Text Generation
Transformers
Safetensors
qwen3_5_text
agentic-coding
reasoning
tool-use
on-device
laptop-scale
sft
reinforcement-learning
conversational
Instructions to use jsbaicenter/JSBAI-Coder-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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library_name: transformers
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pipeline_tag: text-generation
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# JSBAI-Coder-4B
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library_name: transformers
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pipeline_tag: text-generation
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---
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/69b0868703a7e83e476092ec/RIr7Vy21qoIEFJrMAE8qn.png" alt="SDSU JSBCAI Center Logo" width="400">
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</p>
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# James Silberrad Brown Center for AI Research
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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.
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The Center brings together faculty, researchers, and students across disciplines including business, engineering, psychology, public health, computer science, and the social sciences to develop AI systems that address real-world challenges while prioritizing ethical and human-centered design.
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# JSBAI-Coder-4B
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