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Model: prithivMLmods/Monoceros-QwenM-1.5B
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+tags:
+- text-generation-inference
+- Code
+- Math
+- RL
+- CoT
+---
+![M.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/JXIomwktKoqTBjJQNy3rj.png)
+
+# **Monoceros-QwenM-1.5B**
+
+> **Monoceros-QwenM-1.5B** is a **chain-of-thought reasoning model** fine-tuned from **Qwen-1.5B**, specifically designed for solving **mathematical problems** in both **English** and **Chinese**. It brings advanced reasoning and step-by-step problem-solving capabilities in a compact size, ideal for educational tools, tutoring systems, and math-focused assistants.
+
+## **Key Features**
+
+1. **Chain-of-Thought Math Reasoning**  
+   Trained to produce intermediate steps for math problems, Monoceros-QwenM-1.5B enables interpretability and transparent logic in answers — critical for educational and verification purposes.
+
+2. **Bilingual Proficiency (English + Chinese)**  
+   Capable of understanding, reasoning, and explaining math problems fluently in **both English and Simplified Chinese**, making it suitable for multilingual learning environments.
+
+3. **Compact yet Capable**  
+   While only 1.5B parameters, this model delivers strong performance for arithmetic, algebra, geometry, word problems, and logical puzzles with minimal resource demands.
+
+4. **Step-by-Step Computation**  
+   Provides structured, multi-step answers that mirror human-like problem solving, making it easy to follow and learn from.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Monoceros-QwenM-1.5B"
+
+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 solvers for students across basic to intermediate levels.
+- **Bilingual Educational Apps**: Teaching math in **English** and **Chinese**, improving accessibility.
+- **STEM Reasoning Tools**: Reasoning for science, engineering, and logic-based problems.
+- **Lightweight LLM Applications**: Embedded use cases in browsers, mobile apps, or low-resource environments.
+
+## **Limitations**
+
+1. **Limited Domain Generalization**:  
+   Optimized for math; performance may drop in creative writing, casual conversation, or unrelated topics.
+
+2. **Parameter Scale**:  
+   Though efficient, it may underperform compared to larger models on highly complex or abstract math.
+
+3. **Bias from Base Model**:  
+   Inherits any biases from Qwen-1.5B’s pretraining. Outputs should be validated in sensitive settings.
+
+4. **Prompt Sensitivity**:  
+   Precise, structured input yields better stepwise results.