metadata
language:
- 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
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
!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
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."""
| 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 |