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--- |
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base_model: mistralai/Mistral-7B-v0.3 |
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language: |
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- en |
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license: apache-2.0 |
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model_name: Mistral 7B v0.3 |
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pipeline_tag: text-generation |
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tags: |
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- gptq |
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- 4-bit |
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inference: false |
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model_creator: Mistral AI |
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model_type: mistral |
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prompt_template: '{prompt}' |
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quantized_by: iproskurina |
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base_model_relation: quantized |
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--- |
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# Mistral 7B v0.3 - GPTQ |
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- Model creator: [Mistral AI](https://huggingface.co./mistralai) |
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- Original model: [Mistral 7B v0.3](https://huggingface.co./mistralai/Mistral-7B-v0.3) |
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The model published in this repo was quantized to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ). |
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**Quantization details** |
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**All quantization parameters were taken from [GPTQ paper](https://arxiv.org/abs/2210.17323).** |
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GPTQ calibration data consisted of 128 random 2048 token segments from the [C4 dataset](https://huggingface.co./datasets/c4). |
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The grouping size used for quantization is equal to 128. |
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## How to use this GPTQ model from Python code |
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### Install the necessary packages |
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Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. |
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```shell |
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pip3 install --upgrade transformers optimum |
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# If using PyTorch 2.1 + CUDA 12.x: |
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pip3 install --upgrade auto-gptq |
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# or, if using PyTorch 2.1 + CUDA 11.x: |
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pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ |
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``` |
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If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: |
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```shell |
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pip3 uninstall -y auto-gptq |
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git clone https://github.com/PanQiWei/AutoGPTQ |
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cd AutoGPTQ |
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git checkout v0.5.1 |
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pip3 install . |
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``` |
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### You can then use the following code |
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```python |
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from transformers import AutoTokenizer, TextGenerationPipeline,AutoModelForCausalLM |
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig |
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pretrained_model_dir = "iproskurina/Mistral-7B-v0.3-GPTQ-4bit-g128" |
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True) |
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model = AutoGPTQForCausalLM.from_quantized(pretrained_model_dir, device="cuda:0", model_basename="model") |
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pipeline = TextGenerationPipeline(model=model, tokenizer=tokenizer) |
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print(pipeline("auto-gptq is")[0]["generated_text"]) |
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``` |
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