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

Libraries

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

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

pipe = pipeline("text-generation", model="jsbaicenter/JSBAI-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/JSBAI-Coder-4B")
model = AutoModelForCausalLM.from_pretrained("jsbaicenter/JSBAI-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/JSBAI-Coder-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsbaicenter/JSBAI-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/JSBAI-Coder-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

jsbai-aaron commited on Sep 24

Commit

9083257

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1 Parent(s): c47c36e

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@@ -26,13 +26,13 @@ JSBAI-Coder-4B is a 4B-parameter model, fine-tuned from Qwen3.5-4B, that investi
 We reserved 121 real software bugs that the model never saw during training. Before fine-tuning, it solved 10% of them. After training, it solved **83%**, verified by running each project's hidden test suite. The model learned to fix bugs in general, not just the ones it practiced on.
-| Benchmark | Qwen3.5-4B (base) | **JSBAI-Coder-4B** | Change |
 |---|---|---|---|
-| **Generalization test** (121 unseen bugs, tests run to verify) | 10.1% | **82.9%** | **+72.8 points** |
-| **Live-60** (60 real-world engineering tasks, solved end-to-end in containers) | 15.0% | **21.7%** | **+44% relative** |
-| **Instruction-following** (IFEval) | 84.66 | **87.21** | +2.55 |
-| MMLU-Pro | TBD | TBD | TBD |
-| Terminal-Bench 2.1 | TBD | TBD | TBD |
 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 reserved 121 real software bugs that the model never saw during training. Before fine-tuning, it solved 10% of them. After training, it solved **83%**, verified by running each project's hidden test suite. The model learned to fix bugs in general, not just the ones it practiced on.
+| Benchmark | Qwen3.5-4B (base) | **JSBAI-Coder-4B** | JSBAI-Coder-4B-NVFP4 |
 |---|---|---|---|
+| **Generalization test** (121 unseen bugs, tests run to verify) | 10.1% | **82.9%** | coming soon |
+| **Live-60** (60 real-world engineering tasks, solved end-to-end in containers) | 15.0% | **21.7%** | coming soon |
+| **Instruction-following** (IFEval) | 84.66 | **87.21** | coming soon |
+| MMLU-Pro | TBD | TBD | coming soon |
+| Terminal-Bench 2.1 | TBD | TBD | coming soon |
 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.