Instructions to use NeuronicL/Nero1-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeuronicL/Nero1-0.5B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NeuronicL/Nero1-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NeuronicL/Nero1-0.5B")
model = AutoModelForCausalLM.from_pretrained("NeuronicL/Nero1-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NeuronicL/Nero1-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeuronicL/Nero1-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuronicL/Nero1-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NeuronicL/Nero1-0.5B

SGLang

How to use NeuronicL/Nero1-0.5B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NeuronicL/Nero1-0.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuronicL/Nero1-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NeuronicL/Nero1-0.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuronicL/Nero1-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NeuronicL/Nero1-0.5B with Docker Model Runner:
```
docker model run hf.co/NeuronicL/Nero1-0.5B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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)

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Safetensors

Model size

0.5B params

Tensor type

BF16

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Quantizations

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Nero1-0.5B

Model Description

Key Specifications

Usage

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Dataset used to train NeuronicL/Nero1-0.5B