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--- |
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language: |
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- en |
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license: other |
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library_name: transformers |
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tags: |
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- orpo |
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- qwen2 |
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- rlhf |
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- sft |
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base_model: |
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- Qwen/Qwen2-72B-Instruct |
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datasets: |
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- mlabonne/orpo-dpo-mix-40k |
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license_name: tongyi-qianwen |
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license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE |
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model-index: |
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- name: Qwen2-72B-Orpo-v0.1 |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: IFEval (0-Shot) |
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type: HuggingFaceH4/ifeval |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: inst_level_strict_acc and prompt_level_strict_acc |
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value: 78.8 |
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name: strict accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Qwen2-72B-Orpo-v0.1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: BBH (3-Shot) |
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type: BBH |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc_norm |
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value: 57.41 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Qwen2-72B-Orpo-v0.1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MATH Lvl 5 (4-Shot) |
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type: hendrycks/competition_math |
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args: |
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num_few_shot: 4 |
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metrics: |
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- type: exact_match |
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value: 35.42 |
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name: exact match |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Qwen2-72B-Orpo-v0.1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GPQA (0-shot) |
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type: Idavidrein/gpqa |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 17.9 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Qwen2-72B-Orpo-v0.1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MuSR (0-shot) |
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type: TAUR-Lab/MuSR |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 20.87 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Qwen2-72B-Orpo-v0.1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU-PRO (5-shot) |
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type: TIGER-Lab/MMLU-Pro |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 49.5 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Qwen2-72B-Orpo-v0.1 |
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name: Open LLM Leaderboard |
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--- |
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# dfurman/Qwen2-72B-Orpo-v0.1 |
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## Model |
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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. |
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Thanks go out to [mlabonne](https://huggingface.co/mlabonne), [Qwen](https://huggingface.com/Qwen), et al. for the source dataset and base model. |
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You can find the experiment on W&B at [this address](https://wandb.ai/dryanfurman/huggingface/runs/fw7mtub1?nw=nwuserdryanfurman). |
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## 💻 Usage |
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<details> |
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<summary>Setup</summary> |
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```python |
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!pip install -qU transformers accelerate bitsandbytes |
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!huggingface-cli download dfurman/Qwen2-72B-Orpo-v0.1 |
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``` |
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```python |
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from transformers import AutoTokenizer, BitsAndBytesConfig |
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import transformers |
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import torch |
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if torch.cuda.get_device_capability()[0] >= 8: |
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!pip install -qqq flash-attn |
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attn_implementation = "flash_attention_2" |
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torch_dtype = torch.bfloat16 |
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else: |
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attn_implementation = "eager" |
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torch_dtype = torch.float16 |
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# quantize if necessary |
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# bnb_config = BitsAndBytesConfig( |
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# load_in_4bit=True, |
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# bnb_4bit_quant_type="nf4", |
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# bnb_4bit_compute_dtype=torch_dtype, |
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# bnb_4bit_use_double_quant=True, |
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# ) |
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model = "dfurman/Qwen2-72B-Orpo-v0.1" |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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model_kwargs={ |
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"torch_dtype": torch_dtype, |
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# "quantization_config": bnb_config, |
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"device_map": "auto", |
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"attn_implementation": attn_implementation, |
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} |
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) |
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``` |
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</details> |
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### Run |
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```python |
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question = """The bakers at the Beverly Hills Bakery baked 200 loaves of bread on Monday morning. |
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They sold 93 loaves in the morning and 39 loaves in the afternoon. |
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A grocery store then returned 6 unsold loaves back to the bakery. |
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How many loaves of bread did the bakery have left? |
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Respond as succinctly as possible. Format the response as a completion of this table: |
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|step|subquestion|procedure|result| |
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|:---|:----------|:--------|:-----:|""" |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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{"role": "user", "content": question}, |
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] |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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# print("***Prompt:\n", prompt) |
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outputs = pipeline(prompt, max_new_tokens=1000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print("***Generation:") |
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print(outputs[0]["generated_text"][len(prompt):]) |
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``` |
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``` |
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***Generation: |
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|1|Initial loaves|Start with total loaves|200| |
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|2|Sold in morning|Subtract morning sales|200 - 93 = 107| |
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|3|Sold in afternoon|Subtract afternoon sales|107 - 39 = 68| |
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|4|Returned loaves|Add returned loaves|68 + 6 = 74| |
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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dfurman__Qwen2-72B-Orpo-v0.1) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |43.32| |
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|IFEval (0-Shot) |78.80| |
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|BBH (3-Shot) |57.41| |
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|MATH Lvl 5 (4-Shot)|35.42| |
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|GPQA (0-shot) |17.90| |
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|MuSR (0-shot) |20.87| |
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|MMLU-PRO (5-shot) |49.50| |
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