--- 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 --- ![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ) # QuantFactory/FineLlama-3.1-8B-GGUF This is quantized version of [mlabonne/FineLlama-3.1-8B](https://huggingface.co/mlabonne/FineLlama-3.1-8B) created using llama.cpp # Original Model Card # 🍷 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. [](https://github.com/unslothai/unsloth)