Instructions to use Jabs2/LFM2.5-1.2B-JAPT-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 Jabs2/LFM2.5-1.2B-JAPT-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 Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_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 Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_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 Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M

Use Docker

docker model run hf.co/Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M

LM Studio
Jan
Ollama
How to use Jabs2/LFM2.5-1.2B-JAPT-GGUF with Ollama:
```
ollama run hf.co/Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M
```
Unsloth Desktop

How to use Jabs2/LFM2.5-1.2B-JAPT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_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": "Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Jabs2/LFM2.5-1.2B-JAPT-GGUF with Docker Model Runner:
```
docker model run hf.co/Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M
```

Lemonade

How to use Jabs2/LFM2.5-1.2B-JAPT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M

Run and chat with the model

lemonade run user.LFM2.5-1.2B-JAPT-GGUF-IQ3_M

List all available models

lemonade list

Hermes Agent

How to use Jabs2/LFM2.5-1.2B-JAPT-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 Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_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 Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Jabs2/LFM2.5-1.2B-JAPT-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_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 "Jabs2/LFM2.5-1.2B-JAPT-GGUF:IQ3_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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

LFM2.5-1.2B-JAPT-GGUF

GGUF quants of a Japanese→Portuguese fine-tune of LiquidAI/LFM2.5-1.2B-Instruct, built for offline anime subtitle translation (GoAnime TV project).

Method

Base: LiquidAI/LFM2.5-1.2B-Instruct (license lfm1.0, see LICENSE file of the base model)
QLoRA SFT (r=32, 6000 steps): 147k JA-PT pairs (Tatoeba + OpenSubtitles v2024, cleaned)
- 15k EN-PT pairs (Tatoeba, keeps the EN→PT direction alive)
Merge → GGUF (F16) → quantize with llama-quantize
IQ3_M uses an importance matrix calibrated on JA-PT translation data

Quality gate (6 sentences, greedy, temp 0)

Same protocol as the project gate: PASS = meaning preserved + zero hallucination.

Quant	Size	Score
`LFM2.5-1.2B-JAPT-Q4_K_M.gguf`	731 MB	5/6
`LFM2.5-1.2B-JAPT-IQ3_M.gguf`	567 MB	5/6

Known gap: polite request with honorific (先輩…いただけますか) still fails in every small model tested — only the 1.8B teacher (Hy-MT2) gets it right.

Usage (llama.cpp)

llama-cli -m LFM2.5-1.2B-JAPT-IQ3_M.gguf \
  -sys "You are a helpful translation assistant." \
  -p "Translate the following text into Portuguese. Output only the translated result without any additional explanation:

おはよう、今日はいい天気だね." --temp 0 -n 128

Downloads last month: 121

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

3-bit

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