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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="javierbits/Javi0.4-4B")
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/Javi0.4-4B")
model = AutoModelForMultimodalLM.from_pretrained("javierbits/Javi0.4-4B", 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/Javi0.4-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "javierbits/Javi0.4-4B"
# 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/Javi0.4-4B",
		"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/Javi0.4-4B

SGLang

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

Docker Model Runner
How to use javierbits/Javi0.4-4B with Docker Model Runner:
```
docker model run hf.co/javierbits/Javi0.4-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Javi0.4-4B (control)

Javi0.4-4B is Javi0.3-4B with the same embedding row changed: token 51187 (=open_page) is multiplied by 0.84. No fine-tuning, no new data, no gradients. Every other weight is byte-identical to Javi0.3-4B.

It exists as the 4B control for the Javi0.4 weight-space experiment: the same row edit applied to the larger model.

Measured effect

On 8 held-out eval-v3 canonical fail questions with the first web_search forced to fail, Javi0.3-4B already chose web_search in 4/4 cases, so that probe sees no change on 4B. On an opencode 403 replay, both models fetch another typed URL instead of searching again.

Full eval v3

175 held-out seeds, 3 runs, date pin 2026-10-07, cached search results. Javi0.3-4B answers on the same seeds are the baseline. Judges: DeepSeek-V4.1-Flash-1017 and mimo-v26-dual.

metric	Javi0.3-4B	Javi0.4-4B
core pass (judge average)	0.50 [0.45, 0.55]	0.56 [0.52, 0.61]
core pass (deepseek)	0.64	0.74
core pass (mimo)	0.36	0.39
recovery after a tool failure	0.58 (n=12)	0.75 (n=12)
made-up links	0.01	0.00
loops	0.03	0.02

Paired core pass difference: +0.06 [0.03, 0.10] (deepseek +0.10 [0.05, 0.14], mimo +0.04 [0.00, 0.07]), 95% bootstrap over 175 seeds. The deepseek interval clears zero; the mimo interval touches it. Conflict questions are the one category down (0.26 vs 0.40, n=7, directional at that n); unknowable questions are up (0.55 vs 0.32, n=10).

Two answer episodes (2 seeds, one per run) carry no deepseek verdict because the judge omits the tools or style key; both seeds are judged in the other runs, so the table covers 175 seeds. Incomplete episodes (7 across the three runs) are scored 0 for core pass and skipped by the judges. Full scoreboard: data/v2/eval/javi04-vs-03/score.md in the source tree.

This control measures slightly above Javi0.3-4B, but the targeted probe cannot attribute the gain to the open_page row alone. The 4B's remaining recovery gap in the webfetch/web_fetch route is untouched by the edit.

Files

Merged bf16 safetensors (two shards, 8.8 GB), tokenizer, processor files and chat template. The vision tower is Qwen3.5-4B's, unchanged; the edit is in the language model only.

Run it

Thinking off, temperature 0.2, top-p 0.8, top-k 20.

GGUF for llama.cpp or lmdeck: Javi0.4-4B-GGUF.

lmdeck

lmdeck use javi0.4-4b

Transformers

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "javierbits/Javi0.4-4B"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="auto", device_map="auto")

messages = [{"role": "user", "content": [{"type": "text", "text": "What's the latest stable Rust release?"}]}]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False,
                                       tokenize=True, return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.2, top_p=0.8, top_k=20)
print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

The edit

embed_tokens[51187] *= 0.84  # =open_page

Token 51187 is a single token in the Qwen3.5 tokenizer. The row is tied between input and output embeddings, so the output logit for =open_page is reduced.