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Model: harsh762011/numinao14-new Source: Original Platform
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README.md
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README.md
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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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1
chat_template.jinja
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chat_template.jinja
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{{ '<|system|>Your name is Phi, an AI math expert developed by Microsoft.' }}{% for message in messages %}{% if message['role'] == 'system' %} {{ message['content'] }}{% if 'tools' in message and message['tools'] is not none %}{{ '<|tool|>' + message['tools'] + '<|/tool|>' }}{% endif %}{% endif %}{% endfor %}{{ '<|end|>' }}{% for message in messages %}{% if message['role'] != 'system' %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}
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config.json
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config.json
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{
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"architectures": [
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"Phi3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 199999,
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"dtype": "float16",
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"embd_pdrop": 0.0,
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"full_attn_mod": 1,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"ignore_keys_at_rope_validation": null,
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"initializer_range": 0.02,
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"interpolate_factor": 1,
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"lm_head_bias": false,
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"mlp_bias": false,
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"model_type": "phi3",
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"num_attention_heads": 24,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"original_max_position_embeddings": 4096,
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"partial_rotary_factor": 0.75,
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"resid_pdrop": 0.0,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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generation_config.json
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model.safetensors
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tokenizer_config.json
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