---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- ExLlamaV2
- 4bit
- Mistral
- Mistral-7B
- quantized
- exl2
- 5.0-bpw
---

# Mistral-7B-Instruct-v0.2-5.0-bpw-exl2

<!-- Provide a quick summary of what the model is/does. -->
This repo contains 4-bit quantized (using ExLlamaV2) model Mistral AI_'s Mistral-7B-Instruct-v0.2



## Model Details

- Model creator: [Mistral AI_](https://huggingface.co/mistralai)
- Original model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)


### About 4 bit quantization using ExLlamaV2


- ExLlamaV2 github repo: [ExLlamaV2 github repo](https://github.com/turboderp/exllamav2)



# How to Get Started with the Model

Use the code below to get started with the model.


## How to run from Python code

#### First install the package

# Import 

```python
import torch
import os
from torch import bfloat16
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig, LlamaForCausalLM
```

# Use a pipeline as a high-level helper



## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]


## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->


#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]


## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

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