Mistral-7B-Instruct-v0.2-bnb-4bit

Model Card for alokabhishek/Mistral-7B-Instruct-v0.2-bnb-4bit

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This repo contains 4-bit quantized (using bitsandbytes) model Mistral AI_'s Mistral-7B-Instruct-v0.2

Model Details

• Model creator: Mistral AI_
• Original model: Mistral-7B-Instruct-v0.2

About 4 bit quantization using bitsandbytes

• QLoRA: Efficient Finetuning of Quantized LLMs: arXiv - QLoRA: Efficient Finetuning of Quantized LLMs

• Hugging Face Blog post on 4-bit quantization using bitsandbytes: Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

• bitsandbytes github repo: bitsandbytes github repo

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

pip install -q -U bitsandbytes accelerate torch huggingface_hub
pip install -q -U git+https://github.com/huggingface/transformers.git # Install latest version of transformers
pip install -q -U git+https://github.com/huggingface/peft.git
pip install flash-attn --no-build-isolation

Import

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

Use a pipeline as a high-level helper

model_id_mistral = "alokabhishek/Mistral-7B-Instruct-v0.2-bnb-4bit"

tokenizer_mistral = AutoTokenizer.from_pretrained(model_id_mistral, use_fast=True)

model_mistral = AutoModelForCausalLM.from_pretrained(
    model_id_mistral,
    device_map="auto"
)


pipe_mistral = pipeline(model=model_mistral, tokenizer=tokenizer_mistral, task='text-generation')

prompt_mistral = "Tell me a funny joke about Large Language Models meeting a Blackhole in an intergalactic Bar."

output_mistral = pipe_llama(prompt_mistral, max_new_tokens=512)

print(output_mistral[0]["generated_text"])

Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Evaluation

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Results

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