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--- |
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library_name: transformers |
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tags: |
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- math |
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- cot |
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- text-generation-inference |
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- preview |
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- experimental |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-1.5B-Instruct |
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pipeline_tag: text-generation |
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--- |
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# **Deepmath-Competitive-1.5B-Preview** |
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> **Deepmath-Competitive-1.5B-Preview** is a **chain-of-thought reasoning model** fine-tuned from **Qwen-1.5B**, purpose-built for solving **mathematical problems** in both **English** and **Chinese** with a focus on **long-context understanding**. It enables advanced reasoning and detailed step-by-step problem solving in a compact form — ideal for competitive exam preparation, tutoring systems, and math-focused AI assistants. |
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## **Key Features** |
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1. **Chain-of-Thought Math Reasoning** |
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Specifically trained to output detailed intermediate steps for math problems, Deepmath-Competitive-1.5B-Preview ensures interpretability and logical clarity — vital for learning and validation. |
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2. **Bilingual Proficiency (English + Chinese)** |
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Proficient in understanding and solving math problems in **both English and Simplified Chinese**, supporting diverse educational needs. |
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3. **Long-Context Reasoning** |
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Optimized for **long-form math problems** and word problem comprehension, enabling reasoning over extended contexts and compound queries. |
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4. **Compact yet Powerful** |
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With just 1.5B parameters, it delivers robust performance on arithmetic, algebra, geometry, logic, and competitive exam-style word problems with minimal computational cost. |
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5. **Structured Step-by-Step Computation** |
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Produces clean, stepwise outputs that mimic expert human problem-solving, helping learners follow the process and logic intuitively. |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Deepmath-Competitive-1.5B-Preview" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?" |
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messages = [ |
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{"role": "system", "content": "You are a helpful tutor skilled in solving math problems with step-by-step explanations."}, |
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{"role": "user", "content": prompt} |
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] |
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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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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## **Intended Use** |
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- **Math Tutoring Bots**: Delivers in-depth, multi-step solutions for students preparing for competitive and school-level math. |
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- **Bilingual Educational Apps**: Effective in English and Chinese teaching environments. |
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- **STEM Reasoning Tools**: Supports structured reasoning across science and engineering questions. |
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- **Compact LLM Deployments**: Suitable for low-latency environments like mobile apps, edge devices, or web integrations. |
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## **Limitations** |
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1. **Domain Focus**: |
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Primarily tuned for mathematics; performance may drop outside STEM or logical domains. |
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2. **Model Scale**: |
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While efficient, it may underperform on abstract or research-level problems compared to larger models. |
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3. **Inherited Biases**: |
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As a fine-tune of Qwen-1.5B, some pretraining biases may persist. Review is advised in critical applications. |
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4. **Prompt Sensitivity**: |
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Performs best with clearly structured prompts and formal question phrasing. |
