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--- |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- PRM |
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- Code |
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- Math |
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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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# **Deepthink-1.5B-Open-PRM** |
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> **Deepthink-1.5B-Open-PRM** is a **process-supervised reasoning model** fine-tuned from **Qwen2.5 1.5B** using **Process Reward Models (PRM)**. It excels at **step-by-step mathematical problem solving** in both **English** and **Simplified Chinese**, offering interpretable, logically structured responses for use in **education**, **STEM tutoring**, and **lightweight math agents**. |
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## **Key Features** |
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1. **Process Reward Model Supervision (PRM)** |
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Fine-tuned with PRMs to reward high-quality intermediate reasoning steps — fostering step-by-step interpretability, accuracy, and educational transparency. |
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2. **Compact Foundation (Qwen2.5 0.5B)** |
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Built upon the highly efficient Qwen2.5 1.5B architecture and scaled up through distillation and reward-based alignment to 1.5B parameters, balancing reasoning quality and deployment efficiency. |
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3. **Bilingual Math Capability** |
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Fluent in solving and explaining math problems in both **English** and **Simplified Chinese**, making it ideal for multilingual classrooms and tutoring platforms. |
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4. **Process-Supervised Math Reasoning** |
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Trained to reason like a teacher — showing each logical step before delivering an answer. Ideal for learners who need to understand the “how” and “why” behind each solution. |
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5. **Long-Context & Word Problem Reasoning** |
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Especially proficient with multi-step arithmetic, word problems, logic puzzles, and middle school to early college-level math. |
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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/Deepthink-1.5B-Open-PRM" |
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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 tank can be filled by one pipe in 6 hours and emptied by another in 9 hours. How long will it take to fill the tank if both pipes are opened together?" |
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messages = [ |
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{"role": "system", "content": "You are a helpful math tutor who explains each step clearly."}, |
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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 Education Agents**: Tutors that explain problems step by step, helping users build understanding through reasoning. |
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- **Bilingual Learning Platforms**: Apps that teach math in both Chinese and English. |
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- **STEM-Oriented Assistants**: Supports early-stage problem solving in science and engineering contexts. |
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- **Lightweight LLM Deployments**: Optimized for low-resource environments, from browsers to mobile devices. |
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## **Limitations** |
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1. **Domain Specificity** |
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Primarily tuned for math reasoning — performance may degrade on unrelated tasks like creative writing or open dialogue. |
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2. **Model Size Constraint** |
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While efficient, 1.5B parameters may struggle with highly abstract or very long multi-domain tasks. |
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3. **PRM Bias Generalization** |
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PRM training can bias toward rewardable structures — results should still be reviewed for correctness and completeness. |
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4. **Prompt Structure Sensitivity** |
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Well-structured queries yield more accurate and educationally useful outputs. |
