85 lines
2.1 KiB
Markdown
85 lines
2.1 KiB
Markdown
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---
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license: cc-by-nc-3.0
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base_model: unsloth/phi-4-mini-reasoning
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- phi-4
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language:
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- en
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---
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# Phi-4 Mini Reasoning – JEE Mathematics Finetuned Model
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A new version of the model present at `harsh762011/numiano14`.
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# Uploaded Finetuned Model
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- **Developed by:** Harsh Srivastava
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- **License:** cc-by-nc-3.0
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- **Finetuned from model:** unsloth/phi-4-mini-reasoning
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This Phi-4 model was trained faster using Unsloth and Hugging Face TRL.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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---
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# Description
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This model is a finetuned version of Phi-4 Mini Reasoning designed for solving JEE-level mathematics problems.
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The model is optimized for:
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- Step-by-step mathematical reasoning
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- Symbolic problem solving
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- Competitive exam-style question solving
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---
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# Training Dataset
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Total samples used: 500k+ filtered mathematics and reasoning samples.
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The training pipeline focuses on JEE-level mathematical difficulty using keyword-based dataset filtering.
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## Sources
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- AI-MO/NuminaMath-CoT — 293k samples (2 epochs)
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- AI-MO/NuminaMath-TIR — 68,850 samples
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- MetaMathQA — 70k samples
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- TIGER-Lab MathInstruct — 125,220 samples
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- PhysicsWallahAI JEE Main 2025 (Jan) — 182 samples
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- PhysicsWallahAI JEE Main 2025 (Apr) — 169 samples
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- MMLU High School Mathematics — 78 samples
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- MMLU College Mathematics — 50 samples
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- MMLU Abstract Algebra — 25 samples
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---
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# Training Details
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- **Base model:** Phi-4 Mini Reasoning
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- **Framework:** Unsloth + Hugging Face TRL
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- **Training method:** LoRA finetuning
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- **Sequence length:** 2048
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- **Optimizer:** AdamW 8bit
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---
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# Intended Purpose
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The model is designed for:
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- JEE mathematics reasoning
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- Step-by-step mathematical explanations
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- Competitive exam problem solving
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- Mathematical chain-of-thought reasoning
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---
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# Limitations
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- The model may still generate incorrect mathematical reasoning.
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- Outputs should be verified for high-stakes usage.
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- The model is still under active improvement and continued training.
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