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

Libraries

How to use anakin87/LFM2-2.6B-mr-tictactoe with Transformers:

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

pipe = pipeline("text-generation", model="anakin87/LFM2-2.6B-mr-tictactoe")
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-mr-tictactoe")
model = AutoModelForCausalLM.from_pretrained("anakin87/LFM2-2.6B-mr-tictactoe", 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 anakin87/LFM2-2.6B-mr-tictactoe 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-mr-tictactoe"
# 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-mr-tictactoe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anakin87/LFM2-2.6B-mr-tictactoe

SGLang

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

Docker Model Runner
How to use anakin87/LFM2-2.6B-mr-tictactoe with Docker Model Runner:
```
docker model run hf.co/anakin87/LFM2-2.6B-mr-tictactoe
```

anakin87 commited on Apr 5

Commit

10ebf3b

verified ·

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@@ -1,199 +1,60 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: other
+license_name: lfm1.0
+license_link: https://www.liquid.ai/legal/lfm-license
+base_model:
+- anakin87/LFM2-2.6B-ttt-rl-merged
 library_name: transformers
+tags:
+- rl
+- cispo
+- tictactoe
+- rlvr
+pipeline_tag: text-generation
+language:
+- en
 ---
+# LFM2-2.6B-mr-tictactoe
+A 2.6B parameter model that plays near-perfect Tic Tac Toe, outperforming `openai/gpt-5-mini` on this task.
+Built from [LiquidAI/LFM2-2.6B](https://huggingface.co/LiquidAI/LFM2-2.6B) through a full training pipeline: Supervised Fine-Tuning on synthetic data, followed by two rounds of Reinforcement Learning (CISPO) in a verifiable Tic Tac Toe environment.
+This model was developed as part of 🎓 **[LLM RL Environments Lil Course](https://github.com/anakin87/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).
+🤗🕹️ **[Play against Mr. Tic Tac Toe](https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe)**
+![Tic Tac Toe performance](https://raw.githubusercontent.com/anakin87/llm-rl-environments-lil-course/main/images/model_comparison.png)
+## Training pipeline
+| Step | Model | Method |
+|------|-------|--------|
+| 1. SFT warm-up | [anakin87/LFM2-2.6B-ttt-sft](https://huggingface.co/anakin87/LFM2-2.6B-ttt-sft) | SFT on 174 synthetic games from `gpt-5-mini` |
+| 2. RL round 1 | [anakin87/LFM2-2.6B-ttt-rl](https://huggingface.co/anakin87/LFM2-2.6B-ttt-rl) + [merged](https://huggingface.co/anakin87/LFM2-2.6B-ttt-rl-merged) | CISPO, 600 steps, opponents at 20-70% random |
+| 3. RL round 2 | [anakin87/LFM2-2.6B-ttt-rl-2](https://huggingface.co/anakin87/LFM2-2.6B-ttt-rl-2) + this model | CISPO, 400 steps, opponents at 0-25% random, temp 1.25 |
 ## Evaluation
+100 games per setting. The model plays as X (first mover) against a Minimax-based opponent.
+| **Model vs random opponent** | **% Wins** | **% Draws** | **% Losses** | **% Follows format** | **% Games w invalid moves** |
+|------------------------------|------------|-------------|--------------|----------------------|---------------------|
+| openai/gpt-5-mini | 90 | 9 | 1 | 100 | 0 |
+| LiquidAI/LFM2-2.6B | 40 | 11 | 49 | 27.8 | 40 |
+| **anakin87/LFM2-2.6B-mr-tictactoe** | **90** | **10** | **0** | **100** | **0** |
+| | | | | | |
+| **Model vs optimal opponent** | **% Wins** | **% Draws** | **% Losses** | **% Follows format** | **% Games w invalid moves** |
+| openai/gpt-5-mini | 0 | 76 | 24 | 100 | 0 |
+| LiquidAI/LFM2-2.6B | 0 | 11 | 89 | 24.7 | 43 |
+| **anakin87/LFM2-2.6B-mr-tictactoe** | **0** | **97** | **3** | **99.8** | **0** |
+## Training details
+- **Algorithm:** CISPO (two rounds), using [Verifiers](https://github.com/PrimeIntellect-ai/verifiers) RLTrainer
+- **Environment:** [anakin87/tictactoe](https://app.primeintellect.ai/dashboard/environments/anakin87/tictactoe) (Verifiers environment)
+- **LoRA rank:** 8
+- **Hardware:** 2x NVIDIA RTX Pro 6000 (round 1), 2x NVIDIA H200 (round 2)
+- **Training time:** ~8 hours per round
+- **W&B project:** [LFM2-2.6B Tic Tac Toe](https://wandb.ai/stefanofiorucci/LFM2-2.6B%20Tic%20Tac%20Toe/table)