94 lines
3.7 KiB
Markdown
94 lines
3.7 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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- 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. |