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 4 hours ago

Commit

20eaa79

verified ·

1 Parent(s): 5052d07

Correct generalization row: audit found 27 of 121 gate instances had entered the training pool pre-measurement; honest split (94 never-trained: 68.1%/60.8%; 27 overlapping: 81.5%/73.6%) replaces the inflated 82.9%. Correction note added with methodology disclosure.

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -33,7 +33,7 @@ We reserved 121 real software bugs that the model never saw during training. Bef
 | Benchmark | Qwen3.5-4B (base) | **Aztec-Coder-4B** | Aztec-Coder-4B-NVFP4 |
 |---|---|---|---|
-| **Generalization test** (121 unseen bugs, tests run to verify) | 10.1% | **82.9%** | 38.0%* |
 | **Live-60** (60 real-world engineering tasks, solved end-to-end in containers) | 15.0% | **21.7%** | 15.0% |
 | **Instruction-following** (IFEval) | 84.66 | **87.21** | 86.37 |
 | MMLU-Pro | 64.0% | **70.0%** | 66.85% |
@@ -66,7 +66,7 @@ Three stages, each with a plain-language summary:
 2. **Reinforcement learning on real bugs.** The model then practiced on 237 curated software engineering problems: ones it could sometimes solve, but not reliably. For each problem, the model repeatedly attempted a fix. Solutions that made the real hidden tests pass were reinforced; failures were not. This phase, 145 batches of on-policy GRPO, built the actual problem-solving ability.
-3. **Generalization checks.** At every stage boundary, we re-tested the model on problems it had never trained on. The 10.1% to 82.9% jump above is the result.
 ### Training data

 | Benchmark | Qwen3.5-4B (base) | **Aztec-Coder-4B** | Aztec-Coder-4B-NVFP4 |
 |---|---|---|---|
+| **Generalization test** (121 never-trained bugs, tests run to verify) | 10.1% | **68.1%** (instance) / 60.8% (per-attempt) on the 94 fully held-out; 81.5%/73.6% on 27 later-found overlapping | 38.0%* |
 | **Live-60** (60 real-world engineering tasks, solved end-to-end in containers) | 15.0% | **21.7%** | 15.0% |
 | **Instruction-following** (IFEval) | 84.66 | **87.21** | 86.37 |
 | MMLU-Pro | 64.0% | **70.0%** | 66.85% |
 2. **Reinforcement learning on real bugs.** The model then practiced on 237 curated software engineering problems: ones it could sometimes solve, but not reliably. For each problem, the model repeatedly attempted a fix. Solutions that made the real hidden tests pass were reinforced; failures were not. This phase, 145 batches of on-policy GRPO, built the actual problem-solving ability.
+3. **Generalization checks.** At every stage boundary, we re-tested the model on problems it had never trained on. *Correction (2026-09-29): an earlier version of this card reported 82.9% on the full 121-instance set. A post-release audit found 27 of those instances had entered the training pool before the measurement, inflating the number. The corrected figures above separate the 94 genuinely never-trained instances (68.1% instance-level, 60.8% per-attempt) from the 27 overlapping ones (81.5%/73.6%). The training gains remain large: the same 94 instances went from 7.0% before training to 60.8% after.*
 ### Training data