From 832ef4fccf8921e36c964f1d775d34eb769a2b6a Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Wed, 13 May 2026 15:06:36 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: PhysicsWallahAI/Aryabhata-1.0 Source: Original Platform --- .gitattributes | 49 +++++ README.md | 229 ++++++++++++++++++++ accuracy-vs-token.png | Bin 0 -> 104948 bytes accuracy.png | Bin 0 -> 212943 bytes benchmark.png | Bin 0 -> 49701 bytes chat_template.jinja | 54 +++++ config.json | 60 ++++++ configuration.json | 1 + generation_config.json | 6 + model-00001-of-00007.safetensors | 3 + model-00002-of-00007.safetensors | 3 + model-00003-of-00007.safetensors | 3 + model-00004-of-00007.safetensors | 3 + model-00005-of-00007.safetensors | 3 + model-00006-of-00007.safetensors | 3 + 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special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..21b3632 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth 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diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..69a31e3 --- /dev/null +++ b/README.md @@ -0,0 +1,229 @@ +--- +license: cc-by-nc-4.0 +tags: +- small-language-model +- jee +- exam-centric +- indian-education +- reinforcement-learning +- supervised-finetuning +- model-merging +- rejection-sampling +- mathematics +- ai4education +- physicswallah +language: +- en +model_name: PhysicsWallah/Aryabhata-1.0 +model_creator: Physics Wallah AI Research +model_type: Causal decoder-based model +base_model: Qwen/Qwen2.5-Math-7B +pipeline_tag: text-generation +library_name: transformers +--- + +# Aryabhatta 1.0 : An exam-focused language model for JEE Math + +![](benchmark.png) + +## Overview + +**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. + + +> 🚧 *Aryabhata 1.0 is an **experimental release**. We are actively seeking feedback — please contribute in the Discussion tab of this repo.* +--- + +## 🧠 Key Features + +- **Architecture**: 7B parameter causal decoder-based model. +- **Exam-Centric Optimization**: Specifically tuned for JEE-level Mathematics reasoning. +- **High Accuracy**: + - **86%** on **JEE Mains January 2025** session. + - **90.2%** on **JEE Mains April 2025** session. +- **Token Efficiency**: Operates effectively around a **~2K token window**, compared to ~8K required by other reasoning models. +- **Compute Efficient**: Trained on a **1x2 NVIDIA H100 GPU** using optimized pipeline. + +--- + +## 🛠️ Training Details + +- **Training Data**: ~130K problem-solution pairs curated from proprietary Physics Wallah exam datasets. +- **Training Pipeline**: + - **Model Merging** + - **Rejection Sampling** + - **Supervised Fine-Tuning (SFT)** + - **Reinforcement Learning with Verifiable Rewards (RLVR)** + +### 🔀 Model Merging +We began with model merging (Weighted average) to build a strong initialization (Aryabhata 0.5) by combining diverse model capabilities: +* Qwen 2.5 Math: A robust math-centric LLM with solid symbolic math foundations. +* Ace Math: An enhanced version of Qwen 2.5 Math, fine-tuned by NVIDIA for improved accuracy in mathematics benchmarks. +* DeepSeek R1 Distill Qwen: A long-form reasoning model, fine-tuned on reasoning traces distilled from DeepSeek R1. + +### 📚 Data Curation + Rejection Sampling +We extracted ~250K raw questions from Physics Wallah's internal database and applied aggressive filtering and cleaning: +* Removed: diagram-based, non-English, and option-heavy questions. +* Kept: questions matching the distribution of JEE Main 2019–2024. +Final curated dataset: ~130K high-quality questions. + +For each question: +* Generated 4 CoTs using Aryabhata 0.5. +* Retained only those leading to correct final answers. + +Resulting Dataset: +* ~100K questions +* ~350K high-quality CoTs + +We used this dataset for SFT. + +### 🎯 Reinforcement Learning with Verifiable Rewards (RLVR) +We used a custom in-house variant of Group Relative Policy Optimization (GRPO), adapted for math-specific reward functions. +* Removed KL-divergence penalty +* Removed clipping + +We used RLVR on the remaining ~30K questions. + +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. + +--- + +## 📊 Performance Highlights + +### Evaluation Setup +All evaluations were performed with temperature = 0.0, and we report pass@1 accuracy. + +#### Evaluation Datasets +We evaluated the model on two sets of official JEE Mains 2025 mathematics papers: +* January Session: 10 question papers containing 250 questions. +* April Session: 9 question papers containing 225 questions. + +Each paper includes a mix of: +* Multiple Choice Questions (MCQs) with one correct option +* Numeric Answer Type (NAT) questions requiring precise numerical responses + +#### Evaluation Metric +We used a composite evaluation metric to reflect real-world grading rigor and reduce false positives: + +1. Float Match + * Compares predicted and target answers within a tolerance (±1e-9) + * Handles rounding artifacts and small numerical errors robustly +2. String Match + * Used for symbolic answers (e.g., fractions, radicals) + * Uses strict exact match — predictions must match ground truth character-for-character +3. LLM-as-Judge (GPT-4o-mini) + * Used for Mathematical equivalence for ambiguous formats + +### 🔹 Accuracy Comparison Across Models +![](accuracy.png) +> *Aryabhata has the best accuracy on JEE Main Maths, on par with frontier models* + +### 🔹 Accuracy vs Token Usage +![](accuracy-vs-token.png) +> *Aryabhata is on par with frontier models in terms of accuracy vs token usage* + +--- + +## 🔧 Intended Use + +**Primary Use Cases**: +- Competitive exam preparation (JEE Main level mathematics problems) +- Question answering and doubt-solving systems +- Educational tutoring and concept explanation + + +## 💡 How to Use + +### 🧪 Using with 🤗 Transformers + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig + +model_id = "PhysicsWallahAI/Aryabhata-1.0" + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained(model_id) + + +# Define stop strings +stop_strings = ["<|im_end|>", "<|end|>", "", "⁠```python\n", "⁠<|im_start|>", "]}}]}}]"] + +def strip_bad_tokens(s, stop_strings): + for suffix in stop_strings: + if s.endswith(suffix): + return s[:-len(suffix)] + return s + + +# Create generation config (can also set temperature, top_p, etc.) +generation_config = GenerationConfig( + max_new_tokens=4096, + stop_strings = stop_strings +) + +query = 'Find all the values of \\sqrt[3]{1}' +messages = [{'role': 'system', 'content': 'Think step-by-step; put only the final answer inside \\boxed{}.'}, + {'role': 'user', 'content': query}] + +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) +inputs = tokenizer([text], return_tensors="pt") +outputs = model.generate(**inputs, generation_config=generation_config, tokenizer=tokenizer) + +print(strip_bad_tokens(tokenizer.decode(outputs[0], skip_special_tokens=True), stop_strings)) +```` + +--- + +### ⚡ Using with vLLM + +To run the model efficiently using vLLM: + +```python +from vllm import LLM, SamplingParams + +# Initialize model (downloads from Hugging Face if not local) +llm = LLM(model="PhysicsWallahAI/Aryabhata-1.0") + +# Define prompt and sampling configuration +query = 'Find all the values of \\sqrt[3]{1}' +messages = [{'role': 'system', 'content': 'Think step-by-step; put only the final answer inside \\boxed{}.'}, + {'role': 'user', 'content': query}] +sampling_params = SamplingParams(temperature=0.0, max_tokens=4*1024, stop=["<|im_end|>", "<|end|>", "", "⁠```python\n", "⁠<|im_start|>", "]}}]}}]"]) + +# Run inference +results = llm.chat(messages, sampling_params) + +# Print result +print(results[0].outputs[0].text.strip()) +``` + +--- + +Read more about Aryabhata 1.0 in our [Technical Report](https://arxiv.org/abs/2508.08665) + +--- + +## 🚀 Roadmap + +**Aryabhata 2.0** (Upcoming): +- Extending domain coverage to **Physics** and **Chemistry** +- Supporting **JEE Advanced**, **NEET**, and **Foundation syllabus** +- Further optimization for affordability and accuracy in real-time deployments + +--- + +## 🤝 Citation + +If you use this model, please cite: + +```bibtex +@misc{Aryabhata2025, + title = {Aryabhata 1.0: A compact, exam-focused language model tailored for mathematics in Indian competitive exams, especially JEE Main.}, + author = {Physics Wallah AI Research}, + year = {2025}, + note = {\url{https://huggingface.co/PhysicsWallahAI/Aryabhata-1.0}}, +} \ No newline at end of file diff --git a/accuracy-vs-token.png b/accuracy-vs-token.png new file mode 100644 index 0000000000000000000000000000000000000000..9ea118aaafb61ce3803035c365aa3eebdf80b366 GIT binary patch literal 104948 zcmd431yq%5+ck>1QMb9>wg{+T&=w$Vf{1{CbQyFv(pVcCK?P|QkXSScpH9I9s13SlyHu?;*7wxRfEbYvUFYR#9x3M+0v^c~o zz$hmoC~m8~!zpZUN3fY;K-kgsAae+(D--RiWuEdv9~8}hZtqS9WRVG#p^^vPrA zuLSqEIJzp0%@uuj?76XN-$9Li;U73}#V!8Ox{qnuXO(luj#+!I+I(fB!iMu3Hf+~g zw{g*vJVrgvbCLo2(gBNBq*eO)J(!vMT;Kdsx-nvd?~+7%`?K4^O&%Z@CoihLDU!FxTqF4X(&u?lQsJQdLUo3ZD 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with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0]['role'] == 'system' %} + {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }} + {%- else %} + {{- '<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- for message in messages %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} + {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {{- '<|im_start|>' + message.role }} + {%- if message.content %} + {{- '\n' + message.content }} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {{- tool_call.arguments | tojson }} + {{- '}\n' }} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- message.content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..25e8e25 --- /dev/null +++ b/config.json @@ -0,0 +1,60 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "attention_dropout": 0.0, + 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