Initial GPTQ model commit
Browse files
README.md
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---
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inference: false
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license: other
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---
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<!-- header start -->
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<div style="width: 100%;">
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
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</div>
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<div style="display: flex; flex-direction: column; align-items: flex-end;">
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<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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</div>
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</div>
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<!-- header end -->
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# Camel AI's CAMEL 13B Role Playing Data GPTQ
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These files are GPTQ 4bit model files for [Camel AI's CAMEL 13B Role Playing Data](https://huggingface.co/camel-ai/CAMEL-13B-Role-Playing-Data).
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It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
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## Repositories available
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* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ)
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* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-GGML)
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* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-fp16)
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## How to easily download and use this model in text-generation-webui
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Please make sure you're using the latest version of text-generation-webui
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1. Click the **Model tab**.
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2. Under **Download custom model or LoRA**, enter `TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ`.
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3. Click **Download**.
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4. The model will start downloading, and once finished it will be automatically loaded.
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5. 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.
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* Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
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6. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
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## How to use this GPTQ model from Python code
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First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
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`pip install auto-gptq`
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Then try the following example code:
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```python
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from transformers import AutoTokenizer, pipeline, logging
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
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import argparse
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model_name_or_path = "TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ"
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model_basename = "camel-13b-roleplay-GPTQ-4bit--1g.act.order"
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use_triton = False
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
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model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
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model_basename=model_basename,
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use_safetensors=True,
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trust_remote_code=True,
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device="cuda:0",
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use_triton=use_triton,
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quantize_config=None)
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print("\n\n*** Generate:")
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input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
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output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
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print(tokenizer.decode(output[0]))
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# Inference can also be done using transformers' pipeline
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# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
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logging.set_verbosity(logging.CRITICAL)
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prompt = "Tell me about AI"
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prompt_template=f'''### Human: {prompt}
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### Assistant:'''
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print("*** Pipeline:")
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.95,
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repetition_penalty=1.15
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)
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print(pipe(prompt_template)[0]['generated_text'])
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```
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## Provided files
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**camel-13b-roleplay-GPTQ-4bit--1g.act.order.safetensors**
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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.
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It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.
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* `camel-13b-roleplay-GPTQ-4bit--1g.act.order.safetensors`
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* Works with AutoGPTQ in CUDA or Triton modes.
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* Works with GPTQ-for-LLaMa in CUDA mode. May have issues with GPTQ-for-LLaMa Triton mode.
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* Works with text-generation-webui, including one-click-installers.
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* Parameters: Groupsize = -1. Act Order / desc_act = True.
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<!-- footer start -->
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## Discord
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For further support, and discussions on these models and AI in general, join us at:
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[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
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## Thanks, and how to contribute.
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Thanks to the [chirper.ai](https://chirper.ai) team!
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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.
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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.
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Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
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* Patreon: https://patreon.com/TheBlokeAI
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* Ko-Fi: https://ko-fi.com/TheBlokeAI
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**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
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**Patreon special mentions**: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.
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Thank you to all my generous patrons and donaters!
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<!-- footer end -->
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# Original model card: Camel AI's CAMEL 13B Role Playing Data
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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](https://arxiv.org/abs/2303.17760). 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)!
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| Model | size | ARC-C (25 shots, acc_norm) | HellaSwag (10 shots, acc_norm) | MMLU (5 shots, acc_norm) | TruthfulQA (0 shot, mc2) | Average | Delta |
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|-------------|:----:|:---------------------------:|:-------------------------------:|:-------------------------:|:-------------------------:|:-------:|-------|
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| LLaMA | 13B | 50.8 | 78.9 | 37.7 | 39.9 | 51.8 | - |
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| Vicuna | 13B | 47.4 | 75.2 | 39.6 | 49.8 | 53.7 | 1.9 |
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| CAMEL | 13B | 54.9 | 79.3 | 48.5 | 46.2 | **57.2** | 5.4 |
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| LLaMA | 30B | 57.1 | 82.6 | 45.7 | 42.3 | 56.9 | 5.1 |
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---
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license: cc-by-nc-4.0
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---
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