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
```

javierbits commited on 5 days ago

Commit

b4e9de0

verified ·

1 Parent(s): 658270e

docs: drop lmswitch

Browse files

Files changed (1) hide show

README.md +1 -7

README.md CHANGED Viewed

@@ -39,7 +39,7 @@ Merged bf16 safetensors (two shards, 8.8 GB), tokenizer, processor files and cha
 Thinking off, temperature 0.2, top-p 0.8, top-k 20.
-**GGUF** for llama.cpp, lmdeck or lmswitch: [Javi0.4-4B-GGUF](https://huggingface.co/javierbits/Javi0.4-4B-GGUF).
 **lmdeck**
@@ -47,12 +47,6 @@ Thinking off, temperature 0.2, top-p 0.8, top-k 20.
 lmdeck use javi0.4-4b
 ```
-**lmswitch**
-```bash
-lmswitch on javi0.4-4b
-```
 **Transformers**
 ```python

 Thinking off, temperature 0.2, top-p 0.8, top-k 20.
+**GGUF** for llama.cpp or lmdeck: [Javi0.4-4B-GGUF](https://huggingface.co/javierbits/Javi0.4-4B-GGUF).
 **lmdeck**
 lmdeck use javi0.4-4b
 ```
 **Transformers**
 ```python