Text Generation
Transformers
Safetensors
qwen3_5_text
agentic-coding
reasoning
tool-use
on-device
laptop-scale
sft
reinforcement-learning
conversational
Instructions to use jsbaicenter/Aztec-Coder-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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=256) 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
Commit History
19 days ago
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Upload README.md with huggingface_hub
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Add preview-release notice: research preview, not recommended for production until 1.0 with full evaluations
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Revert premature re-evaluation annotation; hold card updates until the re-measurement completes
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IMPORTANT: Mark generalization and Live-60 rows as under re-evaluation due to discovered RL environment flaws (git-checkout shortcut + broken edit tool). Safety results unaffected. Full re-measurement in progress.
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20 days ago
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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.
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Fill NVFP4 XSTest cell: 99.8% benign compliance (the workstation's leg)
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Add XSTest over-refusal results: 97.1% benign compliance (base 54.0%), 0% over-refusal — both safety axes verified
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Add NVFP4 HarmBench safety cell: 96.9% refusal (quant noise pushes 2 borderline cases; hard-harm categories clean)
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Add HarmBench safety verification: 98.1% refusal vs base 99.4% (159 standard behaviors, judge-confirmed across two independent runs)
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22 days ago
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