Asterope-21-OpenR1

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.

Key Features

1. Code-Centric Chain-of-Thought Reasoning
   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.

2. Distributed Reinforcement Learning Fine-Tuning
   Trained with reinforcement learning across distributed environments to reinforce optimal coding strategies and accurate logical reasoning pathways.

3. Multilingual Programming Support
   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.

4. Lightweight, Developer-Ready (1.5B Parameters)
   Designed for low-latency environments like IDE extensions, browser dev tools, and CLI bots, making it both fast and resource-efficient.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Asterope-21-OpenR1"

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

prompt = "Debug the following Python code:\ndef add(a, b):\n  return a + b\nprint(add(5))"
messages = [
    {"role": "system", "content": "You are a skilled coding assistant capable of reasoning step-by-step to solve software development tasks."},
    {"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

• Code Debugging Assistants: Identifying, explaining, and fixing bugs with precision.
• Educational Coding Tools: Helping users learn how and why code works, with rich step-by-step walkthroughs.
• Multi-language Code Generation: Write clean, working code across languages and platforms.
• Lightweight IDE Integration: Embed into editors, terminals, or web-based environments.

Limitations

1. Focused Domain:
   Optimized for development workflows. May underperform in creative or non-technical tasks.

2. Model Scale:
   Though efficient, complex multi-file or large-context debugging tasks may benefit from larger models.

3. RL Bias Toward Code Tasks:
   Reinforcement learning favors coding reasoning paths — outputs for general-purpose Q&A may be limited.

4. Prompt Structure Matters:
   More effective when inputs include structured error messages, full code context, or clear questions.