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Etherll/LFM2-350M-Q4_K_M-GGUF overview

Etherll/LFM2 350M Q4 K M GGUF This model was converted to GGUF format from LiquidAI/LFM2 350M https://huggingface.co/LiquidAI/LFM2 350M using llama.cpp via the…

transformersggufliquidlfm2edgellama-cppgguf-my-repotext-generationenarzhfrdejakoesbase_model:LiquidAI/LFM2-350Mbase_model:quantized:LiquidAI/LFM2-350Mlicense:otherendpoints_compatibleregion:us

Runs locally from ~218.7 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation
Author

Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
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lfm2-350m-q4_k_m.ggufGGUFQ4_K_M218.7 MBDownload

Model Details

Model IDEtherll/LFM2-350M-Q4_K_M-GGUF
AuthorEtherll
Pipelinetext-generation
Licenseother
Base modelLiquidAI/LFM2-350M
Last modified2026-06-27T13:20:59.000Z

Model README

---

library_name: transformers

license: other

license_name: lfm1.0

license_link: LICENSE

language:

  • en
  • ar
  • zh
  • fr
  • de
  • ja
  • ko
  • es

pipeline_tag: text-generation

tags:

  • liquid
  • lfm2
  • edge
  • llama-cpp
  • gguf-my-repo

new_version: LiquidAI/LFM2.5-350M

base_model: LiquidAI/LFM2-350M

---

Etherll/LFM2-350M-Q4_K_M-GGUF

This model was converted to GGUF format from LiquidAI/LFM2-350M using llama.cpp via the ggml.ai's GGUF-my-repo space.

Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Etherll/LFM2-350M-Q4_K_M-GGUF --hf-file lfm2-350m-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Etherll/LFM2-350M-Q4_K_M-GGUF --hf-file lfm2-350m-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Etherll/LFM2-350M-Q4_K_M-GGUF --hf-file lfm2-350m-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Etherll/LFM2-350M-Q4_K_M-GGUF --hf-file lfm2-350m-q4_k_m.gguf -c 2048

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