Instructions to use chris0809/tinyLLM-0.51B-VLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chris0809/tinyLLM-0.51B-VLM with Transformers:

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

pipe = pipeline("image-text-to-text", model="chris0809/tinyLLM-0.51B-VLM", trust_remote_code=True)
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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("chris0809/tinyLLM-0.51B-VLM", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chris0809/tinyLLM-0.51B-VLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chris0809/tinyLLM-0.51B-VLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chris0809/tinyLLM-0.51B-VLM",
		"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/chris0809/tinyLLM-0.51B-VLM

SGLang

How to use chris0809/tinyLLM-0.51B-VLM 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 "chris0809/tinyLLM-0.51B-VLM" \
    --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": "chris0809/tinyLLM-0.51B-VLM",
		"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 "chris0809/tinyLLM-0.51B-VLM" \
        --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": "chris0809/tinyLLM-0.51B-VLM",
		"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 chris0809/tinyLLM-0.51B-VLM with Docker Model Runner:
```
docker model run hf.co/chris0809/tinyLLM-0.51B-VLM
```

tinyLLM VLM 0.51B

中文项目文档 · English project overview · Live demo

The local demo switches to the VLM checkpoint, encodes five image views, and streams the checked output.

tinyLLM is a custom 0.51B decoder-only model trained from scratch for Chinese and English. Loading requires trust_remote_code=True because the architecture is implemented in this repository.

This release is the step 18000 VLM checkpoint. It combines the language model with a 4-layer Q-Former, projector and rank-128 visual LoRA. The visual continual-SFT stage used 201,748 image examples plus 20% bilingual text replay.

Load

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "chris0809/tinyLLM-0.51B-VLM"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
)
model.load_lora_pretrained(model_id, subfolder="adapter")

Thinking mode

The bundled tokenizer also accepts enable_thinking=True in apply_chat_template, mainly for text-only mathematics or multi-step reasoning. Keep it off for ordinary image description; the GitHub demo uses a separate conservative visual system prompt to reduce hallucination.

Vision encoder

The frozen vision tower is OpenGVLab/InternViT-300M-448px-V2_5 and is loaded separately. This repository contains the language model, Q-Former, projector and the visual LoRA adapter. The model forward accepts precomputed vision_feats, vision_mask, global_pos and global_off tensors. The five-view image preprocessing and end-to-end chat path are in the GitHub project. This repository does not claim generic AutoProcessor support.

Source

Training code and full project documentation: https://github.com/Huanz86251/tinyLLM

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Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for chris0809/tinyLLM-0.51B-VLM

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Finetuned

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