README.md

metadata

language:
  - en
license: llama3
library_name: transformers
tags:
  - orpo
  - llama 3
  - rlhf
  - sft
datasets:
  - mlabonne/orpo-dpo-mix-40k
base_model:
  - meta-llama/Meta-Llama-3-70B

dfurman/Llama-3-70B-Orpo-v0.1

This is an ORPO fine-tune of meta-llama/Meta-Llama-3-70B on 2k samples of mlabonne/orpo-dpo-mix-40k.

It's a successful fine-tune that follows the ChatML template!

🔎 Application

This model uses a context window of 8k. It was trained with the ChatML template.

🏆 Evaluation

Open LLM Leaderboard

TBD.

📈 Training curves

You can find the experiment on W&B at this address.

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "dfurman/Llama-3-70B-Orpo-v0.1"
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"])