Instructions to use OrionLLM/NanoCoder-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OrionLLM/NanoCoder-0.6b with Transformers:

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

pipe = pipeline("text-generation", model="OrionLLM/NanoCoder-0.6b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OrionLLM/NanoCoder-0.6b")
model = AutoModelForCausalLM.from_pretrained("OrionLLM/NanoCoder-0.6b", device_map="auto")
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 Settings

vLLM

How to use OrionLLM/NanoCoder-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OrionLLM/NanoCoder-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OrionLLM/NanoCoder-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OrionLLM/NanoCoder-0.6b

SGLang

How to use OrionLLM/NanoCoder-0.6b 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 "OrionLLM/NanoCoder-0.6b" \
    --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": "OrionLLM/NanoCoder-0.6b",
		"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 "OrionLLM/NanoCoder-0.6b" \
        --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": "OrionLLM/NanoCoder-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OrionLLM/NanoCoder-0.6b with Docker Model Runner:
```
docker model run hf.co/OrionLLM/NanoCoder-0.6b
```

NanoCoder-0.6b

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---
language:
- en
- code
pipeline_tag: text-generation
license: apache-2.0
tags:
- coderion
- code
- coding
- reasoning
- small-language-model
- 0.6b
- chronological-reasoning
- high-reasoning
- compact-model
library_name: transformers
datasets:
- nvidia/OpenCodeReasoning
base_model:
- Qwen/Qwen3-0.6B
---

<p align="center">
  <img src="https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/YF0kEDYMGJhcM3Lbl2EOD.png" alt="logo" width="250">
</p>


<p align="center"><b>A compact 0.6B coding model built for strong reasoning efficiency.</b></p>

---

**NanoCoder** is a **small 0.6B parameter coding-focused language model** designed for **high and xhigh chronological reasoning** in programming tasks.

It is built to deliver **surprisingly strong structured reasoning and coding performance for its size**, focusing on consistency, logical step progression, and efficient problem solving.

While **NanoCoder is not intended to be a general everyday assistant**, it is a **small but capable specialist model** that performs well within its class and remains **reliable for compact code reasoning workloads**.

---

## Key Characteristics

- **0.6B parameters**
- **Dedicated to code**
- **Optimized for high reasoning intensity**
- **Chronological reasoning style**
- **Strong consistency for a compact model**
- **Designed for efficient performance despite its small size**

---

## Limitations

NanoCoder is a **small specialized model**.

Because of that:

- It may not match larger models on broad real-world assistant tasks
- It is not primarily designed for daily casual use
- It performs best when used for **focused coding and reasoning workloads**
- Its main strength is **efficiency, consistency, and reasoning quality relative to size**