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Model: PhysicsWallahAI/Aryabhata-1.0 Source: Original Platform
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---
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license: cc-by-nc-4.0
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tags:
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- small-language-model
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- jee
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- exam-centric
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- indian-education
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- reinforcement-learning
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- supervised-finetuning
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- model-merging
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- rejection-sampling
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- mathematics
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- ai4education
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- physicswallah
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language:
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- en
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model_name: PhysicsWallah/Aryabhata-1.0
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model_creator: Physics Wallah AI Research
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model_type: Causal decoder-based model
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base_model: Qwen/Qwen2.5-Math-7B
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Aryabhatta 1.0 : An exam-focused language model for JEE Math
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## Overview
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**Aryabhata 1.0** is a 7B parameter small language model for mathematics developed by **Physics Wallah AI Research**, optimized for high-stakes Indian competitive exams like **JEE Mains**. Despite its compact size, Aryabhata 1.0 achieves **state-of-the-art performance** on exam-centric reasoning tasks with impressive **token efficiency** and low inference cost.
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> 🚧 *Aryabhata 1.0 is an **experimental release**. We are actively seeking feedback — please contribute in the Discussion tab of this repo.*
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---
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## 🧠 Key Features
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- **Architecture**: 7B parameter causal decoder-based model.
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- **Exam-Centric Optimization**: Specifically tuned for JEE-level Mathematics reasoning.
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- **High Accuracy**:
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- **86%** on **JEE Mains January 2025** session.
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- **90.2%** on **JEE Mains April 2025** session.
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- **Token Efficiency**: Operates effectively around a **~2K token window**, compared to ~8K required by other reasoning models.
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- **Compute Efficient**: Trained on a **1x2 NVIDIA H100 GPU** using optimized pipeline.
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---
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## 🛠️ Training Details
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- **Training Data**: ~130K problem-solution pairs curated from proprietary Physics Wallah exam datasets.
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- **Training Pipeline**:
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- **Model Merging**
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- **Rejection Sampling**
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- **Supervised Fine-Tuning (SFT)**
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- **Reinforcement Learning with Verifiable Rewards (RLVR)**
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### 🔀 Model Merging
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We began with model merging (Weighted average) to build a strong initialization (Aryabhata 0.5) by combining diverse model capabilities:
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* Qwen 2.5 Math: A robust math-centric LLM with solid symbolic math foundations.
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* Ace Math: An enhanced version of Qwen 2.5 Math, fine-tuned by NVIDIA for improved accuracy in mathematics benchmarks.
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* DeepSeek R1 Distill Qwen: A long-form reasoning model, fine-tuned on reasoning traces distilled from DeepSeek R1.
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### 📚 Data Curation + Rejection Sampling
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We extracted ~250K raw questions from Physics Wallah's internal database and applied aggressive filtering and cleaning:
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* Removed: diagram-based, non-English, and option-heavy questions.
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* Kept: questions matching the distribution of JEE Main 2019–2024.
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Final curated dataset: ~130K high-quality questions.
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For each question:
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* Generated 4 CoTs using Aryabhata 0.5.
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* Retained only those leading to correct final answers.
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Resulting Dataset:
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* ~100K questions
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* ~350K high-quality CoTs
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We used this dataset for SFT.
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### 🎯 Reinforcement Learning with Verifiable Rewards (RLVR)
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We used a custom in-house variant of Group Relative Policy Optimization (GRPO), adapted for math-specific reward functions.
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* Removed KL-divergence penalty
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* Removed clipping
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We used RLVR on the remaining ~30K questions.
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This multi-phase training strategy allows Aryabhata 1.0 to capture **pedagogy-aligned reasoning patterns**, making it highly effective for solving real student queries in mathematics.
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---
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## 📊 Performance Highlights
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### Evaluation Setup
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All evaluations were performed with temperature = 0.0, and we report pass@1 accuracy.
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#### Evaluation Datasets
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We evaluated the model on two sets of official JEE Mains 2025 mathematics papers:
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* January Session: 10 question papers containing 250 questions.
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* April Session: 9 question papers containing 225 questions.
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Each paper includes a mix of:
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* Multiple Choice Questions (MCQs) with one correct option
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* Numeric Answer Type (NAT) questions requiring precise numerical responses
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#### Evaluation Metric
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We used a composite evaluation metric to reflect real-world grading rigor and reduce false positives:
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1. Float Match
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* Compares predicted and target answers within a tolerance (±1e-9)
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* Handles rounding artifacts and small numerical errors robustly
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2. String Match
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* Used for symbolic answers (e.g., fractions, radicals)
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* Uses strict exact match — predictions must match ground truth character-for-character
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3. LLM-as-Judge (GPT-4o-mini)
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* Used for Mathematical equivalence for ambiguous formats
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### 🔹 Accuracy Comparison Across Models
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> *Aryabhata has the best accuracy on JEE Main Maths, on par with frontier models*
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### 🔹 Accuracy vs Token Usage
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> *Aryabhata is on par with frontier models in terms of accuracy vs token usage*
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---
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## 🔧 Intended Use
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**Primary Use Cases**:
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- Competitive exam preparation (JEE Main level mathematics problems)
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- Question answering and doubt-solving systems
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- Educational tutoring and concept explanation
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## 💡 How to Use
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### 🧪 Using with 🤗 Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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model_id = "PhysicsWallahAI/Aryabhata-1.0"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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# Define stop strings
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stop_strings = ["<|im_end|>", "<|end|>", "<im_start|>", "```python\n", "<|im_start|>", "]}}]}}]"]
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def strip_bad_tokens(s, stop_strings):
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for suffix in stop_strings:
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if s.endswith(suffix):
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return s[:-len(suffix)]
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return s
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# Create generation config (can also set temperature, top_p, etc.)
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generation_config = GenerationConfig(
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max_new_tokens=4096,
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stop_strings = stop_strings
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)
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query = 'Find all the values of \\sqrt[3]{1}'
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messages = [{'role': 'system', 'content': 'Think step-by-step; put only the final answer inside \\boxed{}.'},
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{'role': 'user', 'content': query}]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer([text], return_tensors="pt")
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outputs = model.generate(**inputs, generation_config=generation_config, tokenizer=tokenizer)
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print(strip_bad_tokens(tokenizer.decode(outputs[0], skip_special_tokens=True), stop_strings))
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````
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---
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### ⚡ Using with vLLM
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To run the model efficiently using vLLM:
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```python
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from vllm import LLM, SamplingParams
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# Initialize model (downloads from Hugging Face if not local)
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llm = LLM(model="PhysicsWallahAI/Aryabhata-1.0")
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# Define prompt and sampling configuration
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query = 'Find all the values of \\sqrt[3]{1}'
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messages = [{'role': 'system', 'content': 'Think step-by-step; put only the final answer inside \\boxed{}.'},
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{'role': 'user', 'content': query}]
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sampling_params = SamplingParams(temperature=0.0, max_tokens=4*1024, stop=["<|im_end|>", "<|end|>", "<im_start|>", "```python\n", "<|im_start|>", "]}}]}}]"])
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# Run inference
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results = llm.chat(messages, sampling_params)
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# Print result
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print(results[0].outputs[0].text.strip())
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```
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---
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Read more about Aryabhata 1.0 in our [Technical Report](https://arxiv.org/abs/2508.08665)
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---
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## 🚀 Roadmap
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**Aryabhata 2.0** (Upcoming):
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- Extending domain coverage to **Physics** and **Chemistry**
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- Supporting **JEE Advanced**, **NEET**, and **Foundation syllabus**
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- Further optimization for affordability and accuracy in real-time deployments
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---
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## 🤝 Citation
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If you use this model, please cite:
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```bibtex
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@misc{Aryabhata2025,
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title = {Aryabhata 1.0: A compact, exam-focused language model tailored for mathematics in Indian competitive exams, especially JEE Main.},
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author = {Physics Wallah AI Research},
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year = {2025},
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note = {\url{https://huggingface.co/PhysicsWallahAI/Aryabhata-1.0}},
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}
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'Please reason step by step, and put your final answer within \\boxed{}.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
|
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{{- '\n</tool_response>' }}
|
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
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{{- '<|im_start|>assistant\n' }}
|
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{%- endif %}
|
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60
config.json
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config.json
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{
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"architectures": [
|
||||
"Qwen2ForCausalLM"
|
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],
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
|
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"eos_token_id": 151643,
|
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"hidden_act": "silu",
|
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"hidden_size": 3584,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 18944,
|
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"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
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"max_position_embeddings": 131072,
|
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"max_window_layers": 28,
|
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"model_type": "qwen2",
|
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"num_attention_heads": 28,
|
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"num_hidden_layers": 28,
|
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"num_key_value_heads": 4,
|
||||
"pad_token_id": 151643,
|
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"rms_norm_eps": 1e-06,
|
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"rope_scaling": null,
|
||||
"rope_theta": 10000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "float32",
|
||||
"transformers_version": "4.53.1",
|
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"use_cache": false,
|
||||
"use_mrope": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 152064
|
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}
|
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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generation_config.json
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{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 151643,
|
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"eos_token_id": 151643,
|
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"transformers_version": "4.53.1"
|
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}
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model-00001-of-00007.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:42c9b8d8d78a684e55c7a3903ac12b9754a4eaf428a24ff04ca87dfd1f4cc5dd
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size 4976687216
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"content": "<｜begin▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|EOT|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151648": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151649": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"bos_token": "<｜begin▁of▁sentence｜>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<｜end▁of▁sentence｜>",
|
||||
"extra_special_tokens": {},
|
||||
"legacy": true,
|
||||
"model_max_length": 16384,
|
||||
"pad_token": "<｜end▁of▁sentence｜>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizerFast",
|
||||
"unk_token": null,
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user