Instructions to use kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-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 kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kth8/LFM2.5-230M-OpenCode-Title-Generator-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 kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kth8/LFM2.5-230M-OpenCode-Title-Generator-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 kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kth8/LFM2.5-230M-OpenCode-Title-Generator-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 kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M

Use Docker

docker model run hf.co/kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kth8/LFM2.5-230M-OpenCode-Title-Generator-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": "kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M

SGLang

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF with Ollama:
```
ollama run hf.co/kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M
```
Unsloth Desktop

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kth8/LFM2.5-230M-OpenCode-Title-Generator-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": "kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF with Docker Model Runner:
```
docker model run hf.co/kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M
```

Lemonade

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-230M-OpenCode-Title-Generator-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-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 kth8/LFM2.5-230M-OpenCode-Title-Generator-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 kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kth8/LFM2.5-230M-OpenCode-Title-Generator-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 "kth8/LFM2.5-230M-OpenCode-Title-Generator-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"

A supervised fine-tune of unsloth/LFM2.5-230M on the kth8/title-generation-10000x dataset. Supports all 10 languages by the base model: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish.

Trained with the system prompt used by OpenCode's title agent. OpenCode generate thread title based on the first user message. OpenCode UI can show title up to 100 characters long before being truncated.

Usage example

Point to this model with small_model key in opencode.jsonc config file.

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "title": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://127.0.0.1:8080/v1",
        "apiKey": "not-needed"
      },
      "models": {
        "generator": {}
      }
    }
  },
  "small_model": "title/generator"
}

User prompt

Troy's home is 75 meters away from school while Emily's home is 98 meters away from school. Troy and Emily walk to school and back home every day. How much farther does Emily walk to school and back in five days?

Title response

Troy vs Emily five-day school walk distance difference

Training Settings

PEFT

Rank: 16
LoRA alpha: 16
Modules: ["q_proj", "k_proj", "v_proj", "out_proj", "in_proj", "w1", "w2", "w3"]
Gradient checkpointing: unsloth

SFT

Epoch: 1
Batch size: 4
Gradient Accumulation steps: 4
Learning rate: 0.0002
Optimizer: adamw_torch_fused
Learning rate scheduler: cosine
Warmup steps: 10
Weight decay: 0.01

Framework versions

Unsloth: 2026.9.4
TRL: 0.23.1
Transformers: 5.5.0
Pytorch: 2.10.0+cu128
Datasets: 4.3.0
Tokenizers: 0.22.2

License

This model is released under LFM Open License v1.0.

Downloads last month: 132

GGUF

Model size

0.2B params

Architecture

lfm2

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF

Base model

LiquidAI/LFM2.5-230M-Base

Finetuned

LiquidAI/LFM2.5-230M

Finetuned

unsloth/LFM2.5-230M

Finetuned

kth8/LFM2.5-230M-OpenCode-Title-Generator

Quantized

(2)

this model

kth8
/

LFM2.5-230M-OpenCode-Title-Generator-GGUF

Usage example

Training Settings

PEFT

SFT

Framework versions

License

Model tree for kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF

Dataset used to train kth8/LFM2.5-230M-OpenCode-Title-Generator-GGUF