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 15 days ago

Commit

82b12f2

verified ·

1 Parent(s): 4a69a0e

Upload README.md with huggingface_hub

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README.md +13 -5

README.md CHANGED Viewed

@@ -16,22 +16,30 @@ datasets:
 - nvidia/Nemotron-Post-Training-Dataset-v2
 ---
 # James Silberrad Brown Center for AI Research
 The **James Silberrad Brown Center for Artificial Intelligence (JSBCAI)** is an interdisciplinary research hub at San Diego State University dedicated to advancing artificial intelligence through foundational research, applied innovation, and student-driven inquiry.
-# JSBAI-Coder-4B
 **An agentic coding model that runs on your laptop.**
-JSBAI-Coder-4B is a 4B-parameter model, fine-tuned from Qwen3.5-4B, that investigates bugs, edits files, runs commands, and verifies its own fixes in real software repositories. Agentic coding at this level has required 27B+ models. This one fits on a consumer GPU.
 ## Results
 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%** | 38.0%* |
 | **Live-60** (60 real-world engineering tasks, solved end-to-end in containers) | 15.0% | **21.7%** | 15.0% |
@@ -73,7 +81,7 @@ Three stages, each with a plain-language summary:
 ```python
 from vllm import LLM
-llm = LLM(model="jsbaicenter/JSBAI-Coder-4B", max_model_len=131072)
 ```
 Recommended sampling: `temperature 1.0, top_p 0.95`. The model uses the Qwen3.5 chat template with interleaved thinking (the `qwen3` reasoning parser in vLLM) and `qwen3_coder` tool-call format.
@@ -102,7 +110,7 @@ Apache-2.0, matching the base model.
 ```bibtex
 @misc{jsbai_coder_4b,
-  title={JSBAI-Coder-4B: Agentic Coding at Laptop Scale via Teacher-Seeded RL},
   author={James Silberrad Brown Center for AI},
   year={2026},
   publisher={HuggingFace}

 - nvidia/Nemotron-Post-Training-Dataset-v2
 ---
+# ⚠️ This model has moved
+**JSBAI-Coder-4B has been renamed to [Aztech-4B-Coder](https://huggingface.co/jsbaicenter/Aztech-4B-Coder).**
+Please use the new repository for downloads and citations. This repo remains for continuity.
+---
 # James Silberrad Brown Center for AI Research
 The **James Silberrad Brown Center for Artificial Intelligence (JSBCAI)** is an interdisciplinary research hub at San Diego State University dedicated to advancing artificial intelligence through foundational research, applied innovation, and student-driven inquiry.
+# Aztech-4B-Coder
 **An agentic coding model that runs on your laptop.**
+Aztech-4B-Coder is a 4B-parameter model, fine-tuned from Qwen3.5-4B, that investigates bugs, edits files, runs commands, and verifies its own fixes in real software repositories. Agentic coding at this level has required 27B+ models. This one fits on a consumer GPU.
 ## Results
 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) | **Aztech-4B-Coder** | Aztech-4B-Coder-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% |
 ```python
 from vllm import LLM
+llm = LLM(model="jsbaicenter/Aztech-4B-Coder", max_model_len=131072)
 ```
 Recommended sampling: `temperature 1.0, top_p 0.95`. The model uses the Qwen3.5 chat template with interleaved thinking (the `qwen3` reasoning parser in vLLM) and `qwen3_coder` tool-call format.
 ```bibtex
 @misc{jsbai_coder_4b,
+  title={Aztech-4B-Coder: Agentic Coding at Laptop Scale via Teacher-Seeded RL},
   author={James Silberrad Brown Center for AI},
   year={2026},
   publisher={HuggingFace}