en license: llama3 library_name: transformers tags:
orpo
llama 3
rlhf
sft base_model:
meta-llama/Meta-Llama-3-8B datasets:
mlabonne/orpo-dpo-mix-40k model-index:
name: Llama-3-8B-Orpo-v0.1 results:
- task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics:
  - type: inst_level_strict_acc and prompt_level_strict_acc value: 30.0 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics:
  - type: acc_norm value: 13.77 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics:
  - type: exact_match value: 3.78 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics:
  - type: acc_norm value: 1.57 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics:
  - type: acc_norm value: 2.73 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 14.23 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1 name: Open LLM Leaderboard

dfurman/Llama-3-8B-Orpo-v0.1

This is an ORPO fine-tune of meta-llama/Meta-Llama-3-8B on 4k 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

Model ID	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
meta-llama/Meta-Llama-3-8B-Instruct 📄	66.87	60.75	78.55	67.07	51.65	74.51	68.69
dfurman/Llama-3-8B-Orpo-v0.1 📄	64.67	60.67	82.56	66.59	50.47	79.01	48.75
meta-llama/Meta-Llama-3-8B 📄	62.35	59.22	82.02	66.49	43.95	77.11	45.34

📈 Training curves

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

💻 Usage

Setup

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
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

model = "dfurman/Llama-3-8B-Orpo-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={
        "torch_dtype": torch_dtype,
        "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

"""***Prompt:
 <|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Tell me a recipe for a spicy margarita.<|im_end|>
<|im_start|>assistant

***Generation:
 Sure! Here's a recipe for a spicy margarita:

Ingredients:

- 2 oz silver tequila
- 1 oz triple sec
- 1 oz fresh lime juice
- 1/2 oz simple syrup
- 1/2 oz fresh lemon juice
- 1/2 tsp jalapeño, sliced (adjust to taste)
- Ice cubes
- Salt for rimming the glass

Instructions:

1. Prepare the glass by running a lime wedge around the rim of the glass. Dip the rim into a shallow plate of salt to coat.
2. Combine the tequila, triple sec, lime juice, simple syrup, lemon juice, and jalapeño slices in a cocktail shaker.
3. Add ice cubes to the cocktail shaker and shake vigorously for 30 seconds to 1 minute.
4. Strain the cocktail into the prepared glass.
5. Garnish with a lime wedge and jalapeño slice.

Enjoy! This spicy margarita has a nice balance of sweetness and acidity, with a subtle heat from the jalapeño that builds gradually as you sip."""

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dfurman__Llama-3-8B-Orpo-v0.1)

Metric	Value
Avg.	11.01
IFEval (0-Shot)	30.00
BBH (3-Shot)	13.77
MATH Lvl 5 (4-Shot)	3.78
GPQA (0-shot)	1.57
MuSR (0-shot)	2.73
MMLU-PRO (5-shot)	14.23

RichardErkhov/dfurman_-_Llama-3-8B-Orpo-v0.1-4bits

Original model description: