Text Classification
Transformers
Safetensors
English
qwen3_5
image-text-to-text
unsloth
classifier
shell
bash
powershell
Instructions to use tomngdev/AutoShell-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tomngdev/AutoShell-0.8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tomngdev/AutoShell-0.8B")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tomngdev/AutoShell-0.8B") model = AutoModelForMultimodalLM.from_pretrained("tomngdev/AutoShell-0.8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use tomngdev/AutoShell-0.8B 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 tomngdev/AutoShell-0.8B 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 tomngdev/AutoShell-0.8B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tomngdev/AutoShell-0.8B to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="tomngdev/AutoShell-0.8B", max_seq_length=2048, )
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README.md
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@@ -25,6 +25,15 @@ Inspired by [mistralai/Shieldstral-1.0-3B](https://huggingface.co/mistralai/Shie
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Using [Qwen/Qwen3.5-0.8B-Base](Qwen/Qwen3.5-0.8B-Base) as base checkpoint and synthetic dataset from [tomngdev/shell-safety-common](https://huggingface.co/datasets/tomngdev/shell-safety-common),
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**AutoShell** is trained to identify which commands is safe or unsafe with a `<SessionContext></SessionContext> block`.
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## Usage
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### vLLM
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print(f"safe score = {score:.3f} -> {SAFE if flagged else unsafe}")
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```
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Using [Qwen/Qwen3.5-0.8B-Base](Qwen/Qwen3.5-0.8B-Base) as base checkpoint and synthetic dataset from [tomngdev/shell-safety-common](https://huggingface.co/datasets/tomngdev/shell-safety-common),
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**AutoShell** is trained to identify which commands is safe or unsafe with a `<SessionContext></SessionContext> block`.
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## Versions
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| Model | Description |
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| ----- | ----------- |
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| [**AutoShell-0.8B**](https://huggingface.co/tomngdev/AutoShell-0.8B) | Recommended for hosting |
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| [AutoShell-0.8B-GGUF](https://huggingface.co/tomngdev/AutoShell-0.8B-GGUF) | GGUF quantizations for 0.8B |
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| [AutoShell-350M](https://huggingface.co/tomngdev/AutoShell-350M) | Recommended for local CPU |
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| [AutoShell-350M-GGUF](https://huggingface.co/tomngdev/AutoShell-350M-GGUF) | GGUF quantizations for 350M |
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## Usage
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### vLLM
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print(f"safe score = {score:.3f} -> {SAFE if flagged else unsafe}")
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```
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## License
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Follow Qwen3.5-0.8B-Base [Apache 2.0 license](https://huggingface.co/Qwen/Qwen3.5-0.8B-Base/blob/main/LICENSE)
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