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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Use Docker

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

LM Studio
Jan

vLLM

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

Use Docker

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

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

Unsloth Studio

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

How to use NANI-Nithin/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 NANI-Nithin/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": "NANI-Nithin/LFM2.5-2.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/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

Hermes Agent

How to use NANI-Nithin/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 NANI-Nithin/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 NANI-Nithin/LFM2.5-2.6B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

LFM2.5-2.6B-GGUF

GGUF quantizations of LiquidAI/LFM2.5-2.6B, a 2.69B-parameter dense hybrid model built for agentic, on-device workloads with a 128K-token context window and native tool calling [web:41][web:39].

Model Details

LFM2.5-2.6B combines 22 double-gated short convolution (LIV) blocks with 8 grouped-query attention (GQA) blocks across 30 total layers, an architecture selected via hardware-in-the-loop search on real edge silicon [web:40][web:44]. It was pre-trained on roughly 34 trillion tokens and post-trained through a four-stage pipeline (SFT, teacher specialization, multi-domain on-policy distillation, and agentic RL) to reliably plan, call tools, and execute multi-step tasks inside agent harnesses [web:38][web:44].

Property	Value
Parameters	2.69B (dense)
Layers	30 (22 conv + 8 GQA)
Embedding dimension	2048
Context length	131,072 tokens [web:41]
Vocabulary size	128,000 tokens
Training data	~34 trillion tokens
Languages	16, including English, Arabic, Chinese, French, German, Hindi, Japanese, Korean, Russian, Spanish [web:41]
License	LFM Open License v1.0 [web:50]

Files

Quantized with llama.cpp's convert_hf_to_gguf.py and llama-quantize. Tested for compatibility on an RTX 4060 Laptop (8 GB VRAM).

Quantization	Size	Notes
Q2_K	1.09 GB	Smallest, largest quality loss
Q3_K_S	1.27 GB
Q3_K_M	1.37 GB	Balanced 3-bit
Q3_K_L	1.45 GB
Q4_K_S	1.6 GB
Q4_K_M	1.67 GB	Recommended default for most users
Q5_K_S	1.9 GB
Q5_K_M	1.94 GB	Near-F16 quality, moderate size
Q6_K	2.22 GB	Very close to F16 quality
Q8_0	2.87 GB	Minimal quality loss
F16	5.4 GB	Full precision, reference file

Usage

Run with llama.cpp, Ollama, LM Studio, or any GGUF-compatible inference engine:

./llama-cli -m LFM2.5-2.6B-Q4_K_M.gguf -p "Your prompt here" -n 256

The model uses a ChatML-like chat template with native tool-call tokens (<|tool_call_start|>, <|tool_call_end|>) and a Pythonic tool-call format (function_name(arg="value")) [web:40].

Recommended Quantization

For 8 GB VRAM laptops (e.g. RTX 4060 Laptop), Q4_K_M offers the best balance of quality and footprint (~1.67 GB), leaving headroom for KV cache at long context lengths. For maximum fidelity on the same hardware, Q6_K or Q8_0 still fit comfortably given the model's small base size [web:51].

License

This model inherits the LFM Open License v1.0 from the original LiquidAI/LFM2.5-2.6B release, not a permissive license like Apache 2.0 or MIT — review the terms before commercial deployment [web:50][web:51].

Credits

Original model and architecture by Liquid AI [web:38]. GGUF conversion by NANI-Nithin.

Downloads last month: 301

GGUF

Model size

3B params

Architecture

lfm2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for NANI-Nithin/LFM2.5-2.6B-GGUF

Base model

LiquidAI/LFM2.5-2.6B-Base

Finetuned

LiquidAI/LFM2.5-2.6B

Quantized

(54)

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