Instructions to use LiquidAI/LFM2-2.6B-Transcript-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LiquidAI/LFM2-2.6B-Transcript-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LiquidAI/LFM2-2.6B-Transcript-GGUF", dtype="auto")

llama-cpp-python

How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LiquidAI/LFM2-2.6B-Transcript-GGUF",
	filename="LFM2-2.6B-Transcript-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LiquidAI/LFM2-2.6B-Transcript-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 LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LiquidAI/LFM2-2.6B-Transcript-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 LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LiquidAI/LFM2-2.6B-Transcript-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 LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2-2.6B-Transcript-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2-2.6B-Transcript-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M

SGLang

How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LiquidAI/LFM2-2.6B-Transcript-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2-2.6B-Transcript-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LiquidAI/LFM2-2.6B-Transcript-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2-2.6B-Transcript-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M
```

Unsloth Studio new

How to use LiquidAI/LFM2-2.6B-Transcript-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 LiquidAI/LFM2-2.6B-Transcript-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 LiquidAI/LFM2-2.6B-Transcript-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LiquidAI/LFM2-2.6B-Transcript-GGUF to start chatting

Pi new

How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M
```

Lemonade

How to use LiquidAI/LFM2-2.6B-Transcript-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/LFM2-2.6B-Transcript-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2-2.6B-Transcript-GGUF-Q4_K_M

List all available models

lemonade list

Try LFM • Documentation • LEAP

LFM2-2.6B-Transcript-GGUF

Based on LFM2-2.6B, LFM2-2.6B-Transcript is designed for private, on-device meeting summarization. We partnered with AMD to deliver cloud-level summary quality while running entirely locally, ensuring your meeting data never leaves your device.

Highlights:

Cloud-level summary quality, approaching much larger models
Under 3GB of RAM usage for long meetings
Fast summaries in seconds, not minutes
Runs fully locally across CPU, GPU, and NPU

You can find more information about this model here.

🏃 How to run

Example usage with llama.cpp:

llama-cli -hf LiquidAI/LFM2-2.6B-Transcript-GGUF

📬 Contact

If you are interested in custom solutions with edge deployment, please contact our sales team.

Downloads last month: 822

GGUF

Model size

3B params

Architecture

lfm2

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for LiquidAI/LFM2-2.6B-Transcript-GGUF

Base model

LiquidAI/LFM2-2.6B

Finetuned

LiquidAI/LFM2-2.6B-Transcript

Quantized

(11)

this model

Collection including LiquidAI/LFM2-2.6B-Transcript-GGUF

🎯 Liquid Nanos

Collection

Library of task-specific models: https://www.liquid.ai/blog/introducing-liquid-nanos-frontier-grade-performance-on-everyday-devices • 26 items • Updated Apr 8 • 113