How to use from
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 "prithivMLmods/jpt-4b-GGUF" \
    --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": "prithivMLmods/jpt-4b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'
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 "prithivMLmods/jpt-4b-GGUF" \
        --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": "prithivMLmods/jpt-4b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'
Quick Links

jpt-4b-GGUF

JPT-4B is a fast, open multimodal decision model built by kirp and based on Qwen3.5-4B. It is designed for typed decision-making, accepting a given situation with structured questions and returning calibrated probabilities for each available option in a single forward pass. It supports choice, score, and noul question types, with support for text as well as screenshots, photos, and video frames through its unchanged vision tower. JPT-4B is implemented as a merged LoRA fine-tune and is designed for low-latency decision inference without generated explanations or reasoning tokens. JPT-4B on Hugging Face

Model Files

File Name Quant Type File Size File Link Description
jpt-4b.BF16.gguf BF16 8.42 GB Link Full BF16 weights. Highest quality, largest file size.
jpt-4b.Q3_K_L.gguf Q3_K_L 2.42 GB Link Lower quality but usable, good for low RAM availability.
jpt-4b.Q3_K_M.gguf Q3_K_M 2.26 GB Link Low quality.
jpt-4b.Q4_K_M.gguf Q4_K_M 2.71 GB Link Good quality, default size for most use cases, recommended.
jpt-4b.Q4_K_S.gguf Q4_K_S 2.56 GB Link Slightly lower quality with more space savings, recommended.
jpt-4b.Q5_K_M.gguf Q5_K_M 3.07 GB Link High quality, recommended.
jpt-4b.Q5_K_S.gguf Q5_K_S 2.99 GB Link High quality, recommended.
jpt-4b.mmproj-bf16.gguf mmproj-bf16 676 MB Link Multimodal projection file in BF16 format. Used for vision/language models.

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp

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GGUF
Model size
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Architecture
qwen35
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