57 lines
2.3 KiB
Markdown
57 lines
2.3 KiB
Markdown
---
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language: en
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- conversational
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- code
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datasets:
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- smirki/Agentic-Coding-Tessa
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library_name: transformers
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---
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# Nero1-0.5B
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**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.
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## Model Description
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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:
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- Writing functional, production-ready code.
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- Understanding and executing multi-step agentic instructions.
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- Maintaining high performance in low-latency environments (Edge/Local development).
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### Key Specifications
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- **Base Model:** [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
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- **Training Data:** [smirki/Agentic-Coding-Tessa](https://huggingface.co/datasets/smirki/Agentic-Coding-Tessa)
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- **Fine-tuning Method:** Full Parameter Fine-tuning (Full FT)
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- **Parameters:** 0.49 Billion
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- **Context Length:** 32,768 tokens
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## Usage
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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.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "NeuronicL/Nero1-0.5B"
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device = "cuda" # or "cpu"
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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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messages = [
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{"role": "system", "content": "You are a helpful coding assistant specialized in agentic tasks."},
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{"role": "user", "content": "Write a Python script to scrape news headlines and save them to a JSON file."}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512)
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response) |