--- library_name: transformers license: mit datasets: - mlabonne/orpo-dpo-mix-40k base_model: - meta-llama/Llama-3.2-1B pipeline_tag: text-generation --- # Orpo-Llama-3.2-1B-15k AdamLucek/Orpo-Llama-3.2-1B-15k is an [ORPO](https://arxiv.org/abs/2403.07691) fine tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co./meta-llama/Llama-3.2-1B) on a subset of 15,000 shuffled entries of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co./datasets/mlabonne/orpo-dpo-mix-40k). Trained for 7 hours on an L4 GPU with [this training script](https://colab.research.google.com/drive/1KV9AFAfhQCSjF8Ej4rI2ejDmx5AUnqHq?usp=sharing), modified from [Maxime Labonne's original guide](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) For full model details, refer to the base model page [meta-llama/Llama-3.2-1B](https://huggingface.co./meta-llama/Llama-3.2-1B) ## Evaluations In comparsion to [AdamLucek/Orpo-Llama-3.2-1B-40k](https://huggingface.co./AdamLucek/Orpo-Llama-3.2-1B-40k) using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). | Benchmark | 15k Accuracy | 15k Normalized | 40k Accuracy | 40k Normalized | Notes | |----------------|--------------|----------------|--------------|----------------|-------------------------------------------| | AGIEval | 22.14% | 21.01% | 23.57% | 23.26% | 0-Shot Average across multiple reasoning tasks | | GPT4ALL | 51.15% | 54.38% | 51.63% | 55.00% | 0-Shot Average across all categories | | TruthfulQA | 42.79% | N/A | 42.14% | N/A | MC2 accuracy | | MMLU | 31.22% | N/A | 31.01% | N/A | 5-Shot Average across all categories | | Winogrande | 61.72% | N/A | 61.12% | N/A | 0-shot evaluation | | ARC Challenge | 32.94% | 36.01% | 33.36% | 37.63% | 0-shot evaluation | | ARC Easy | 64.52% | 60.40% | 65.91% | 60.90% | 0-shot evaluation | | BoolQ | 50.24% | N/A | 52.29% | N/A | 0-shot evaluation | | PIQA | 75.46% | 74.37% | 75.63% | 75.19% | 0-shot evaluation | | HellaSwag | 48.56% | 64.71% | 48.46% | 64.50% | 0-shot evaluation | ## Using this Model ```python from transformers import AutoTokenizer import transformers import torch # Load Model and Pipeline model = "AdamLucek/Orpo-Llama-3.2-1B-15k" pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) # Load Tokenizer tokenizer = AutoTokenizer.from_pretrained(model) # Generate Message messages = [{"role": "user", "content": "What is a language model?"}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) 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"]) ``` ## Training Statistics