Instructions to use kth8/LFM2.5-230M-OpenCode-Title-Generator 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 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")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kth8/LFM2.5-230M-OpenCode-Title-Generator")
model = AutoModelForCausalLM.from_pretrained("kth8/LFM2.5-230M-OpenCode-Title-Generator", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use kth8/LFM2.5-230M-OpenCode-Title-Generator 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use kth8/LFM2.5-230M-OpenCode-Title-Generator with Docker Model Runner:
```
docker model run hf.co/kth8/LFM2.5-230M-OpenCode-Title-Generator
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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: 526

Safetensors

Model size

0.2B params

Tensor type

BF16

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

Base model

LiquidAI/LFM2.5-230M-Base

Finetuned

LiquidAI/LFM2.5-230M

Finetuned

unsloth/LFM2.5-230M

Finetuned

(5)

this model

Quantizations

2 models

kth8
/

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

Usage example

Training Settings

PEFT

SFT

Framework versions

License

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

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