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--- |
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base_model: bigscience/bloom-7b1 |
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inference: false |
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model_creator: bigscience |
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model_name: bloom-7b1 |
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model_type: bloom |
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pipeline_tag: text-generation |
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quantized_by: iproskurina |
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tags: |
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- pretrained |
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license: bigscience-bloom-rail-1.0 |
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language: |
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- ak |
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- ar |
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- as |
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- bm |
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- bn |
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- ca |
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- code |
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- en |
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- es |
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- eu |
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- fon |
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- fr |
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- gu |
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- hi |
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- id |
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- ig |
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- ki |
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- kn |
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- lg |
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- ln |
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- ml |
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- mr |
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- ne |
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- nso |
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- ny |
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- or |
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- pa |
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- pt |
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- rn |
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- rw |
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- sn |
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- st |
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- sw |
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- ta |
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- te |
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- tn |
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- ts |
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- tum |
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- tw |
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- ur |
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- vi |
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- wo |
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- xh |
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- yo |
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- zh |
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- zhs |
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- zht |
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- zu |
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datasets: |
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- c4 |
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--- |
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# 🌸 BLOOM 7b1 - GPTQ |
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- Model creator: [BigScience](https://huggingface.co/bigscience) |
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- Original model: [BLOOM 7b1](https://huggingface.co/bigscience/bloom-7b1) |
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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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```shell |
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pip install accelerate==0.26.1 datasets==2.16.1 dill==0.3.7 gekko==1.0.6 multiprocess==0.70.15 peft==0.7.1 rouge==1.0.1 sentencepiece==0.1.99 |
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git clone https://github.com/upunaprosk/AutoGPTQ |
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cd AutoGPTQ |
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pip install -v . |
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``` |
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Recommended transformers version: 4.35.2. |
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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/bloom-7b1-gptq-4bit" |
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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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