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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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- Deepseek |
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- code |
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- math |
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- RL |
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- R1 |
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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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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
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pipeline_tag: text-generation |
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--- |
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# **Asterope-21-OpenR1** |
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> **Asterope-21-OpenR1** is a **distributed reinforcement learning (RL)** fine-tuned model based on **Qwen-1.5B**, purpose-built to enhance **coding proficiency**, **debugging accuracy**, and **step-by-step reasoning** in **software development tasks** across multiple programming languages. Compact yet capable, it's ideal for intelligent coding assistants, developer tools, and embedded reasoning engines. |
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## **Key Features** |
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1. **Code-Centric Chain-of-Thought Reasoning** |
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Optimized to generate structured, multi-step solutions for programming problems — including algorithm design, debugging, and code explanation — enabling developers to understand the "why" behind each step. |
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2. **Distributed Reinforcement Learning Fine-Tuning** |
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Trained with reinforcement learning across distributed environments to reinforce optimal coding strategies and accurate logical reasoning pathways. |
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3. **Multilingual Programming Support** |
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Supports various programming languages (e.g., **Python**, **JavaScript**, **C++**, **Java**, **Go**) and adapts to a wide range of development contexts from scripting to systems programming. |
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4. **Lightweight, Developer-Ready (1.5B Parameters)** |
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Designed for low-latency environments like IDE extensions, browser dev tools, and CLI bots, making it both fast and resource-efficient. |
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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/Asterope-21-OpenR1" |
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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 = "Debug the following Python code:\ndef add(a, b):\n return a + b\nprint(add(5))" |
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messages = [ |
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{"role": "system", "content": "You are a skilled coding assistant capable of reasoning step-by-step to solve software development tasks."}, |
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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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- **Code Debugging Assistants**: Identifying, explaining, and fixing bugs with precision. |
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- **Educational Coding Tools**: Helping users learn how and why code works, with rich step-by-step walkthroughs. |
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- **Multi-language Code Generation**: Write clean, working code across languages and platforms. |
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- **Lightweight IDE Integration**: Embed into **editors**, **terminals**, or **web-based environments**. |
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## **Limitations** |
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1. **Focused Domain**: |
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Optimized for development workflows. May underperform in creative or non-technical tasks. |
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2. **Model Scale**: |
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Though efficient, complex multi-file or large-context debugging tasks may benefit from larger models. |
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3. **RL Bias Toward Code Tasks**: |
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Reinforcement learning favors coding reasoning paths — outputs for general-purpose Q&A may be limited. |
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4. **Prompt Structure Matters**: |
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More effective when inputs include structured error messages, full code context, or clear questions. |
