/).
This page currently stores the PyTorch model weights and model definition, a HuggingFace pipeline will be implemented in the future.
## Using the model
To use the model, you need to have the `model` folder, that you can dowload from this repository as well as on [GitHub](https://github.com/dblasko/low-light-event-img-enhancer/), present in your project folder.
Then, the following code can be used to download the model weights from HuggingFace and load them in PyTorch for downstream use of the model:
```python
import torch
import torchvision.transforms as T
from PIL import Image
from huggingface_hub import hf_hub_download
from model.MIRNet.model import MIRNet
device = (
torch.device("cuda")
if torch.cuda.is_available()
else torch.device("mps")
if torch.backends.mps.is_available()
else torch.device("cpu")
)
# Download the model weights from the Hugging Face Hub
model_path = hf_hub_download(
repo_id="dblasko/mirnet-low-light-img-enhancement", filename="mirnet_finetuned.pth"
)
# Load the model
model = MIRNet().to(device)
model.load_state_dict(torch.load(model_path, map_location=device)["model_state_dict"])
# Use the model, for example for inference on an image
model.eval()
with torch.no_grad():
img = Image.open("image_path.png").convert("RGB")
img_tensor = T.Compose(
[
T.Resize(400), # Adjust image resizing depending on hardware
T.ToTensor(),
T.Normalize([0.0, 0.0, 0.0], [1.0, 1.0, 1.0]),
]
)(img).unsqueeze(0)
img_tensor = img_tensor.to(device)
if img_tensor.shape[2] % 8 != 0:
img_tensor = img_tensor[:, :, : -(img_tensor.shape[2] % 8), :]
if img_tensor.shape[3] % 8 != 0:
img_tensor = img_tensor[:, :, :, : -(img_tensor.shape[3] % 8)]
output = model(img_tensor)
```
|
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zhengr/zephyr-7b-dpo-lora
|
zhengr
| 2023-12-16T13:14:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:14:54Z |
Entry not found
|
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LoneStriker/PiVoT-10.7B-Mistral-v0.2-RP-5.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T14:27:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:17:35Z |
---
license: cc-by-sa-4.0
language:
- en
pipeline_tag: text-generation
---
# PiVoT-10.7B-Mistral-v0.2-RP
# **Model Details**
### Description
PivoT is RP Finetuned model based on PiVoT-10.7B-Mistral-v0.2. Private RP dataset collected from ArcaLive Ai Chat Chan used.
This model has sequence length(Sliding Window) of **8192**
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
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sd-dreambooth-library/sleeve-tattoo
|
sd-dreambooth-library
| 2023-12-16T13:19:56Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-16T13:18:19Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
---
### sleeve tattoo on Stable Diffusion via Dreambooth
#### model by ersinyildirim
This your the Stable Diffusion model fine-tuned the sleeve tattoo concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **<sleeve-tattoo> tattoo**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
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GHArt/PhotoPedia_XL_V4.5_xl_fp16
|
GHArt
| 2023-12-16T13:28:31Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-16T13:21:14Z |
Entry not found
|
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giuliadc/mbart-large-cc25-mlsum-de-bildung
|
giuliadc
| 2023-12-16T13:21:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:21:29Z |
Entry not found
|
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] |
baseten/mixtral
|
baseten
| 2023-12-16T13:30:51Z | 0 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T13:23:10Z |
Entry not found
|
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] |
cngvng/VNlaw-phoGPT
|
cngvng
| 2023-12-16T13:23:31Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T13:23:31Z |
---
license: apache-2.0
---
|
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masonanalytics/PEFT_Base_Mistral
|
masonanalytics
| 2023-12-16T13:24:30Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T13:23:51Z |
---
license: apache-2.0
---
|
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ProstamidGermany/Prostamid
|
ProstamidGermany
| 2023-12-16T13:26:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:25:25Z |
Prostamid: Tee, Bewertungen, Preis, Zutaten, Kosten, Leistungen, Nebenwirkungen, Apotheke, Germany!
Prostamid wurde zur Behandlung von Prostataproblemen entwickelt, die bei Männern mit zunehmendem Alter auftreten können, wie z. B. Prostatavergrößerung und damit verbundene Harnwegsbeschwerden. Prostamid Germany
<a href="https://www.excaliburnutrition.com/ProstGerma">Prostamid Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website: <a href="https://www.excaliburnutrition.com/ProstGerma">www.prostamid.com</a>
<p><a href="https://www.excaliburnutrition.com/ProstGerma"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOFRXDEJVt4qSMkzKWpke7kR4wHKVFz3CqBOEFNZXHjyF8K7qi_FsJ-IFYAp2b7aaTG5hsJNGeWZqNCScWvikRP4vN3D5KLGjh0FAJqOKS3sY1cHUAq5j6D9UTVTRh9J3IDjAXnlUk13XbsBE-BjeyK2SZBX7LWu-uV5PWGYbLByBv7_As5d4p941yrtM/w629-h207/Prostamid%20Germany%201.png" alt="enter image description here"> </a></p>
➢Produktname – Prostamid
➢Kategorie – Prostatagesundheit
➢Hauptvorteile – Erhaltung der Prostatagesundheit
➢ Zusammensetzung – Natürliche organische Verbindung
➢ Nebenwirkungen – Nicht zutreffend
➢Endgültige Bewertung: – 4,8
➢ Verfügbarkeit – Online
➢Angebote und Rabatte; SPAREN SIE HEUTE! JETZT EINKAUFEN, UM SONDERANGEBOT zu kaufen!!!
Was ist Prostamid?
Dies ist ein Nahrungsergänzungsmittel, das speziell zur Förderung und Erhaltung der Prostatagesundheit entwickelt wurde. Dies ist für viele Männer mit zunehmendem Alter ein großes Problem, und Nahrungsergänzungsmittel wie Prostamid haben aufgrund ihres Potenzials zur Unterstützung der Prostatafunktion Aufmerksamkeit erregt. Prostamid wird häufig aus einer Mischung natürlicher Inhaltsstoffe hergestellt, die für ihre potenziellen Vorteile bei der Unterstützung der Prostatagesundheit bekannt sind.
<a href="https://www.excaliburnutrition.com/ProstGerma">Prostamid Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website: <a href="https://www.excaliburnutrition.com/ProstGerma">www.prostamid.com</a>
<p><a href="https://www.excaliburnutrition.com/ProstGerma"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOFRXDEJVt4qSMkzKWpke7kR4wHKVFz3CqBOEFNZXHjyF8K7qi_FsJ-IFYAp2b7aaTG5hsJNGeWZqNCScWvikRP4vN3D5KLGjh0FAJqOKS3sY1cHUAq5j6D9UTVTRh9J3IDjAXnlUk13XbsBE-BjeyK2SZBX7LWu-uV5PWGYbLByBv7_As5d4p941yrtM/w629-h207/Prostamid%20Germany%201.png" alt="enter image description here"> </a></p>
Prostamid Prostamid Tee Prostamid Preis Prostamid Bewertungen Prostamid Zutaten Prostamid Leistungen Prostamid Nebenwirkungen Prostamid Tee preis Prostamid Tee Bewertungen Prostamid Komposition Prostamid Beschwerde Prostamid Wo zu kaufen Prostamid Wie benutzt man Prostamid Kosten Prostamid funktioniert Prostamid Forum Prostamid Original Prostamid Apotheke
Wie funktioniert Prostamid?
Prostamid wirkt typischerweise durch die Verwendung einer Kombination natürlicher Inhaltsstoffe, die mit der Gesundheit der Prostata in Verbindung gebracht werden. Einer der Hauptbestandteile darin, Sägepalmenextrakt, ist für sein Potenzial bekannt, die Symptome einer vergrößerten Prostata, einschließlich häufigem Wasserlassen und Schwierigkeiten beim Wasserlassen, zu lindern. Es wird angenommen, dass dies den Harnfluss verbessert und die mit Prostataproblemen verbundenen Harnsymptome verringert. Dies ist ein weiterer Nährstoff, der die Gesundheit der Prostata unterstützen kann.
Wo kann man Prostamid kaufen?
Prostamid kann aus verschiedenen Quellen erworben werden, was den Käufern Flexibilität und Komfort bietet. Offizielle Website Der Kauf auf der offiziellen Website ist oft eine sichere Option, um sicherzustellen, dass Sie echte Produkte erhalten. Beim Kauf von Nahrungsergänzungsmitteln ist es wichtig, vertrauenswürdige Verkäufer auszuwählen, auf Produktzertifizierungen zu achten und die empfohlenen Dosierungsanweisungen zu befolgen, um die besten Ergebnisse zu erzielen.
<a href="https://www.excaliburnutrition.com/ProstGerma">Prostamid Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website: <a href="https://www.excaliburnutrition.com/ProstGerma">www.prostamid.com</a>
<p><a href="https://www.excaliburnutrition.com/ProstGerma"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOFRXDEJVt4qSMkzKWpke7kR4wHKVFz3CqBOEFNZXHjyF8K7qi_FsJ-IFYAp2b7aaTG5hsJNGeWZqNCScWvikRP4vN3D5KLGjh0FAJqOKS3sY1cHUAq5j6D9UTVTRh9J3IDjAXnlUk13XbsBE-BjeyK2SZBX7LWu-uV5PWGYbLByBv7_As5d4p941yrtM/w629-h207/Prostamid%20Germany%201.png" alt="enter image description here"> </a></p>
Zusammenfassung:
Prostamid ist ein Nahrungsergänzungsmittel zur Unterstützung und Erhaltung der Prostatagesundheit bei Männern. Durch die Kombination natürlicher Inhaltsstoffe wie Sägepalme zielt dieses Nahrungsergänzungsmittel darauf ab, Harnbeschwerden zu reduzieren und die allgemeine Gesundheit der Prostata zu fördern. Für Männer, die eine Linderung häufiger Prostataprobleme suchen, ist Prostamid möglicherweise eine wertvolle Ergänzung ihrer Wellness-Routine. Sie können es auf offiziellen Websites, bei seriösen Online-Händlern oder in ausgewählten physischen Geschäften kaufen, um die Gesundheit Ihrer Prostata effektiv zu erhalten. Konsultieren Sie immer einen Arzt, bevor Sie mit einer neuen Nahrungsergänzungskur beginnen, um sicherzustellen, dass diese Ihren spezifischen Gesundheitsbedürfnissen entspricht.
Prostamid Prostamid Tee Prostamid Preis Prostamid Bewertungen Prostamid Zutaten Prostamid Leistungen Prostamid Nebenwirkungen Prostamid Tee preis Prostamid Tee Bewertungen Prostamid Komposition Prostamid Beschwerde Prostamid Wo zu kaufen Prostamid Wie benutzt man Prostamid Kosten Prostamid funktioniert Prostamid Forum Prostamid Original Prostamid Apotheke
mehr lessen: https://www.excaliburnutrition.com/ProstGerma
https://sites.google.com/view/prostamid-preis/home
https://www.excaliburnutrition.com/ProstGerma
https://sites.google.com/view/prostamid-preis/home
https://healthtoned.blogspot.com/2023/12/prostamid-germany.html
https://www.weddingwire.com/website/prostamid-and-germany
https://medium.com/@healthytalk24x7/prostamid-germany-6f60958783a2
https://medium.com/@healthytalk24x7/prostamid-tee-bewertungen-preis-zutaten-kosten-leistungen-nebenwirkungen-apotheke-germany-0fa4d5e8359d
https://infogram.com/prostamid-germany-1hnp27mw033ky2g?live
https://softisenilspain.hashnode.dev/prostamid-germany
https://prostamidgermany.company.site/
https://sway.office.com/aPiUQTnOt0JqS1VG
https://healthytalk24x7.wixsite.com/sunshine/post/prostamid-germany
https://gamma.app/docs/Prostamid-Germany-7jp37ytaqu14l5v?mode=doc
https://groups.google.com/g/snshine/c/m2e8ULV1X9w
https://www.sunflower-cissp.com/glossary/cissp/7380/prostamid-germany
https://community.thebatraanumerology.com/
https://community.thebatraanumerology.com/post/prostamid-tee-bewertungen-preis-zutaten-kosten-leistungen-nebenwirkungen-ap--657da47e3dd575a01529bde0
https://enkling.com/read-blog/9145
https://nepal.tribe.so/post/prostamid-germany-657da4a1456d270aec10e690
https://soundcloud.com/prostamid-germany/prostamid-germany?si=2416c4f12f7d43bab278959f641e9d2c&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing
https://www.deviantart.com/prostamidgermany/art/Prostamid-Germany-1002471775
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LoneStriker/PiVoT-10.7B-Mistral-v0.2-RP-6.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T14:27:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:25:25Z |
---
license: cc-by-sa-4.0
language:
- en
pipeline_tag: text-generation
---
# PiVoT-10.7B-Mistral-v0.2-RP
# **Model Details**
### Description
PivoT is RP Finetuned model based on PiVoT-10.7B-Mistral-v0.2. Private RP dataset collected from ArcaLive Ai Chat Chan used.
This model has sequence length(Sliding Window) of **8192**
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
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] |
LoneStriker/PiVoT-10.7B-Mistral-v0.2-3.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T13:32:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"ko",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:27:22Z |
---
license: cc-by-sa-4.0
language:
- en
- ko
pipeline_tag: text-generation
---
# PiVoT-10.7B-Mistral-v0.2
# **Model Details**
### Description
PivoT is Finetuned model based on merge of Mistral 0.2.
SFT + DPO used when training.
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
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] |
TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ
|
TheBloke
| 2023-12-16T14:14:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:garage-bAInd/Open-Platypus",
"base_model:kyujinpy/SOLAR-Platypus-10.7B-v2",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-16T13:32:18Z |
---
base_model: kyujinpy/SOLAR-Platypus-10.7B-v2
datasets:
- garage-bAInd/Open-Platypus
inference: false
language:
- en
library_name: transformers
license: cc-by-nc-sa-4.0
model_creator: KyujinHan
model_name: Solar Platypus 10.7B v2
model_type: solar
pipeline_tag: text-generation
prompt_template: '### Human: {prompt}
### Assistant:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# Solar Platypus 10.7B v2 - GPTQ
- Model creator: [KyujinHan](https://huggingface.co/kyujinpy)
- Original model: [Solar Platypus 10.7B v2](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v2)
<!-- description start -->
# Description
This repo contains GPTQ model files for [KyujinHan's Solar Platypus 10.7B v2](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v2).
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.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF)
* [KyujinHan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: SUS
```
### Human: {prompt}
### Assistant:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients 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.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<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 and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.98 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/SOLAR-Platypus-10.7B-v2-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 6.59 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/SOLAR-Platypus-10.7B-v2-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 11.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 11.25 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/SOLAR-Platypus-10.7B-v2-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 11.99 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 6.18 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/SOLAR-Platypus-10.7B-v2-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/SOLAR-Platypus-10.7B-v2-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 `SOLAR-Platypus-10.7B-v2-GPTQ`:
```shell
mkdir SOLAR-Platypus-10.7B-v2-GPTQ
huggingface-cli download TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ --local-dir SOLAR-Platypus-10.7B-v2-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir SOLAR-Platypus-10.7B-v2-GPTQ
huggingface-cli download TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir SOLAR-Platypus-10.7B-v2-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 Hugging Face 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 SOLAR-Platypus-10.7B-v2-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ --local-dir SOLAR-Platypus-10.7B-v2-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/SOLAR-Platypus-10.7B-v2-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/SOLAR-Platypus-10.7B-v2-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/SOLAR-Platypus-10.7B-v2-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: `SOLAR-Platypus-10.7B-v2-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-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''### Human: {prompt}
### Assistant:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### 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 --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/SOLAR-Platypus-10.7B-v2-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 = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''### Human: {prompt}
### 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 Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: KyujinHan's Solar Platypus 10.7B v2
# **SOLAR-Platypus-10.7B-v2**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
SOLAR-Platypus-10.7B-v2 is an auto-regressive language model based on the Llama2 architecture.
**Base Model**
[upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)
**Training Dataset**
[garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).
## Notice
While training, I used Q-LoRA.
The lora_r values is 64.
## Q-LoRA config
- LoRA_r: 64
- LoRA_alpha: 16
- LoRA_dropout: 0.05
- LoRA_target_modules: [gate_proj, up_proj, down_proj, q_proj, k_proj, v_proj]
## Prompt
```
## Human:
## Assistant:
```
# **Model Benchmark**
## Open leaderboard
- Follow up as [link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- | --- |
| SOLAR-Platypus-10.7B-v1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| SOLAR-Platypus-10.7B-v2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/SOLAR-Platypus-10.7B-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
---
|
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] |
TheBloke/SOLAR-Platypus-10.7B-v2-GGUF
|
TheBloke
| 2023-12-16T13:38:09Z | 0 | 2 |
transformers
|
[
"transformers",
"gguf",
"solar",
"text-generation",
"en",
"dataset:garage-bAInd/Open-Platypus",
"base_model:kyujinpy/SOLAR-Platypus-10.7B-v2",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
text-generation
| 2023-12-16T13:32:18Z |
---
base_model: kyujinpy/SOLAR-Platypus-10.7B-v2
datasets:
- garage-bAInd/Open-Platypus
inference: false
language:
- en
library_name: transformers
license: cc-by-nc-sa-4.0
model_creator: KyujinHan
model_name: Solar Platypus 10.7B v2
model_type: solar
pipeline_tag: text-generation
prompt_template: '### Human: {prompt}
### Assistant:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
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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 -->
# Solar Platypus 10.7B v2 - GGUF
- Model creator: [KyujinHan](https://huggingface.co/kyujinpy)
- Original model: [Solar Platypus 10.7B v2](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v2)
<!-- description start -->
## Description
This repo contains GGUF format model files for [KyujinHan's Solar Platypus 10.7B v2](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v2).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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 incomplete 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF)
* [KyujinHan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: SUS
```
### Human: {prompt}
### 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [solar-platypus-10.7b-v2.Q2_K.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q2_K.gguf) | Q2_K | 2 | 4.55 GB| 7.05 GB | smallest, significant quality loss - not recommended for most purposes |
| [solar-platypus-10.7b-v2.Q3_K_S.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q3_K_S.gguf) | Q3_K_S | 3 | 4.67 GB| 7.17 GB | very small, high quality loss |
| [solar-platypus-10.7b-v2.Q3_K_M.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q3_K_M.gguf) | Q3_K_M | 3 | 5.19 GB| 7.69 GB | very small, high quality loss |
| [solar-platypus-10.7b-v2.Q3_K_L.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q3_K_L.gguf) | Q3_K_L | 3 | 5.65 GB| 8.15 GB | small, substantial quality loss |
| [solar-platypus-10.7b-v2.Q4_0.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q4_0.gguf) | Q4_0 | 4 | 6.07 GB| 8.57 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [solar-platypus-10.7b-v2.Q4_K_S.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q4_K_S.gguf) | Q4_K_S | 4 | 6.10 GB| 8.60 GB | small, greater quality loss |
| [solar-platypus-10.7b-v2.Q4_K_M.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q4_K_M.gguf) | Q4_K_M | 4 | 6.46 GB| 8.96 GB | medium, balanced quality - recommended |
| [solar-platypus-10.7b-v2.Q5_0.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q5_0.gguf) | Q5_0 | 5 | 7.40 GB| 9.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [solar-platypus-10.7b-v2.Q5_K_S.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q5_K_S.gguf) | Q5_K_S | 5 | 7.40 GB| 9.90 GB | large, low quality loss - recommended |
| [solar-platypus-10.7b-v2.Q5_K_M.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q5_K_M.gguf) | Q5_K_M | 5 | 7.60 GB| 10.10 GB | large, very low quality loss - recommended |
| [solar-platypus-10.7b-v2.Q6_K.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q6_K.gguf) | Q6_K | 6 | 8.81 GB| 11.31 GB | very large, extremely low quality loss |
| [solar-platypus-10.7b-v2.Q8_0.gguf](https://huggingface.co/TheBloke/SOLAR-Platypus-10.7B-v2-GGUF/blob/main/solar-platypus-10.7b-v2.Q8_0.gguf) | Q8_0 | 8 | 11.41 GB| 13.91 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/SOLAR-Platypus-10.7B-v2-GGUF and below it, a specific filename to download, such as: solar-platypus-10.7b-v2.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/SOLAR-Platypus-10.7B-v2-GGUF solar-platypus-10.7b-v2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/SOLAR-Platypus-10.7B-v2-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/SOLAR-Platypus-10.7B-v2-GGUF solar-platypus-10.7b-v2.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 35 -m solar-platypus-10.7b-v2.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Human: {prompt}\n\n### 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./solar-platypus-10.7b-v2.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"### Human: {prompt}\n\n### Assistant:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./solar-platypus-10.7b-v2.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
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: KyujinHan's Solar Platypus 10.7B v2
# **SOLAR-Platypus-10.7B-v2**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
SOLAR-Platypus-10.7B-v2 is an auto-regressive language model based on the Llama2 architecture.
**Base Model**
[upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)
**Training Dataset**
[garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).
## Notice
While training, I used Q-LoRA.
The lora_r values is 64.
## Q-LoRA config
- LoRA_r: 64
- LoRA_alpha: 16
- LoRA_dropout: 0.05
- LoRA_target_modules: [gate_proj, up_proj, down_proj, q_proj, k_proj, v_proj]
## Prompt
```
## Human:
## Assistant:
```
# **Model Benchmark**
## Open leaderboard
- Follow up as [link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- | --- |
| SOLAR-Platypus-10.7B-v1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| SOLAR-Platypus-10.7B-v2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/SOLAR-Platypus-10.7B-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
---
<!-- original-model-card end -->
|
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Gojo00000876/Foxyia
|
Gojo00000876
| 2023-12-16T13:34:13Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T13:32:47Z |
---
license: openrail
---
|
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LoneStriker/PiVoT-10.7B-Mistral-v0.2-RP-8.0bpw-h8-exl2-2
|
LoneStriker
| 2023-12-16T14:27:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:33:28Z |
---
license: cc-by-sa-4.0
language:
- en
pipeline_tag: text-generation
---
# PiVoT-10.7B-Mistral-v0.2-RP
# **Model Details**
### Description
PivoT is RP Finetuned model based on PiVoT-10.7B-Mistral-v0.2. Private RP dataset collected from ArcaLive Ai Chat Chan used.
This model has sequence length(Sliding Window) of **8192**
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
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GHArt/Reproduction_SDXL_V2v1.2_xl_fp16
|
GHArt
| 2023-12-16T13:40:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-16T13:33:48Z |
Entry not found
|
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Pranavsenthilvel/classification-T5-kfold-V1
|
Pranavsenthilvel
| 2023-12-16T13:34:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:34:40Z |
Entry not found
|
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] |
ryuseiken/Paulina_Rubio
|
ryuseiken
| 2023-12-16T13:36:19Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T13:35:28Z |
---
license: openrail
---
|
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] |
wkai052/venom
|
wkai052
| 2023-12-16T13:36:05Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T13:36:05Z |
---
license: mit
---
|
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LoneStriker/PiVoT-10.7B-Mistral-v0.2-4.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T13:43:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"ko",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:36:42Z |
---
license: cc-by-sa-4.0
language:
- en
- ko
pipeline_tag: text-generation
---
# PiVoT-10.7B-Mistral-v0.2
# **Model Details**
### Description
PivoT is Finetuned model based on merge of Mistral 0.2.
SFT + DPO used when training.
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
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kingzzm/chatglm3-6b-ggml
|
kingzzm
| 2023-12-16T14:06:58Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T13:39:57Z |
---
license: mit
---
The quantized version of the ChatGLM3-6B and the quantization format used is GGML.
|
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Shayaan69/test-model
|
Shayaan69
| 2023-12-16T13:40:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:40:27Z |
Entry not found
|
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sdpkjc/Swimmer-v4-ddpg_continuous_action-seed1
|
sdpkjc
| 2023-12-16T15:33:19Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Swimmer-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T13:41:33Z |
---
tags:
- Swimmer-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Swimmer-v4
type: Swimmer-v4
metrics:
- type: mean_reward
value: 138.96 +/- 19.34
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Swimmer-v4**
This is a trained model of a DDPG agent playing Swimmer-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Swimmer-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/Swimmer-v4-ddpg_continuous_action-seed1/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/sdpkjc/Swimmer-v4-ddpg_continuous_action-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Swimmer-v4-ddpg_continuous_action-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Swimmer-v4 --track --seed 1
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'env_id': 'Swimmer-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-aann-indef-non_num_removal-1e-3
|
kanishka
| 2023-12-17T03:10:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual-babylm-aanns_indef_non_num_removal",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:41:46Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-aanns_indef_non_num_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-aann-indef-non_num_removal-1e-3
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual-babylm-aanns_indef_non_num_removal
type: kanishka/counterfactual-babylm-aanns_indef_non_num_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.41229973981063833
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-aann-indef-non_num_removal-1e-3
This model was trained from scratch on the kanishka/counterfactual-babylm-aanns_indef_non_num_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3843
- Accuracy: 0.4123
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6007 | 1.0 | 18592 | 3.7977 | 0.3594 |
| 3.3842 | 2.0 | 37184 | 3.5559 | 0.3813 |
| 3.258 | 3.0 | 55776 | 3.4575 | 0.3925 |
| 3.1779 | 4.0 | 74368 | 3.3995 | 0.3990 |
| 3.1232 | 5.0 | 92960 | 3.3918 | 0.4016 |
| 3.0747 | 6.0 | 111552 | 3.3719 | 0.4039 |
| 3.0457 | 7.0 | 130144 | 3.3350 | 0.4080 |
| 3.0106 | 8.0 | 148736 | 3.3603 | 0.4063 |
| 2.9872 | 9.0 | 167328 | 3.3405 | 0.4092 |
| 2.957 | 10.0 | 185920 | 3.3396 | 0.4107 |
| 2.9414 | 11.0 | 204512 | 3.3313 | 0.4108 |
| 2.9166 | 12.0 | 223104 | 3.3352 | 0.4120 |
| 2.8912 | 13.0 | 241696 | 3.3347 | 0.4120 |
| 2.8764 | 14.0 | 260288 | 3.3473 | 0.4119 |
| 2.854 | 15.0 | 278880 | 3.3459 | 0.4128 |
| 2.8354 | 16.0 | 297472 | 3.3604 | 0.4123 |
| 2.8152 | 17.0 | 316064 | 3.3503 | 0.4136 |
| 2.7971 | 18.0 | 334656 | 3.3643 | 0.4134 |
| 2.7738 | 19.0 | 353248 | 3.3726 | 0.4129 |
| 2.7594 | 20.0 | 371840 | 3.3843 | 0.4123 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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sdpkjc/Walker2d-v4-ddpg_continuous_action-seed1
|
sdpkjc
| 2023-12-16T15:35:57Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Walker2d-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T13:43:40Z |
---
tags:
- Walker2d-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Walker2d-v4
type: Walker2d-v4
metrics:
- type: mean_reward
value: 1673.48 +/- 975.88
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Walker2d-v4**
This is a trained model of a DDPG agent playing Walker2d-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Walker2d-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/Walker2d-v4-ddpg_continuous_action-seed1/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/sdpkjc/Walker2d-v4-ddpg_continuous_action-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Walker2d-v4-ddpg_continuous_action-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Walker2d-v4 --track --seed 1
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'env_id': 'Walker2d-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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] |
OhCherryFire/tsllm-llama2_7b-gsm8k-policy-sft_ep3
|
OhCherryFire
| 2023-12-16T14:11:33Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:43:53Z |
Temporary Redirect. Redirecting to /OhCherryFire/tsllm-llama2_7b-gsm8k-policy-sft_ep2/resolve/main/README.md
|
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] |
LoneStriker/PiVoT-10.7B-Mistral-v0.2-5.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T13:50:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"ko",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:45:54Z |
---
license: cc-by-sa-4.0
language:
- en
- ko
pipeline_tag: text-generation
---
# PiVoT-10.7B-Mistral-v0.2
# **Model Details**
### Description
PivoT is Finetuned model based on merge of Mistral 0.2.
SFT + DPO used when training.
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
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GHArt/FenrisXL_V16.4_xl_fp16
|
GHArt
| 2023-12-16T13:53:37Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-16T13:45:55Z |
Entry not found
|
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giuliadc/mbart-large-cc25-finetuned-mlsum-bildung
|
giuliadc
| 2023-12-16T13:46:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:46:15Z |
Entry not found
|
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agusroccohd/isabelpantoja
|
agusroccohd
| 2023-12-16T13:52:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:48:37Z |
Entry not found
|
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sdpkjc/Humanoid-v4-ddpg_continuous_action-seed1
|
sdpkjc
| 2023-12-16T15:42:03Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Humanoid-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T13:51:07Z |
---
tags:
- Humanoid-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Humanoid-v4
type: Humanoid-v4
metrics:
- type: mean_reward
value: 853.12 +/- 598.65
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Humanoid-v4**
This is a trained model of a DDPG agent playing Humanoid-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Humanoid-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/Humanoid-v4-ddpg_continuous_action-seed1/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/sdpkjc/Humanoid-v4-ddpg_continuous_action-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Humanoid-v4-ddpg_continuous_action-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Humanoid-v4 --track --seed 1
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'env_id': 'Humanoid-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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] |
LoneStriker/PiVoT-10.7B-Mistral-v0.2-6.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T14:00:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"ko",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T13:55:27Z |
---
license: cc-by-sa-4.0
language:
- en
- ko
pipeline_tag: text-generation
---
# PiVoT-10.7B-Mistral-v0.2
# **Model Details**
### Description
PivoT is Finetuned model based on merge of Mistral 0.2.
SFT + DPO used when training.
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
[
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] |
hkivancoral/smids_5x_deit_tiny_rms_001_fold5
|
hkivancoral
| 2023-12-16T14:50:39Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T13:55:50Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7433333333333333
---
<!-- 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. -->
# smids_5x_deit_tiny_rms_001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6094
- Accuracy: 0.7433
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8822 | 1.0 | 375 | 0.8719 | 0.52 |
| 0.8936 | 2.0 | 750 | 0.8470 | 0.535 |
| 0.8252 | 3.0 | 1125 | 0.8071 | 0.595 |
| 0.8333 | 4.0 | 1500 | 0.7970 | 0.6017 |
| 0.8046 | 5.0 | 1875 | 0.8070 | 0.5633 |
| 0.8082 | 6.0 | 2250 | 0.9208 | 0.5167 |
| 0.7481 | 7.0 | 2625 | 0.7984 | 0.5633 |
| 0.8409 | 8.0 | 3000 | 0.7900 | 0.5783 |
| 0.7673 | 9.0 | 3375 | 0.7551 | 0.62 |
| 0.7321 | 10.0 | 3750 | 0.7485 | 0.6133 |
| 0.8282 | 11.0 | 4125 | 0.7517 | 0.6083 |
| 0.7206 | 12.0 | 4500 | 0.7745 | 0.6 |
| 0.6841 | 13.0 | 4875 | 0.8307 | 0.5917 |
| 0.7738 | 14.0 | 5250 | 0.7274 | 0.6683 |
| 0.8416 | 15.0 | 5625 | 0.7353 | 0.67 |
| 0.704 | 16.0 | 6000 | 0.7258 | 0.65 |
| 0.6873 | 17.0 | 6375 | 0.7174 | 0.68 |
| 0.714 | 18.0 | 6750 | 0.7557 | 0.6483 |
| 0.7105 | 19.0 | 7125 | 0.6868 | 0.6917 |
| 0.6559 | 20.0 | 7500 | 0.6845 | 0.6783 |
| 0.6717 | 21.0 | 7875 | 0.7043 | 0.67 |
| 0.7139 | 22.0 | 8250 | 0.6944 | 0.68 |
| 0.6633 | 23.0 | 8625 | 0.7071 | 0.6667 |
| 0.6888 | 24.0 | 9000 | 0.6979 | 0.6883 |
| 0.6621 | 25.0 | 9375 | 0.6468 | 0.7117 |
| 0.6157 | 26.0 | 9750 | 0.6767 | 0.6833 |
| 0.6777 | 27.0 | 10125 | 0.7097 | 0.67 |
| 0.7108 | 28.0 | 10500 | 0.6811 | 0.6917 |
| 0.8139 | 29.0 | 10875 | 0.6750 | 0.7067 |
| 0.6291 | 30.0 | 11250 | 0.6415 | 0.7133 |
| 0.5725 | 31.0 | 11625 | 0.6769 | 0.6833 |
| 0.6243 | 32.0 | 12000 | 0.6733 | 0.7267 |
| 0.6311 | 33.0 | 12375 | 0.6227 | 0.7217 |
| 0.6254 | 34.0 | 12750 | 0.6222 | 0.72 |
| 0.567 | 35.0 | 13125 | 0.6040 | 0.735 |
| 0.5363 | 36.0 | 13500 | 0.5935 | 0.7533 |
| 0.6308 | 37.0 | 13875 | 0.6047 | 0.7267 |
| 0.5334 | 38.0 | 14250 | 0.6481 | 0.7217 |
| 0.5951 | 39.0 | 14625 | 0.6059 | 0.7317 |
| 0.6325 | 40.0 | 15000 | 0.6172 | 0.735 |
| 0.5905 | 41.0 | 15375 | 0.6255 | 0.7233 |
| 0.6095 | 42.0 | 15750 | 0.5896 | 0.7433 |
| 0.49 | 43.0 | 16125 | 0.5925 | 0.7367 |
| 0.4891 | 44.0 | 16500 | 0.5937 | 0.7367 |
| 0.4867 | 45.0 | 16875 | 0.5918 | 0.7583 |
| 0.5178 | 46.0 | 17250 | 0.6030 | 0.735 |
| 0.561 | 47.0 | 17625 | 0.6183 | 0.74 |
| 0.4632 | 48.0 | 18000 | 0.5943 | 0.7517 |
| 0.4666 | 49.0 | 18375 | 0.6107 | 0.7417 |
| 0.4901 | 50.0 | 18750 | 0.6094 | 0.7433 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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denisgr04/model
|
denisgr04
| 2023-12-16T13:57:32Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Open-Orca/Mistral-7B-OpenOrca",
"region:us"
] | null | 2023-12-16T13:57:22Z |
---
library_name: peft
base_model: Open-Orca/Mistral-7B-OpenOrca
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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## Uses
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### Direct Use
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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
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
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] |
kejolong/tes
|
kejolong
| 2023-12-16T14:45:24Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T13:57:22Z |
---
license: creativeml-openrail-m
---
|
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kumar1993/test
|
kumar1993
| 2023-12-16T13:58:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T13:58:51Z |
Entry not found
|
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GHArt/Hentai_Mix_XL_Road_to_freedom_V3.0_xl_fp16
|
GHArt
| 2023-12-16T14:06:02Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-16T13:59:27Z |
Entry not found
|
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] |
liewchooichin/my_model
|
liewchooichin
| 2023-12-17T09:42:33Z | 0 | 0 |
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T14:00:31Z |
---
license: apache-2.0
---
|
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] |
OhCherryFire/tsllm-llama2_7b-gsm8k-value-ep0
|
OhCherryFire
| 2023-12-16T14:08:04Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
feature-extraction
| 2023-12-16T14:00:49Z |
Entry not found
|
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EWCH/speecht5_tts_voxpopuli_nl
|
EWCH
| 2023-12-16T14:03:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:03:04Z |
Entry not found
|
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testoygermany/TestoYGermany
|
testoygermany
| 2023-12-16T14:04:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:03:13Z |
TestoY: Germany, Kapsel, Preis, Bewertungen, Pille, Vorteile, Zutaten, Tablette, kosten, Apotheke!
TestoY ist ein natürliches Nahrungsergänzungsmittel zur Unterstützung der sexuellen Gesundheit und Leistungsfähigkeit von Männern. TestoY Germany
<a href="https://www.boxdrug.com/TestGerm">TestoY Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website: <a href="https://www.boxdrug.com/TestGerm">www.TestoY.com</a>
<p><a href="https://www.boxdrug.com/TestGerm"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjHEBONE-bKZxbh9mcBsgpEzIply3EDbEG8prDN5PE4y6wwqlLgactEHKGYRd5fnKUf7BJ4PZTYWTFkT1I-v1NUQUV0qrdhoLKPNhSJQmotD_ZHSltucrgigcxioHnkNbSHgdFEDPFEg9MZ0XGUaVDnTh4nqENVigC1a338RVZ2PHgWGcEJWOr_2AVKCKA/w616-h375/Testoy%20Germany%201.png" alt="enter image description here"> </a></p>
➢Produktname – TestoY
➢Kategorie – Männliche Verbesserung
➢Hauptvorteile – Verbessert die Gesundheit und die sexuelle Leistungsfähigkeit
➢ Zusammensetzung – Natürliche organische Verbindung
➢ Nebenwirkungen – Nicht zutreffend
➢Endgültige Bewertung: – 4,8
➢ Verfügbarkeit – Online
➢Angebote und Rabatte; SPAREN SIE HEUTE! JETZT EINKAUFEN, UM SONDERANGEBOT zu kaufen!!!
Was ist TestoY?
Die Formel TestoY besteht aus einer Mischung wirksamer Kräuter und Extrakte und zielt darauf ab, verschiedene Aspekte des sexuellen Wohlbefindens anzusprechen, darunter Ausdauer, Ausdauer und Libido. Im Gegensatz zu pharmazeutischen Alternativen ist TestoY stolz darauf, die Kraft natürlicher Inhaltsstoffe zu nutzen, um die allgemeine männliche Vitalität zu steigern.
<a href="https://www.boxdrug.com/TestGerm">TestoY Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website: <a href="https://www.boxdrug.com/TestGerm">www.TestoY.com</a>
<p><a href="https://www.boxdrug.com/TestGerm"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjHEBONE-bKZxbh9mcBsgpEzIply3EDbEG8prDN5PE4y6wwqlLgactEHKGYRd5fnKUf7BJ4PZTYWTFkT1I-v1NUQUV0qrdhoLKPNhSJQmotD_ZHSltucrgigcxioHnkNbSHgdFEDPFEg9MZ0XGUaVDnTh4nqENVigC1a338RVZ2PHgWGcEJWOr_2AVKCKA/w616-h375/Testoy%20Germany%201.png" alt="enter image description here"> </a></p>
TestoY TestoY Pille TestoY Kapsel TestoY Tablette TestoY Preis TestoY Bewertungen TestoY Zutaten TestoY Vorteile TestoY Nebenwirkungen TestoY Kapsel preis TestoY Kapsel Bewertungen TestoY Komposition TestoY Beschwerde TestoY Wo zu kaufen TestoY Wie benutzt man TestoY kosten TestoY funktioniert TestoY Forum TestoY Original TestoY Apotheke
Wie funktioniert TestoY?
Die Wirksamkeit von TestoY liegt in den sorgfältig ausgewählten Inhaltsstoffen, die jeweils aufgrund ihres Potenzials ausgewählt wurden, die sexuelle Gesundheit des Mannes positiv zu beeinflussen. Die Formel enthält typischerweise Inhaltsstoffe, die für ihre aphrodisierenden Eigenschaften, die Durchblutungsförderung und die Testosteronunterstützung bekannt sind. TestoY enthält Inhaltsstoffe, von denen angenommen wird, dass sie das sexuelle Verlangen anregen und so eine gesteigerte Libido und Erregung fördern
Wo kann man TestoY kaufen?
Für diejenigen, die TestoY ausprobieren möchten, ist es wichtig, es bei seriösen Quellen zu kaufen. Die offizielle Website des Produkts ist eine zuverlässige Option und stellt sicher, dass Sie ein Originalprodukt mit allen Vorteilen der Formel erhalten. Hüten Sie sich vor Drittanbietern und achten Sie stets auf Kundenrezensionen, um die Wirksamkeit des Produkts beurteilen zu können.
<a href="https://www.boxdrug.com/TestGerm">TestoY Jetzt kaufen!! Klicken Sie auf den Link unten für weitere Informationen und erhalten Sie jetzt 50 % Rabatt!! Beeil dich !!</a>
Offizielle Website: <a href="https://www.boxdrug.com/TestGerm">www.TestoY.com</a>
<p><a href="https://www.boxdrug.com/TestGerm"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjHEBONE-bKZxbh9mcBsgpEzIply3EDbEG8prDN5PE4y6wwqlLgactEHKGYRd5fnKUf7BJ4PZTYWTFkT1I-v1NUQUV0qrdhoLKPNhSJQmotD_ZHSltucrgigcxioHnkNbSHgdFEDPFEg9MZ0XGUaVDnTh4nqENVigC1a338RVZ2PHgWGcEJWOr_2AVKCKA/w616-h375/Testoy%20Germany%201.png" alt="enter image description here"> </a></p>
Zusammenfassung:
Zusammenfassend lässt sich sagen, dass TestoY ein natürliches Nahrungsergänzungsmittel ist, das verspricht, die sexuelle Gesundheit des Mannes durch eine Mischung sorgfältig ausgewählter Inhaltsstoffe zu verbessern. Mit potenziellen Vorteilen, die von erhöhter Ausdauer bis hin zu gesteigerter Libido reichen, zielt dieses Produkt darauf ab, Männer dabei zu unterstützen, ihr Selbstvertrauen im Schlafzimmer zurückzugewinnen. Wenn Sie TestoY ausprobieren möchten, kaufen Sie es unbedingt auf der offiziellen Website, um die Authentizität und Qualität des Produkts sicherzustellen. Begeben Sie sich mit TestoY auf Ihre Reise zu mehr Vitalität und sexuellem Wohlbefinden.
TestoY TestoY Pille TestoY Kapsel TestoY Tablette TestoY Preis TestoY Bewertungen TestoY Zutaten TestoY Vorteile TestoY Nebenwirkungen TestoY Kapsel preis TestoY Kapsel Bewertungen TestoY Komposition TestoY Beschwerde TestoY Wo zu kaufen TestoY Wie benutzt man TestoY kosten TestoY funktioniert TestoY Forum TestoY Original TestoY Apotheke
https://www.boxdrug.com/TestGerm
https://sites.google.com/view/testoy-germany/home
https://healthtoned.blogspot.com/2023/12/testoy-germany.html
https://medium.com/@healthytalk24x7/testoy-germany-8d71ce738cba
https://www.weddingwire.com/website/testoy-and-germany
https://medium.com/@healthytalk24x7/testoy-germany-kapsel-preis-bewertungen-pille-vorteile-zutaten-tablette-kosten-apotheke-10379197b245
https://infogram.com/testoy-germany-1h1749vp0lklq6z?live
https://softisenilspain.hashnode.dev/testoy-germany
https://testoygermany.company.site/
https://sway.office.com/0w5gWqhqEO6ZKBFN
https://gamma.app/docs/TestoY-Germany-n569wiraeh16g2t?mode=doc
https://groups.google.com/g/snshine/c/RDH3V6aXXRo
https://healthytalk24x7.wixsite.com/sunshine/post/testoy-germany
https://www.sunflower-cissp.com/glossary/cissp/7383/testoy-germany
https://replit.com/@testoygermany
|
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] |
rehaidib/swe628
|
rehaidib
| 2023-12-16T14:39:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T14:04:56Z |
Entry not found
|
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] |
LoneStriker/PiVoT-10.7B-Mistral-v0.2-8.0bpw-h8-exl2-2
|
LoneStriker
| 2023-12-16T14:13:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"ko",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T14:06:59Z |
---
license: cc-by-sa-4.0
language:
- en
- ko
pipeline_tag: text-generation
---
# PiVoT-10.7B-Mistral-v0.2
# **Model Details**
### Description
PivoT is Finetuned model based on merge of Mistral 0.2.
SFT + DPO used when training.
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
|
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] |
goosebok/krutoi
|
goosebok
| 2023-12-16T14:09:04Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T14:08:07Z |
---
license: openrail
---
|
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Patcas/newnew
|
Patcas
| 2023-12-16T14:08:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:08:27Z |
Entry not found
|
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deneesk/new-roop
|
deneesk
| 2023-12-16T15:42:13Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2023-12-16T14:13:33Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_rms_0001_fold4
|
hkivancoral
| 2023-12-16T15:34:28Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T14:13:42Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.87
---
<!-- 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. -->
# smids_5x_beit_base_rms_0001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5932
- Accuracy: 0.87
## 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: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3203 | 1.0 | 375 | 0.4417 | 0.8283 |
| 0.2426 | 2.0 | 750 | 0.3953 | 0.8467 |
| 0.1231 | 3.0 | 1125 | 0.5794 | 0.86 |
| 0.1615 | 4.0 | 1500 | 0.5639 | 0.8417 |
| 0.0891 | 5.0 | 1875 | 0.5564 | 0.8583 |
| 0.0575 | 6.0 | 2250 | 0.6614 | 0.8617 |
| 0.0358 | 7.0 | 2625 | 0.6797 | 0.8667 |
| 0.0806 | 8.0 | 3000 | 0.6344 | 0.865 |
| 0.0789 | 9.0 | 3375 | 0.6957 | 0.8717 |
| 0.0241 | 10.0 | 3750 | 0.9064 | 0.8567 |
| 0.0385 | 11.0 | 4125 | 0.7288 | 0.865 |
| 0.0734 | 12.0 | 4500 | 0.9419 | 0.8617 |
| 0.0068 | 13.0 | 4875 | 1.1180 | 0.8417 |
| 0.0709 | 14.0 | 5250 | 0.8470 | 0.8517 |
| 0.0043 | 15.0 | 5625 | 0.9006 | 0.8733 |
| 0.0276 | 16.0 | 6000 | 0.9685 | 0.8617 |
| 0.0864 | 17.0 | 6375 | 0.9433 | 0.865 |
| 0.0263 | 18.0 | 6750 | 1.0594 | 0.8667 |
| 0.0288 | 19.0 | 7125 | 0.9857 | 0.8617 |
| 0.0437 | 20.0 | 7500 | 0.9535 | 0.8617 |
| 0.0182 | 21.0 | 7875 | 1.0851 | 0.8633 |
| 0.0247 | 22.0 | 8250 | 0.9263 | 0.8683 |
| 0.0006 | 23.0 | 8625 | 0.9868 | 0.8717 |
| 0.0399 | 24.0 | 9000 | 1.0128 | 0.86 |
| 0.0299 | 25.0 | 9375 | 0.9782 | 0.8867 |
| 0.0002 | 26.0 | 9750 | 1.2403 | 0.8567 |
| 0.0051 | 27.0 | 10125 | 1.1638 | 0.8567 |
| 0.0126 | 28.0 | 10500 | 1.0178 | 0.87 |
| 0.0001 | 29.0 | 10875 | 1.0674 | 0.8717 |
| 0.008 | 30.0 | 11250 | 1.0641 | 0.8633 |
| 0.0072 | 31.0 | 11625 | 1.1590 | 0.8667 |
| 0.0003 | 32.0 | 12000 | 1.3669 | 0.8567 |
| 0.0001 | 33.0 | 12375 | 1.2305 | 0.8733 |
| 0.0072 | 34.0 | 12750 | 1.1660 | 0.865 |
| 0.0 | 35.0 | 13125 | 1.3099 | 0.8683 |
| 0.001 | 36.0 | 13500 | 1.3895 | 0.8617 |
| 0.0003 | 37.0 | 13875 | 1.2632 | 0.8633 |
| 0.0099 | 38.0 | 14250 | 1.2864 | 0.8583 |
| 0.0 | 39.0 | 14625 | 1.2372 | 0.87 |
| 0.0 | 40.0 | 15000 | 1.3431 | 0.85 |
| 0.0 | 41.0 | 15375 | 1.3348 | 0.86 |
| 0.0 | 42.0 | 15750 | 1.4149 | 0.8633 |
| 0.0 | 43.0 | 16125 | 1.5031 | 0.86 |
| 0.0 | 44.0 | 16500 | 1.5165 | 0.8667 |
| 0.0 | 45.0 | 16875 | 1.5362 | 0.8633 |
| 0.0 | 46.0 | 17250 | 1.5276 | 0.865 |
| 0.0 | 47.0 | 17625 | 1.5857 | 0.8667 |
| 0.0 | 48.0 | 18000 | 1.5842 | 0.8667 |
| 0.0 | 49.0 | 18375 | 1.5927 | 0.8683 |
| 0.0 | 50.0 | 18750 | 1.5932 | 0.87 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_5x_beit_base_rms_00001_fold4
|
hkivancoral
| 2023-12-16T15:34:51Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T14:14:22Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_00001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8933333333333333
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_5x_beit_base_rms_00001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3109
- Accuracy: 0.8933
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1968 | 1.0 | 375 | 0.3151 | 0.8917 |
| 0.1474 | 2.0 | 750 | 0.4474 | 0.8667 |
| 0.0741 | 3.0 | 1125 | 0.4618 | 0.89 |
| 0.0638 | 4.0 | 1500 | 0.5553 | 0.9083 |
| 0.0357 | 5.0 | 1875 | 0.7199 | 0.8767 |
| 0.027 | 6.0 | 2250 | 0.8598 | 0.8783 |
| 0.0037 | 7.0 | 2625 | 1.0235 | 0.8817 |
| 0.0229 | 8.0 | 3000 | 1.0021 | 0.8817 |
| 0.0002 | 9.0 | 3375 | 1.0533 | 0.88 |
| 0.0003 | 10.0 | 3750 | 1.0170 | 0.8917 |
| 0.0047 | 11.0 | 4125 | 1.0274 | 0.885 |
| 0.0161 | 12.0 | 4500 | 0.9972 | 0.8883 |
| 0.0395 | 13.0 | 4875 | 1.1208 | 0.8817 |
| 0.0195 | 14.0 | 5250 | 1.1819 | 0.8833 |
| 0.0231 | 15.0 | 5625 | 1.2063 | 0.8867 |
| 0.002 | 16.0 | 6000 | 1.1906 | 0.8817 |
| 0.0189 | 17.0 | 6375 | 1.3367 | 0.8683 |
| 0.006 | 18.0 | 6750 | 1.3216 | 0.8767 |
| 0.0201 | 19.0 | 7125 | 1.2482 | 0.8883 |
| 0.0004 | 20.0 | 7500 | 1.3064 | 0.88 |
| 0.0 | 21.0 | 7875 | 1.2624 | 0.8833 |
| 0.0332 | 22.0 | 8250 | 1.2916 | 0.8783 |
| 0.0001 | 23.0 | 8625 | 1.2718 | 0.875 |
| 0.0134 | 24.0 | 9000 | 1.2861 | 0.8767 |
| 0.0091 | 25.0 | 9375 | 1.2558 | 0.8867 |
| 0.0 | 26.0 | 9750 | 1.1412 | 0.875 |
| 0.0003 | 27.0 | 10125 | 1.1757 | 0.8883 |
| 0.0 | 28.0 | 10500 | 1.1969 | 0.885 |
| 0.0001 | 29.0 | 10875 | 1.2159 | 0.8833 |
| 0.0439 | 30.0 | 11250 | 1.2112 | 0.885 |
| 0.0 | 31.0 | 11625 | 1.1996 | 0.8867 |
| 0.0011 | 32.0 | 12000 | 1.2726 | 0.8917 |
| 0.0 | 33.0 | 12375 | 1.2290 | 0.895 |
| 0.003 | 34.0 | 12750 | 1.2689 | 0.885 |
| 0.0001 | 35.0 | 13125 | 1.2685 | 0.8833 |
| 0.0 | 36.0 | 13500 | 1.2338 | 0.89 |
| 0.0 | 37.0 | 13875 | 1.2931 | 0.8817 |
| 0.0037 | 38.0 | 14250 | 1.2980 | 0.8833 |
| 0.0 | 39.0 | 14625 | 1.3078 | 0.895 |
| 0.0 | 40.0 | 15000 | 1.3295 | 0.8867 |
| 0.0 | 41.0 | 15375 | 1.2988 | 0.8917 |
| 0.0 | 42.0 | 15750 | 1.3679 | 0.8817 |
| 0.0 | 43.0 | 16125 | 1.3182 | 0.8833 |
| 0.0 | 44.0 | 16500 | 1.3785 | 0.885 |
| 0.0 | 45.0 | 16875 | 1.3130 | 0.8833 |
| 0.0008 | 46.0 | 17250 | 1.3226 | 0.8917 |
| 0.028 | 47.0 | 17625 | 1.3211 | 0.89 |
| 0.0214 | 48.0 | 18000 | 1.3040 | 0.8933 |
| 0.0 | 49.0 | 18375 | 1.3082 | 0.8917 |
| 0.0 | 50.0 | 18750 | 1.3109 | 0.8933 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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] |
neph1/bellman-7b-mistral-instruct-v0.2
|
neph1
| 2023-12-17T20:10:39Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"sv",
"dataset:neph1/bellman-7b-finetune",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T14:17:21Z |
---
license: apache-2.0
datasets:
- neph1/bellman-7b-finetune
language:
- sv
---
Qlora trained for 5 epochs on 6400 rows of q&a from around 1000 pages from wikipedia + around 100 of python questions and examples from
eph1/Alpaca-Lora-GPT4-Swedish-Refined (because I had spent so much time cleaning them and didn't want to throw them away). Also a couple of hundred rows of manually
gathered examples and some generated using chat-gpt.
Dataset otherwise generated using gpt-3.5-turbo.
Rank: 16
Alpha: 16
Dropout: 0.1
Context length: 1024
I may run another 5 epochs on this. But it feels like it's 'aligned' pretty well. (Regular mistral insists Magdalena Andersson is prime minister, still.)
Example (q8, temp: 0.7):
```
Prompt: Detta är en konversation mellan en användare och en hjälpsam AI-assistent som talar flytande svenska.
Användare: vem är statsminister i sverige 2023?
Assistent: Statsministern i Sverige år 2023 är Ulf Kristersson.
Användare: hur många bor det i sverige?
Assistent: År 2023 har Sverige ungefär 10,4 miljoner invånare.
Användare: berätta om några vanliga djurarter i sverige
Assistent: I Sverige finns ett brett utbud av djurarter. Några vanligt förekommande djur inkluderar älg, björn, lodjur, hare, rådjur samt en mångfald av fåglar och fiskar.
Den svenska naturen erbjuder en rik biologisk diversitet.
Användare: sjung lite som bellman
Assistent: Sverige har en rik kulturhistoria och en av dess mest berömda bidrag är den klassiska svenska diktsamlingen 'Fredmans epistlar', skriven av Carl Michael Bellman.
Här är ett exempel på en av hans dikter:
'År 1790 gick jag i parken ut,
Och så såg jag en dam,
Hon satt där på en bänk och slöt
Et styver band om kam.'
Bellmans poesi kännetecknas av humor och detaljrikedom. Den reflekterar den svenska samhällsklassstrukturen och livet under 1700-talet på ett unik väg.
'Fredman' är verkligen en viktig del i Sveriges kulturella arv.
```
|
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] |
htzznc/test
|
htzznc
| 2023-12-16T14:19:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T14:19:15Z |
---
license: apache-2.0
---
|
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Kahyos/test
|
Kahyos
| 2023-12-16T14:20:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:20:56Z |
Entry not found
|
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] |
MOKSHm/t5-base-grammar-synthesis
|
MOKSHm
| 2023-12-16T14:50:34Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"grammar correction",
"en",
"dataset:jfleg",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-16T14:27:16Z |
---
license: apache-2.0
datasets:
- jfleg
language:
- en
pipeline_tag: text2text-generation
tags:
- grammar correction
---
# Model
This model utilizes the [Flan-T5-base](https://huggingface.co/google/flan-t5-base) pre-trained model and has been fine-tuned using the [JFLEG](https://huggingface.co/datasets/jfleg) dataset with the assistance of the [Happy Transformer](https://github.com/EricFillion/happy-transformer) framework. Its primary objective is to correct a wide range of potential grammatical errors that sentences might contain including issues with punctuation, typos, prepositions, and more.
# Usage with Transformers
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Sajid030/t5-base-grammar-synthesis")
model = AutoModelForSeq2SeqLM.from_pretrained("Sajid030/t5-base-grammar-synthesis")
text = "One person if do n't have good health that means so many things they could lost ."
inputs = tokenizer("grammar:"+text, truncation=True, return_tensors='pt')
output = model.generate(inputs['input_ids'])
correction=tokenizer.batch_decode(output, skip_special_tokens=True)
print("".join(correction)) #Correction: If one person doesn't have good health, so many things could be lost.
```
# Usage with HappyTransformers
```
from happytransformer import HappyTextToText, TTSettings
happy_tt = HappyTextToText("T5", "Sajid030/t5-base-grammar-synthesis")
args = TTSettings()
sentence = "Much many brands and sellers still in the market."
result = happy_tt.generate_text("grammar: "+ sentence, args=args)
print(result.text) # Many brands and sellers are still in the market.
```
|
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JKuang96/q-FrozenLake-v1-4x4-noSlippery
|
JKuang96
| 2023-12-16T14:32:37Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T14:32:34Z |
---
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="JKuang96/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"])
```
|
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necrophiliac/grr
|
necrophiliac
| 2023-12-16T14:34:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:34:05Z |
Entry not found
|
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