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---
base_model: mistralai/Mistral-7B-Instruct-v0.2
inference: false
license: apache-2.0
model_creator: mistralai
model_name: Mistral-7B-Instruct-v0.2-GPTQ
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
tags:
- finetuned
- quantized
- 4-bit
- gptq
- transformers
- pytorch
- safetensors
- mistral
- text-generation
- finetuned
- arxiv:2310.06825
- license:apache-2.0
- autotrain_compatible
- has_space
- text-generation-inference
- region:us
---
# Description
[MaziyarPanahi/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-v0.2-GPTQ) is a quantized (GPTQ) version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
## How to use
### Install the necessary packages
```
pip install --upgrade accelerate auto-gptq transformers
```
### Example Python code
```python
from transformers import AutoTokenizer, pipeline
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import torch
model_id = "MaziyarPanahi/Mistral-7B-Instruct-v0.2-GPTQ"
quantize_config = BaseQuantizeConfig(
bits=4,
group_size=128,
desc_act=False
)
model = AutoGPTQForCausalLM.from_quantized(
model_id,
use_safetensors=True,
device="cuda:0",
quantize_config=quantize_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.1
)
outputs = pipe("What is a large language model?")
print(outputs[0]["generated_text"])
``` |