模型:

TheBloke/LongChat-13B-GPTQ

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LmSys' Long Chat 13B GPTQ

These files are GPTQ 4bit model files for LmSys' Long Chat 13B .

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

This GPTQ offers up to 16K context size

The increased context is tested to work with ExLlama , via the latest release of text-generation-webui .

This model should NOT be used at 2048 context. For that, please use the standard Vicuna 1.3 model.

It has also been tested from Python code using AutoGPTQ, and trust_remote_code=True .

Please read carefully below to see how to use it.

Repositories available

Prompt template

A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input
USER: prompt
ASSISTANT:

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

Please make sure you're using the latest version of text-generation-webui

  • Click the Model tab .
  • Under Download custom model or LoRA , enter TheBloke/LongChat-13B-GPTQ .
  • Click Download .
  • The model will start downloading. Once it's finished it will say "Done"
  • Untick Autoload the model
  • In the top left, click the refresh icon next to Model .
  • In the Model dropdown, choose the model you just downloaded: LongChat-13B-GPTQ
  • To use the increased context, set the Loader to ExLlama , set max_seq_len to 16384, 8192 or 4096, and set compress_pos_emb to 8 for 16384 context, 4 for 8192 context, or to 2 for 4096 context.
  • Now click Save Settings followed by Reload
  • The model will automatically load, and is now ready for use!
  • 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 with AutoGPTQ

    First make sure you have AutoGPTQ and Einops installed:

    pip3 install einops auto-gptq
    

    Then run the following code. Note that in order to get this to work, config.json has been hardcoded to a sequence length of 8192.

    If you want to try 4096 or 16384 instead, please manually edit config.json to set max_position_embeddings to the value you want.

    from transformers import AutoTokenizer, pipeline, logging
    from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
    import argparse
    
    model_name_or_path = "TheBloke/LongChat-13B-GPTQ"
    model_basename = "longchat-13b-16k-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=True,
            device_map='auto',
            use_triton=use_triton,
            quantize_config=None)
    
    model.seqlen = 8192
    
    # Note: check the prompt template is correct for this model.
    prompt = "Tell me about AI"
    prompt_template=f'''USER: {prompt}
    ASSISTANT:'''
    
    print("\n\n*** Generate:")
    
    input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
    output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
    print(tokenizer.decode(output[0]))
    
    # Inference can also be done using transformers' pipeline
    
    # Prevent printing spurious transformers error when using pipeline with AutoGPTQ
    logging.set_verbosity(logging.CRITICAL)
    
    print("*** Pipeline:")
    pipe = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        max_new_tokens=512,
        temperature=0.7,
        top_p=0.95,
        repetition_penalty=1.15
    )
    
    print(pipe(prompt_template)[0]['generated_text'])
    

    Provided files

    longchat-13b-16k-GPTQ-4bit-128g.no-act.order.safetensors

    This will work with AutoGPTQ, ExLlama, 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.

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

    • longchat-13b-16k-GPTQ-4bit-128g.no-act.order.safetensors
      • Works for use with ExLlama with increased context (4096, 8192, 16384, or other values in-between)
      • Works with AutoGPTQ in Python code, including with increased context, if trust_remote_code=True is set.
      • Should work with GPTQ-for-LLaMa in CUDA mode, but unknown if increased context works - TBC. 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.

    Special thanks to : Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

    Patreon special mentions : Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire.

    Thank you to all my generous patrons and donaters!

    Original model card: LmSys' Long Chat 13B

    longchat-13b-16k Model Card

    Model details

    Model type: longchat-13b-16k is an open-source chatbot trained by fine-tuning llama-13b on user-shared conversations collected from ShareGPT, using the condensing rotary embedding technique reported in the blog .

    Model date: longchat-13b-16k was trained on June 2023.

    Organizations developing the model: The LongChat developers: Dacheng Li*, Rulin Shao*, Anze Xie, Ying Sheng, Lianmin Zheng, Ion Stoica, Xuezhe Ma, and Hao Zhang

    Paper or resources for more information: https://github.com/DachengLi1/LongChat

    Where to send questions or comments about the model: https://github.com/DachengLi1/LongChat

    Intended use

    Primary intended uses: The primary use of longchat-13b-16k is for research purposes.

    Primary intended users: The primary intended users of the model are researchers in natural language processing, machine learning, and artificial intelligence.

    Training dataset

    18K conversations collected from ShareGPT.com.

    Evaluation dataset

    A preliminary evaluation of the model quality is conducted by our released LongEval .