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Model: NeuronicL/Nero1-0.5B
Source: Original Platform

README.md

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+---
+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)