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

Setup

!pip install -qU transformers accelerate bitsandbytes

from transformers import AutoTokenizer, BitsAndBytesConfig
import transformers
import torch

if torch.cuda.get_device_capability()[0] >= 8:
    !pip install -qqq flash-attn
    attn_implementation = "flash_attention_2"
    torch_dtype = torch.bfloat16
else:
    attn_implementation = "eager"
    torch_dtype = torch.float16

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch_dtype,
    bnb_4bit_use_double_quant=True,
)

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

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={
        "torch_dtype": torch_dtype,
        "quantization_config": bnb_config,
        "device_map": "auto",
        "attn_implementation": attn_implementation,
    }
)

Run

messages = [{"role": "user", "content": "What is a large language model?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print("***Prompt:\n", prompt)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print("***Generation:\n", outputs[0]["generated_text"])

Output

coming