---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.3
model-index:
- name: mistral-v0.3-vi-alpaca
  results: []
language:
- vi
- en
pipeline_tag: text-generation
datasets:
- bkai-foundation-models/vi-alpaca
---

<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/Fine%20tuning%20mistral%20Instruct%207B-v3/runs/w1jgfsao)

# Mistral-7B-Instruct-v0.3 LoRA

This model is a LoRA fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on [bkai-foundation-models/vi-alpaca](https://huggingface.co/datasets/bkai-foundation-models/vi-alpaca) dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4744
- eval_runtime: 241.8465
- eval_samples_per_second: 31.016
- eval_steps_per_second: 3.878
- epoch: 1.0
- step: 10627

## Usage

```python
# !pip install accelerate bitsandbytes peft
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
import torch

model_name = "mistralai/Mistral-7B-Instruct-v0.3"
peft_model_id = "date3k2/Mistral-7B-Instruct-vi-alpaca"

bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)


model.load_adapter(peft_model_id)
device = "cuda"

messages = [
    {
        "role": "user",
        "content": """You are a helpful Vietnamese AI chatbot. Below is an instruction that describes a task. Write a response that appropriately completes the request. Your response should be in Vietnamese.
    Instruction:
    Viết công thức để nấu một món ngon từ thịt bò.""",
    },
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=500, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

```

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4

### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1