Instructions to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M # Run inference directly in the terminal: llama cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M # Run inference directly in the terminal: llama cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
Use Docker
docker model run hf.co/nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with Ollama:
ollama run hf.co/nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with Docker Model Runner:
docker model run hf.co/nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
- Lemonade
How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
Run and chat with the model
lemonade run user.SmolLM2-360M-Assignment-Metadata-Extractor-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 417 Bytes
a073422 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<|endoftext|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"errors": "replace",
"extra_special_tokens": [],
"is_local": false,
"model_max_length": 8192,
"pad_token": "<|endoftext|>",
"padding_side": "left",
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|endoftext|>",
"vocab_size": 49152
}
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