Instructions to use tiny-random/step3-vl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/step3-vl with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/step3-vl", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("tiny-random/step3-vl", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tiny-random/step3-vl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/step3-vl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/step3-vl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/step3-vl

SGLang

How to use tiny-random/step3-vl 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 "tiny-random/step3-vl" \
    --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": "tiny-random/step3-vl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "tiny-random/step3-vl" \
        --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": "tiny-random/step3-vl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/step3-vl with Docker Model Runner:
```
docker model run hf.co/tiny-random/step3-vl
```

yujiepan commited on Jan 28

Commit

f286307

verified ·

1 Parent(s): 48fbf47

Upload folder using huggingface_hub

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README.md +2 -0

README.md CHANGED Viewed

@@ -168,6 +168,7 @@ save_file(state_dict, f"{save_folder}/model.safetensors")
 ### Printing the model:
 <details><summary>Click to expand</summary>
 ```text
 Step3VL10BForCausalLM(
   (model): StepRoboticsModel(
@@ -227,4 +228,5 @@ Step3VL10BForCausalLM(
   (lm_head): Linear(in_features=8, out_features=151936, bias=False)
 )
 ```
 </details>

 ### Printing the model:
 <details><summary>Click to expand</summary>
 ```text
 Step3VL10BForCausalLM(
   (model): StepRoboticsModel(
   (lm_head): Linear(in_features=8, out_features=151936, bias=False)
 )
 ```
 </details>