Model Card for alokabhishek/Mistral-7B-Instruct-v0.2-bnb-8bit

This repo contains 8-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 8 bit quantization using bitsandbytes

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

• Hugging Face Blog post on 8-bit quantization using bitsandbytes: A Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using Hugging Face Transformers, Accelerate and bitsandbytes

• 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 --quiet bitsandbytes
!pip install --quiet --upgrade transformers # Install latest version of transformers
!pip install --quiet --upgrade accelerate
!pip install --quiet sentencepiece
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-8bit"

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

Direct Use

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Bias, Risks, and Limitations

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Evaluation

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Results

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