Instructions to use anakin87/LFM2-2.6B-ttt-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anakin87/LFM2-2.6B-ttt-sft with Transformers:

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

pipe = pipeline("text-generation", model="anakin87/LFM2-2.6B-ttt-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anakin87/LFM2-2.6B-ttt-sft")
model = AutoModelForCausalLM.from_pretrained("anakin87/LFM2-2.6B-ttt-sft")
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 anakin87/LFM2-2.6B-ttt-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anakin87/LFM2-2.6B-ttt-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anakin87/LFM2-2.6B-ttt-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anakin87/LFM2-2.6B-ttt-sft

SGLang

How to use anakin87/LFM2-2.6B-ttt-sft 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 "anakin87/LFM2-2.6B-ttt-sft" \
    --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": "anakin87/LFM2-2.6B-ttt-sft",
		"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 "anakin87/LFM2-2.6B-ttt-sft" \
        --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": "anakin87/LFM2-2.6B-ttt-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anakin87/LFM2-2.6B-ttt-sft with Docker Model Runner:
```
docker model run hf.co/anakin87/LFM2-2.6B-ttt-sft
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LFM2-2.6B-ttt-sft

Supervised Fine-Tuning checkpoint of LiquidAI/LFM2-2.6B for Tic Tac Toe.

The goal of this SFT warm-up was to teach the model the correct output format and valid move syntax, before applying Reinforcement Learning. The model is not a strong player at this stage.

This is an intermediate checkpoint from 🎓 LLM RL Environments Lil Course, a hands-on course on building RL environments for Language Models, where models learn from rewards, not examples. It walks through the full process of turning a small open model into a specialist that outperforms a large proprietary one on a specific task (Tic Tac Toe). The final model is anakin87/LFM2-2.6B-mr-tictactoe.

🤗🕹️ Play against the final model

Training

Method: SFT with PRIME-RL
Dataset: anakin87/tictactoe-filtered (174 examples, ~5.5 epochs)
Steps: 30, batch size 32, lr 1e-5, seq_len 700
Hardware: NVIDIA RTX Pro 6000 96GB (~5 min)

Evaluation

100 games per setting.

Model vs random opponent	% Wins	% Draws	% Losses	% Follows format	% Games w invalid moves
LiquidAI/LFM2-2.6B	40	11	49	27.8	40
anakin87/LFM2-2.6B-ttt-sft	74	13	13	99.8	11

Model vs optimal opponent	% Wins	% Draws	% Losses	% Follows format	% Games w invalid moves
LiquidAI/LFM2-2.6B	0	11	89	24.7	43
anakin87/LFM2-2.6B-ttt-sft	0	52	48	99	14