---
license: mit
library_name: peft
tags:
- PEFT
- Qlora
- mistral-7b
- fine-tuning
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: mistral7b-fine-tuned-qlora
  results: []
datasets:
- timdettmers/openassistant-guanaco
pipeline_tag: text-generation
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Mistral7b-fine-tuned-qlora

<img src="https://www.kdnuggets.com/wp-content/uploads/selvaraj_mistral_7bv02_finetuning_mistral_new_opensource_llm_hugging_face_3.png" alt="im" width="700" />

# Model version and Dataset

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on  [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset.

## Usage guidance

Please refer to [this notebook](https://github.com/shirinyamani/mistral7b-lora-finetuning/blob/main/misral_7B_updated.ipynb) for a complete demo including notes regarding cloud deployment

## Inference

```python
import os
from os.path import exists, join, isdir
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, GenerationConfig
from peft import PeftModel
from peft.tuners.lora import LoraLayer

# Update variables!
max_new_tokens = 100
top_p = 0.9
temperature=0.7
user_question = "What is  central limit theorem?"

# Base model
model_name_or_path = 'mistralai/Mistral-7B-v0.1' # Change it to 'YOUR_BASE_MODEL'
adapter_path = 'ShirinYamani/mistral7b-fine-tuned-qlora' # Change it to 'YOUR_ADAPTER_PATH'

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
# if you wanna use LLaMA HF then fix the early conversion issues.
tokenizer.bos_token_id = 1

# Load the model (use bf16 for faster inference)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    torch_dtype=torch.bfloat16,
    device_map={"": 0},
    # Qlora -- 4-bit config
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type='nf4',
    )
)

model = PeftModel.from_pretrained(model, adapter_path)
model.eval()

prompt = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the user's questions. "
    "### Human: {user_question}"
    "### Assistant: "
)

def generate(model, user_question, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature):
    inputs = tokenizer(prompt.format(user_question=user_question), return_tensors="pt").to('cuda')

    outputs = model.generate(
        **inputs,
        generation_config=GenerationConfig(
            do_sample=True,
            max_new_tokens=max_new_tokens,
            top_p=top_p,
            temperature=temperature,
        )
    )

    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(text)
    return text

generate(model, user_question)
```

### Training hyperparameters

```python
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 10
- mixed_precision_training: Native AMP
```

### Framework versions
```python
- PEFT 0.11.2.dev0
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
```