Instructions to use jsbaicenter/Aztec-Coder-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsbaicenter/Aztec-Coder-4B with Transformers:

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

pipe = pipeline("text-generation", model="jsbaicenter/Aztec-Coder-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jsbaicenter/Aztec-Coder-4B")
model = AutoModelForCausalLM.from_pretrained("jsbaicenter/Aztec-Coder-4B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jsbaicenter/Aztec-Coder-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsbaicenter/Aztec-Coder-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jsbaicenter/Aztec-Coder-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jsbaicenter/Aztec-Coder-4B

SGLang

How to use jsbaicenter/Aztec-Coder-4B 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 "jsbaicenter/Aztec-Coder-4B" \
    --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": "jsbaicenter/Aztec-Coder-4B",
		"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 "jsbaicenter/Aztec-Coder-4B" \
        --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": "jsbaicenter/Aztec-Coder-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jsbaicenter/Aztec-Coder-4B with Docker Model Runner:
```
docker model run hf.co/jsbaicenter/Aztec-Coder-4B
```

jsbai-aaron commited on about 8 hours ago

Commit

8faf351

verified ·

1 Parent(s): b8c177f

Add preview-release notice: research preview, not recommended for production until 1.0 with full evaluations

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README.md CHANGED Viewed

@@ -23,6 +23,8 @@ The **James Silberrad Brown Center for Artificial Intelligence (JSBCAI)** is an
 # Aztec-Coder-4B
 **An agentic coding model that runs on your laptop.**
 Aztec-Coder-4B is a 4B-parameter model, fine-tuned from Qwen3.5-4B, that investigates bugs, edits files, runs commands, and verifies its own fixes in real software repositories. Agentic coding at this level has required 27B+ models. This one fits on a consumer GPU.

 # Aztec-Coder-4B
+> ⚠ **PREVIEW RELEASE** — This is a research preview, not recommended for production use. Full evaluations are in progress. Release 1.0 with complete, validated benchmark results is coming. Safety results (HarmBench, XSTest) are measured and reported below.
 **An agentic coding model that runs on your laptop.**
 Aztec-Coder-4B is a 4B-parameter model, fine-tuned from Qwen3.5-4B, that investigates bugs, edits files, runs commands, and verifies its own fixes in real software repositories. Agentic coding at this level has required 27B+ models. This one fits on a consumer GPU.