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 5 days ago

Commit

acf504d

verified ·

1 Parent(s): cdc43fb

Upload hf_quant_config.json with huggingface_hub

Browse files

Files changed (1) hide show

hf_quant_config.json +46 -0

hf_quant_config.json ADDED Viewed

	@@ -0,0 +1,46 @@

+{
+    "producer": {
+        "name": "modelopt",
+        "version": "0.44.0"
+    },
+    "quantization": {
+        "quant_algo": "NVFP4",
+        "kv_cache_quant_algo": null,
+        "group_size": 16,
+        "exclude_modules": [
+            "lm_head",
+            "model.layers.0.linear_attn*",
+            "model.layers.1.linear_attn*",
+            "model.layers.10.linear_attn*",
+            "model.layers.11.self_attn*",
+            "model.layers.12.linear_attn*",
+            "model.layers.13.linear_attn*",
+            "model.layers.14.linear_attn*",
+            "model.layers.15.self_attn*",
+            "model.layers.16.linear_attn*",
+            "model.layers.17.linear_attn*",
+            "model.layers.18.linear_attn*",
+            "model.layers.19.self_attn*",
+            "model.layers.2.linear_attn*",
+            "model.layers.20.linear_attn*",
+            "model.layers.21.linear_attn*",
+            "model.layers.22.linear_attn*",
+            "model.layers.23.self_attn*",
+            "model.layers.24.linear_attn*",
+            "model.layers.25.linear_attn*",
+            "model.layers.26.linear_attn*",
+            "model.layers.27.self_attn*",
+            "model.layers.28.linear_attn*",
+            "model.layers.29.linear_attn*",
+            "model.layers.3.self_attn*",
+            "model.layers.30.linear_attn*",
+            "model.layers.31.self_attn*",
+            "model.layers.4.linear_attn*",
+            "model.layers.5.linear_attn*",
+            "model.layers.6.linear_attn*",
+            "model.layers.7.self_attn*",
+            "model.layers.8.linear_attn*",
+            "model.layers.9.linear_attn*"
+        ]
+    }
+}