92 lines
3.6 KiB
Markdown
92 lines
3.6 KiB
Markdown
---
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library_name: transformers
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tags:
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- text-generation-inference
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- code
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- grpo
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- math
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- RL
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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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# **Lota-Carinae-Open-GRPO**
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> **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.
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## **Key Features**
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1. **Chain-of-Thought Math Reasoning**
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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.
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2. **Bilingual Proficiency (English + Chinese)**
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Fluently understands and explains math problems in **English** and **Simplified Chinese**, serving diverse educational ecosystems and multilingual environments.
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3. **Compact yet Intelligent**
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Despite its **1.5B parameter** size, it achieves strong performance in arithmetic, algebra, geometry, word problems, and logic puzzles, with optimized efficiency via GRPO.
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4. **Structured Step-by-Step Computation**
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Delivers coherent, human-readable step-by-step solutions, making complex problems easier to follow and learn from.
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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/Monoceros-QwenM-1.5B" # (Update with new repo name if applicable)
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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**: Step-by-step assistants for learners from basic to intermediate levels.
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- **Bilingual Educational Apps**: Math learning in **English** and **Chinese**, improving access and comprehension.
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- **STEM Reasoning Tools**: Supports science, technology, engineering, and logical thinking tasks.
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- **RL-Enhanced Lightweight LLMs**: Powered by **GRPO**, suitable for embedded or resource-constrained deployments (mobile, web, or on-device).
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## **Limitations**
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1. **Domain Focused**:
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Primarily optimized for mathematical reasoning; general-purpose tasks may yield reduced quality.
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2. **Model Scale**:
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Smaller size means it may not match the depth of larger models for complex or abstract scenarios.
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3. **Inherited Biases**:
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As it builds upon Qwen-1.5B, it may retain pretraining biases—careful use is advised in sensitive contexts.
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4. **Prompt Sensitivity**:
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Structured, math-specific prompts deliver the most accurate results. |