Instructions to use javierbits/Javi0.3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use javierbits/Javi0.3-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="javierbits/Javi0.3-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("javierbits/Javi0.3-4B")
model = AutoModelForMultimodalLM.from_pretrained("javierbits/Javi0.3-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use javierbits/Javi0.3-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "javierbits/Javi0.3-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "javierbits/Javi0.3-4B",
		"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

docker model run hf.co/javierbits/Javi0.3-4B

SGLang

How to use javierbits/Javi0.3-4B 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 "javierbits/Javi0.3-4B" \
    --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": "javierbits/Javi0.3-4B",
		"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 "javierbits/Javi0.3-4B" \
        --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": "javierbits/Javi0.3-4B",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use javierbits/Javi0.3-4B with Docker Model Runner:
```
docker model run hf.co/javierbits/Javi0.3-4B
```

Javier commited on 18 days ago

Commit

34fc9bb

verified ·

1 Parent(s): 1f7d593

Card: lead with first-principles reasoning and never guessing

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ tags:
 # Javi0.3-4B
-A 4B agent model that looks things up instead of guessing, says plainly when it can't verify something, and works around a failed tool instead of looping or giving up. Fine-tuned from [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) to work inside coding and research agents ([grok build](https://x.ai), [omp](https://www.npmjs.com/package/@oh-my-pi/pi-coding-agent), [opencode](https://opencode.ai)) with their own system prompts and tools.
 This repo holds the merged bf16 weights. For llama.cpp or [lmswitch](https://github.com/jvr0x/lmswitch), use [Javi0.3-4B-GGUF](https://huggingface.co/jvr0x/Javi0.3-4B-GGUF). Smaller sibling: [Javi0.3-2B](https://huggingface.co/jvr0x/Javi0.3-2B).

 # Javi0.3-4B
+A 4B agent model trained to think from first principles and use its tools effectively to find the answer to your prompt, or, when the tools can't get there, to guide you to it. It's trained to never guess: if it can't verify something or doesn't know, it says so. When a tool fails, it works out why and takes another route instead of looping or giving up. Fine-tuned from [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) to work inside coding and research agents ([grok build](https://x.ai), [omp](https://www.npmjs.com/package/@oh-my-pi/pi-coding-agent), [opencode](https://opencode.ai)) with their own system prompts and tools.
 This repo holds the merged bf16 weights. For llama.cpp or [lmswitch](https://github.com/jvr0x/lmswitch), use [Javi0.3-4B-GGUF](https://huggingface.co/jvr0x/Javi0.3-4B-GGUF). Smaller sibling: [Javi0.3-2B](https://huggingface.co/jvr0x/Javi0.3-2B).