Instructions to use lsadouk1111/VisualStep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lsadouk1111/VisualStep with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/phi-4-mini-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "lsadouk1111/VisualStep") - Transformers
How to use lsadouk1111/VisualStep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lsadouk1111/VisualStep") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lsadouk1111/VisualStep", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lsadouk1111/VisualStep with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lsadouk1111/VisualStep" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lsadouk1111/VisualStep", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lsadouk1111/VisualStep
- SGLang
How to use lsadouk1111/VisualStep 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 "lsadouk1111/VisualStep" \ --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": "lsadouk1111/VisualStep", "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 "lsadouk1111/VisualStep" \ --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": "lsadouk1111/VisualStep", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use lsadouk1111/VisualStep with Docker Model Runner:
docker model run hf.co/lsadouk1111/VisualStep
Download checkpoint-600/trainer_state.json from lsadouk1111/VisualStep: direct link, hf CLI and curl.
- Browser
- Download file 5.22 kB
-
https://huggingface.co/lsadouk1111/VisualStep/resolve/main/checkpoint-600/trainer_state.json
- Command line
-
hf download hf://lsadouk1111/VisualStep/checkpoint-600/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/lsadouk1111/VisualStep/resolve/main/checkpoint-600/trainer_state.json
5.22 kB
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