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TheBlokeAI

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)


Vigogne 2 70B Chat - GPTQ

Description

This repo contains GPTQ model files for bofeng huang's Vigogne 2 70B Chat.

Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.

These files were quantised using hardware kindly provided by Massed Compute.

Repositories available

Prompt template: Vigogne-Llama-2-Chat

<s>[INST] <<SYS>>
Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.
<</SYS>>

{prompt} [/INST] 

Known compatible clients / servers

These GPTQ models are known to work in the following inference servers/webuis.

This may not be a complete list; if you know of others, please let me know!

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.

Explanation of GPTQ parameters
  • 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.
Branch Bits GS Act Order Damp % GPTQ Dataset Seq Len Size ExLlama Desc
main 4 None Yes 0.1 french 4096 35.33 GB Yes 4-bit, with Act Order. No group size, to lower VRAM requirements.
gptq-4bit-128g-actorder_True 4 128 Yes 0.1 french 4096 36.65 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 4 32 Yes 0.1 french 4096 40.66 GB Yes 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage.
gptq-3bit--1g-actorder_True 3 None Yes 0.1 french 4096 26.77 GB No 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g.
gptq-3bit-128g-actorder_True 3 128 Yes 0.1 french 4096 28.03 GB No 3-bit, with group size 128g and act-order. Higher quality than 128g-False.
gptq-3bit-32g-actorder_True 3 32 Yes 0.1 french 4096 31.84 GB No 3-bit, with group size 64g and act-order. Highest quality 3-bit option.

How to download, including from branches

In text-generation-webui

To download from the main branch, enter TheBloke/vigogne-2-70B-chat-GPTQ in the "Download model" box.

To download from another branch, add :branchname to the end of the download name, eg TheBloke/vigogne-2-70B-chat-GPTQ:gptq-4bit-128g-actorder_True

From the command line

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

To download the main branch to a folder called vigogne-2-70B-chat-GPTQ:

mkdir vigogne-2-70B-chat-GPTQ
huggingface-cli download TheBloke/vigogne-2-70B-chat-GPTQ --local-dir vigogne-2-70B-chat-GPTQ --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir vigogne-2-70B-chat-GPTQ
huggingface-cli download TheBloke/vigogne-2-70B-chat-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir vigogne-2-70B-chat-GPTQ --local-dir-use-symlinks False
More advanced huggingface-cli download usage

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.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

mkdir vigogne-2-70B-chat-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/vigogne-2-70B-chat-GPTQ --local-dir vigogne-2-70B-chat-GPTQ --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

With git (not recommended)

To clone a specific branch with git, use a command like this:

git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/vigogne-2-70B-chat-GPTQ

Note that using Git with HF repos is strongly discouraged. It will be much slower than using huggingface-hub, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the .git folder as a blob.)

How to easily download and use this model in text-generation-webui

Please make sure you're using the latest version of 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/vigogne-2-70B-chat-GPTQ.

    • To download from a specific branch, enter for example TheBloke/vigogne-2-70B-chat-GPTQ:gptq-4bit-128g-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: vigogne-2-70B-chat-GPTQ

  7. The model will automatically load, and is now ready for use!

  8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.

    • Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file quantize_config.json.
  9. Once you're ready, click the Text Generation tab and enter a prompt to get started!

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:

--model-id TheBloke/vigogne-2-70B-chat-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):

pip3 install huggingface-hub
from huggingface_hub import InferenceClient

endpoint_url = "https://your-endpoint-url-here"

prompt = "Tell me about AI"
prompt_template=f'''<s>[INST] <<SYS>>
Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.
<</SYS>>

{prompt} [/INST] 
'''

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}")

How to use this GPTQ model from Python code

Install the necessary packages

Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  # Use cu117 if on CUDA 11.7

If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:

pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .

You can then use the following code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/vigogne-2-70B-chat-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Tell me about AI"
prompt_template=f'''<s>[INST] <<SYS>>
Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.
<</SYS>>

{prompt} [/INST] 
'''

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'])

Compatibility

The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.

ExLlama is compatible with Llama and Mistral models 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.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

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.

Special thanks to: Aemon Algiz.

Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: bofeng huang's Vigogne 2 70B Chat

Vigogne

Vigogne-2-70B-Chat: A Llama-2-based French Chat LLM

Vigogne-2-70B-Chat is a French chat LLM, based on Llama-2-70B, optimized to generate helpful and coherent responses in conversations with users.

Check out our release blog and GitHub repository for more information.

Usage and License Notices: Vigogne-2-70B-Chat follows Llama-2's usage policy. A significant portion of the training data is distilled from GPT-3.5-Turbo and GPT-4, kindly use it cautiously to avoid any violations of OpenAI's terms of use.

Prompt Template

We used a prompt template adapted from the chat format of Llama-2.

You can apply this formatting using the chat template through the apply_chat_template() method.

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bofenghuang/vigogne-2-70b-chat")

conversation = [
    {"role": "user", "content": "Bonjour ! Comment ça va aujourd'hui ?"},
    {"role": "assistant", "content": "Bonjour ! Je suis une IA, donc je n'ai pas de sentiments, mais je suis prêt à vous aider. Comment puis-je vous assister aujourd'hui ?"},
    {"role": "user", "content": "Quelle est la hauteur de la Tour Eiffel ?"},
    {"role": "assistant", "content": "La Tour Eiffel mesure environ 330 mètres de hauteur."},
    {"role": "user", "content": "Comment monter en haut ?"},
]

print(tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True))

You will get

<s>[INST] <<SYS>>
Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.
<</SYS>>

Bonjour ! Comment ça va aujourd'hui ? [/INST] Bonjour ! Je suis une IA, donc je n'ai pas de sentiments, mais je suis prêt à vous aider. Comment puis-je vous assister aujourd'hui ? </s>[INST] Quelle est la hauteur de la Tour Eiffel ? [/INST] La Tour Eiffel mesure environ 330 mètres de hauteur. </s>[INST] Comment monter en haut ? [/INST]

Usage

Inference using the unquantized model with 🤗 Transformers

from typing import Dict, List, Optional
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TextStreamer

model_name_or_path = "bofenghuang/vigogne-2-70b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto")

streamer = TextStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)


def chat(
    query: str,
    history: Optional[List[Dict]] = None,
    temperature: float = 0.7,
    top_p: float = 1.0,
    top_k: float = 0,
    repetition_penalty: float = 1.1,
    max_new_tokens: int = 1024,
    **kwargs,
):
    if history is None:
        history = []

    history.append({"role": "user", "content": query})

    input_ids = tokenizer.apply_chat_template(history, return_tensors="pt").to(model.device)
    input_length = input_ids.shape[1]

    generated_outputs = model.generate(
        input_ids=input_ids,
        generation_config=GenerationConfig(
            temperature=temperature,
            do_sample=temperature > 0.0,
            top_p=top_p,
            top_k=top_k,
            repetition_penalty=repetition_penalty,
            max_new_tokens=max_new_tokens,
            pad_token_id=tokenizer.eos_token_id,
            **kwargs,
        ),
        streamer=streamer,
        return_dict_in_generate=True,
    )

    generated_tokens = generated_outputs.sequences[0, input_length:]
    generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)

    history.append({"role": "assistant", "content": generated_text})

    return generated_text, history


# 1st round
response, history = chat("Un escargot parcourt 100 mètres en 5 heures. Quelle est sa vitesse ?", history=None)

# 2nd round
response, history = chat("Quand il peut dépasser le lapin ?", history=history)

# 3rd round
response, history = chat("Écris une histoire imaginative qui met en scène une compétition de course entre un escargot et un lapin.", history=history)

You can also use the Google Colab Notebook provided below.

Open In Colab

Limitations

Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.

Acknowledgements

The model training was conducted on the Jean-Zay supercomputer at GENCI, and we extend our gratitude to the IDRIS team for their responsive support throughout the project.

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