Text Classification
Transformers
Safetensors
English
lfm2
text-generation
unsloth
classifier
shell
bash
powershell
Instructions to use tomngdev/AutoShell-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tomngdev/AutoShell-350M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tomngdev/AutoShell-350M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tomngdev/AutoShell-350M") model = AutoModelForCausalLM.from_pretrained("tomngdev/AutoShell-350M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use tomngdev/AutoShell-350M 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-350M 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-350M 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-350M to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="tomngdev/AutoShell-350M", max_seq_length=2048, )
File size: 1,365 Bytes
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"architectures": [
"Lfm2ForCausalLM"
],
"block_auto_adjust_ff_dim": true,
"block_dim": 1024,
"block_ffn_dim_multiplier": 1.0,
"block_mlp_init_scale": 1.0,
"block_multiple_of": 256,
"block_norm_eps": 1e-05,
"block_out_init_scale": 1.0,
"block_use_swiglu": true,
"block_use_xavier_init": true,
"bos_token_id": 1,
"conv_L_cache": 3,
"conv_bias": false,
"conv_dim": 1024,
"conv_use_xavier_init": true,
"dtype": "float32",
"eos_token_id": 7,
"full_attn_idxs": null,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 6656,
"layer_types": [
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv"
],
"max_position_embeddings": 128000,
"model_name": "LiquidAI/LFM2.5-350M-Base",
"model_type": "lfm2",
"norm_eps": 1e-05,
"num_attention_heads": 16,
"num_heads": 16,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": 0,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"tie_word_embeddings": true,
"transformers_version": "5.15.0",
"unsloth_version": "2026.8.19",
"use_cache": true,
"use_pos_enc": true,
"vocab_size": 65536
}
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