modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-05 18:32:21
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 540
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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| card
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|---|---|---|---|---|---|---|---|---|---|
LoneStriker/NeuralMarcoro14-7B-3.0bpw-h6-exl2
|
LoneStriker
| 2024-01-26T16:52:36Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mlabonne/Marcoro14-7B-slerp",
"dpo",
"rlhf",
"conversational",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:mlabonne/Marcoro14-7B-slerp",
"base_model:finetune:mlabonne/Marcoro14-7B-slerp",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T16:51:16Z |
---
base_model: mlabonne/Marcoro14-7B-slerp
license: cc-by-nc-4.0
tags:
- mlabonne/Marcoro14-7B-slerp
- dpo
- rlhf
datasets:
- mlabonne/chatml_dpo_pairs
---
# NeuralMarcoro14-7B
This is a DPO fine-tuned version of [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) using the [chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) preference dataset.
It improves the performance of the model on Nous benchmark suite and the Open LLM Benchmark.
It is currently the best-performing 7B LLM on the Open LLM Leaderboard (08/01/24).
You can try it out in this [Space](https://huggingface.co/spaces/mlabonne/NeuralMarcoro14-7B-GGUF-Chat) (GGUF Q4_K_M).
## ⚡ Quantized models
* **GGUF**: https://huggingface.co/mlabonne/NeuralMarcoro14-7B-GGUF
## 🏆 Evaluation
### Open LLM Leaderboard
### Nous
| Model |AGIEval|GPT4ALL|TruthfulQA|Bigbench|Average|
|-------------------------|------:|------:|---------:|-------:|------:|
|[NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)| 44.59| 76.17| 65.94| 46.9| 58.4|
|[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) | 44.66| 76.24| 64.15| 45.64| 57.67|
|Change | -0.07| -0.07| +1.79| +1.26| +0.73|
## 🧩 Training hyperparameters
**LoRA**:
* r=16
* lora_alpha=16
* lora_dropout=0.05
* bias="none"
* task_type="CAUSAL_LM"
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
**Training arguments**:
* per_device_train_batch_size=4
* gradient_accumulation_steps=4
* gradient_checkpointing=True
* learning_rate=5e-5
* lr_scheduler_type="cosine"
* max_steps=200
* optim="paged_adamw_32bit"
* warmup_steps=100
**DPOTrainer**:
* beta=0.1
* max_prompt_length=1024
* max_length=1536
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/NeuralMarcoro14-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
TheBloke/EstopianMaid-13B-GPTQ
|
TheBloke
| 2024-01-26T16:41:31Z | 108 | 17 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"roleplay",
"text-generation-inference",
"en",
"base_model:KatyTheCutie/EstopianMaid-13B",
"base_model:quantized:KatyTheCutie/EstopianMaid-13B",
"license:apache-2.0",
"autotrain_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-01-26T15:56:26Z |
---
base_model: KatyTheCutie/EstopianMaid-13B
inference: false
language:
- en
library_name: transformers
license: apache-2.0
model_creator: Katy Vetteriano
model_name: EstopianMaid 13B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- roleplay
- text-generation-inference
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
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</div>
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</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 -->
# EstopianMaid 13B - GPTQ
- Model creator: [Katy Vetteriano](https://huggingface.co/KatyTheCutie)
- Original model: [EstopianMaid 13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Katy Vetteriano's EstopianMaid 13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
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/EstopianMaid-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/EstopianMaid-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF)
* [Katy Vetteriano's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/KatyTheCutie/EstopianMaid-13B)
<!-- 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 -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `apache-2.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Katy Vetteriano's EstopianMaid 13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B).
<!-- licensing 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/EstopianMaid-13B-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/EstopianMaid-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/EstopianMaid-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/EstopianMaid-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/EstopianMaid-13B-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/EstopianMaid-13B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/EstopianMaid-13B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/EstopianMaid-13B-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `EstopianMaid-13B-GPTQ`:
```shell
mkdir EstopianMaid-13B-GPTQ
huggingface-cli download TheBloke/EstopianMaid-13B-GPTQ --local-dir EstopianMaid-13B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir EstopianMaid-13B-GPTQ
huggingface-cli download TheBloke/EstopianMaid-13B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir EstopianMaid-13B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central 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 EstopianMaid-13B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/EstopianMaid-13B-GPTQ --local-dir EstopianMaid-13B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/EstopianMaid-13B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/EstopianMaid-13B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/EstopianMaid-13B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `EstopianMaid-13B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-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/EstopianMaid-13B-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_template,
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/EstopianMaid-13B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "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: Katy Vetteriano's EstopianMaid 13B
Based on feedback Estopian made can:
- EstopianMaid is good at sticking to the character card.
- maintains coherency in a setting with multiple characters.
- Able to create new scenario's
- Prompt Template: Alpaca
### Instruction:
{prompt}
### Response:
Recommended settings:
- SillyTavern Default Preset.
- Temperature: 0.7
- Min-P: 0.3
- Amount to Gen: 256
- Top P: 1
- Repetition penalty: 1.10
Models used:
BlueNipples/TimeCrystal-l2-13B
cgato/Thespis-13b-DPO-v0.7
KoboldAI/LLaMA2-13B-Estopia
NeverSleep/Noromaid-13B-0.4-DPO
Doctor-Shotgun/cat-v1.0-13b
Feedback is always appreciated!
Thank you KoboldAI for their usage of their MergeBox and Caitlyn G. for their support and feedback.
|
SaiMaruthi/code-llama-7b-text-to-sql
|
SaiMaruthi
| 2024-01-26T16:37:20Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-01-26T15:03:47Z |
---
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: codellama/CodeLlama-7b-hf
model-index:
- name: code-llama-7b-text-to-sql
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. -->
# code-llama-7b-text-to-sql
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
KatyTestHistorical/NaberiusMaid-7B-GGUF
|
KatyTestHistorical
| 2024-01-26T16:27:34Z | 14 | 1 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T13:35:26Z |
experimental 7B model, feedback appreciated!
|
TheBloke/EstopianMaid-13B-AWQ
|
TheBloke
| 2024-01-26T16:24:08Z | 184 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"roleplay",
"text-generation-inference",
"en",
"base_model:KatyTheCutie/EstopianMaid-13B",
"base_model:quantized:KatyTheCutie/EstopianMaid-13B",
"license:apache-2.0",
"autotrain_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2024-01-26T15:56:26Z |
---
base_model: KatyTheCutie/EstopianMaid-13B
inference: false
language:
- en
library_name: transformers
license: apache-2.0
model_creator: Katy Vetteriano
model_name: EstopianMaid 13B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- roleplay
- text-generation-inference
---
<!-- 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 -->
# EstopianMaid 13B - AWQ
- Model creator: [Katy Vetteriano](https://huggingface.co/KatyTheCutie)
- Original model: [EstopianMaid 13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B)
<!-- description start -->
## Description
This repo contains AWQ model files for [Katy Vetteriano's EstopianMaid 13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B).
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/EstopianMaid-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/EstopianMaid-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF)
* [Katy Vetteriano's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/KatyTheCutie/EstopianMaid-13B)
<!-- 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 -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `apache-2.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Katy Vetteriano's EstopianMaid 13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B).
<!-- licensing 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/EstopianMaid-13B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.25 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/EstopianMaid-13B-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: `EstopianMaid-13B-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/EstopianMaid-13B-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/EstopianMaid-13B-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/EstopianMaid-13B-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/EstopianMaid-13B-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: Katy Vetteriano's EstopianMaid 13B
Based on feedback Estopian made can:
- EstopianMaid is good at sticking to the character card.
- maintains coherency in a setting with multiple characters.
- Able to create new scenario's
- Prompt Template: Alpaca
### Instruction:
{prompt}
### Response:
Recommended settings:
- SillyTavern Default Preset.
- Temperature: 0.7
- Min-P: 0.3
- Amount to Gen: 256
- Top P: 1
- Repetition penalty: 1.10
Models used:
BlueNipples/TimeCrystal-l2-13B
cgato/Thespis-13b-DPO-v0.7
KoboldAI/LLaMA2-13B-Estopia
NeverSleep/Noromaid-13B-0.4-DPO
Doctor-Shotgun/cat-v1.0-13b
Feedback is always appreciated!
Thank you KoboldAI for their usage of their MergeBox and Caitlyn G. for their support and feedback.
|
gilbertcarey/llama2-qlora-finetunined-french
|
gilbertcarey
| 2024-01-26T16:23:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T16:03:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TheBloke/EstopianMaid-13B-GGUF
|
TheBloke
| 2024-01-26T16:18:21Z | 2,769 | 46 |
transformers
|
[
"transformers",
"gguf",
"llama",
"roleplay",
"text-generation-inference",
"en",
"base_model:KatyTheCutie/EstopianMaid-13B",
"base_model:quantized:KatyTheCutie/EstopianMaid-13B",
"license:apache-2.0",
"region:us"
] | null | 2024-01-26T15:56:26Z |
---
base_model: KatyTheCutie/EstopianMaid-13B
inference: false
language:
- en
library_name: transformers
license: apache-2.0
model_creator: Katy Vetteriano
model_name: EstopianMaid 13B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- roleplay
- text-generation-inference
---
<!-- 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 -->
# EstopianMaid 13B - GGUF
- Model creator: [Katy Vetteriano](https://huggingface.co/KatyTheCutie)
- Original model: [EstopianMaid 13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Katy Vetteriano's EstopianMaid 13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B).
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/EstopianMaid-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/EstopianMaid-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF)
* [Katy Vetteriano's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/KatyTheCutie/EstopianMaid-13B)
<!-- 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 -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `apache-2.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Katy Vetteriano's EstopianMaid 13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B).
<!-- licensing 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [estopianmaid-13b.Q2_K.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q2_K.gguf) | Q2_K | 2 | 4.85 GB| 7.35 GB | significant quality loss - not recommended for most purposes |
| [estopianmaid-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [estopianmaid-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [estopianmaid-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [estopianmaid-13b.Q4_0.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [estopianmaid-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.42 GB| 9.92 GB | small, greater quality loss |
| [estopianmaid-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [estopianmaid-13b.Q5_0.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [estopianmaid-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [estopianmaid-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [estopianmaid-13b.Q6_K.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [estopianmaid-13b.Q8_0.gguf](https://huggingface.co/TheBloke/EstopianMaid-13B-GGUF/blob/main/estopianmaid-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/EstopianMaid-13B-GGUF and below it, a specific filename to download, such as: estopianmaid-13b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/EstopianMaid-13B-GGUF estopianmaid-13b.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/EstopianMaid-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/EstopianMaid-13B-GGUF estopianmaid-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m estopianmaid-13b.Q4_K_M.gguf --color -c 4096 --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 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="./estopianmaid-13b.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(
"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="./estopianmaid-13b.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: Katy Vetteriano's EstopianMaid 13B
Based on feedback Estopian made can:
- EstopianMaid is good at sticking to the character card.
- maintains coherency in a setting with multiple characters.
- Able to create new scenario's
- Prompt Template: Alpaca
### Instruction:
{prompt}
### Response:
Recommended settings:
- SillyTavern Default Preset.
- Temperature: 0.7
- Min-P: 0.3
- Amount to Gen: 256
- Top P: 1
- Repetition penalty: 1.10
Models used:
BlueNipples/TimeCrystal-l2-13B
cgato/Thespis-13b-DPO-v0.7
KoboldAI/LLaMA2-13B-Estopia
NeverSleep/Noromaid-13B-0.4-DPO
Doctor-Shotgun/cat-v1.0-13b
Feedback is always appreciated!
Thank you KoboldAI for their usage of their MergeBox and Caitlyn G. for their support and feedback.
<!-- original-model-card end -->
|
bartowski/deepseek-coder-7b-base-v1.5-exl2
|
bartowski
| 2024-01-26T16:15:55Z | 4 | 3 | null |
[
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2024-01-26T16:02:26Z |
---
license: other
license_name: deepseek-license
license_link: LICENSE
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of deepseek-coder-7b-base-v1.5
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.12">turboderp's ExLlamaV2 v0.0.12</a> for quantization.
# The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/deepseek-ai/deepseek-coder-7b-base-v1.5
No GQA - VRAM requirements will be higher
| Branch | Bits | lm_head bits | Size (4k) | Size (16k) | Description |
| -------------------------------------------------------------- | ---- | ------------ | --------- | ---------- | ----------- |
| [8_0](https://huggingface.co/Bartowski/deepseek-coder-7b-base-v1.5-exl2/tree/8_0) | 8.0 | 8.0 | 9.4 GB | 15.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/Bartowski/deepseek-coder-7b-base-v1.5-exl2/tree/6_5) | 6.5 | 8.0 | 8.6 GB | 14.8 GB | Near unquantized performance at vastly reduced size, **recommended**. |
| [5_0](https://huggingface.co/Bartowski/deepseek-coder-7b-base-v1.5-exl2/tree/5_0) | 5.0 | 6.0 | 7.2 GB | 13.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards with 4k context. |
| [4_25](https://huggingface.co/Bartowski/deepseek-coder-7b-base-v1.5-exl2/tree/4_25) | 4.25 | 6.0 | 6.5 GB | 12.7 GB | GPTQ equivalent bits per weight. |
| [3_5](https://huggingface.co/Bartowski/deepseek-coder-7b-base-v1.5-exl2/tree/3_5) | 3.5 | 6.0 | 5.9 GB | 12.1 GB | Lower quality, not recommended. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/deepseek-coder-7b-base-v1.5-exl2 deepseek-coder-7b-base-v1.5-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `deepseek-coder-7b-base-v1.5-exl2`:
```shell
mkdir deepseek-coder-7b-base-v1.5-exl2
huggingface-cli download bartowski/deepseek-coder-7b-base-v1.5-exl2 --local-dir deepseek-coder-7b-base-v1.5-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir deepseek-coder-7b-base-v1.5-exl2-6_5
huggingface-cli download bartowski/deepseek-coder-7b-base-v1.5-exl2 --revision 6_5 --local-dir deepseek-coder-7b-base-v1.5-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir deepseek-coder-7b-base-v1.5-exl2-6.5
huggingface-cli download bartowski/deepseek-coder-7b-base-v1.5-exl2 --revision 6_5 --local-dir deepseek-coder-7b-base-v1.5-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
NandGate1110/mistral_7b_guanaco_new
|
NandGate1110
| 2024-01-26T16:15:51Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"arxiv:1910.09700",
"region:us"
] | null | 2024-01-26T10:57:43Z |
---
library_name: peft
base_model: Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
Devdeshitha/Mistral_7B_new
|
Devdeshitha
| 2024-01-26T16:09:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T15:17:13Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
dsfsi/en-ss-m2m100-combo
|
dsfsi
| 2024-01-26T16:05:34Z | 119 | 0 |
transformers
|
[
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"siswati",
"m2m100",
"south-africa",
"ss",
"en",
"dataset:dsfsi/vukuzenzele-sentence-aligned",
"dataset:dsfsi/gov-za-monolingual",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-25T09:40:21Z |
---
license: cc-by-4.0
datasets:
- dsfsi/vukuzenzele-sentence-aligned
- dsfsi/gov-za-monolingual
language:
- ss
- en
metrics:
- bleu
tags:
- siswati
- m2m100
- south-africa
---
# [en-ss] English to Siswati Translation Model
Based on M2M100 model using The ZA-gov-multilingual and Vuk'uzenzele South African multilingual corpora, and the SADiLaR Bilingual English-Siswati Corpus.
Model created from English to Siswati aligned sentences from [ZA-gov-multilingual](), [Vuk'uzenzele](), and [SADiLaR]()
## Authors
- Richard Lastrucci
- Vukosi Marivate - [@vukosi](https://twitter.com/vukosi)
|
jlbaker361/dcgan-wikiart25
|
jlbaker361
| 2024-01-26T16:01:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-25T03:57:26Z |
---
{}
---
Creative Adversarial Network
epochs: 2
dataset jlbaker361/wikiart-balanced25
n classes 27
batch_size 4
images where resized to 512
and then center cropped to: 512
used clip=False
discriminator parameters:
init_dim: 32
final_dim 512
generator parameters:
input noise_dim: 100
|
YouKnwMe/Mistral-7B-Instruct-exp-e2
|
YouKnwMe
| 2024-01-26T15:58:00Z | 0 | 0 | null |
[
"safetensors",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-01-26T15:57:26Z |
---
license: cc-by-nc-4.0
---
Description TBD, thanks for checking in!
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(repo_id)
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
Heithem777/NeuralPipe-7B-slerp
|
Heithem777
| 2024-01-26T15:56:03Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"samir-fama/SamirGPT-v1",
"abacusai/Slerp-CM-mist-dpo",
"base_model:abacusai/Slerp-CM-mist-dpo",
"base_model:merge:abacusai/Slerp-CM-mist-dpo",
"base_model:samir-fama/SamirGPT-v1",
"base_model:merge:samir-fama/SamirGPT-v1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-24T12:50:43Z |
---
tags:
- merge
- mergekit
- lazymergekit
- samir-fama/SamirGPT-v1
- abacusai/Slerp-CM-mist-dpo
base_model:
- samir-fama/SamirGPT-v1
- abacusai/Slerp-CM-mist-dpo
---
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [samir-fama/SamirGPT-v1](https://huggingface.co/samir-fama/SamirGPT-v1)
* [abacusai/Slerp-CM-mist-dpo](https://huggingface.co/abacusai/Slerp-CM-mist-dpo)
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# No parameters necessary for base model
- model: samir-fama/SamirGPT-v1
parameters:
density: 0.53
weight: 0.4
- model: abacusai/Slerp-CM-mist-dpo
parameters:
density: 0.53
weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Heithem777/NeuralPipe-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-26T15:53:44Z | 39 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"SciPhi/SciPhi-Mistral-7B-32k",
"pytorch",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"license:apache-2.0",
"base_model:MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-26T15:45:09Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- SciPhi/SciPhi-Mistral-7B-32k
- pytorch
- license:mit
- autotrain_compatible
- endpoints_compatible
- region:us
- license:apache-2.0
model_name: SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF.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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./SciPhi-Mistral-7B-32k-Mistral-7B-Instruct-v0.1-GGUF.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)
|
NAQarabash/flan-t5-base-finetunes_QA_tr
|
NAQarabash
| 2024-01-26T15:53:14Z | 90 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-26T15:52:38Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-finetunes_QA_tr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-finetunes_QA_tr
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
mlx-community/SOLAR-10.7b-Instruct-dpo-4bit-mlx
|
mlx-community
| 2024-01-26T15:51:34Z | 2 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"mlx",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T14:37:31Z |
---
license: cc-by-nc-4.0
library_name: transformers
tags:
- mlx
---
# mlx-community/SOLAR-10.7b-Instruct-dpo-4bit-mlx
This model was converted to MLX format from [`macadeliccc/SOLAR-10.7b-Instruct-dpo`]().
Refer to the [original model card](https://huggingface.co/macadeliccc/SOLAR-10.7b-Instruct-dpo) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/SOLAR-10.7b-Instruct-dpo-4bit-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
alnrg2arg/blockchainlabs_7B_merged_test2_4_sft_4bit_DPO_orca
|
alnrg2arg
| 2024-01-26T15:48:17Z | 61 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"dataset:Intel/orca_dpo_pairs",
"base_model:alnrg2arg/blockchainlabs_7B_merged_test2_4",
"base_model:quantized:alnrg2arg/blockchainlabs_7B_merged_test2_4",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-24T06:47:22Z |
---
language:
- en
license: cc-by-nc-4.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: alnrg2arg/blockchainlabs_7B_merged_test2_4
datasets:
- Intel/orca_dpo_pairs
---
This is a model from blockchainlab test 2.4 which are merged - alnrg2arg/blockchainlabs_7B_merged_test2_4.
The project is running to make a small LLM for a on-device purpose.
Overall pipeline for this iteration is
1.Merging to make a base model (7B) 2.Prune the model to reduce the parameter (50% sparcity) 3.For recovery phase of the pruning, the DPO is chosen.
This model which is not pruned is intended to compare with the pruned model.
This is the code and parameters I chose for this model(DPO).
```
from transformers import TrainingArguments, AutoModelForCausalLM
from trl import DPOTrainer
dpo_trainer = DPOTrainer(
model = model,
ref_model = None,
args = TrainingArguments(
per_device_train_batch_size = 8,
gradient_accumulation_steps = 8,
warmup_ratio = 0.1,
num_train_epochs = 3,
learning_rate = 5e-6,
fp16 = not torch.cuda.is_bf16_supported(),
bf16 = torch.cuda.is_bf16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.0,
lr_scheduler_type = "linear",
seed = 42,
output_dir = "output_DPO",
),
beta = 0.1,
train_dataset = dataset,
# eval_dataset = raw_datasets["test"],
tokenizer = tokenizer,
max_length = 1024,
max_prompt_length = 512,
)
```
The code and parameters are borrowed from https://colab.research.google.com/drive/1SKrKGV-BZoU4kv5q3g0jtE_OhRgPtrrQ?usp=sharing
Benchmark scores
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|----------|------:|------|-----:|------|-----:|---|-----:|
|winogrande| 1|none | 5|acc |0.8248|± |0.0107|
|
alnrg2arg/blockchainlabs_7B_merged_test2_4_sft_4bit_DPO_orca2
|
alnrg2arg
| 2024-01-26T15:43:03Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-25T06:31:33Z |
---
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|--------------|------:|------|-----:|------|-----:|---|-----:|
|truthfulqa_mc2| 2|none | 0|acc |0.6786|± |0.0153|
|
gromdimon/beLLM
|
gromdimon
| 2024-01-26T15:41:05Z | 0 | 0 | null |
[
"art",
"bigram-language-model",
"text-generation",
"en",
"be",
"license:mit",
"region:us"
] |
text-generation
| 2024-01-26T13:33:52Z |
---
license: mit
language:
- en
- be
inference: false
tags:
- art
- bigram-language-model
- text-generation
---
# beLLM
## Model Description
The beLLM or `belarusian Large Language Model (LLM)` is a pretrained generative language model for the Belarusian language. It is based on the previous work
of [RuPoemGPT](https://github.com/gromdimon/ml-random/tree/master/rupoemgpt). The model was trained on a collection of belarusian poems and prose, which
were collected from different sources.
For more information about beLLM, please refer to [github-repo](https://github.com/gromdimon/beLLM).
### Intended Use
This model is intended for natural language generation tasks, such as creative writing assistance or text completion.
### Limitations and Bias
The model was trained just on 10mb of data, so it's very biased and very limited.
## Training and Evaluation Data
The dataset was collected from different sources and manually preprocessed. It contains over 9.5 million characters and is available on the [github-repo](https://github.com/gromdimon/beLLM). The dataset includes the following sources:
- [Belaruskaja Palichka](https://knihi.com/)
- [Ejka](https://ejka.ru/)
- [LitBel](https://lit-bel.org/)
- [RuLit](https://www.rulit.me/)
- [Stihi.by](https://stihi.by/)
- [BelSputnik](https://bel.sputnik.by/)
Some of the authors included in the dataset:
- Maxim Tank (Максім Танк)
- Yanka Kupala (Янка Купала)
- Yakub Kolas (Якуб Колас)
- Maxim Bogdanovich (Максім Багдановіч)
- Vasyl Bykov (Васіль Быкаў)
- Francishak Bagushevich (Францішак Багушэвіч)
- Yanka Bryl (Янка Брыль)
### Training Procedure
Hyperparameters for the training included:
```
# # Hyperparameters
BATCH_SIZE = 32 # how many independent sequences will we process in parallel?
BLOCK_SIZE = 256 # what is the maximum context length for predictions?
MAX_ITERATIONS = 10000
EVALUATION_INTERVAL = 500
LEARNING_RATE = 4e-4
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
EVALUATION_ITERATIONS = 200
NUMBER_OF_EMBEDDINGS = 512
NUMBER_OF_HEADS = 8
NUMBER_OF_LAYERS = 8
DROPOUT = 0.0
# -----------
```
After every 2000 epochs the weights were saved. You can find them in this repo. Every model has the following semantics: "model_<number_of_epochs>".
### Evaluation Results
Currently the latest `model_9999.pt` can make following generations:
```
Хапаць, дзе к попле можна
Займаць зрабіць.
Так маўчаў кашлянуць, зноў барадучыся словы, зноў трагічна і шум пачаў упалы, як дрыготкімі вушамі.
Габрыня пацалавала Ганна лаючася:
– Зноў не знаёмую, за штаб мне кашлянулася, што будзе член такі рэч, на колішняй Нёмане! Як трэба дагледзець кожным? Што з табой: вялікі год кашляніць будуць, колькі Яўхіма! Ну што ж, колькі хітры! І не горш за ўсіх! Хадзіць на вуліцы – нясіць ды, за важней! Заявіць – конь бароўскі, дахаты!.. Пад Куранятком!
– Го-га, дзела хадзіць па хатах! – Яўхім свой, жвавы, запярэчыла Яўхіма.
– Няма начы! Не трэба ведаць нікому! – неахвотна засмяялася за Зайчыка. – Пакуль не пішаш! На добры малы чалавек!
Ніякі нячас, канешне, чакаў маладых панылы дыялектар, у Петрака, вячэрам, у турме які яго раней.
«Э-е, аднак! Не, не ведаю, якая чаго гэта яна».
— А ты, хлопец, кажа! Хлопчыкі, хлопчыкі! От хлопчыкі!
— Гэта ўжо толькі добра ведаюць, што. Найшла сушчэня і на гарышчы месяцяцца ўволю, ці славакі турмаюць?
— Пад бокам, — скамандаваў ката, — прадаваў Брык.
Апошняя нібы набок ад яго ці здурнела, быццам адчуваючы сябе чаканне нешта сваім, хоць яна гаварыла.
Дзёмчыхі неўпрыкмет пагорквалі з вачэй сетку. Ён магла дастаць з роспаччу астраўкаю трохпрыбы любіў адным ліхам, заслугачу было такое, што ж была пры сабе Лена такая грамада, якімі былі бліжэй да ўсіх магіл часам дабраўся.
— А хіба ён жа смуглы? — спытала яна.
— Выглядаў бы, каб аб нашым такім ваенным час ісці стаў і маладзіца не чапала. Толькі лапамі ўжо зусім недадзеленым быў незразумелы, але калі на Івана зноў кароценька прасіла.
— Вось што, барыс падкінуў? — спытаўся нарэшце, як змоўкла з вераспіскай у кішэню, прыпаўшы: — Выходзіць яна ўжо няма для яе! Годзе за бацьку. Ідзіце, a людзі стараліся бацькамі. Высадзіце, што ўсе роўныя!
Яна лёгенька штанула: Джулія дагоніць — барадаты ад шмат штабе чалавекі. Яна ісці маці не дагоніць, а яна не адчулася. Ён баяўся збірацца ў горад. Дзяўчыны яшчэ больш не былі, каб у печы, вядома, ніколі, яна не гаварыла. Ніколі
```
## Usage
For usage and other information, please refer to [github-repo](https://github.com/gromdimon/beLLM).
## Source and Contributions
This model was developed by [Dzmitry Hramyka](https://github.com/gromdimon). Contributions and feedback are welcome.
|
wucng/res18_flower_c5
|
wucng
| 2024-01-26T15:36:25Z | 309 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"resnet",
"image-classification",
"generated_from_trainer",
"custom_code",
"base_model:wucng/custom-resnet18",
"base_model:finetune:wucng/custom-resnet18",
"autotrain_compatible",
"region:us"
] |
image-classification
| 2024-01-26T14:56:31Z |
---
base_model: wucng/custom-resnet18
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: res18_flower_c5
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. -->
# res18_flower_c5
This model is a fine-tuned version of [wucng/custom-resnet18](https://huggingface.co/wucng/custom-resnet18) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2866
- Accuracy: 0.8954
## 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: 0.0005
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4384 | 1.0 | 46 | 0.2866 | 0.8954 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
NAQarabash/flan-t5-base-QA_tr
|
NAQarabash
| 2024-01-26T15:34:20Z | 92 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad_tr",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-26T15:33:36Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
datasets:
- squad_tr
model-index:
- name: flan-t5-base-QA_tr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-QA_tr
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the squad_tr dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-26T15:28:39Z | 51 | 3 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"GreenNode/GreenNode-mini-7B-multilingual-v1olet",
"pytorch",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-26T13:28:59Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- GreenNode/GreenNode-mini-7B-multilingual-v1olet
- pytorch
- en
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF.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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1-GGUF.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)
|
anilbhatt1/phi2-proj-peft-model
|
anilbhatt1
| 2024-01-26T15:19:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T02:27:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
portilla911/instructor-large-sharded
|
portilla911
| 2024-01-26T15:18:40Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T15:18:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
c-demartino/llama-2-7b-chat-paragraphs
|
c-demartino
| 2024-01-26T15:09:13Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"story maps",
"events",
"conversational",
"dataset:c-demartino/llama-2-7b-chat-paragraphs",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T14:57:37Z |
---
license: apache-2.0
datasets:
- c-demartino/llama-2-7b-chat-paragraphs
pipeline_tag: text-generation
tags:
- story maps
- events
---
|
MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-26T15:08:27Z | 50 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"argilla/notus-7b-v1",
"tensorboard",
"dpo",
"rlaif",
"preference",
"ultrafeedback",
"en",
"dataset:argilla/ultrafeedback-binarized-preferences",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"license:apache-2.0",
"base_model:MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-26T14:59:41Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- argilla/notus-7b-v1
- tensorboard
- dpo
- rlaif
- preference
- ultrafeedback
- en
- dataset:argilla/ultrafeedback-binarized-preferences
- base_model:alignment-handbook/zephyr-7b-sft-full
- license:mit
- model-index
- autotrain_compatible
- endpoints_compatible
- region:us
- license:apache-2.0
model_name: notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./notus-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.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)
|
magixn/q-FrozenLake-v1-4x4-noSlippery
|
magixn
| 2024-01-26T14:58:02Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T14:57:58Z |
---
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="magixn/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"])
```
|
mudogruer/mistral-8x7b-dolly
|
mudogruer
| 2024-01-26T14:57:02Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"base_model:adapter:mistralai/Mixtral-8x7B-v0.1",
"region:us"
] | null | 2024-01-26T14:56:55Z |
---
library_name: peft
base_model: mistralai/Mixtral-8x7B-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
samitizerxu/segformer-b1-kelp-rgb-agg-imgaug-jan-26
|
samitizerxu
| 2024-01-26T14:56:40Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T13:13:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
y629/distilbert-base-uncased-finetuned-emotion
|
y629
| 2024-01-26T14:54:25Z | 93 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T14:37:37Z |
---
license: apache-2.0
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: 0.919
- name: F1
type: f1
value: 0.9192117062934999
---
<!-- 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: 0.2241
- Accuracy: 0.919
- F1: 0.9192
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8491 | 1.0 | 250 | 0.3189 | 0.905 | 0.9024 |
| 0.2564 | 2.0 | 500 | 0.2241 | 0.919 | 0.9192 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.13.3
|
colable/llama-ko-peft
|
colable
| 2024-01-26T14:52:50Z | 58 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T14:34:31Z |
---
license: mit
language:
- ko
---
# open-llama-2-ko based model with inhouse dataset
This is an Korean Model based on
* [beomi/open-llama-2-ko-7b]
gpu code example
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math
## v2 models
model_path = "colable/llama-ko-peft"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
)
print(tokenizer.decode(generation_output[0]))
prompt = input("please input prompt:")
```
|
linoyts/2000_ads_linoy_multi
|
linoyts
| 2024-01-26T14:40:36Z | 152 | 2 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-01-25T09:50:50Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: '<s0><s1> ad of a <s2><s3> woman wearing headphones'
output:
url:
"image_0.png"
- text: '<s0><s1> ad of a <s2><s3> woman wearing headphones'
output:
url:
"image_1.png"
- text: '<s0><s1> ad of a <s2><s3> woman wearing headphones'
output:
url:
"image_2.png"
- text: '<s0><s1> ad of a <s2><s3> woman wearing headphones'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: an ad in the style of <s0><s1> of a <s2><s3> woman
license: openrail++
---
# SDXL LoRA DreamBooth - linoyts/2000_ads_linoy_multi
<Gallery />
## Model description
### These are linoyts/2000_ads_linoy_multi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`2000_ads_linoy_multi.safetensors` here 💾](/linoyts/2000_ads_linoy_multi/blob/main/2000_ads_linoy_multi.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:2000_ads_linoy_multi:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`2000_ads_linoy_multi_emb.safetensors` here 💾](/linoyts/2000_ads_linoy_multi/blob/main/2000_ads_linoy_multi_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `2000_ads_linoy_multi_emb` to your prompt. For example, `an ad in the style of 2000_ads_linoy_multi_emb of a woman`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('linoyts/2000_ads_linoy_multi', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='linoyts/2000_ads_linoy_multi', filename='2000_ads_linoy_multi_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('<s0><s1> ad of a <s2><s3> woman wearing headphones').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
to trigger concept `T2K` → use `<s2><s3>` in your prompt
## Details
All [Files & versions](/linoyts/2000_ads_linoy_multi/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
anilguven/albert_tr_turkish_hotel_reviews
|
anilguven
| 2024-01-26T14:29:37Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"hotel",
"review",
"turkish",
"sentiment",
"bert",
"tr",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:02:14Z |
---
license: unknown
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- hotel
- review
- turkish
- sentiment
- bert
---
### Model Info
This model was developed/finetuned for hotel review task for the Turkish Language. This model was finetuned via the Turkish hotel review dataset.
- LABEL_0: positive review
- LABEL_1: negative review
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
- **Paper:** https://dl.acm.org/doi/10.1145/3557892
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
- **Finetuned from model [optional]:** https://huggingface.co/loodos/ALBERT-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- auprc = 0.9967569041911343
- auroc = 0.9959888228299643
- eval_loss = 0.20936161253005187
- fn = 184
- fp = 11
- mcc = 0.934422786276581
- tn = 2889
- tp = 2716
- Accuracy: %96.63
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{10.1145/3557892,
author = {Guven, Zekeriya Anil},
title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3557892},
doi = {10.1145/3557892},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {dec},
articleno = {55},
numpages = {16},
keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
}*
**APA:**
*Guven, Z. A. (2022). The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-16.*
|
SpotLab/filarias_species_detection
|
SpotLab
| 2024-01-26T14:27:27Z | 4 | 0 |
transformers
|
[
"transformers",
"tflite",
"mobilenetV2",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2023-10-10T15:58:05Z |
---
license: cc-by-nc-sa-4.0
---
This model is an object detection model trained with tensorflow object detection API, published with the paper Edge Artificial Intelligence for real-time automatic quantification of filariasis in mobile microscopy
- Model description:
- Developed by: Spotlab
- Model type: SSD mobilenet v2
- Model input: image resized to 640 and normalized to with mean=127.5 and std = 127.5.
- Classes: Loa loa, Mansonella perstans, Wuchereria bancrofti, Brugia malayi
- Datasets:
- Training set: 1203 field of view images (400 magnification) from 85 independent samples with 906 L. loa, 378 M. perstans, 35 W. bancrofti, and 58 B. malayi parasites.
- Validation set: 311 field of view images (100 magnification) from 30 independent samples with 138 L. loa, 102 M. perstans, 29 W. bancrofti, and 5 B. malayi parasites.
- Test set: 624 field of view images (100 magnification) from 18 independent samples with with 658 L. loa, 15 M. perstans, 21 W. bancrofti, and 23 B. malayi parasites.
- Performance:
- On validation set: the species differentiation algorithm achieved a weighted precision of 84.08%, recall of 95.33%, and an F1 score of 94.70%. Breaking down the results per class, the precision rates were 94.85% for L. loa, 97.03% for M. perstans, 94.00% for W. bancrofti, and 66.67% for B. malayi. The corresponding recall rates were 93.48%, 96.08%, 97.92%, and 92.31% respectively.
- On test set: overall precision of 95.46%, recall of 97.81%, and F1-score of 96.62%. The per-class precision values were determined as 98.80% for L. loa, 60.00% for M. perstans, 100.00% for W. bancrofti, and 58.97% for B. malayi. The corresponding recall rates were calculated as 98.50%, 100.00%, 76.00%, and 100.00%, respectively.
Example predictions:
You can create your own android app to run this model following this tutorial: (TensorFlow Lite Object Detection Android Demo
)[https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android]
|
paths1551/cethu-v1-b8
|
paths1551
| 2024-01-26T14:27:22Z | 2 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:Lykon/DreamShaper",
"base_model:adapter:Lykon/DreamShaper",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-26T12:02:26Z |
---
license: creativeml-openrail-m
base_model: Lykon/DreamShaper
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - paths1551/cethu-v1-b8
These are LoRA adaption weights for Lykon/DreamShaper. The weights were fine-tuned on the /workspace/cethu_lora dataset. You can find some example images in the following.
|
anilguven/bert_tr_turkish_hotel_reviews
|
anilguven
| 2024-01-26T14:25:01Z | 91 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"turkish",
"hotel",
"review",
"sentiment",
"tr",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T13:58:10Z |
---
license: unknown
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- turkish
- hotel
- review
- sentiment
- bert
---
### Model Info
This model was developed/finetuned for hotel review task for the Turkish Language. This model was finetuned via the Turkish hotel review dataset.
- LABEL_0: positive review
- LABEL_1: negative review
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
- **Paper:** https://dl.acm.org/doi/10.1145/3557892
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/bert-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- auprc = 0.9955237027430079
- auroc = 0.9931789536266349
- eval_loss = 0.16509666319230998
- fn = 107
- fp = 17
- mcc = 0.9577026908774097
- tn = 2883
- tp = 2793
- Accuracy: %97.86
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{10.1145/3557892,
author = {Guven, Zekeriya Anil},
title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3557892},
doi = {10.1145/3557892},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {dec},
articleno = {55},
numpages = {16},
keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
}*
**APA:**
*Guven, Z. A. (2022). The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-16.*
|
anilguven/bert_tr_turkish_movie_reviews
|
anilguven
| 2024-01-26T14:24:46Z | 97 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"movie",
"review",
"sentiment",
"turkish",
"tr",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:10:18Z |
---
license: unknown
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- movie
- review
- sentiment
- turkish
- bert
---
### Model Info
This model was developed/finetuned for movie review task for the Turkish Language. This model was finetuned via the Turkish movie review dataset.
- LABEL_0: positive review
- LABEL_1: negative review
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
- **Paper:** https://dl.acm.org/doi/10.1145/3557892
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/bert-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- auprc = 0.9547155589592419
- auroc = 0.9567033960358541
- eval_loss = 0.4520341001172079
- fn = 1368
- fp = 1668
- mcc = 0.7727794159832003
- tn = 11682
- tp = 11982
- Accuracy: %92.11
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{10.1145/3557892,
author = {Guven, Zekeriya Anil},
title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3557892},
doi = {10.1145/3557892},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {dec},
articleno = {55},
numpages = {16},
keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
}*
**APA:**
*Guven, Z. A. (2022). The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-16.*
|
AdityaKumar2408/rare-puppers
|
AdityaKumar2408
| 2024-01-26T14:19:05Z | 179 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-26T14:18:54Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9710144996643066
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Saturn
#### Tea
#### car
#### kangaroo
#### rat
|
Gayathri142214002/Question_Generation_ComQ_onT5base_withDataGen7
|
Gayathri142214002
| 2024-01-26T14:14:20Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-26T09:24:21Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
model-index:
- name: Question_Generation_ComQ_onT5base_withDataGen7
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. -->
# Question_Generation_ComQ_onT5base_withDataGen7
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3923
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7546 | 0.23 | 1000 | 0.5504 |
| 0.6013 | 0.47 | 2000 | 0.5134 |
| 0.557 | 0.7 | 3000 | 0.4834 |
| 0.5217 | 0.94 | 4000 | 0.4513 |
| 0.449 | 1.17 | 5000 | 0.4519 |
| 0.4441 | 1.41 | 6000 | 0.4389 |
| 0.439 | 1.64 | 7000 | 0.4322 |
| 0.4355 | 1.88 | 8000 | 0.4153 |
| 0.3996 | 2.11 | 9000 | 0.4263 |
| 0.388 | 2.35 | 10000 | 0.4183 |
| 0.3856 | 2.58 | 11000 | 0.4129 |
| 0.3782 | 2.82 | 12000 | 0.4101 |
| 0.3719 | 3.05 | 13000 | 0.4091 |
| 0.3395 | 3.29 | 14000 | 0.4091 |
| 0.3453 | 3.52 | 15000 | 0.4053 |
| 0.3538 | 3.76 | 16000 | 0.3933 |
| 0.3468 | 3.99 | 17000 | 0.3897 |
| 0.3128 | 4.22 | 18000 | 0.4035 |
| 0.3191 | 4.46 | 19000 | 0.4005 |
| 0.322 | 4.69 | 20000 | 0.3944 |
| 0.3204 | 4.93 | 21000 | 0.3881 |
| 0.302 | 5.16 | 22000 | 0.3951 |
| 0.2947 | 5.4 | 23000 | 0.3948 |
| 0.3011 | 5.63 | 24000 | 0.3932 |
| 0.303 | 5.87 | 25000 | 0.3873 |
| 0.2902 | 6.1 | 26000 | 0.3916 |
| 0.2777 | 6.34 | 27000 | 0.3940 |
| 0.2811 | 6.57 | 28000 | 0.3937 |
| 0.2815 | 6.81 | 29000 | 0.3923 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
esahit/SAS-finetuned-cochrane-medeasi
|
esahit
| 2024-01-26T14:13:02Z | 91 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:haining/scientific_abstract_simplification",
"base_model:finetune:haining/scientific_abstract_simplification",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-25T18:57:27Z |
---
license: mit
base_model: haining/scientific_abstract_simplification
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: SAS-finetuned-cochrane-medeasi
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. -->
# SAS-finetuned-cochrane-medeasi
This model is a fine-tuned version of [haining/scientific_abstract_simplification](https://huggingface.co/haining/scientific_abstract_simplification) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Bleu: {'bleu': 3.5213074954706223e-06, 'precisions': [0.49951576455396535, 0.15234465234465233, 0.06880219369313224, 0.036816459122902004], 'brevity_penalty': 2.9884691172035265e-05, 'length_ratio': 0.08757975289560735, 'translation_length': 9293, 'reference_length': 106109}
- Sari: {'sari': 2.5441859559296094}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Sari |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------:|
| No log | 1.0 | 159 | nan | {'bleu': 3.5213074954706223e-06, 'precisions': [0.49951576455396535, 0.15234465234465233, 0.06880219369313224, 0.036816459122902004], 'brevity_penalty': 2.9884691172035265e-05, 'length_ratio': 0.08757975289560735, 'translation_length': 9293, 'reference_length': 106109} | {'sari': 2.5441859559296094} |
| No log | 2.0 | 318 | nan | {'bleu': 3.5213074954706223e-06, 'precisions': [0.49951576455396535, 0.15234465234465233, 0.06880219369313224, 0.036816459122902004], 'brevity_penalty': 2.9884691172035265e-05, 'length_ratio': 0.08757975289560735, 'translation_length': 9293, 'reference_length': 106109} | {'sari': 2.5441859559296094} |
| No log | 3.0 | 477 | nan | {'bleu': 3.5213074954706223e-06, 'precisions': [0.49951576455396535, 0.15234465234465233, 0.06880219369313224, 0.036816459122902004], 'brevity_penalty': 2.9884691172035265e-05, 'length_ratio': 0.08757975289560735, 'translation_length': 9293, 'reference_length': 106109} | {'sari': 2.5441859559296094} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
RuHae/HAPO
|
RuHae
| 2024-01-26T14:07:25Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"question-answering",
"qa",
"en",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-26T08:55:44Z |
---
language:
- en
license: apache-2.0
tags:
- question-answering
- qa
datasets:
- squad
metrics:
- squad
---
|
anilguven/distilbert_tr_turkish_tweet
|
anilguven
| 2024-01-26T14:01:10Z | 91 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"turkish",
"tweet",
"emotion",
"sentiment",
"bert",
"tr",
"dataset:anilguven/turkish_tweet_emotion_dataset",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T13:47:57Z |
---
license: unknown
datasets:
- anilguven/turkish_tweet_emotion_dataset
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- turkish
- tweet
- emotion
- sentiment
- bert
---
### Model Info
This model was developed/finetuned for tweet emotion detection task for the Turkish Language. This model was finetuned via tweet dataset. This dataset contains 5 classes: angry, happy, sad, surprised and afraid.
- LABEL_0: angry
- LABEL_1: afraid
- LABEL_2: happy
- LABEL_3: surprised
- LABEL_4: sad
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_tweet_emotion_dataset
- **Paper:** https://ieeexplore.ieee.org/document/9559014
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_tweet_emotion_analysis_with_language_models
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/distilbert-base-turkish-cased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- eval_loss = 0.05249839214870008
- mcc = 0.9828118433102754
- Accuracy: %98.63
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@INPROCEEDINGS{9559014,
author={Guven, Zekeriya Anil},
booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)},
title={Comparison of BERT Models and Machine Learning Methods for Sentiment Analysis on Turkish Tweets},
year={2021},
volume={},
number={},
pages={98-101},
keywords={Computer science;Sentiment analysis;Analytical models;Social networking (online);Computational modeling;Bit error rate;Random forests;Sentiment Analysis;BERT;Machine Learning;Text Classification;Tweet Analysis.},
doi={10.1109/UBMK52708.2021.9559014}}*
**APA:**
*Guven, Z. A. (2021, September). Comparison of BERT models and machine learning methods for sentiment analysis on Turkish tweets. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 98-101). IEEE.*
|
golesheed/whisper-non-native-children-0-dutch
|
golesheed
| 2024-01-26T14:00:53Z | 18 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"nl",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-26T11:31:03Z |
---
language:
- nl
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Large V2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large V2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3707
- Wer: 12.5219
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6724 | 0.71 | 30 | 0.3868 | 19.2016 |
| 0.2748 | 1.43 | 60 | 0.3584 | 15.3846 |
| 0.1701 | 2.14 | 90 | 0.3415 | 13.5346 |
| 0.0814 | 2.86 | 120 | 0.3366 | 13.3398 |
| 0.0419 | 3.57 | 150 | 0.3567 | 13.3982 |
| 0.0254 | 4.29 | 180 | 0.3627 | 12.7167 |
| 0.0124 | 5.0 | 210 | 0.3707 | 12.5219 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
youngbreadho/distilbert-base-uncased-distilled-clinc
|
youngbreadho
| 2024-01-26T13:55:11Z | 97 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:41:19Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
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. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1160
- Accuracy: 0.9419
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1786 | 1.0 | 318 | 0.7011 | 0.7113 |
| 0.5333 | 2.0 | 636 | 0.3054 | 0.8581 |
| 0.2694 | 3.0 | 954 | 0.1794 | 0.9187 |
| 0.1792 | 4.0 | 1272 | 0.1441 | 0.9313 |
| 0.1468 | 5.0 | 1590 | 0.1316 | 0.9358 |
| 0.1323 | 6.0 | 1908 | 0.1242 | 0.9406 |
| 0.1239 | 7.0 | 2226 | 0.1207 | 0.9381 |
| 0.1189 | 8.0 | 2544 | 0.1179 | 0.9406 |
| 0.116 | 9.0 | 2862 | 0.1163 | 0.9426 |
| 0.1143 | 10.0 | 3180 | 0.1160 | 0.9419 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
YanSte/fine_tuning_llama-2_chat_alpaca_dolly_hf
|
YanSte
| 2024-01-26T13:43:22Z | 2 | 1 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-26T12:58:42Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
s3nh/latxa-7b-v1-GGUF
|
s3nh
| 2024-01-26T13:40:29Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T12:47:53Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/HiTZ/latxa-7b-v1).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### inference
User: Tell me story about what is an quantization and what do we need to build. nobody, add new story
User: I want to get 20$ for my idea of building a service where people can share ideas in anonymous! It's like "secret" but without any chances to make it happen. Idea is about making money from advertisement. Add new story
User: Tell me what is an quantization and why we need it
nobody, add new story▅ Eskolaren urteurreneko bazkaria egin dute zelaiarrek
Urretxuko Lizeoko ikasleak gaur arrats
# Original model card
|
sabayo/Marcaps-GPT-adapters-ft
|
sabayo
| 2024-01-26T13:37:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T13:36:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
anilguven/bert_tr_turkish_spam_email
|
anilguven
| 2024-01-26T13:35:13Z | 94 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"turkish",
"spam",
"ham",
"email",
"tr",
"dataset:anilguven/turkish_spam_email",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T19:36:37Z |
---
license: unknown
datasets:
- anilguven/turkish_spam_email
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- turkish
- spam
- ham
- email
- bert
---
### Model Info
This model was developed/finetuned for spam detection task for Turkish Language. This model was finetuned via spam/ham email dataset.
- LABEL_0: ham/normal mail
- LABEL_1: spam mail
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_spam_email
- **Paper:** https://dergipark.org.tr/tr/pub/ejosat/issue/75736/1234079
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_spam_email_detection_with_language_models
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/bert-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
# Model Load safetensors
<!-- Provide a quick summary of what the model is/does. -->
Detailed https://huggingface.co/docs/diffusers/using-diffusers/using_safetensors
### Results
- F1-score: %94.0
- Accuracy: %94.08
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{article_1234079, title={Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={1–6}, year={2023}, DOI={10.31590/ejosat.1234079}, author={GÜVEN, Zekeriya Anıl}, keywords={Siber Güvenlik, Spam Tespiti, Dil Modeli, Makine Öğrenmesi, Doğal Dil İşleme, Metin Sınıflandırma, Cyber Security, Spam Detection, Language Model, Machine Learning, Natural Language Processing, Text Classification}, number={47}, publisher={Osman SAĞDIÇ} }*
**APA:**
*GÜVEN, Z. A. (2023). Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi. Avrupa Bilim ve Teknoloji Dergisi, (47), 1-6.*
|
anilguven/distilbert_tr_turkish_spam_email
|
anilguven
| 2024-01-26T13:34:39Z | 92 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"turkish",
"spam",
"ham",
"email",
"bert",
"tr",
"dataset:anilguven/turkish_spam_email",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T19:28:52Z |
---
license: unknown
datasets:
- anilguven/turkish_spam_email
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- turkish
- spam
- ham
- email
- distilbert
- bert
---
### Model Info
This model was developed/finetuned for spam detection task for Turkish Language. This model was finetuned via spam/ham email dataset.
- LABEL_0: ham/normal mail
- LABEL_1: spam mail
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_spam_email
- **Paper:** https://dergipark.org.tr/tr/pub/ejosat/issue/75736/1234079
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_spam_email_detection_with_language_models
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/distilbert-base-turkish-cased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
# Model Load safetensors
<!-- Provide a quick summary of what the model is/does. -->
Detailed https://huggingface.co/docs/diffusers/using-diffusers/using_safetensors
### Results
- F1-score: %94.00
- Accuracy: %93.60
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{article_1234079, title={Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={1–6}, year={2023}, DOI={10.31590/ejosat.1234079}, author={GÜVEN, Zekeriya Anıl}, keywords={Siber Güvenlik, Spam Tespiti, Dil Modeli, Makine Öğrenmesi, Doğal Dil İşleme, Metin Sınıflandırma, Cyber Security, Spam Detection, Language Model, Machine Learning, Natural Language Processing, Text Classification}, number={47}, publisher={Osman SAĞDIÇ} }*
**APA:**
*GÜVEN, Z. A. (2023). Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi. Avrupa Bilim ve Teknoloji Dergisi, (47), 1-6.*
|
NandGate1110/mistral_7b_guanaco
|
NandGate1110
| 2024-01-26T13:34:36Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"arxiv:1910.09700",
"region:us"
] | null | 2024-01-18T15:23:41Z |
---
library_name: peft
base_model: Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
anilguven/albert_tr_turkish_spam_email
|
anilguven
| 2024-01-26T13:34:19Z | 121 | 1 |
transformers
|
[
"transformers",
"safetensors",
"albert",
"text-classification",
"turkish",
"spam",
"ham",
"email",
"bert",
"tr",
"dataset:anilguven/turkish_spam_email",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T19:34:03Z |
---
license: unknown
datasets:
- anilguven/turkish_spam_email
language:
- tr
metrics:
- accuracy
- f1
- recall
- precision
tags:
- turkish
- spam
- ham
- email
- albert
- bert
---
### Model Info
This model was developed/finetuned for spam detection task for Turkish Language. This model was finetuned via spam/ham email dataset.
- LABEL_0: ham/normal mail
- LABEL_1: spam mail
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_spam_email
- **Paper:** https://dergipark.org.tr/tr/pub/ejosat/issue/75736/1234079
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_spam_email_detection_with_language_models
- **Finetuned from model [optional]:** https://huggingface.co/loodos/albert-base-turkish-uncased
#### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
# Model Load safetensors
<!-- Provide a quick summary of what the model is/does. -->
Detailed https://huggingface.co/docs/diffusers/using-diffusers/using_safetensors
### Results
- F1-score: %93.55
- Accuracy: %93.10
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
*@article{article_1234079, title={Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={1–6}, year={2023}, DOI={10.31590/ejosat.1234079}, author={GÜVEN, Zekeriya Anıl}, keywords={Siber Güvenlik, Spam Tespiti, Dil Modeli, Makine Öğrenmesi, Doğal Dil İşleme, Metin Sınıflandırma, Cyber Security, Spam Detection, Language Model, Machine Learning, Natural Language Processing, Text Classification}, number={47}, publisher={Osman SAĞDIÇ} }*
**APA:**
*GÜVEN, Z. A. (2023). Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi. Avrupa Bilim ve Teknoloji Dergisi, (47), 1-6.*
|
paths1551/cethu-v1-b2
|
paths1551
| 2024-01-26T13:32:58Z | 2 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:Lykon/DreamShaper",
"base_model:adapter:Lykon/DreamShaper",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-26T11:26:44Z |
---
license: creativeml-openrail-m
base_model: Lykon/DreamShaper
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - paths1551/cethu-v1-b2
These are LoRA adaption weights for Lykon/DreamShaper. The weights were fine-tuned on the /workspace/cethu_lora dataset. You can find some example images in the following.
|
ntc-ai/SDXL-LoRA-slider.scowling
|
ntc-ai
| 2024-01-26T13:28:27Z | 12 | 2 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2024-01-26T13:28:19Z |
---
language:
- en
thumbnail: "images/evaluate/scowling.../scowling_17_3.0.png"
widget:
- text: scowling
output:
url: images/scowling_17_3.0.png
- text: scowling
output:
url: images/scowling_19_3.0.png
- text: scowling
output:
url: images/scowling_20_3.0.png
- text: scowling
output:
url: images/scowling_21_3.0.png
- text: scowling
output:
url: images/scowling_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "scowling"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - scowling (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/scowling_17_-3.0.png" width=256 height=256 /> | <img src="images/scowling_17_0.0.png" width=256 height=256 /> | <img src="images/scowling_17_3.0.png" width=256 height=256 /> |
| <img src="images/scowling_19_-3.0.png" width=256 height=256 /> | <img src="images/scowling_19_0.0.png" width=256 height=256 /> | <img src="images/scowling_19_3.0.png" width=256 height=256 /> |
| <img src="images/scowling_20_-3.0.png" width=256 height=256 /> | <img src="images/scowling_20_0.0.png" width=256 height=256 /> | <img src="images/scowling_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
scowling
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.scowling', weight_name='scowling.safetensors', adapter_name="scowling")
# Activate the LoRA
pipe.set_adapters(["scowling"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, scowling"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1140+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
triet1102/distilbert-base-uncased-finetuned-clinc
|
triet1102
| 2024-01-26T13:22:58Z | 97 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T13:18:57Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7583
- Accuracy: 0.9203
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2885 | 1.0 | 318 | 3.2661 | 0.7310 |
| 2.5978 | 2.0 | 636 | 1.8508 | 0.8458 |
| 1.5196 | 3.0 | 954 | 1.1364 | 0.8990 |
| 0.9933 | 4.0 | 1272 | 0.8393 | 0.9148 |
| 0.7755 | 5.0 | 1590 | 0.7583 | 0.9203 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
AlekseyKorshuk/evol-codealpaca-v1-sft-4e-5-dpo-3ep
|
AlekseyKorshuk
| 2024-01-26T13:20:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"phi",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"custom_code",
"base_model:AlekseyKorshuk/evol-codealpaca-v1-sft-4e-5",
"base_model:finetune:AlekseyKorshuk/evol-codealpaca-v1-sft-4e-5",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T08:19:13Z |
---
license: mit
base_model: AlekseyKorshuk/evol-codealpaca-v1-sft-4e-5
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: evol-codealpaca-v1-sft-4e-5-dpo-3ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: AlekseyKorshuk/evol-codealpaca-v1-sft-4e-5
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
hub_model_id: AlekseyKorshuk/evol-codealpaca-v1-sft-4e-5-dpo-3ep
hub_strategy: every_save
load_in_8bit: false
load_in_4bit: false
strict: false
rl: dpo
datasets:
- path: AlekseyKorshuk/evol-codealpaca-v1-dpo
split: train
type: chatml.intel
dataset_prepared_path:
#val_set_size: 0.001
output_dir: ./output
sequence_len: 2048
#sample_packing: false # currently unsupported
pad_to_sequence_len:
lora_r:
lora_alpha:
lora_dropout:
lora_target_modules:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project: ui-thesis
wandb_entity:
wandb_watch:
wandb_name: phi-2-chatml-dpo
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 8
num_epochs: 3
optimizer: paged_adamw_8bit
adam_beta1: 0.9
adam_beta2: 0.95
max_grad_norm: 1.0
adam_epsilon: 0.00001
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
learning_rate: 5.0e-7
warmup_steps: 32
#warmup_ratio: 0.1
weight_decay: 0.01
dpo_beta: 0.01
train_on_inputs: false
group_by_length: false
bf16: false
fp16: true
tf32: false
#float16: false
#bloat16: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
#evals_per_epoch: 5
#eval_table_size: 8 # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
#eval_table_max_new_tokens: 768 # Total number of tokens generated for predictions sent to wandb. Default is 128
chat_template: chatml
#saves_per_epoch: 1
save_steps: 1000
save_total_limit: 1
seed: 42
debug:
deepspeed:
fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true
```
</details><br>
# evol-codealpaca-v1-sft-4e-5-dpo-3ep
This model is a fine-tuned version of [AlekseyKorshuk/evol-codealpaca-v1-sft-4e-5](https://huggingface.co/AlekseyKorshuk/evol-codealpaca-v1-sft-4e-5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 32
- training_steps: 935
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
mamurak/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters
|
mamurak
| 2024-01-26T13:20:23Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2024-01-25T13:04:51Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
timelord7000/jan-comic3000
|
timelord7000
| 2024-01-26T13:16:00Z | 3 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-01-26T13:15:54Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: a comic strip with a girl in a pink hat and a purple bag in the style of <s0><s1>
output:
url: image-0.png
- text: a comic strip with a cartoon character and text in the style of <s0><s1>
output:
url: image-1.png
- text: the game cover for bryna and the minic knight in the style of <s0><s1>
output:
url: image-2.png
- text: a cartoon comic with a girl wearing a hat with horns in the style of <s0><s1>
output:
url: image-3.png
- text: a comic strip with a woman in a purple hat and a purple dragon in the style
of <s0><s1>
output:
url: image-4.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: in the style of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - timelord7000/jan-comic3000
<Gallery />
## Model description
### These are timelord7000/jan-comic3000 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`jan-comic3000.safetensors` here 💾](/timelord7000/jan-comic3000/blob/main/jan-comic3000.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:jan-comic3000:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`jan-comic3000_emb.safetensors` here 💾](/timelord7000/jan-comic3000/blob/main/jan-comic3000_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `jan-comic3000_emb` to your prompt. For example, `in the style of jan-comic3000_emb`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('timelord7000/jan-comic3000', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='timelord7000/jan-comic3000', filename='jan-comic3000_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('in the style of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/timelord7000/jan-comic3000/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
golesheed/whisper-non-native-children-1-dutch
|
golesheed
| 2024-01-26T13:13:55Z | 55 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"nl",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-26T12:38:13Z |
---
language:
- nl
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Large V2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large V2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3649
- Wer: 11.9003
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6619 | 0.71 | 30 | 0.3733 | 16.9978 |
| 0.2708 | 1.43 | 60 | 0.3423 | 15.2736 |
| 0.1655 | 2.14 | 90 | 0.3352 | 13.9055 |
| 0.0767 | 2.86 | 120 | 0.3321 | 12.6874 |
| 0.0416 | 3.57 | 150 | 0.3421 | 12.1439 |
| 0.0237 | 4.29 | 180 | 0.3498 | 12.2751 |
| 0.0114 | 5.0 | 210 | 0.3649 | 11.9003 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
simpragma/breeze-listen-dsw-base-ta
|
simpragma
| 2024-01-26T13:12:28Z | 62 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"ta",
"dataset:mozilla-foundation/common_voice_16_0",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-23T08:26:57Z |
---
language:
- ta
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Breeze DSW Tamil - base
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_0 ta
type: mozilla-foundation/common_voice_16_0
config: ta
split: test
args: ta
metrics:
- name: Wer
type: wer
value: 21.407068619939793
---
<!-- 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. -->
# Breeze DSW Tamil - base
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_16_0 ta dataset.
It achieves the following results on the evaluation set:
- Loss: 0.375
- Wer: 21.4071
## 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: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1698 | 0.1 | 100 | 0.5723 | 30.4406 |
| 0.3578 | 0.2 | 200 | 0.4302 | 25.6862 |
| 0.2832 | 0.3 | 300 | 0.3967 | 23.2048 |
| 0.2663 | 0.4 | 400 | 0.4038 | 23.8525 |
| 0.5175 | 0.5 | 500 | 0.3962 | 24.1466 |
| 0.2365 | 0.6 | 600 | 0.3850 | 22.2595 |
| 0.1692 | 0.7 | 700 | 0.3960 | 21.8687 |
| 0.1815 | 0.8 | 800 | 0.3823 | 22.0772 |
| 0.1612 | 0.9 | 900 | 0.3701 | 21.8056 |
| 0.1393 | 1.0 | 1000 | 0.375 | 21.4071 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
|
Kralley/mistral-7b-da-instr-fn
|
Kralley
| 2024-01-26T13:11:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:danish-foundation-models/munin-7b-alpha",
"base_model:adapter:danish-foundation-models/munin-7b-alpha",
"license:apache-2.0",
"region:us"
] | null | 2024-01-26T11:42:24Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: danish-foundation-models/munin-7b-alpha
model-index:
- name: ft-results
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. -->
# ft-results
This model is a fine-tuned version of [danish-foundation-models/munin-7b-alpha](https://huggingface.co/danish-foundation-models/munin-7b-alpha) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
|
not-lain/test-dynamic-pipeline
|
not-lain
| 2024-01-26T13:11:10Z | 94 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T12:58:51Z |
---
pipeline_tag: text-classification
---
# how to load the pipeline
```python
from transformers import pipeline
pipe = pipeline(model="not-lain/test-dynamic-pipeline",trust_remote_code=True)
pipe("hi",second_text="hello")
```
|
youngbreadho/distilbert-base-uncased-finetuned-clinc
|
youngbreadho
| 2024-01-26T13:06:42Z | 97 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T14:05:01Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7682
- Accuracy: 0.9184
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2967 | 1.0 | 318 | 3.2810 | 0.7181 |
| 2.6146 | 2.0 | 636 | 1.8653 | 0.8403 |
| 1.5377 | 3.0 | 954 | 1.1478 | 0.8981 |
| 1.0043 | 4.0 | 1272 | 0.8491 | 0.9135 |
| 0.7902 | 5.0 | 1590 | 0.7682 | 0.9184 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
trakss1436/phi2-webglm-qlora-v2
|
trakss1436
| 2024-01-26T13:04:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T13:04:34Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mamsis25/cubao
|
mamsis25
| 2024-01-26T13:04:00Z | 0 | 0 | null |
[
"conversational",
"aa",
"region:us"
] |
text-generation
| 2024-01-26T13:03:46Z |
---
language:
- aa
pipeline_tag: conversational
---
|
bartowski/deepseek-coder-7b-instruct-v1.5-exl2
|
bartowski
| 2024-01-26T12:59:37Z | 4 | 3 | null |
[
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2024-01-26T12:46:00Z |
---
license: other
license_name: deepseek
license_link: LICENSE
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of deepseek-coder-7b-instruct-v1.5
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.12">turboderp's ExLlamaV2 v0.0.12</a> for quantization.
# The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5
No GQA - VRAM requirements will be higher
| Branch | Bits | lm_head bits | Size (4k) | Size (16k) | Description |
| -------------------------------------------------------------- | ---- | ------------ | --------- | ---------- | ----------- |
| [8_0](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/8_0) | 8.0 | 8.0 | 9.4 GB | 15.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/6_5) | 6.5 | 8.0 | 8.6 GB | 14.8 GB | Near unquantized performance at vastly reduced size, **recommended**. |
| [5_0](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/5_0) | 5.0 | 6.0 | 7.2 GB | 13.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards with 4k context. |
| [4_25](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/4_25) | 4.25 | 6.0 | 6.5 GB | 12.7 GB | GPTQ equivalent bits per weight. |
| [3_5](https://huggingface.co/Bartowski/deepseek-coder-7b-instruct-v1.5-exl2/tree/3_5) | 3.5 | 6.0 | 5.9 GB | 12.1 GB | Lower quality, not recommended. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/deepseek-coder-7b-instruct-v1.5-exl2 deepseek-coder-7b-instruct-v1.5-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `deepseek-coder-7b-instruct-v1.5-exl2`:
```shell
mkdir deepseek-coder-7b-instruct-v1.5-exl2
huggingface-cli download bartowski/deepseek-coder-7b-instruct-v1.5-exl2 --local-dir deepseek-coder-7b-instruct-v1.5-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir deepseek-coder-7b-instruct-v1.5-exl2-6_5
huggingface-cli download bartowski/deepseek-coder-7b-instruct-v1.5-exl2 --revision 6_5 --local-dir deepseek-coder-7b-instruct-v1.5-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir deepseek-coder-7b-instruct-v1.5-exl2-6.5
huggingface-cli download bartowski/deepseek-coder-7b-instruct-v1.5-exl2 --revision 6_5 --local-dir deepseek-coder-7b-instruct-v1.5-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
AntoineGourru/Mistral_drome_full
|
AntoineGourru
| 2024-01-26T12:51:10Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T12:45:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sannysayril/distilgpt2-kitpot-data-test-02
|
sannysayril
| 2024-01-26T12:44:03Z | 46 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T12:38:34Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_keras_callback
model-index:
- name: sannysayril/distilgpt2-kitpot-data-test-02
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. -->
# sannysayril/distilgpt2-kitpot-data-test-02
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: 1.5898
- Validation Loss: 1.3760
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 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 |
|:----------:|:---------------:|:-----:|
| 2.6143 | 1.9087 | 0 |
| 1.8442 | 1.5438 | 1 |
| 1.5898 | 1.3760 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF
|
MaziyarPanahi
| 2024-01-26T12:32:49Z | 45 | 5 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"migtissera/Tess-XS-v1-3-yarn-128K",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2",
"base_model:quantized:MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2",
"conversational"
] |
text-generation
| 2024-01-26T11:36:52Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- migtissera/Tess-XS-v1-3-yarn-128K
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF
base_model: MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2)
## Description
[MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF) contains GGUF format model files for [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF) and below it, a specific filename to download, such as: Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.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 MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-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 MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.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>
## 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 Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.2-GGUF.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)
|
paths1551/cethu-v1-b1
|
paths1551
| 2024-01-26T12:31:34Z | 3 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:Lykon/DreamShaper",
"base_model:adapter:Lykon/DreamShaper",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-26T11:11:45Z |
---
license: creativeml-openrail-m
base_model: Lykon/DreamShaper
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - paths1551/cethu-v1-b1
These are LoRA adaption weights for Lykon/DreamShaper. The weights were fine-tuned on the /workspace/cethu_lora dataset. You can find some example images in the following.
|
xyfJASON/VCNet-pytorch
|
xyfJASON
| 2024-01-26T12:17:50Z | 0 | 0 | null |
[
"tensorboard",
"license:mit",
"region:us"
] | null | 2024-01-26T11:22:52Z |
---
license: mit
---
Checkpoints and training logs for GitHub repository: [xyfJASON/VCNet-pytorch](https://github.com/xyfJASON/VCNet-pytorch).
|
dataequity/dataequity-kde4-en-de-qlora
|
dataequity
| 2024-01-26T12:12:49Z | 120 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-23T19:08:32Z |
---
license: apache-2.0
library_name: transformers
datasets:
- kde4
widget:
- text: Hi! How are you?
---
## Model Summary
dataequity-kde4-en-de-qlora is a Transformer based language translator fine tuned using the kde dataset. The base model used is Helsinki-NLP/opus-mt-en-de
Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
### eng-spa
* source group: English
* target group: German
* model: transformer
* source language(s): en
* target language(s): de
* model: transformer
### Inference Code:
```python
from transformers import MarianMTModel, MarianTokenizer,
hub_repo_name = 'dataequity/dataequity-kde4-en-de-qlora'
tokenizer = MarianTokenizer.from_pretrained(hub_repo_name)
finetuned_model = MarianMTModel.from_pretrained(hub_repo_name)
questions = [
"How are the first days of each season chosen?",
"Why are laws requiring identification for voting scrutinized by the media?",
"Why aren't there many new operating systems being created?"
]
translated = finetuned_model.generate(**tokenizer(questions, return_tensors="pt", padding=True))
[tokenizer.decode(t, skip_special_tokens=True) for t in translated]
```
|
dataequity/dataequity-kde4-en-es-qlora
|
dataequity
| 2024-01-26T12:12:30Z | 120 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-23T19:40:23Z |
---
license: apache-2.0
library_name: transformers
datasets:
- kde4
widget:
- text: Hi! How are you?
---
## Model Summary
dataequity-kde4-en-es-qlora is a Transformer based language translator fine tuned using the kde dataset. The base model used is Helsinki-NLP/opus-mt-en-es
Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
### eng-spa
* source group: English
* target group: Spanish
* model: transformer
* source language(s): en
* target language(s): es
* model: transformer
### Inference Code:
```python
from transformers import MarianMTModel, MarianTokenizer,
hub_repo_name = 'dataequity/dataequity-kde4-en-es-qlora'
tokenizer = MarianTokenizer.from_pretrained(hub_repo_name)
finetuned_model = MarianMTModel.from_pretrained(hub_repo_name)
questions = [
"How are the first days of each season chosen?",
"Why are laws requiring identification for voting scrutinized by the media?",
"Why aren't there many new operating systems being created?"
]
translated = finetuned_model.generate(**tokenizer(questions, return_tensors="pt", padding=True))
[tokenizer.decode(t, skip_special_tokens=True) for t in translated]
```
|
sqiangcao/sd-class-butterflies-32
|
sqiangcao
| 2024-01-26T12:06:56Z | 44 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2024-01-26T12:05:59Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('sqiangcao/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Heithem777/mol_test
|
Heithem777
| 2024-01-26T11:42:17Z | 0 | 0 | null |
[
"merge",
"mergekit",
"lazymergekit",
"ibm/MoLFormer-XL-both-10pct",
"base_model:ibm/MoLFormer-XL-both-10pct",
"base_model:finetune:ibm/MoLFormer-XL-both-10pct",
"region:us"
] | null | 2024-01-26T11:42:16Z |
---
tags:
- merge
- mergekit
- lazymergekit
- ibm/MoLFormer-XL-both-10pct
- ibm/MoLFormer-XL-both-10pct
base_model:
- ibm/MoLFormer-XL-both-10pct
- ibm/MoLFormer-XL-both-10pct
---
# mol_test
mol_test is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [ibm/MoLFormer-XL-both-10pct](https://huggingface.co/ibm/MoLFormer-XL-both-10pct)
* [ibm/MoLFormer-XL-both-10pct](https://huggingface.co/ibm/MoLFormer-XL-both-10pct)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: ibm/MoLFormer-XL-both-10pct
layer_range: [0, 11]
- model: ibm/MoLFormer-XL-both-10pct
layer_range: [0, 11]
merge_method: slerp
base_model: ibm/MoLFormer-XL-both-10pct
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Heithem777/mol_test"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
SilverCoder66/Mistral-7B-Instruct-adapt-v0.23
|
SilverCoder66
| 2024-01-26T11:31:17Z | 0 | 0 | null |
[
"safetensors",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-01-26T11:30:20Z |
---
license: cc-by-nc-4.0
---
Description TBD, thanks for checking in!
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(repo_id)
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
PJM124/PJ-roberta-base-lora-paws-x
|
PJM124
| 2024-01-26T11:31:11Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:adapter:FacebookAI/xlm-roberta-base",
"region:us"
] | null | 2024-01-26T11:31:09Z |
---
library_name: peft
base_model: xlm-roberta-base
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
SilverCoder66/Mistral-7B-Instruct-adapt-v0.22
|
SilverCoder66
| 2024-01-26T11:29:38Z | 0 | 0 | null |
[
"safetensors",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-01-26T11:28:28Z |
---
license: cc-by-nc-4.0
---
Description TBD, thanks for checking in!
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(repo_id)
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
EssJayB/ddpm-celebahq-finetuned-butterflies-2epoch_us
|
EssJayB
| 2024-01-26T11:28:23Z | 44 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2024-01-26T11:28:02Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('EssJayB/ddpm-celebahq-finetuned-butterflies-2epoch_us')
image = pipeline().images[0]
image
```
|
SilverCoder66/Mistral-7B-Instruct-adapt-v0.21
|
SilverCoder66
| 2024-01-26T11:27:54Z | 0 | 0 | null |
[
"safetensors",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-01-26T11:26:51Z |
---
license: cc-by-nc-4.0
---
Description TBD, thanks for checking in!
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(repo_id)
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
xyfJASON/PartialConv-Inpainting-pytorch
|
xyfJASON
| 2024-01-26T11:16:19Z | 0 | 0 | null |
[
"tensorboard",
"license:mit",
"region:us"
] | null | 2024-01-26T11:02:46Z |
---
license: mit
---
Checkpoints and training logs for GitHub repository: [xyfJASON/PartialConv-Inpainting-pytorch](https://github.com/xyfJASON/PartialConv-Inpainting-pytorch/).
Note: The PSNR values recorded in the log and tensorboard are incorrect.
注意：log 和 tensorboard 里记录的 psnr 是错误的，应以最后测试结果为准
|
Maikou/Michelangelo
|
Maikou
| 2024-01-26T11:12:45Z | 0 | 15 | null |
[
"image-to-3d",
"text-to-3d",
"arxiv:2306.17115",
"license:lgpl-3.0",
"region:us"
] |
text-to-3d
| 2023-10-25T09:26:10Z |
---
license: lgpl-3.0
pipeline_tag: text-to-3d
tags:
- image-to-3d
---
# Michelangelo
* [Project Page](https://neuralcarver.github.io/michelangelo/)
* [Paper](https://arxiv.org/abs/2306.17115)
* [Code](https://github.com/NeuralCarver/Michelangelo)
* [Demo](https://huggingface.co/spaces/Maikou/Michelangelo)
|
SanjiWatsuki/SUS-Wizard-34B
|
SanjiWatsuki
| 2024-01-26T11:09:13Z | 6 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T08:55:59Z |
---
library_name: transformers
tags: []
---
SUS-Chat-34B finetuned on the Wizard-Vicuna dataset.
Prompt format:
```
### Human: {message}
### Assistant:
```
|
s3nh/EstopianMaid-13B-GGUF
|
s3nh
| 2024-01-26T11:05:51Z | 468 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T09:24:46Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/KatyTheCutie/EstopianMaid-13B).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### inference
User: Tell me story about what is an quantization and what do we need to build.
Answer: Once upon a time, in the magical world of digital music, there was a problem that needed solving. The problem was how to take sound waves, which are continuous and smooth, and turn them into something that computers could understand and manipulate easily. This process is called "quantization."
In order to build this solution, we needed clever engineers who understood both the art of music and the science of technology. They worked tirelessly, experimenting with different methods and algorithms, until they finally created a system that could transform sound waves into digital data.
Their invention was called an "
# Original model card
|
cloudyu/Mixtral_7Bx5_MoE_30B_DPO
|
cloudyu
| 2024-01-26T10:49:36Z | 54 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-24T13:34:09Z |
---
license: mit
tags:
- moe
---
* [This is DPO improved version of cloudyu/Mixtral_7Bx5_MoE_30B](https://huggingface.co/cloudyu/Mixtral_7Bx5_MoE_30B)
* [DPO Trainer](https://huggingface.co/docs/trl/main/en/dpo_trainer)
* metric not test
|
LazarusNLP/all-indobert-base-v2
|
LazarusNLP
| 2024-01-26T10:48:47Z | 103 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"ind",
"dataset:indonli",
"dataset:indolem/indo_story_cloze",
"dataset:unicamp-dl/mmarco",
"dataset:miracl/miracl",
"dataset:SEACrowd/wrete",
"dataset:SEACrowd/indolem_ntp",
"dataset:khalidalt/tydiqa-goldp",
"dataset:SEACrowd/facqa",
"dataset:indonesian-nlp/lfqa_id",
"dataset:jakartaresearch/indoqa",
"dataset:jakartaresearch/id-paraphrase-detection",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-01-26T10:05:02Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- indonli
- indolem/indo_story_cloze
- unicamp-dl/mmarco
- miracl/miracl
- SEACrowd/wrete
- SEACrowd/indolem_ntp
- khalidalt/tydiqa-goldp
- SEACrowd/facqa
- indonesian-nlp/lfqa_id
- jakartaresearch/indoqa
- jakartaresearch/id-paraphrase-detection
language:
- ind
---
# LazarusNLP/all-indobert-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('LazarusNLP/all-indobert-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('LazarusNLP/all-indobert-base-v2')
model = AutoModel.from_pretrained('LazarusNLP/all-indobert-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=LazarusNLP/all-indobert-base-v2)
## Training
The model was trained with the parameters:
**DataLoader**:
`MultiDatasetDataLoader.MultiDatasetDataLoader` of length 968 with parameters:
```
{'batch_size_pairs': 384, 'batch_size_triplets': 256}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 484,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Augustya07/Llama-2-7b-hf-neitzsche-books
|
Augustya07
| 2024-01-26T10:47:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T10:47:17Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
csukuangfj/icefall-asr-conv-emformer-transducer-stateless2-zh
|
csukuangfj
| 2024-01-26T10:43:12Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-01-26T10:36:18Z |
---
license: apache-2.0
---
This repo is forked from
https://huggingface.co/PingfengLuo/icefall-asr-conv-emformer-transducer-stateless2-zh
## Chinese-English-mixed ASR model using icefall_conv_emformer2
### Wenetspeech testset results
| TEST_NET | TEST_MEETING |
|----------|--------------|
| 9.64 | 9.2 | |
as log in `decoding_results/modified_beam_search_result`
### Training commond
```
python3 conv_emformer_transducer_stateless2/train.py --world-size 8 --num-epochs 30 --start-epoch 1 --exp-dir conv_emformer_transducer_stateless2/exp --max-duration 400 --master-port 12321 --num-encoder-layers 12 --chunk-length 32 --cnn-module-kernel 31 --left-context-length 32 --right-context-length 8 --memory-size 32
```
### Model unit is char+bpe as `data/lang_char_bpe/tokens.txt`
|
ertyazilim/emotion-analiysis-with-distilbert
|
ertyazilim
| 2024-01-26T10:31:19Z | 46 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T10:14:07Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: ertyazilim/emotion-analiysis-with-distilbert
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. -->
# ertyazilim/emotion-analiysis-with-distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1339
- Validation Loss: 0.1353
- Train Accuracy: 0.9385
- Epoch: 1
## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3922 | 0.1544 | 0.941 | 0 |
| 0.1339 | 0.1353 | 0.9385 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
unikei/t5-base-split-and-rephrase
|
unikei
| 2024-01-26T10:15:16Z | 444 | 18 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"split and rephrase",
"en",
"dataset:wiki_split",
"dataset:web_split",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-19T11:25:06Z |
---
license: bigscience-openrail-m
tags:
- split and rephrase
widget:
- text: >-
Cystic Fibrosis (CF) is an autosomal recessive disorder that affects
multiple organs, which is common in the Caucasian population,
symptomatically affecting 1 in 2500 newborns in the UK, and more than 80,000
individuals globally.
datasets:
- wiki_split
- web_split
language:
- en
---
# T5 model for splitting complex sentences to simple sentences in English
Split-and-rephrase is the task of splitting a complex input sentence into shorter sentences while preserving meaning. (Narayan et al., 2017)
E.g.:
```
Cystic Fibrosis (CF) is an autosomal recessive disorder that affects multiple organs,
which is common in the Caucasian population, symptomatically affecting 1 in 2500 newborns in the UK,
and more than 80,000 individuals globally.
```
could be split into
```
Cystic Fibrosis is an autosomal recessive disorder that affects multiple organs.
```
```
Cystic Fibrosis is common in the Caucasian population.
```
```
Cystic Fibrosis affects 1 in 2500 newborns in the UK.
```
```
Cystic Fibrosis affects more than 80,000 individuals globally.
```
## How to use it in your code:
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
checkpoint="unikei/t5-base-split-and-rephrase"
tokenizer = T5Tokenizer.from_pretrained(checkpoint)
model = T5ForConditionalGeneration.from_pretrained(checkpoint)
complex_sentence = "Cystic Fibrosis (CF) is an autosomal recessive disorder that \
affects multiple organs, which is common in the Caucasian \
population, symptomatically affecting 1 in 2500 newborns in \
the UK, and more than 80,000 individuals globally."
complex_tokenized = tokenizer(complex_sentence,
padding="max_length",
truncation=True,
max_length=256,
return_tensors='pt')
simple_tokenized = model.generate(complex_tokenized['input_ids'], attention_mask = complex_tokenized['attention_mask'], max_length=256, num_beams=5)
simple_sentences = tokenizer.batch_decode(simple_tokenized, skip_special_tokens=True)
print(simple_sentences)
"""
Output:
Cystic Fibrosis is an autosomal recessive disorder that affects multiple organs. Cystic Fibrosis is common in the Caucasian population. Cystic Fibrosis affects 1 in 2500 newborns in the UK. Cystic Fibrosis affects more than 80,000 individuals globally.
"""
```
|
vierlinglukas/a2c-PandaReachDense-v3
|
vierlinglukas
| 2024-01-26T10:14:17Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T10:10:04Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.19 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hojzas/setfit-proj8-multilabel
|
hojzas
| 2024-01-26T10:07:59Z | 49 | 0 |
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"dataset:hojzas/proj8-multilabel",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"co2_eq_emissions",
"region:us"
] |
text-classification
| 2024-01-26T10:07:33Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- hojzas/proj8-multilabel
metrics:
- accuracy
widget:
- text: 'def first_with_given_key(iterable, key=lambda x: x):\n keys_used = {}\n for
item in iterable:\n rp = repr(key(item))\n if rp not in keys_used.keys():\n keys_used[rp]
= repr(item)\n yield item'
- text: 'def first_with_given_key(iterable, key=lambda x: x):\n keys=[]\n for
i in iterable:\n if key(i) not in keys:\n yield i\n keys.append(key(i))'
- text: 'def first_with_given_key(iterable, key=repr):\n set_of_keys = set()\n lambda_key
= (lambda x: key(x))\n for item in iterable:\n key = lambda_key(item)\n try:\n key_for_set
= hash(key)\n except TypeError:\n key_for_set = repr(key)\n if
key_for_set in set_of_keys:\n continue\n set_of_keys.add(key_for_set)\n yield
item'
- text: 'def first_with_given_key(iterable, key = lambda x: x):\n found_keys={}\n for
i in iterable:\n if key(i) not in found_keys.keys():\n found_keys[key(i)]=i\n yield
i'
- text: 'def first_with_given_key(the_iterable, key=lambda x: x):\n temp_keys=[]\n for
i in range(len(the_iterable)):\n if (key(the_iterable[i]) not in temp_keys):\n temp_keys.append(key(the_iterable[i]))\n yield
the_iterable[i]\n del temp_keys'
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
emissions: 0.2716104726718793
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
ram_total_size: 251.49160385131836
hours_used: 0.005
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj8-multilabel](https://huggingface.co/datasets/hojzas/proj8-multilabel) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [hojzas/proj8-multilabel](https://huggingface.co/datasets/hojzas/proj8-multilabel)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("hojzas/setfit-proj8-multilabel")
# Run inference
preds = model("def first_with_given_key(iterable, key=lambda x: x):\n keys=[]\n for i in iterable:\n if key(i) not in keys:\n yield i\n keys.append(key(i))")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 43 | 92.5185 | 125 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0147 | 1 | 0.3001 | - |
| 0.7353 | 50 | 0.0104 | - |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.000 kg of CO2
- **Hours Used**: 0.005 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: No GPU used
- **CPU Model**: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
- **RAM Size**: 251.49 GB
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.36.1
- PyTorch: 2.1.2+cu121
- Datasets: 2.14.7
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
jendragora/pipka
|
jendragora
| 2024-01-26T10:02:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-26T10:01:12Z |
tiny very fluffy rabbit eats carrots
|
ertyazilim/distilbert-emotion
|
ertyazilim
| 2024-01-26T09:43:47Z | 92 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-23T07:08:45Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: distilbert-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: 0.937
---
<!-- 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-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: 0.1411
- Accuracy: 0.937
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 250 | 0.1860 | 0.929 |
| 0.3337 | 2.0 | 500 | 0.1411 | 0.937 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
csukuangfj/icefall_asr_wenetspeech_pruned_transducer_stateless5_streaming
|
csukuangfj
| 2024-01-26T09:39:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-26T09:31:34Z |
This repo is forked from
https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_streaming
See https://github.com/k2-fsa/icefall/pull/447 .
|
sevvalozdamar/bert-base-cased-finetuned
|
sevvalozdamar
| 2024-01-26T09:20:37Z | 93 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-21T10:59:05Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned
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. -->
# bert-base-cased-finetuned
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2396
- Accuracy: 0.9575
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2096 | 1.0 | 100 | 0.2535 | 0.9375 |
| 0.0835 | 2.0 | 200 | 0.2931 | 0.9425 |
| 0.0358 | 3.0 | 300 | 0.2112 | 0.9525 |
| 0.0124 | 4.0 | 400 | 0.2400 | 0.9475 |
| 0.0071 | 5.0 | 500 | 0.2241 | 0.955 |
| 0.0024 | 6.0 | 600 | 0.2342 | 0.9575 |
| 0.0035 | 7.0 | 700 | 0.2345 | 0.96 |
| 0.0005 | 8.0 | 800 | 0.2278 | 0.9575 |
| 0.0059 | 9.0 | 900 | 0.2243 | 0.96 |
| 0.0003 | 10.0 | 1000 | 0.2396 | 0.9575 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
hojzas/setfit-multilabel-test
|
hojzas
| 2024-01-26T09:18:35Z | 49 | 0 |
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"dataset:ethos",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"co2_eq_emissions",
"region:us"
] |
text-classification
| 2024-01-26T09:18:08Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- ethos
metrics:
- accuracy
widget:
- text: apparently east Asians commit less crime than us......well this is because
they are submissive conformist losers..WHITE POWER
- text: Because everyone knows this islam men is the devil
- text: Syria has a religion problem.
- text: Or maybe just don't follow degenerate sandnigger religions from the middle
east?
- text: ONLY LEGAL IMMIGRANTS... Like the hero officer Singh murdered in the line
of duty. NO TO ILLEGAL ALIENS THEY DO NOT BELONG HERE. Deport today
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
emissions: 0.4430446693845021
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
ram_total_size: 251.49160385131836
hours_used: 0.009
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: ethos
type: ethos
split: test
metrics:
- type: accuracy
value: 0.4509283819628647
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [ethos](https://huggingface.co/datasets/ethos) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [ethos](https://huggingface.co/datasets/ethos)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.4509 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("hojzas/setfit-multilabel-test")
# Run inference
preds = model("Syria has a religion problem.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 5 | 20.2344 | 182 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0063 | 1 | 0.2441 | - |
| 0.3125 | 50 | 0.1594 | - |
| 0.625 | 100 | 0.1721 | - |
| 0.9375 | 150 | 0.12 | - |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.000 kg of CO2
- **Hours Used**: 0.009 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: No GPU used
- **CPU Model**: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
- **RAM Size**: 251.49 GB
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.36.1
- PyTorch: 2.1.2+cu121
- Datasets: 2.14.7
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
ginami/distilbert-base-uncased-finetuned-emotion
|
ginami
| 2024-01-26T09:15:20Z | 93 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T09:08:19Z |
---
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: 0.926
- name: F1
type: f1
value: 0.9260951796167063
---
<!-- 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: 0.2160
- Accuracy: 0.926
- F1: 0.9261
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8118 | 1.0 | 250 | 0.3167 | 0.9065 | 0.9058 |
| 0.2434 | 2.0 | 500 | 0.2160 | 0.926 | 0.9261 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
hasiburrahman/ppo-LunarLander-v2
|
hasiburrahman
| 2024-01-26T09:13:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T09:13:32Z |
---
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: 280.56 +/- 16.93
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
...
```
|
DMetaSoul/sbert-chinese-general-v1
|
DMetaSoul
| 2024-01-26T09:08:08Z | 715 | 6 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"mteb",
"zh",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-25T08:49:55Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
- mteb
model-index:
- name: sbert-chinese-general-v1
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 22.293919432958074
- type: cos_sim_spearman
value: 22.56718923553609
- type: euclidean_pearson
value: 22.525656322797026
- type: euclidean_spearman
value: 22.56718923553609
- type: manhattan_pearson
value: 22.501773028824065
- type: manhattan_spearman
value: 22.536992587828397
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 30.33575274463879
- type: cos_sim_spearman
value: 30.298708742167772
- type: euclidean_pearson
value: 32.33094743729218
- type: euclidean_spearman
value: 30.298710993858734
- type: manhattan_pearson
value: 32.31155376195945
- type: manhattan_spearman
value: 30.267669681690744
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.507999999999996
- type: f1
value: 36.436808400753286
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 41.493256724214255
- type: cos_sim_spearman
value: 40.98395961967895
- type: euclidean_pearson
value: 41.12345737966565
- type: euclidean_spearman
value: 40.983959619555996
- type: manhattan_pearson
value: 41.02584539471014
- type: manhattan_spearman
value: 40.87549513383032
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 9.794628751974724
- type: f1
value: 9.350535369492716
- type: precision
value: 9.179392662804986
- type: recall
value: 9.794628751974724
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 34.984726547788284
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 27.81945732281589
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 53.06586280826805
- type: mrr
value: 59.58781746031746
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 52.83635946154306
- type: mrr
value: 59.315079365079356
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 5.721
- type: map_at_10
value: 8.645
- type: map_at_100
value: 9.434
- type: map_at_1000
value: 9.586
- type: map_at_3
value: 7.413
- type: map_at_5
value: 8.05
- type: mrr_at_1
value: 9.626999999999999
- type: mrr_at_10
value: 13.094
- type: mrr_at_100
value: 13.854
- type: mrr_at_1000
value: 13.958
- type: mrr_at_3
value: 11.724
- type: mrr_at_5
value: 12.409
- type: ndcg_at_1
value: 9.626999999999999
- type: ndcg_at_10
value: 11.35
- type: ndcg_at_100
value: 15.593000000000002
- type: ndcg_at_1000
value: 19.619
- type: ndcg_at_3
value: 9.317
- type: ndcg_at_5
value: 10.049
- type: precision_at_1
value: 9.626999999999999
- type: precision_at_10
value: 2.796
- type: precision_at_100
value: 0.629
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 5.476
- type: precision_at_5
value: 4.1209999999999996
- type: recall_at_1
value: 5.721
- type: recall_at_10
value: 15.190000000000001
- type: recall_at_100
value: 33.633
- type: recall_at_1000
value: 62.019999999999996
- type: recall_at_3
value: 9.099
- type: recall_at_5
value: 11.423
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 77.36620565243535
- type: cos_sim_ap
value: 85.92291866877001
- type: cos_sim_f1
value: 78.19390231037029
- type: cos_sim_precision
value: 71.24183006535948
- type: cos_sim_recall
value: 86.64952069207388
- type: dot_accuracy
value: 77.36620565243535
- type: dot_ap
value: 85.94113738490068
- type: dot_f1
value: 78.19390231037029
- type: dot_precision
value: 71.24183006535948
- type: dot_recall
value: 86.64952069207388
- type: euclidean_accuracy
value: 77.36620565243535
- type: euclidean_ap
value: 85.92291893444687
- type: euclidean_f1
value: 78.19390231037029
- type: euclidean_precision
value: 71.24183006535948
- type: euclidean_recall
value: 86.64952069207388
- type: manhattan_accuracy
value: 77.29404690318701
- type: manhattan_ap
value: 85.88284362100919
- type: manhattan_f1
value: 78.17836812144213
- type: manhattan_precision
value: 71.18448838548666
- type: manhattan_recall
value: 86.69628244096329
- type: max_accuracy
value: 77.36620565243535
- type: max_ap
value: 85.94113738490068
- type: max_f1
value: 78.19390231037029
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.976
- type: map_at_10
value: 35.18
- type: map_at_100
value: 35.921
- type: map_at_1000
value: 35.998999999999995
- type: map_at_3
value: 32.763
- type: map_at_5
value: 34.165
- type: mrr_at_1
value: 26.976
- type: mrr_at_10
value: 35.234
- type: mrr_at_100
value: 35.939
- type: mrr_at_1000
value: 36.016
- type: mrr_at_3
value: 32.771
- type: mrr_at_5
value: 34.172999999999995
- type: ndcg_at_1
value: 26.976
- type: ndcg_at_10
value: 39.635
- type: ndcg_at_100
value: 43.54
- type: ndcg_at_1000
value: 45.723
- type: ndcg_at_3
value: 34.652
- type: ndcg_at_5
value: 37.186
- type: precision_at_1
value: 26.976
- type: precision_at_10
value: 5.406
- type: precision_at_100
value: 0.736
- type: precision_at_1000
value: 0.091
- type: precision_at_3
value: 13.418
- type: precision_at_5
value: 9.293999999999999
- type: recall_at_1
value: 26.976
- type: recall_at_10
value: 53.766999999999996
- type: recall_at_100
value: 72.761
- type: recall_at_1000
value: 90.148
- type: recall_at_3
value: 40.095
- type: recall_at_5
value: 46.233000000000004
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 11.285
- type: map_at_10
value: 30.259000000000004
- type: map_at_100
value: 33.772000000000006
- type: map_at_1000
value: 34.037
- type: map_at_3
value: 21.038999999999998
- type: map_at_5
value: 25.939
- type: mrr_at_1
value: 45.1
- type: mrr_at_10
value: 55.803999999999995
- type: mrr_at_100
value: 56.301
- type: mrr_at_1000
value: 56.330999999999996
- type: mrr_at_3
value: 53.333
- type: mrr_at_5
value: 54.798
- type: ndcg_at_1
value: 45.1
- type: ndcg_at_10
value: 41.156
- type: ndcg_at_100
value: 49.518
- type: ndcg_at_1000
value: 52.947
- type: ndcg_at_3
value: 39.708
- type: ndcg_at_5
value: 38.704
- type: precision_at_1
value: 45.1
- type: precision_at_10
value: 20.75
- type: precision_at_100
value: 3.424
- type: precision_at_1000
value: 0.42700000000000005
- type: precision_at_3
value: 35.632999999999996
- type: precision_at_5
value: 30.080000000000002
- type: recall_at_1
value: 11.285
- type: recall_at_10
value: 43.242000000000004
- type: recall_at_100
value: 68.604
- type: recall_at_1000
value: 85.904
- type: recall_at_3
value: 24.404
- type: recall_at_5
value: 32.757
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21
- type: map_at_10
value: 28.364
- type: map_at_100
value: 29.199
- type: map_at_1000
value: 29.265
- type: map_at_3
value: 25.717000000000002
- type: map_at_5
value: 27.311999999999998
- type: mrr_at_1
value: 21
- type: mrr_at_10
value: 28.364
- type: mrr_at_100
value: 29.199
- type: mrr_at_1000
value: 29.265
- type: mrr_at_3
value: 25.717000000000002
- type: mrr_at_5
value: 27.311999999999998
- type: ndcg_at_1
value: 21
- type: ndcg_at_10
value: 32.708
- type: ndcg_at_100
value: 37.184
- type: ndcg_at_1000
value: 39.273
- type: ndcg_at_3
value: 27.372000000000003
- type: ndcg_at_5
value: 30.23
- type: precision_at_1
value: 21
- type: precision_at_10
value: 4.66
- type: precision_at_100
value: 0.685
- type: precision_at_1000
value: 0.086
- type: precision_at_3
value: 10.732999999999999
- type: precision_at_5
value: 7.82
- type: recall_at_1
value: 21
- type: recall_at_10
value: 46.6
- type: recall_at_100
value: 68.5
- type: recall_at_1000
value: 85.6
- type: recall_at_3
value: 32.2
- type: recall_at_5
value: 39.1
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 44.878799538283964
- type: f1
value: 33.84678310261366
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 82.1951219512195
- type: ap
value: 46.78292030042397
- type: f1
value: 76.20482468514128
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 62.84331627244547
- type: cos_sim_spearman
value: 68.39990265073726
- type: euclidean_pearson
value: 66.87431827169324
- type: euclidean_spearman
value: 68.39990264979167
- type: manhattan_pearson
value: 66.89702078900328
- type: manhattan_spearman
value: 68.42107302159141
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 9.28600891904827
- type: mrr
value: 8.057936507936509
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.820999999999998
- type: map_at_10
value: 30.44
- type: map_at_100
value: 31.35
- type: map_at_1000
value: 31.419000000000004
- type: map_at_3
value: 28.134999999999998
- type: map_at_5
value: 29.482000000000003
- type: mrr_at_1
value: 23.782
- type: mrr_at_10
value: 31.141999999999996
- type: mrr_at_100
value: 32.004
- type: mrr_at_1000
value: 32.068000000000005
- type: mrr_at_3
value: 28.904000000000003
- type: mrr_at_5
value: 30.214999999999996
- type: ndcg_at_1
value: 23.782
- type: ndcg_at_10
value: 34.625
- type: ndcg_at_100
value: 39.226
- type: ndcg_at_1000
value: 41.128
- type: ndcg_at_3
value: 29.968
- type: ndcg_at_5
value: 32.35
- type: precision_at_1
value: 23.782
- type: precision_at_10
value: 4.994
- type: precision_at_100
value: 0.736
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 12.13
- type: precision_at_5
value: 8.495999999999999
- type: recall_at_1
value: 22.820999999999998
- type: recall_at_10
value: 47.141
- type: recall_at_100
value: 68.952
- type: recall_at_1000
value: 83.985
- type: recall_at_3
value: 34.508
- type: recall_at_5
value: 40.232
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (zh-CN)
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 57.343644922663074
- type: f1
value: 56.744802953803486
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.363819771351714
- type: f1
value: 62.15920863434656
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 14.6
- type: map_at_10
value: 18.231
- type: map_at_100
value: 18.744
- type: map_at_1000
value: 18.811
- type: map_at_3
value: 17.133000000000003
- type: map_at_5
value: 17.663
- type: mrr_at_1
value: 14.6
- type: mrr_at_10
value: 18.231
- type: mrr_at_100
value: 18.744
- type: mrr_at_1000
value: 18.811
- type: mrr_at_3
value: 17.133000000000003
- type: mrr_at_5
value: 17.663
- type: ndcg_at_1
value: 14.6
- type: ndcg_at_10
value: 20.349
- type: ndcg_at_100
value: 23.204
- type: ndcg_at_1000
value: 25.44
- type: ndcg_at_3
value: 17.995
- type: ndcg_at_5
value: 18.945999999999998
- type: precision_at_1
value: 14.6
- type: precision_at_10
value: 2.7199999999999998
- type: precision_at_100
value: 0.414
- type: precision_at_1000
value: 0.06
- type: precision_at_3
value: 6.833
- type: precision_at_5
value: 4.5600000000000005
- type: recall_at_1
value: 14.6
- type: recall_at_10
value: 27.200000000000003
- type: recall_at_100
value: 41.4
- type: recall_at_1000
value: 60
- type: recall_at_3
value: 20.5
- type: recall_at_5
value: 22.8
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 66.58333333333333
- type: f1
value: 66.26700927460007
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 72.00866269626421
- type: cos_sim_ap
value: 77.00520104243304
- type: cos_sim_f1
value: 74.39303710490151
- type: cos_sim_precision
value: 65.69579288025889
- type: cos_sim_recall
value: 85.74445617740233
- type: dot_accuracy
value: 72.00866269626421
- type: dot_ap
value: 77.00520104243304
- type: dot_f1
value: 74.39303710490151
- type: dot_precision
value: 65.69579288025889
- type: dot_recall
value: 85.74445617740233
- type: euclidean_accuracy
value: 72.00866269626421
- type: euclidean_ap
value: 77.00520104243304
- type: euclidean_f1
value: 74.39303710490151
- type: euclidean_precision
value: 65.69579288025889
- type: euclidean_recall
value: 85.74445617740233
- type: manhattan_accuracy
value: 72.1710882512182
- type: manhattan_ap
value: 77.00551017913976
- type: manhattan_f1
value: 74.23423423423424
- type: manhattan_precision
value: 64.72898664571878
- type: manhattan_recall
value: 87.0116156282999
- type: max_accuracy
value: 72.1710882512182
- type: max_ap
value: 77.00551017913976
- type: max_f1
value: 74.39303710490151
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 88.19000000000001
- type: ap
value: 85.13415594781077
- type: f1
value: 88.17344156114062
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 13.70522140998517
- type: cos_sim_spearman
value: 15.07546667334743
- type: euclidean_pearson
value: 17.49511420225285
- type: euclidean_spearman
value: 15.093970931789618
- type: manhattan_pearson
value: 17.44069961390521
- type: manhattan_spearman
value: 15.076029291596962
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 26.835294224547155
- type: cos_sim_spearman
value: 27.920204597498856
- type: euclidean_pearson
value: 26.153796707702803
- type: euclidean_spearman
value: 27.920971379720548
- type: manhattan_pearson
value: 26.21954147857523
- type: manhattan_spearman
value: 27.996860049937478
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.15901259718581
- type: cos_sim_spearman
value: 61.57967880874167
- type: euclidean_pearson
value: 53.83523291596683
- type: euclidean_spearman
value: 61.57967880874167
- type: manhattan_pearson
value: 54.99971428907956
- type: manhattan_spearman
value: 61.61229543613867
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 34.20930208460845
- type: cos_sim_spearman
value: 33.879011104224524
- type: euclidean_pearson
value: 35.08526425284862
- type: euclidean_spearman
value: 33.879011104224524
- type: manhattan_pearson
value: 35.509419089701275
- type: manhattan_spearman
value: 33.30035487147621
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 82.30068282185835
- type: cos_sim_spearman
value: 82.16763221361724
- type: euclidean_pearson
value: 80.52772752433374
- type: euclidean_spearman
value: 82.16797037220333
- type: manhattan_pearson
value: 80.51093859500105
- type: manhattan_spearman
value: 82.17643310049654
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 65.14113035189213
- type: mrr
value: 74.9589270937443
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 12.013
- type: map_at_10
value: 30.885
- type: map_at_100
value: 34.643
- type: map_at_1000
value: 34.927
- type: map_at_3
value: 21.901
- type: map_at_5
value: 26.467000000000002
- type: mrr_at_1
value: 49.623
- type: mrr_at_10
value: 58.05200000000001
- type: mrr_at_100
value: 58.61300000000001
- type: mrr_at_1000
value: 58.643
- type: mrr_at_3
value: 55.947
- type: mrr_at_5
value: 57.229
- type: ndcg_at_1
value: 49.623
- type: ndcg_at_10
value: 41.802
- type: ndcg_at_100
value: 49.975
- type: ndcg_at_1000
value: 53.504
- type: ndcg_at_3
value: 43.515
- type: ndcg_at_5
value: 41.576
- type: precision_at_1
value: 49.623
- type: precision_at_10
value: 22.052
- type: precision_at_100
value: 3.6450000000000005
- type: precision_at_1000
value: 0.45399999999999996
- type: precision_at_3
value: 38.616
- type: precision_at_5
value: 31.966
- type: recall_at_1
value: 12.013
- type: recall_at_10
value: 41.891
- type: recall_at_100
value: 67.096
- type: recall_at_1000
value: 84.756
- type: recall_at_3
value: 24.695
- type: recall_at_5
value: 32.09
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 39.800999999999995
- type: f1
value: 38.5345899934575
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 40.16574242797479
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 24.232617974671754
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 24.6
- type: map_at_10
value: 31.328
- type: map_at_100
value: 32.088
- type: map_at_1000
value: 32.164
- type: map_at_3
value: 29.133
- type: map_at_5
value: 30.358
- type: mrr_at_1
value: 24.6
- type: mrr_at_10
value: 31.328
- type: mrr_at_100
value: 32.088
- type: mrr_at_1000
value: 32.164
- type: mrr_at_3
value: 29.133
- type: mrr_at_5
value: 30.358
- type: ndcg_at_1
value: 24.6
- type: ndcg_at_10
value: 35.150999999999996
- type: ndcg_at_100
value: 39.024
- type: ndcg_at_1000
value: 41.157
- type: ndcg_at_3
value: 30.637999999999998
- type: ndcg_at_5
value: 32.833
- type: precision_at_1
value: 24.6
- type: precision_at_10
value: 4.74
- type: precision_at_100
value: 0.66
- type: precision_at_1000
value: 0.083
- type: precision_at_3
value: 11.667
- type: precision_at_5
value: 8.06
- type: recall_at_1
value: 24.6
- type: recall_at_10
value: 47.4
- type: recall_at_100
value: 66
- type: recall_at_1000
value: 83
- type: recall_at_3
value: 35
- type: recall_at_5
value: 40.300000000000004
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 83.96000000000001
- type: ap
value: 65.11027167433211
- type: f1
value: 82.03549710974653
license: apache-2.0
language:
- zh
---
# DMetaSoul/sbert-chinese-general-v1
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在 NLI、PAWS-X、PKU-Paraphrase-Bank、STS 等语义相似数据集上进行训练，适用于**通用语义匹配**场景（此模型在 Chinese-STS 任务上效果较好，但在其它任务上效果并非最优，存在一定过拟合风险），比如文本特征抽取、文本向量聚类、文本语义搜索等业务场景。
注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-general-v1-distill)，也已经开源啦！
# Usage
## 1. Sentence-Transformers
通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
```
pip install -U sentence-transformers
```
然后使用下面的代码来载入该模型并进行文本表征向量的提取：
```python
from sentence_transformers import SentenceTransformer
sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
model = SentenceTransformer('DMetaSoul/sbert-chinese-general-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## 2. HuggingFace Transformers
如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-general-v1')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v1')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation
该模型在公开的几个语义匹配数据集上进行了评测，计算了向量相似度跟真实标签之间的相关性系数：
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** |
| ------------ | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- |
| **spearman** | 84.54% | 82.17% | 23.80% | 65.94% | 45.52% | 11.52% | 48.51% |
## Citing & Authors
E-mail: xiaowenbin@dmetasoul.com
|
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