---
language: en
license: apache-2.0
pipeline_tag: text-generation
tags:
- conversational
- code
datasets:
- smirki/Agentic-Coding-Tessa
library_name: 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](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
- **Training Data:** [smirki/Agentic-Coding-Tessa](https://huggingface.co/datasets/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.

```python
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)