Instructions to use gitcat404/IntroSVG-Qwen2.5-VL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gitcat404/IntroSVG-Qwen2.5-VL-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="gitcat404/IntroSVG-Qwen2.5-VL-7B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("gitcat404/IntroSVG-Qwen2.5-VL-7B")
model = AutoModelForMultimodalLM.from_pretrained("gitcat404/IntroSVG-Qwen2.5-VL-7B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gitcat404/IntroSVG-Qwen2.5-VL-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gitcat404/IntroSVG-Qwen2.5-VL-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gitcat404/IntroSVG-Qwen2.5-VL-7B",
		"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/gitcat404/IntroSVG-Qwen2.5-VL-7B

SGLang

How to use gitcat404/IntroSVG-Qwen2.5-VL-7B 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 "gitcat404/IntroSVG-Qwen2.5-VL-7B" \
    --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": "gitcat404/IntroSVG-Qwen2.5-VL-7B",
		"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 "gitcat404/IntroSVG-Qwen2.5-VL-7B" \
        --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": "gitcat404/IntroSVG-Qwen2.5-VL-7B",
		"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 gitcat404/IntroSVG-Qwen2.5-VL-7B with Docker Model Runner:
```
docker model run hf.co/gitcat404/IntroSVG-Qwen2.5-VL-7B
```

IntroSVG-Qwen2.5-VL-7B / llamaboard_config.yaml

gitcat404

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	top.booster: auto
	top.checkpoint_path: null
	top.finetuning_type: full
	top.model_name: Qwen2.5-VL-7B-Instruct
	top.quantization_bit: none
	top.quantization_method: bitsandbytes
	top.rope_scaling: none
	top.template: qwen2_vl
	train.additional_target: ''
	train.apollo_rank: 16
	train.apollo_scale: 32
	train.apollo_target: all
	train.apollo_update_interval: 200
	train.badam_mode: layer
	train.badam_switch_interval: 50
	train.badam_switch_mode: ascending
	train.badam_update_ratio: 0.05
	train.batch_size: 2
	train.compute_type: bf16
	train.create_new_adapter: false
	train.cutoff_len: 6000
	train.dataset:
	- critic_training_data
	- refined
	- omnisvg
	- svgen
	- llm4svg
	train.dataset_dir: data
	train.ds_offload: false
	train.ds_stage: '3'
	train.extra_args: '{"optim": "adamw_torch"}'
	train.freeze_extra_modules: ''
	train.freeze_trainable_layers: 2
	train.freeze_trainable_modules: all
	train.galore_rank: 16
	train.galore_scale: 2
	train.galore_target: all
	train.galore_update_interval: 200
	train.gradient_accumulation_steps: 8
	train.learning_rate: 1e-4
	train.logging_steps: 21
	train.lora_alpha: 16
	train.lora_dropout: 0
	train.lora_rank: 8
	train.lora_target: ''
	train.loraplus_lr_ratio: 0
	train.lr_scheduler_type: cosine
	train.mask_history: false
	train.max_grad_norm: '1.0'
	train.max_samples: '1000000'
	train.neat_packing: false
	train.neftune_alpha: 0
	train.num_train_epochs: '3.0'
	train.packing: false
	train.ppo_score_norm: false
	train.ppo_whiten_rewards: false
	train.pref_beta: 0.1
	train.pref_ftx: 0
	train.pref_loss: sigmoid
	train.report_to:
	- none
	train.resize_vocab: false
	train.reward_model: []
	train.save_steps: 5000
	train.swanlab_api_key: ''
	train.swanlab_mode: cloud
	train.swanlab_project: llamafactory
	train.swanlab_run_name: ''
	train.swanlab_workspace: ''
	train.train_on_prompt: false
	train.training_stage: Supervised Fine-Tuning
	train.use_apollo: false
	train.use_badam: false
	train.use_dora: false
	train.use_galore: false
	train.use_llama_pro: false
	train.use_pissa: false
	train.use_rslora: false
	train.use_swanlab: false
	train.val_size: 0.03
	train.warmup_steps: 0