---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
datasets:
- mlabonne/FineTome-100k
---

# 🍷 FineLlama-3.1-8B

![](https://i.imgur.com/jUDo6ID.jpeg)

This is a finetune of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B) made for my article ["Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth"](https://huggingface.co./blog/mlabonne/sft-llama3).

It was trained on 100k super high-quality samples from the [mlabonne/FineTome-100k](https://huggingface.co./datasets/mlabonne/FineTome-100k) dataset.

**Try the demo**: https://huggingface.co./spaces/mlabonne/FineLlama-3.1-8B

## 🔎 Applications

This model was made for educational purposes. I recommend using Meta's instruct model for real applications.

## ⚡ Quantization

* **GGUF**: https://huggingface.co./mlabonne/FineLlama-3.1-8B-GGUF

## 🏆 Evaluation

TBD.

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/FineLlama-3.1-8B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```

---

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)