README
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
| 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
!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": "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 |
Contributors
dfurman
16 commits
leaderboard-pr-bot
1 commits
Similar repos
Linked in READMEs
README
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
| 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
!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": "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 |
Similar repos
Linked in READMEs
Contributors
dfurman
16 commits
leaderboard-pr-bot
1 commits