TheBloke/openchat_v2_w-GPTQ

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

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OpenChat's OpenChat v2 W GPTQ

These files are GPTQ 4bit model files for OpenChat's OpenChat v2 W.

It is the result of quantising to 4bit using GPTQ-for-LLaMa.

Repositories available

• 4-bit GPTQ models for GPU inference
• Unquantised fp16 model in pytorch format, for GPU inference and for further conversions

GGML models have not been made due to the custom prompt templating required, which I believe can't work with GGML at this time.

Prompt template: custom

This model uses a custom prompt template. This will likely mean it will NOT work in UIs like text-generation-webui until special support is added.

The conversation template involves concatenating tokens, and cannot be expressed in plain-text.

Besides base model vocabulary, an end-of-turn token <|end_of_turn|> is added.

Here is an example of single-round conversation template:

def tokenize_single_input(tokenizer, prompt):
    # OpenChat V2
    human_prefix = "User:"
    prefix    = "Assistant GPT4:"
    eot_token = "<|end_of_turn|>"
    bos_token = "<s>"

    def _tokenize(text):
        return tokenizer.convert_tokens_to_ids(tokenizer._tokenize(text))

    def _tokenize_special(special_name):
        return tokenizer.convert_tokens_to_ids(special_name)

    return [_tokenize_special(bos_token)] + _tokenize(human_prefix) + _tokenize(prompt) + [_tokenize_special(eot_token)] + \
          _tokenize(prefix)

To explore conditional language models, you can also set prefix = "Assistant GPT3:" to mimic ChatGPT behavior (this may cause performance degradation).

Hint: In BPE, tokenize(A) + tokenize(B) does not always equals to tokenize(A + B)

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 know how to make a manual install.

1. Click the Model tab.
2. Under Download custom model or LoRA, enter TheBloke/openchat_v2_w-GPTQ.
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: openchat_v2_w-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 set 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!

How to use this GPTQ model from Python code

First make sure you have AutoGPTQ installed:

GITHUB_ACTIONS=true pip install auto-gptq

Then try the following example code:

from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import argparse
def tokenize_single_input(tokenizer, prompt):
    # OpenChat V2
    human_prefix = "User:"
    prefix    = "Assistant GPT4:"
    eot_token = "<|end_of_turn|>"
    bos_token = "<s>"

    def _tokenize(text):
        return tokenizer.convert_tokens_to_ids(tokenizer._tokenize(text))

    def _tokenize_special(special_name):
        return tokenizer.convert_tokens_to_ids(special_name)

    return [_tokenize_special(bos_token)] + _tokenize(human_prefix) + _tokenize(prompt) + [_tokenize_special(eot_token)] + \
          _tokenize(prefix)

model_name_or_path = "TheBloke/openchat_v2-GPTQ"
model_basename = "openchat_v2-GPTQ-4bit-128g.no-act.order"

use_triton = False

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

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

prompt_ids = tokenizer_single_input(tokenizer, "Tell me about AI")

print("\n\n*** Generate:")

output = model.generate(inputs=prompt_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

Provided files

openchat_v2_w-GPTQ-4bit-128g.no-act.order.safetensors

This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.

If a Llama model, it will also be supported by ExLlama, which will provide 2x speedup over AutoGPTQ and GPTQ-for-LLaMa.

It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.

• openchat_v2_w-GPTQ-4bit-128g.no-act.order.safetensors
  • Works with AutoGPTQ in CUDA or Triton modes.
  • ExLlama suupports Llama 4-bit GPTQs, and will provide 2x speedup over AutoGPTQ and GPTQ-for-LLaMa.
  • Works with GPTQ-for-LLaMa in CUDA mode. May have issues with GPTQ-for-LLaMa Triton mode.
  • Works with text-generation-webui, including one-click-installers.
  • Parameters: Groupsize = 128. Act Order / desc_act = False.

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!

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: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: OpenChat's OpenChat v2 W

OpenChat: Advancing Open-source Language Models with Imperfect Data

The OpenChat v2 family is inspired by offline reinforcement learning, including conditional behavior cloning (OpenChat-v2) and weighted behavior cloning (OpenChat-v2-w).

• OpenChat-v2-w: ~80k cleaned ShareGPT data with conditioning and weighted loss, based on LLaMA-13B with a context length of 2048.
  • Achieves 50.9% win-rate over ChatGPT on MT-bench.
  • Achieves 79.4% win-rate over ChatGPT on Vicuna-bench.
  • Achieves 87.1% win-rate over text-davinci-003 on AlpacaEval.
• OpenChat-v2: ~80k cleaned ShareGPT data with only conditioning, based on LLaMA-13B with a context length of 2048.
  • Achieves 48.1% win-rate over ChatGPT on MT-bench.
  • Achieves 80.6% win-rate over ChatGPT on Vicuna-bench.
  • Achieves 85.0% win-rate over text-davinci-003 on AlpacaEval.

Code and Inference Server

We provide the full source code, including an inference server compatible with the "ChatCompletions" API, in the OpenChat GitHub repository.

Web UI

OpenChat also includes a web UI for a better user experience. See the GitHub repository for instructions.

Conversation Template

The conversation template involves concatenating tokens, and cannot be expressed in plain-text.

Besides base model vocabulary, an end-of-turn token <|end_of_turn|> is added.

Here is an example of single-round conversation template:

def tokenize_single_input(tokenizer, prompt):
    # OpenChat V2
    human_prefix = "User:"
    prefix    = "Assistant GPT4:"
    eot_token = "<|end_of_turn|>"
    bos_token = "<s>"

    def _tokenize(text):
        return tokenizer.convert_tokens_to_ids(tokenizer._tokenize(text))

    def _tokenize_special(special_name):
        return tokenizer.convert_tokens_to_ids(special_name)

    return [_tokenize_special(bos_token)] + _tokenize(human_prefix) + _tokenize(prompt) + [_tokenize_special(eot_token)] + \
           _tokenize(prefix)

To explore conditional language models, you can also set prefix = "Assistant GPT3:" to mimic ChatGPT behavior (this may cause performance degradation).

Hint: In BPE, tokenize(A) + tokenize(B) does not always equals to tokenize(A + B)

Limitations

Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:

• Complex reasoning
• Mathematical and arithmetic tasks
• Programming and coding challenges

Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.