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 1 day ago

Commit

f6ca4bd

verified ·

1 Parent(s): f6a24c3

Add NVFP4 HarmBench safety cell: 96.9% refusal (quant noise pushes 2 borderline cases; hard-harm categories clean)

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -39,11 +39,11 @@ 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% |
 | Terminal-Bench 2.1 (89 tasks, both models, same protocol) | 14.6% | 11.2% | not evaluated |
-| HarmBench (harmful-behavior refusal rate, 159 standard behaviors) | 99.4% | **98.1%** | not evaluated |
 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.
-We also verified that safety alignment survived training. Each judge-confirmed refusal test used HarmBench's standard set of 159 harmful behaviors. The base model refuses 99.4% of them; our model refuses 98.1%. The serious harm categories (chemical and biological, illegal activity, harassment) are clean on both models. The few requests each model does answer are edge cases, like writing a persuasive article about a disputed topic, and they barely overlap between the two models. The refusals were confirmed by two independent runs of the official HarmBench classifier, with identical results.
 *NVFP4 generalization: 12/32 on a 32-instance subset (the same slice our comparisons use). The quantization costs roughly half the generalization capability.

 | MMLU-Pro | 64.0% | **70.0%** | 66.85% |
 | Terminal-Bench 1.0 (core, 80 tasks) | 33.8% | **33.8%** | 18.8% |
 | Terminal-Bench 2.1 (89 tasks, both models, same protocol) | 14.6% | 11.2% | not evaluated |
+| 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.
+We also verified that safety alignment survived training. Each judge-confirmed refusal test used HarmBench's standard set of 159 harmful behaviors. The base model refuses 99.4% of them; our model refuses 98.1%, and the NVFP4 quantized version refuses 96.9%. The serious harm categories (chemical and biological, illegal activity, harassment) are clean on all three models. The few requests each model does answer are edge cases, like writing a persuasive article about a disputed topic, and they barely overlap between the models. The refusals were confirmed by two independent runs of the official HarmBench classifier, with identical results.
 *NVFP4 generalization: 12/32 on a 32-instance subset (the same slice our comparisons use). The quantization costs roughly half the generalization capability.