Instructions to use jsbaicenter/Aztec-Coder-4B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsbaicenter/Aztec-Coder-4B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="jsbaicenter/Aztec-Coder-4B-NVFP4")
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-NVFP4")
model = AutoModelForCausalLM.from_pretrained("jsbaicenter/Aztec-Coder-4B-NVFP4", 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-NVFP4 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-NVFP4"
# 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-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jsbaicenter/Aztec-Coder-4B-NVFP4

SGLang

How to use jsbaicenter/Aztec-Coder-4B-NVFP4 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-NVFP4" \
    --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-NVFP4",
		"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-NVFP4" \
        --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-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jsbaicenter/Aztec-Coder-4B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/jsbaicenter/Aztec-Coder-4B-NVFP4
```

jsbai-aaron commited on 1 day ago

Commit

f7efec7

verified ·

1 Parent(s): 10efdfc

Add XSTest row: 99.8% benign compliance — the full three-variant safety table

Browse files

Files changed (1) hide show

README.md +2 -1

README.md CHANGED Viewed

@@ -41,10 +41,11 @@ We reserved 121 real software bugs that the model never saw during training. Bef
 | MMLU-Pro | 64.0% | 70.0% | **66.85%** |
 | Terminal-Bench 1.0 (core, 80 tasks) | 33.8% | 33.8% | **18.8%** |
 | HarmBench (harmful-behavior refusal rate, 159 standard behaviors) | 99.4% | 98.1% | **96.9%** |
 The instruction-following score *improved* over the base model. The coding gains cost nothing on general quality. Gains of this kind usually trade one for the other.
-Safety alignment survived both the RL training and the quantization: the refusal rate on HarmBench's 159 standard harmful behaviors holds at 96.9% (vs 98.1% for the BF16 and 99.4% for the base model), with the serious harm categories clean on all three. See the BF16 model card for the full safety verification.
 The NVFP4 quantization preserves instruction-following (within ~1 point of BF16) and trades real coding capability: Live-60 drops from 21.7% to 15.0%. The BF16 remains the best model; this variant trades that margin for a 40% smaller footprint and ~5GB VRAM.

 | MMLU-Pro | 64.0% | 70.0% | **66.85%** |
 | Terminal-Bench 1.0 (core, 80 tasks) | 33.8% | 33.8% | **18.8%** |
 | HarmBench (harmful-behavior refusal rate, 159 standard behaviors) | 99.4% | 98.1% | **96.9%** |
+| XSTest (benign-but-scary request compliance, 450 prompts) | 54.0% | 97.1% | **99.8%** |
 The instruction-following score *improved* over the base model. The coding gains cost nothing on general quality. Gains of this kind usually trade one for the other.
+Safety alignment survived both the RL training and the quantization: the refusal rate on HarmBench's 159 standard harmful behaviors holds at 96.9% (vs 98.1% for the BF16 and 99.4% for the base model), with the serious harm categories clean on all three. On the over-refusal side the quant answers 99.8% of benign-but-scary requests (vs 54.0% for the base model) — the deployment artifact's best safety number. See the BF16 model card for the full safety verification. See the BF16 model card for the full safety verification.
 The NVFP4 quantization preserves instruction-following (within ~1 point of BF16) and trades real coding capability: Live-60 drops from 21.7% to 15.0%. The BF16 remains the best model; this variant trades that margin for a 40% smaller footprint and ~5GB VRAM.