Instructions to use LiquidAI/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LiquidAI/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M

Ollama
How to use LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M
```

Unsloth Studio

How to use LiquidAI/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-GGUF to start chatting

How to use LiquidAI/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M
```

Lemonade

How to use LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Instruct-DSpark-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use LiquidAI/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LiquidAI/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-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/LFM2.5-1.2B-Instruct-DSpark-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"

Try LFM • Docs • LEAP • Discord

LFM2.5-1.2B-Instruct-DSpark-GGUF

GGUF build of LiquidAI/LFM2.5-1.2B-Instruct-DSpark for llama.cpp (DSpark speculative decoding is in mainline, ggml-org/llama.cpp #25173).

This is a standalone draft sidecar: it carries only the drafter (5 attention layers, rank-256 Markov head, confidence head, block size 9). Token embeddings and the LM head are shared from the target model at load time, so it must be paired with a LFM2.5-1.2B-Instruct-GGUF target file.

Find more information about LFM2.5-DSpark in our blog post.

📦 Files

file	quant	size	notes
`LFM2.5-1.2B-Instruct-DSpark-F16.gguf`	F16	594 MB	best accept length, recommended when memory allows
`LFM2.5-1.2B-Instruct-DSpark-Q8_0.gguf`	Q8_0	315 MB	accept length −2% vs F16
`LFM2.5-1.2B-Instruct-DSpark-Q4_K_M.gguf`	Q4_K_M	174 MB	accept length −3% vs F16, smallest recommended — sub-4-bit draft quants measurably hurt both accept length and throughput

Draft quantization changes speed only marginally (the drafter is a small share of each cycle); choose by memory budget. The target model quant is the main speed/quality lever and is independent of this file.

🏃 How to run (llama.cpp)

llama-server -m LFM2.5-1.2B-Instruct-F16.gguf \
  -md LFM2.5-1.2B-Instruct-DSpark-F16.gguf \
  --spec-type draft-dspark --spec-draft-n-max 10 --spec-draft-n-min 0 \
  -fa on -ngl 99

The block size is read from the sidecar metadata (n-max is clamped to it). Speculative decoding is exact: the target verifies every proposed token, so greedy output equals the target alone; per-response timings report draft_n / draft_n_accepted.

Other models in the LFM2.5-DSpark GGUF family:

Draft (GGUF)	Target (GGUF)
LFM2.5-1.2B-Instruct-DSpark-GGUF	LFM2.5-1.2B-Instruct-GGUF
LFM2.5-2.6B-DSpark-GGUF	LFM2.5-2.6B-GGUF
LFM2.5-8B-A1B-DSpark-GGUF	LFM2.5-8B-A1B-GGUF

📊 Acceptance and benchmarks

See LiquidAI/LFM2.5-1.2B-Instruct-DSpark for acceptance-length tables (H100 and Apple silicon) and target benchmarks.

📬 Contact

Got questions or want to connect? Join our Discord community
If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

@article{liquidAI202626B,
 author  = {Liquid AI},
 title   = {LFM2.5-2.6B: Agents Everywhere},
 journal = {Liquid AI Blog},
 year    = {2026},
 note    = {www.liquid.ai/blog/lfm2-5-2-6b},
}

@article{liquidAI2026dspark,
  author = {Liquid AI},
  title = {LFM2.5-DSpark: Up to 3.2x Faster Inference from H100 to MacBook},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2.5-dspark},
}