---
library_name: transformers
tags:
- text-generation-inference
- PRM
- Code
- Math
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---

![PRM.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/2inJGKPx_BrMcID7Osto-.png)

# **Deepthink-1.5B-Open-PRM**

> **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**.

## **Key Features**

1. **Process Reward Model Supervision (PRM)**  
   Fine-tuned with PRMs to reward high-quality intermediate reasoning steps — fostering step-by-step interpretability, accuracy, and educational transparency.

2. **Compact Foundation (Qwen2.5 0.5B)**  
   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.

3. **Bilingual Math Capability**  
   Fluent in solving and explaining math problems in both **English** and **Simplified Chinese**, making it ideal for multilingual classrooms and tutoring platforms.

4. **Process-Supervised Math Reasoning**  
   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.

5. **Long-Context & Word Problem Reasoning**  
   Especially proficient with multi-step arithmetic, word problems, logic puzzles, and middle school to early college-level math.

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Deepthink-1.5B-Open-PRM"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

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?"

messages = [
    {"role": "system", "content": "You are a helpful math tutor who explains each step clearly."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```

## **Intended Use**

- **Math Education Agents**: Tutors that explain problems step by step, helping users build understanding through reasoning.
- **Bilingual Learning Platforms**: Apps that teach math in both Chinese and English.
- **STEM-Oriented Assistants**: Supports early-stage problem solving in science and engineering contexts.
- **Lightweight LLM Deployments**: Optimized for low-resource environments, from browsers to mobile devices.

## **Limitations**

1. **Domain Specificity**  
   Primarily tuned for math reasoning — performance may degrade on unrelated tasks like creative writing or open dialogue.

2. **Model Size Constraint**  
   While efficient, 1.5B parameters may struggle with highly abstract or very long multi-domain tasks.

3. **PRM Bias Generalization**  
   PRM training can bias toward rewardable structures — results should still be reviewed for correctness and completeness.

4. **Prompt Structure Sensitivity**  
   Well-structured queries yield more accurate and educationally useful outputs.