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

Use Docker

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

LM Studio
Jan

vLLM

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

Use Docker

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

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

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

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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/LFM2.5-2.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LiquidAI/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 LiquidAI/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 "LiquidAI/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"

LFM2.5-2.6B-GGUF / qad /README.md

adityatadimeti

Add full-precision QAD safetensors source checkpoint

e7caca5 verified 12 days ago

preview code

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metadata

library_name: transformers
pipeline_tag: text-generation
license: other
license_name: lfm1.0
license_link: LICENSE
base_model: LiquidAI/LFM2.5-2.6B
tags:
  - liquid
  - lfm2.5
  - qad
  - safetensors

LFM2.5-2.6B QAD — FP32 source checkpoint

This directory contains the unquantized FP32 safetensors source weights for LFM2.5-2.6B-QAD-Q4_0.gguf. These are the weights after Quantization-Aware Distillation (QAD), before GGUF conversion and quantization. The architecture and tokenizer are those of LiquidAI/LFM2.5-2.6B.

The checkpoint is provided for fine-tuning, inspecting weights, and experimenting with deployment formats. It was optimized for Q4_0 deployment; published QAD Q4_0 benchmark results do not describe direct FP32/BF16 inference or other quantization formats. See the QAD release.

Usage

Install current torch, transformers, and accelerate. This package was checked with Transformers 5.9.0. dtype="auto" preserves the stored FP32 weights.

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "LiquidAI/LFM2.5-2.6B-GGUF"
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="qad")
model = AutoModelForCausalLM.from_pretrained(
    repo_id, subfolder="qad", dtype="auto", device_map="auto"
)

inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": "What is 2 + 2?"}],
    tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Files and validation

model.safetensors: original FP32 QAD weights.
config.json: the validated source architecture/configuration, including its FP32 dtype.
tokenizer.json, tokenizer_config.json: tokenizer files.
chat_template.jinja: exactly the template embedded in the public QAD GGUF.
generation_config.json: defaults from the corresponding public HF model.
LICENSE: the repository's LFM license.
provenance.json: source hash and the validated GGUF reference.

The FP32 weights passed finite-value checks and Transformers loading, generation, and cache-consistency smoke tests. Converting these weights to F16 GGUF and then Q4_0 with llama.cpp revision 74ade52741203e5c8f81eaf06a96cb1cfe15f2a3 reproduced every tensor byte of the released QAD artifact. Applying the same release metadata also reproduced its complete SHA-256. File names and descriptive GGUF metadata can otherwise cause different whole-file hashes despite identical tensors.

Preserve FP32 weights when reproducing that conversion: saving a BF16 copy first changes the source precision. Generation defaults follow the public model; use do_sample=False for deterministic greedy decoding.

License

This checkpoint is distributed under the same LFM license as the corresponding model release.