Instructions to use starvector/starvector-8b-im2svg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use starvector/starvector-8b-im2svg with Transformers:

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

pipe = pipeline("text-generation", model="starvector/starvector-8b-im2svg", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("starvector/starvector-8b-im2svg", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use starvector/starvector-8b-im2svg with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "starvector/starvector-8b-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-8b-im2svg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/starvector/starvector-8b-im2svg

SGLang

How to use starvector/starvector-8b-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-8b-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-8b-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-8b-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-8b-im2svg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use starvector/starvector-8b-im2svg with Docker Model Runner:
```
docker model run hf.co/starvector/starvector-8b-im2svg
```

joanrodai commited on Feb 3, 2025

Commit

4760193

verified ·

1 Parent(s): 3e83e79

Update starvector architecture file

Browse files

Files changed (1) hide show

starvector_arch.py +5 -4

starvector_arch.py CHANGED Viewed

@@ -2,7 +2,7 @@ from transformers import (
     PretrainedConfig,
     PreTrainedModel
 )
 class StarVectorConfig(PretrainedConfig):
     model_type = "starvector"
@@ -18,9 +18,10 @@ class StarVectorConfig(PretrainedConfig):
         use_cache: bool = True,
         num_attention_heads: int = 16,
         num_hidden_layers: int = 24,
-        vocab_size: int = 32000,
-        hidden_size: int = 1024,
         num_kv_heads: int = 4,
         **kwargs,
     ):
         self.starcoder_model_name = starcoder_model_name
@@ -36,7 +37,7 @@ class StarVectorConfig(PretrainedConfig):
         self.vocab_size = vocab_size
         self.hidden_size = hidden_size
         self.num_kv_heads = num_kv_heads
         super().__init__(**kwargs)
 class StarVectorForCausalLM(PreTrainedModel):

     PretrainedConfig,
     PreTrainedModel
 )
+import torch
 class StarVectorConfig(PretrainedConfig):
     model_type = "starvector"
         use_cache: bool = True,
         num_attention_heads: int = 16,
         num_hidden_layers: int = 24,
+        vocab_size: int = 49152,
+        hidden_size: int = 2048,
         num_kv_heads: int = 4,
+        torch_dtype: str = "bfloat16",
         **kwargs,
     ):
         self.starcoder_model_name = starcoder_model_name
         self.vocab_size = vocab_size
         self.hidden_size = hidden_size
         self.num_kv_heads = num_kv_heads
+        self.torch_dtype = torch_dtype
         super().__init__(**kwargs)
 class StarVectorForCausalLM(PreTrainedModel):