---
library_name: transformers
tags:
- text-generation-inference
- code
- grpo
- math
- RL
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---
![as.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/uMfcuixpsyl3mYbz0YACN.png)

# **Lota-Carinae-Open-GRPO**

> **Lota-Carinae-Open-GRPO** is a **chain-of-thought reasoning model** fine-tuned from **Qwen-1.5B**, leveraging an advanced reinforcement learning strategy — **Group Relative Policy Optimization (GRPO)**. It is specifically designed for solving **mathematical problems** in both **English** and **Chinese**, combining stepwise reasoning with lightweight efficiency. Ideal for educational tools, math tutoring systems, and logic-intensive assistants.

## **Key Features**

1. **Chain-of-Thought Math Reasoning**  
   Fine-tuned with GRPO to enhance intermediate step generation, **Lota-Carinae-Open-GRPO** enables high interpretability and logical transparency — essential for both learning and verification.

2. **Bilingual Proficiency (English + Chinese)**  
   Fluently understands and explains math problems in **English** and **Simplified Chinese**, serving diverse educational ecosystems and multilingual environments.

3. **Compact yet Intelligent**  
   Despite its **1.5B parameter** size, it achieves strong performance in arithmetic, algebra, geometry, word problems, and logic puzzles, with optimized efficiency via GRPO.

4. **Structured Step-by-Step Computation**  
   Delivers coherent, human-readable step-by-step solutions, making complex problems easier to follow and learn from.

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Monoceros-QwenM-1.5B"  # (Update with new repo name if applicable)

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

prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?"
messages = [
    {"role": "system", "content": "You are a helpful tutor skilled in solving math problems with step-by-step explanations."},
    {"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 Tutoring Bots**: Step-by-step assistants for learners from basic to intermediate levels.
- **Bilingual Educational Apps**: Math learning in **English** and **Chinese**, improving access and comprehension.
- **STEM Reasoning Tools**: Supports science, technology, engineering, and logical thinking tasks.
- **RL-Enhanced Lightweight LLMs**: Powered by **GRPO**, suitable for embedded or resource-constrained deployments (mobile, web, or on-device).

## **Limitations**

1. **Domain Focused**:  
   Primarily optimized for mathematical reasoning; general-purpose tasks may yield reduced quality.

2. **Model Scale**:  
   Smaller size means it may not match the depth of larger models for complex or abstract scenarios.

3. **Inherited Biases**:  
   As it builds upon Qwen-1.5B, it may retain pretraining biases—careful use is advised in sensitive contexts.

4. **Prompt Sensitivity**:  
   Structured, math-specific prompts deliver the most accurate results.