Instructions to use prithivMLmods/LFM2.5-2.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/LFM2.5-2.6B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/LFM2.5-2.6B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/LFM2.5-2.6B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/LFM2.5-2.6B-GGUF 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 prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LFM2.5-2.6B-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 prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/LFM2.5-2.6B-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 prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/LFM2.5-2.6B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/LFM2.5-2.6B-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": "prithivMLmods/LFM2.5-2.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/LFM2.5-2.6B-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 "prithivMLmods/LFM2.5-2.6B-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": "prithivMLmods/LFM2.5-2.6B-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 "prithivMLmods/LFM2.5-2.6B-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": "prithivMLmods/LFM2.5-2.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/LFM2.5-2.6B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/LFM2.5-2.6B-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 prithivMLmods/LFM2.5-2.6B-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 prithivMLmods/LFM2.5-2.6B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/LFM2.5-2.6B-GGUF to start chatting

How to use prithivMLmods/LFM2.5-2.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LFM2.5-2.6B-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": "prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/LFM2.5-2.6B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LFM2.5-2.6B-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 prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use prithivMLmods/LFM2.5-2.6B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use prithivMLmods/LFM2.5-2.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/LFM2.5-2.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/LFM2.5-2.6B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-2.6B-GGUF-Q4_K_M

List all available models

lemonade list

LFM2.5-2.6B-GGUF

LFM2.5-2.6B is Liquid AI's agent-focused member of the LFM2.5 hybrid model family, a 2.69-billion-parameter, text-only model built on the LFM2 architecture with a 128K-token context window and post-trained specifically for agentic workloads. Its 30-layer architecture (22 double-gated short convolution blocks plus 8 GQA blocks) was pre-trained on ~34 trillion tokens and then transformed into an agent through a four-stage post-training pipeline — two rounds of supervised fine-tuning, per-domain teacher specialization, multi-domain on-policy distillation, and agentic reinforcement learning trained directly inside popular agentic harnesses — making it a pure reasoning model that always emits a <think> block before answering, and supports Pythonic tool calls across 16 languages. Despite its compact size, it proves competitive with models up to 4x larger on tool use, instruction following, and multi-step agentic tasks, outperforming Gemma-4-E4B-it (8B) and rivaling Qwen3.5-9B on benchmarks like IFBench (59.17), Multi-IF (80.07), IFStruct (85.49), and BFCLv4 (56.88), while delivering exceptional efficiency — 220 tok/s on an Apple M5 Max, 113 tok/s on a Ryzen AI Max+ CPU under 2.5GB of memory, and nearly 15K output tokens/sec at high concurrency on a single H100. It's recommended for agentic workloads, tool use, data extraction, RAG, and long-context tasks (though not for agentic coding or knowledge-heavy tasks), ships in native, GGUF, ONNX, and MLX formats, and is released under Liquid AI's LFM1.0 license.

Model Files

File Name	Quant Type	File Size	File Link
LFM2.5-2.6B.BF16.gguf	BF16	5.4 GB	Download
LFM2.5-2.6B.F16.gguf	F16	5.4 GB	Download
LFM2.5-2.6B.F32.gguf	F32	10.8 GB	Download
LFM2.5-2.6B.Q3_K_L.gguf	Q3_K_L	1.45 GB	Download
LFM2.5-2.6B.Q3_K_M.gguf	Q3_K_M	1.37 GB	Download
LFM2.5-2.6B.Q3_K_S.gguf	Q3_K_S	1.27 GB	Download
LFM2.5-2.6B.Q4_K_M.gguf	Q4_K_M	1.67 GB	Download
LFM2.5-2.6B.Q4_K_S.gguf	Q4_K_S	1.6 GB	Download
LFM2.5-2.6B.Q5_K_M.gguf	Q5_K_M	1.94 GB	Download
LFM2.5-2.6B.Q5_K_S.gguf	Q5_K_S	1.9 GB	Download
LFM2.5-2.6B.Q6_K.gguf	Q6_K	2.22 GB	Download
LFM2.5-2.6B.Q8_0.gguf	Q8_0	2.87 GB	Download

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp

Downloads last month: -

GGUF

Model size

3B params

Architecture

lfm2

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/LFM2.5-2.6B-GGUF

Base model

LiquidAI/LFM2.5-2.6B-Base

Finetuned

LiquidAI/LFM2.5-2.6B

Quantized

(16)

this model