| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - deepseek |
| - qwen |
| - distill |
| - cot |
| model-index: |
| - name: Qwen-7B-Distill-Reasoner |
| results: |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: IFEval (0-Shot) |
| type: wis-k/instruction-following-eval |
| split: train |
| args: |
| num_few_shot: 0 |
| metrics: |
| - type: inst_level_strict_acc and prompt_level_strict_acc |
| value: 33.96 |
| name: averaged accuracy |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwen-7B-Distill-Reasoner |
| name: Open LLM Leaderboard |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: BBH (3-Shot) |
| type: SaylorTwift/bbh |
| split: test |
| args: |
| num_few_shot: 3 |
| metrics: |
| - type: acc_norm |
| value: 22.18 |
| name: normalized accuracy |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwen-7B-Distill-Reasoner |
| name: Open LLM Leaderboard |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: MATH Lvl 5 (4-Shot) |
| type: lighteval/MATH-Hard |
| split: test |
| args: |
| num_few_shot: 4 |
| metrics: |
| - type: exact_match |
| value: 21.15 |
| name: exact match |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwen-7B-Distill-Reasoner |
| name: Open LLM Leaderboard |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: GPQA (0-shot) |
| type: Idavidrein/gpqa |
| split: train |
| args: |
| num_few_shot: 0 |
| metrics: |
| - type: acc_norm |
| value: 10.29 |
| name: acc_norm |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwen-7B-Distill-Reasoner |
| 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: 2.78 |
| name: acc_norm |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwen-7B-Distill-Reasoner |
| 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: 20.2 |
| name: accuracy |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwen-7B-Distill-Reasoner |
| name: Open LLM Leaderboard |
| --- |
| # **Qwen-7B-Distill-Reasoner** |
|
|
| Qwen-7B-Distill-Reasoner is based on the *Qwen [ KT ] model*, which was distilled by **DeepSeek-AI/DeepSeek-R1-Distill-Qwen-7B**. It has been fine-tuned on the long chain-of-thought reasoning model and specialized datasets, focusing on chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks. |
|
|
| # **Quickstart with Transformers** |
|
|
| Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_name = "prithivMLmods/Qwen-7B-Distill-Reasoner" |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype="auto", |
| device_map="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| |
| prompt = "Give me a short introduction to large language model." |
| messages = [ |
| {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, |
| {"role": "user", "content": prompt} |
| ] |
| text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| |
| generated_ids = model.generate( |
| **model_inputs, |
| max_new_tokens=512 |
| ) |
| generated_ids = [ |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
| ] |
| |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| ``` |
|
|
| ### **Intended Use:** |
| 1. **Instruction-Following:** The model excels in understanding and executing detailed instructions, making it ideal for automation systems, virtual assistants, and educational tools. |
| 2. **Text Generation:** It can produce coherent, logically structured, and contextually relevant text for use in content creation, summarization, and report writing. |
| 3. **Complex Reasoning Tasks:** With its fine-tuning for chain-of-thought reasoning, the model is well-suited for multi-step problem-solving, logical deduction, and question-answering tasks. |
| 4. **Research and Development:** It can support researchers and developers in exploring advancements in logical reasoning and fine-tuning methodologies. |
| 5. **Educational Applications:** The model can assist in teaching logical reasoning and problem-solving by generating step-by-step solutions. |
|
|
| ### **Limitations:** |
| 1. **Domain-Specific Knowledge:** While fine-tuned on reasoning datasets, the model may lack deep expertise in highly specialized or technical domains. |
| 2. **Hallucination:** Like many large language models, it can generate incorrect or fabricated information, especially when reasoning beyond its training data. |
| 3. **Bias in Training Data:** The model's outputs may reflect biases present in the datasets it was fine-tuned on, which could limit its objectivity in certain contexts. |
| 4. **Performance on Non-Reasoning Tasks:** The model is optimized for chain-of-thought reasoning and may underperform on tasks that require simpler, less structured responses. |
| 5. **Resource-Intensive:** Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments. |
| 6. **Dependence on Input Quality:** The model’s performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results. |
| # [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/prithivMLmods__Qwen-7B-Distill-Reasoner-details)! |
| Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FQwen-7B-Distill-Reasoner&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! |
|
|
| | Metric |Value (%)| |
| |-------------------|--------:| |
| |**Average** | 18.43| |
| |IFEval (0-Shot) | 33.96| |
| |BBH (3-Shot) | 22.18| |
| |MATH Lvl 5 (4-Shot)| 21.15| |
| |GPQA (0-shot) | 10.29| |
| |MuSR (0-shot) | 2.78| |
| |MMLU-PRO (5-shot) | 20.20| |
|
|
|
|
