Model ID	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
meta-llama/Meta-Llama-3-70B-Instruct 📄	77.88	71.42	85.69	80.06	61.81	82.87	85.44
dfurman/Llama-3-70B-Orpo-v0.1 📄	74.67	68.69	88.01	79.39	49.62	85.48	76.8
meta-llama/Meta-Llama-3-70B 📄	73.96	68.77	87.98	79.23	45.56	85.32	76.88

📈 Training curves

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

💻 Usage

Setup

python

!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

python

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Tell me a recipe for a spicy margarita."},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print("***Prompt:\n", prompt)

outputs = pipeline(prompt, max_new_tokens=1000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print("***Generation:\n", outputs[0]["generated_text"][len(prompt):])

Output

"""
"""

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	17.92
IFEval (0-Shot)	20.49
BBH (3-Shot)	24.09
MATH Lvl 5 (4-Shot)	13.52
GPQA (0-shot)	1.01
MuSR (0-shot)	16.28
MMLU-PRO (5-shot)	32.14