Instructions to use mjschock/SmolVLM-Instruct-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjschock/SmolVLM-Instruct-SFT with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mjschock/SmolVLM-Instruct-SFT", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio
How to use mjschock/SmolVLM-Instruct-SFT with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mjschock/SmolVLM-Instruct-SFT to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mjschock/SmolVLM-Instruct-SFT to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mjschock/SmolVLM-Instruct-SFT to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="mjschock/SmolVLM-Instruct-SFT", max_seq_length=2048, )
Training in progress, step 4
Browse files
README.md
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base_model: HuggingFaceTB/SmolVLM-Instruct
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model_name: SmolVLM-Instruct-SFT
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- generated_from_trainer
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### Framework versions
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- TRL: 0.13.0
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- Transformers: 4.48.0.dev0
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- Pytorch: 2.5.1
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base_model: HuggingFaceTB/SmolVLM-Instruct
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library_name: peft
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model_name: SmolVLM-Instruct-SFT
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tags:
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- generated_from_trainer
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### Framework versions
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- PEFT 0.14.0
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- TRL: 0.13.0
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- Transformers: 4.48.0.dev0
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- Pytorch: 2.5.1
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runs/Dec31_18-42-03_pop-os/events.out.tfevents.1735699326.pop-os.232791.0
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