Nero1-0.5B/README.md

language, license, pipeline_tag, tags, datasets, library_name

language	license	pipeline_tag	tags	datasets	library_name
en	apache-2.0	text-generation	conversational code	smirki/Agentic-Coding-Tessa	transformers

Nero1-0.5B

Nero1-0.5B is a specialized, lightweight coding model developed by NeuronicL. It is a full fine-tune of Qwen/Qwen2.5-Coder-0.5B-Instruct, specifically optimized for agentic workflows, tool use, and complex code generation tasks using the smirki/Agentic-Coding-Tessa dataset.

Model Description

Unlike standard parameter-efficient fine-tuning (LoRA), Nero1-0.5B underwent a full parameter update. This allows the model to deeply integrate the agentic reasoning patterns found in the Tessa dataset, making it exceptionally capable of:

• Writing functional, production-ready code.
• Understanding and executing multi-step agentic instructions.
• Maintaining high performance in low-latency environments (Edge/Local development).

Key Specifications

• Base Model: Qwen/Qwen2.5-Coder-0.5B-Instruct
• Training Data: smirki/Agentic-Coding-Tessa
• Fine-tuning Method: Full Parameter Fine-tuning (Full FT)
• Parameters: 0.49 Billion
• Context Length: 32,768 tokens

Usage

You can use Nero1-0.5B with the Hugging Face transformers library. Given its Qwen2.5-Coder backbone, it follows the standard ChatML-style prompt template.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "NeuronicL/Nero1-0.5B"
device = "cuda" # or "cpu"

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

messages = [
    {"role": "system", "content": "You are a helpful coding assistant specialized in agentic tasks."},
    {"role": "user", "content": "Write a Python script to scrape news headlines and save them to a JSON file."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)