Instructions to use cstr/LFM2.5-Encoder-230M-GGUF 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 cstr/LFM2.5-Encoder-230M-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf cstr/LFM2.5-Encoder-230M-GGUF:F16 --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf cstr/LFM2.5-Encoder-230M-GGUF:F16 --embeddings
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible embeddings server: ./llama-server -hf cstr/LFM2.5-Encoder-230M-GGUF:F16 --embeddings
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 embeddings server: ./build/bin/llama-server -hf cstr/LFM2.5-Encoder-230M-GGUF:F16 --embeddings
- LM Studio
- Jan
- Ollama
How to use cstr/LFM2.5-Encoder-230M-GGUF with Ollama:
ollama run hf.co/cstr/LFM2.5-Encoder-230M-GGUF:F16
- Unsloth Desktop
- Docker Model Runner
How to use cstr/LFM2.5-Encoder-230M-GGUF with Docker Model Runner:
docker model run hf.co/cstr/LFM2.5-Encoder-230M-GGUF:F16
- Lemonade
How to use cstr/LFM2.5-Encoder-230M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cstr/LFM2.5-Encoder-230M-GGUF:F16
Run and chat with the model
lemonade run user.LFM2.5-Encoder-230M-GGUF-F16
List all available models
lemonade list
- Atomic Chat
Update model card: license=lfm1.0
Browse files
README.md
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## Run
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Build CrispEmbed from main at ef2cb170 or later (no release binary claim).
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```sh
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CRISPEMBED_LFM2_F32_DOT=1 crispembed -m lfm2-encoder-230m-mixed330 \
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## Run
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Build CrispEmbed from main at ef2cb170 or later (no release binary claim).
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Download and run the 330 MB model with:
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```sh
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CRISPEMBED_LFM2_F32_DOT=1 crispembed -m lfm2-encoder-230m-mixed330 \
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