Create README.md

README.md

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+---
+base_model: facebook/opt-350m
+inference: false
+model_creator: facebook
+model_name: opt-350m
+model_type: opt
+pipeline_tag: text-generation
+quantized_by: iproskurina
+tags:
+- pretrained
+license: other
+language:
+- en
+datasets:
+- c4
+---
+
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/629a3dbcd496c6dcdebf41cc/t-6kpqFpEYJPT6zmvnm49.png" width="200" />
+
+# OPT-350M - GPTQ
+
+
+- Model creator: [Meta AI](https://huggingface.co/facebook)
+- Original model: [OPT-350M](https://huggingface.co/facebook/opt-350m)
+
+The model published in this repo was quantized to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).
+
+**Quantization details**
+
+**All quantization parameters were taken from [GPTQ paper](https://arxiv.org/abs/2210.17323).**
+
+GPTQ calibration data consisted of 128 random 2048 token segments from the [C4 dataset](https://huggingface.co/datasets/c4).
+
+The grouping size used for quantization is equal to 128.
+
+## How to use this GPTQ model from Python code
+
+### Install the necessary packages
+
+```shell
+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
+git clone https://github.com/upunaprosk/AutoGPTQ
+cd AutoGPTQ
+pip install -v .
+```
+Recommended transformers version: 4.35.2.
+
+### You can then use the following code
+
+```python
+
+from transformers import AutoTokenizer, TextGenerationPipeline,AutoModelForCausalLM
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+pretrained_model_dir = "iproskurina/opt-350m-gptq-4bit"
+tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
+model = AutoGPTQForCausalLM.from_quantized(pretrained_model_dir, device="cuda:0", model_basename="model")
+pipeline = TextGenerationPipeline(model=model, tokenizer=tokenizer)
+print(pipeline("auto-gptq is")[0]["generated_text"])
+```
+
+[**LICENSE**](https://huggingface.co/facebook/opt-350m/blob/main/LICENSE.md)