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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

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

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

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf acidsound/LFM2.5-2.6B-Uncensored-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 "acidsound/LFM2.5-2.6B-Uncensored-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 acidsound/LFM2.5-2.6B-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/acidsound/LFM2.5-2.6B-Uncensored-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

LFM2.5 2.6B Uncensored — GGUF

Unofficial GGUF conversions of SC117/LFM2.5-2.6B-Uncensored, prepared from the original BF16 weights for local inference with llama.cpp, Atomic Chat, LM Studio, and other GGUF-compatible applications.

This repository is not affiliated with Liquid AI. Please review the upstream license before redistribution or commercial use.

Available files

Quantization	File size	Recommendation
Q4_K_M	1.67 GB	Smallest; fastest and lowest memory use
Q5_K_M	1.94 GB	Good speed/quality balance
Q6_K	2.22 GB	Recommended default for quality-sensitive prompt generation
Q8_0	2.87 GB	Closest to BF16 quality; use when quality is the priority

Sizes are the local GGUF file sizes in decimal GB. All variants retain the model's tokenizer, chat template, and metadata.

File SHA-256

File	SHA-256
`LFM2.5-2.6B-Uncensored-Q6_K.gguf`	`7CDDB332D4284B2113AD89E7D1C6FDC968EDBAD2704CB05A76D439BB98E06EAF`
`LFM2.5-2.6B-Uncensored-Q8_0.gguf`	`61A09E86309A80522E7E65D5FC0A28798148CA9F388630C390210D3676E03434`

Recommended choice for an M2 MacBook with 16 GB RAM

Q6_K is the recommended everyday choice for short English/Chinese image and video prompts.
Q8_0 is also practical and should preserve a little more quality, at the cost of a larger model and somewhat higher memory bandwidth use.
Keep context moderate (for example, 4k–8k tokens) for the fastest response.

The model weights fit comfortably in 16 GB unified memory; the operating system, application, context, and KV cache still need headroom.

llama.cpp

llama-server \
  -m LFM2.5-2.6B-Uncensored-Q6_K.gguf \
  -c 8192 -ngl 99 -fa on --jinja

Use the Q8 file in the -m argument when maximum quality is preferred. The exact flags available depend on the llama.cpp build and hardware backend.

Atomic Chat

Load one of the GGUF files with the Llama.cpp engine. For structured prompt generation, Atomic Chat's Llama.cpp settings expose Grammar File and JSON Schema File output constraints. A simple flat JSON Schema is usually more reliable than a large schema with nested references.

Atomic Chat's local OpenAI-compatible server is normally available at http://127.0.0.1:1337/v1. With a llama.cpp backend, a request can use response_format with type: "json_schema" to constrain the generated prompt object. Do not combine a custom grammar/schema with native tool-call grammar in the same request.

Example structured output schema

{
  "type": "object",
  "properties": {
    "prompt_en": { "type": "string" },
    "prompt_zh": { "type": "string" },
    "negative_prompt": { "type": "string" },
    "parameters": { "type": "string" }
  },
  "required": ["prompt_en", "prompt_zh", "negative_prompt", "parameters"],
  "additionalProperties": false
}

The schema constrains the output shape; describe the intended fields in the prompt as well. Validate the returned JSON in the calling application.

Quantization provenance

The files were quantized directly from the BF16 GGUF conversion of the upstream model with llama-quantize (llama.cpp build 10278, commit d52ec04a6):

llama-quantize LFM2.5-2.6B-Uncensored-BF16.gguf LFM2.5-2.6B-Uncensored-Q4_K_M.gguf Q4_K_M 8
llama-quantize LFM2.5-2.6B-Uncensored-BF16.gguf LFM2.5-2.6B-Uncensored-Q5_K_M.gguf Q5_K_M 8
llama-quantize LFM2.5-2.6B-Uncensored-BF16.gguf LFM2.5-2.6B-Uncensored-Q6_K.gguf Q6_K 8
llama-quantize LFM2.5-2.6B-Uncensored-BF16.gguf LFM2.5-2.6B-Uncensored-Q8_0.gguf Q8_0 8

Original model: SC117/LFM2.5-2.6B-Uncensored