模型:

TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ

中文

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Camel AI's CAMEL 13B Role Playing Data GPTQ

These files are GPTQ 4bit model files for Camel AI's CAMEL 13B Role Playing Data .

It is the result of quantising to 4bit using AutoGPTQ .

Repositories available

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

  • Click the Model tab .
  • Under Download custom model or LoRA , enter TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ .
  • Click Download .
  • The model will start downloading. Once it's finished it will say "Done"
  • In the top left, click the refresh icon next to Model .
  • In the Model dropdown, choose the model you just downloaded: CAMEL-13B-Role-Playing-Data-GPTQ
  • The model will automatically load, and is now ready for use!
  • 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 .
  • 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:

    pip install auto-gptq

    Then try the following example code:

    from transformers import AutoTokenizer, pipeline, logging
    from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
    import argparse
    
    model_name_or_path = "TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ"
    model_basename = "camel-13b-roleplay-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 = "Tell me about AI"
    prompt_template=f'''### Human: {prompt}
    ### Assistant:'''
    
    print("\n\n*** Generate:")
    
    input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
    output = model.generate(inputs=input_ids, temperature=0.7, 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

    camel-13b-roleplay-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.

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

    • camel-13b-roleplay-GPTQ-4bit-128g.no-act.order.safetensors
      • Works with AutoGPTQ in CUDA or Triton modes.
      • 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.

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

    Patreon special mentions : Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi.

    Thank you to all my generous patrons and donaters!

    Original model card: Camel AI's CAMEL 13B Role Playing Data

    CAMEL-13B-Role-Playing-Data is a chat large language model obtained by finetuning LLaMA-13B model on a total of 229K conversations created through our role-playing framework proposed in CAMEL . We evaluate our model offline using EleutherAI's language model evaluation harness used by Huggingface's Open LLM Benchmark. CAMEL-13B scores an average of 57.2 , outperfroming LLaMA-30B (56.9)!

    Model size ARC-C (25 shots, acc_norm) HellaSwag (10 shots, acc_norm) MMLU (5 shots, acc_norm) TruthfulQA (0 shot, mc2) Average Delta
    LLaMA 13B 50.8 78.9 37.7 39.9 51.8 -
    Vicuna 13B 47.4 75.2 39.6 49.8 53.7 1.9
    CAMEL 13B 54.9 79.3 48.5 46.2 57.2 5.4
    LLaMA 30B 57.1 82.6 45.7 42.3 56.9 5.1

    license: cc-by-nc-4.0