Instructions to use yasserrmd/LFM2-700M-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yasserrmd/LFM2-700M-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="yasserrmd/LFM2-700M-gguf",
	filename="LFM2-700M-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use yasserrmd/LFM2-700M-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf yasserrmd/LFM2-700M-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf yasserrmd/LFM2-700M-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf yasserrmd/LFM2-700M-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf yasserrmd/LFM2-700M-gguf: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 yasserrmd/LFM2-700M-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf yasserrmd/LFM2-700M-gguf: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 yasserrmd/LFM2-700M-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yasserrmd/LFM2-700M-gguf:Q4_K_M

Use Docker

docker model run hf.co/yasserrmd/LFM2-700M-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use yasserrmd/LFM2-700M-gguf with Ollama:
```
ollama run hf.co/yasserrmd/LFM2-700M-gguf:Q4_K_M
```

Unsloth Studio new

How to use yasserrmd/LFM2-700M-gguf 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 yasserrmd/LFM2-700M-gguf 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 yasserrmd/LFM2-700M-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yasserrmd/LFM2-700M-gguf to start chatting

Docker Model Runner
How to use yasserrmd/LFM2-700M-gguf with Docker Model Runner:
```
docker model run hf.co/yasserrmd/LFM2-700M-gguf:Q4_K_M
```

Lemonade

How to use yasserrmd/LFM2-700M-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yasserrmd/LFM2-700M-gguf:Q4_K_M

Run and chat with the model

lemonade run user.LFM2-700M-gguf-Q4_K_M

List all available models

lemonade list

LFM2‑350M • Quantized Version (GGUF)

Quantized GGUF version of the LiquidAI/LFM2-700M model.

✅ Format: GGUF
✅ Use with: liquid_llama.cpp
✅ Supported precisions: Q4_0, Q4_K, etc.

Download

wget https://huggingface.co/yasserrmd/LFM2-700M-gguf/resolve/main/lfm2-700m.Q4_K.gguf

(Adjust filename for other quant formats like Q4_0, if available.)

Notes

Only compatible with liquid_llama.cpp (not llama.cpp).
Replace Q4_K with your chosen quant version.

Downloads last month: 30

GGUF

Model size

0.7B params

Architecture

lfm2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

32-bit

Inference Providers NEW

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Model tree for yasserrmd/LFM2-700M-gguf

Base model

LiquidAI/LFM2-700M

Quantized

(21)

this model