Instructions to use starvector/starvector-1b-im2svg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use starvector/starvector-1b-im2svg with Transformers:

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

pipe = pipeline("text-generation", model="starvector/starvector-1b-im2svg", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("starvector/starvector-1b-im2svg", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use starvector/starvector-1b-im2svg with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "starvector/starvector-1b-im2svg"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "starvector/starvector-1b-im2svg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/starvector/starvector-1b-im2svg

SGLang

How to use starvector/starvector-1b-im2svg 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 "starvector/starvector-1b-im2svg" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "starvector/starvector-1b-im2svg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "starvector/starvector-1b-im2svg" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "starvector/starvector-1b-im2svg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use starvector/starvector-1b-im2svg with Docker Model Runner:
```
docker model run hf.co/starvector/starvector-1b-im2svg
```

starvector-1b-im2svg

File size: 922 Bytes

{
  "_name_or_path": "ServiceNow/starvector-1b-im2svg",
  "adapter_norm": "batch_norm",
  "adapter_size": "large",
  "architectures": [
    "StarVectorForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "starvector_arch.StarVectorConfig",
    "AutoModelForCausalLM": "starvector_arch.StarVectorForCausalLM"
  },
  "dropout": 0.1,
  "hidden_size": 2048,
  "hidden_size_scale": 2,
  "image_encoder_type": "clip",
  "image_size": 224,
  "init_type": "glorot",
  "max_length_train": 8192,
  "max_position_embeddings": 8192,
  "model_type": "starvector",
  "multi_query": true,
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "num_kv_heads": 4,
  "starcoder_model_name": "bigcode/starcoderbase-1b",
  "torch_dtype": "float16",
  "train_LLM": true,
  "train_image_encoder": false,
  "transformers_version": "4.40.1",
  "use_cache": true,
  "use_flash_attn": true,
  "vocab_size": 49156,
  "image_token_index": 49154
}