Improve model card: Add pipeline tag, library name, and link to paper (#1)
Browse files- Improve model card: Add pipeline tag, library name, and link to paper (e440cec9ca1c11fe4f7856aa4aed97790256905b)
Co-authored-by: Niels Rogge <[email protected]>
    	
        README.md
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            license: apache-2.0
         
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            base_model:
         
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            - Qwen/Qwen3-14B
         
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            # Fast-Math-Qwen3-14B
         
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            By applying SFT and GRPO on difficult math problems, we enhanced the performance of `DeepSeek-R1-Distill-Qwen-14B` and developed [`Fast-Math-R1-14B`](https://huggingface.co/RabotniKuma/Fast-Math-R1-14B), 
         
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            which achieves approx. 30% faster inference on average, while maintaining accuracy.
         
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            Technical details can be found in [our github repository](https://github.com/analokmaus/kaggle-aimo2-fast-math-r1/tree/master).
         
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            ## Evaluation
         
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            <img src='https://github.com/analokmaus/kaggle-aimo2-fast-math-r1/blob/master/assets/pass1_aime_all.png?raw=true' max-height='400px'>
         
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            |                     |              | AIME 2024        |                    | AIME 2025        |                    | 
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            | ------------------- | ------------ | ---------------- | ------------------ | ---------------- | ------------------ | 
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            | Model               | Token budget | Pass@1 (avg. 64) | Mean output tokens | Pass@1 (avg. 64) | Mean output tokens | 
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            | Qwen3-14B           | 32000        | 79.3             | 13669              | 69.5             | 16481              | 
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            |                     | 24000        | 75.9             | 13168              | 65.6             | 15235              | 
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            |                     | 16000        | 64.5             | 11351              | 50.4             | 12522              | 
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            |                     | 12000        | 49.7             | 9746               | 36.3             | 10353              | 
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            |                     | 8000         | 28.4             | 7374               | 19.5             | 7485               | 
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            | Fast-Math-Qwen3-14B | 32000        | 77.6             | 9740               | 66.6             | 12281              | 
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            |                     | 24000        | 76.5             | 9634               | 65.3             | 11847              | 
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            |                     | 16000        | 72.6             | 8793               | 60.1             | 10195              | 
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            |                     | 12000        | 65.1             | 7775               | 49.4             | 8733               | 
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            |                     | 8000         | 50.7             | 6260               | 36               | 6618               | 
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            # Inference
         
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            ## vLLM
         
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            messages = [
         
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                {
         
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                    'role': 'user', 
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                    'content': (
         
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                        'Solve the problem, and put the answer in  
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                        'Sarah is twice as old as her youngest brother. If the difference between their ages is 15 years. How old is her youngest brother?'
         
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                    )
         
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                }
         
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            ---
         
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            base_model:
         
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            - Qwen/Qwen3-14B
         
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            license: apache-2.0
         
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            pipeline_tag: text-generation
         
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            library_name: transformers
         
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            ---
         
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            # Fast-Math-Qwen3-14B
         
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            **Fast-Math-Qwen3-14B** is an efficiency-optimized version of `Qwen3-14B`, developed following the two-stage recipe of Supervised Fine-Tuning (SFT) and Reinforcement Learning from Online Inference (GRPO) presented in the paper:
         
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            **[A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning](https://huggingface.co/papers/2507.08267)**
         
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            This model enables **approx. 65% faster inference on average, with minimal loss in performance**, compared to the base `Qwen3-14B`.
         
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            Technical details can be found in [our github repository](https://github.com/analokmaus/kaggle-aimo2-fast-math-r1/tree/master).
         
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            ## Evaluation
         
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            <img src='https://github.com/analokmaus/kaggle-aimo2-fast-math-r1/blob/master/assets/pass1_aime_all.png?raw=true' max-height='400px'>
         
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            |                     |              | AIME 2024        |                    | AIME 2025        |                    |
         
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            | ------------------- | ------------ | ---------------- | ------------------ | ---------------- | ------------------ |
         
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            | Model               | Token budget | Pass@1 (avg. 64) | Mean output tokens | Pass@1 (avg. 64) | Mean output tokens |
         
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            | Qwen3-14B           | 32000        | 79.3             | 13669              | 69.5             | 16481              |
         
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            |                     | 24000        | 75.9             | 13168              | 65.6             | 15235              |
         
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            |                     | 16000        | 64.5             | 11351              | 50.4             | 12522              |
         
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            |                     | 12000        | 49.7             | 9746               | 36.3             | 10353              |
         
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            |                     | 8000         | 28.4             | 7374               | 19.5             | 7485               |
         
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            | Fast-Math-Qwen3-14B | 32000        | 77.6             | 9740               | 66.6             | 12281              |
         
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            |                     | 24000        | 76.5             | 9634               | 65.3             | 11847              |
         
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            |                     | 16000        | 72.6             | 8793               | 60.1             | 10195              |
         
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            |                     | 12000        | 65.1             | 7775               | 49.4             | 8733               |
         
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            |                     | 8000         | 50.7             | 6260               | 36               | 6618               |
         
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            # Inference
         
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            ## vLLM
         
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            )
         
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            messages = [
         
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                {
         
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                    'role': 'user',
         
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                    'content': (
         
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                        'Solve the problem, and put the answer in \\boxed{{}}. '
         
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                        'Sarah is twice as old as her youngest brother. If the difference between their ages is 15 years. How old is her youngest brother?'
         
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                    )
         
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                }
         
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