Instructions to use NobodyWho/LFM2.5-1.2B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NobodyWho/LFM2.5-1.2B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NobodyWho/LFM2.5-1.2B-Instruct-GGUF",
	filename="LFM2.5-1.2B-Instruct-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use NobodyWho/LFM2.5-1.2B-Instruct-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 NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

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 NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

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 NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

Use Docker

docker model run hf.co/NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

LM Studio
Jan
Ollama
How to use NobodyWho/LFM2.5-1.2B-Instruct-GGUF with Ollama:
```
ollama run hf.co/NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16
```

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NobodyWho/LFM2.5-1.2B-Instruct-GGUF to start chatting

How to use NobodyWho/LFM2.5-1.2B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

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": "NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use NobodyWho/LFM2.5-1.2B-Instruct-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 NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

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 NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use NobodyWho/LFM2.5-1.2B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

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 "NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16" \
  --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 NobodyWho/LFM2.5-1.2B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NobodyWho/LFM2.5-1.2B-Instruct-GGUF:F16

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Instruct-GGUF-F16

List all available models

lemonade list

LFM2.5-1.2B-Instruct-GGUF / README.md

gergelyvagujhelyi

Remove penalty_last_n note (the key was stripped from the GGUFs instead)

9439d0f verified 27 days ago

preview code

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	---
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	base_model: LiquidAI/LFM2.5-1.2B-Instruct
	tags:
	- gguf
	- tool-calling
	- liquid
	- lfm2.5
	---

	# LFM2.5-1.2B-Instruct GGUF — with tool_calls chat-template fix

	GGUF builds of [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF)
	prepared for tool calling. Every file is the corresponding upstream quant with
	bit-identical weight tensors; the embedded chat template
	(`tokenizer.chat_template`) is extended to render the `tool_calls` field of
	assistant messages, and the files marked in the table additionally embed
	LiquidAI's recommended sampling as `general.sampling.*` metadata.

	## Model Capabilities

	- Text generation — instruction-following chat model
	- Tool calling — native LFM2 function-calling format; multi-turn tool use works thanks to the template fix in this repo
	- Long context — 128k tokens

	## Getting Started

	Install [NobodyWho](https://docs.nobodywho.ooo/):

	```bash
	pip install nobodywho
	```

	Run — the model is downloaded and cached automatically on first use:

	```python
	from nobodywho import Chat

	chat = Chat("huggingface:NobodyWho/LFM2.5-1.2B-Instruct-GGUF/LFM2.5-1.2B-Instruct-Q8_0-vendor-sampling.gguf")
	response = chat.ask("What is the capital of Denmark?").completed()
	print(response) # Copenhagen!
	```

	### Tool calling

	```python
	from nobodywho import Chat, tool

	@tool(description="Gets the current weather for a city")
	def get_weather(city: str) -> str:
	return f"It is sunny and 22°C in {city}."

	chat = Chat(
	"huggingface:NobodyWho/LFM2.5-1.2B-Instruct-GGUF/LFM2.5-1.2B-Instruct-Q8_0-vendor-sampling.gguf",
	tools=[get_weather],
	)
	print(chat.ask("What is the weather in Paris?").completed())
	```

	> [!NOTE]
	> Tool calling with LFM models ships in the upcoming `nobodywho` release
	> ([PR #564](https://github.com/nobodywho-ooo/nobodywho/pull/564)). These files also work in any other
	> llama.cpp-based runtime; the original unmodified GGUFs live in the upstream
	> [LiquidAI/LFM2.5-1.2B-Instruct-GGUF](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF) repo.

	## Why

	The upstream template renders only `message.content`. Runtimes that store tool
	calls in the structured `tool_calls` field (the HF "unified tool use"
	convention, used by NobodyWho and OpenAI-style APIs) re-render assistant
	tool-call turns as empty turns, so the model never sees its own previous
	calls — causing re-issued tool calls and degraded multi-turn tool use.

	The fixed template renders them in the model's native markup:

	```
	<\|tool_call_start\|>[get_weather(city="Paris")]<\|tool_call_end\|>
	```

	## Files

	\| File \| Fix recipe \| NobodyWho tool-suite score \|
	\|---\|---\|---\|
	\| `LFM2.5-1.2B-Instruct-Q8_0-vendor-sampling.gguf` \| template + vendor sampling \| 14/14 \|
	\| `LFM2.5-1.2B-Instruct-F16.gguf` \| template only \| 14/14 \|
	\| `LFM2.5-1.2B-Instruct-Q4_0-vendor-sampling.gguf` \| template + vendor sampling \| 12/14 (double-calls two tests) \|

	## Sampling notes

	The Q4_0 and Q8_0 files embed LiquidAI's recommended sampling as
	`general.sampling.*` metadata, taken from the vendor's LEAP deployment config:
	`temperature 0.3`, `min_p 0.15`, `repetition_penalty 1.05`. The F16
	deliberately ships without sampling metadata: at full precision, embedding
	those values drops the bash-writing test (13/14 vs 14/14 with default
	sampling), so runtimes fall back to their own defaults.

	> The embedded values were previously `temp 0.1, top_k 50` (the vendor's
	> model-card prose, which conflicts with its LEAP config) and have been
	> corrected to the LEAP values above. All scores re-verified against these
	> files with the corrected sampler: Q8_0 14/14, F16 14/14
	> (sampling-free), F16 + vendor sampling 13/14, Q4_0 12/14 (double-calls
	> two tests).

	## Use

	Verified against [NobodyWho](https://github.com/nobodywho-ooo/nobodywho)'s
	14-test tool-calling suite (single and multi-call, nested arguments,
	multi-turn) — see PR [#564](https://github.com/nobodywho-ooo/nobodywho/pull/564).
	Works as a drop-in replacement for the upstream Q8_0 file in any
	llama.cpp-based runtime.

	## Model Details

	\| Property \| Value \|
	\|---\|---\|
	\| Parameters \| 1.2B \|
	\| Context length \| 128,000 tokens \|
	\| License \| [LFM Open License v1.0](LICENSE) \|
	\| Base model \| [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) \|

	## License

	LFM Open License v1.0, unchanged from upstream — see [LICENSE](LICENSE).
	All credit for the model goes to [Liquid AI](https://huggingface.co/LiquidAI).