dfurman/Qwen2-72B-Orpo-v0.1

Model

This model is a finetune of Qwen/Qwen2-72B-Instruct on 1.5k rows of mlabonne/orpo-dpo-mix-40k. It was trained as a generalist language model for a variety of text generation use cases, including support of agentic capabilities, roleplaying, reasoning, multi-turn conversations, long context coherence, and more.

Thanks go out to mlabonne, Qwen, et al. for the source dataset and base model.

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

💻 Usage

Setup

!pip install -qU transformers accelerate bitsandbytes
!huggingface-cli download dfurman/Qwen2-72B-Orpo-v0.1

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

# quantize if necessary
# 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/Qwen2-72B-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

question = """The bakers at the Beverly Hills Bakery baked 200 loaves of bread on Monday morning. 
They sold 93 loaves in the morning and 39 loaves in the afternoon. 
A grocery store then returned 6 unsold loaves back to the bakery. 
How many loaves of bread did the bakery have left?
Respond as succinctly as possible. Format the response as a completion of this table:
|step|subquestion|procedure|result|
|:---|:----------|:--------|:-----:|"""


messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": question},
]
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:")
print(outputs[0]["generated_text"][len(prompt):])

***Generation:
|1|Initial loaves|Start with total loaves|200|
|2|Sold in morning|Subtract morning sales|200 - 93 = 107|
|3|Sold in afternoon|Subtract afternoon sales|107 - 39 = 68|
|4|Returned loaves|Add returned loaves|68 + 6 = 74|

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	43.32
IFEval (0-Shot)	78.80
BBH (3-Shot)	57.41
MATH Lvl 5 (4-Shot)	35.42
GPQA (0-shot)	17.90
MuSR (0-shot)	20.87
MMLU-PRO (5-shot)	49.50