Instructions to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with Transformers:

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

pipe = pipeline("text-generation", model="HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior")
model = AutoModelForCausalLM.from_pretrained("HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior")
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]:]))

llama-cpp-python

How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior",
	filename="Liquid-LFM2-8B-Polish-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior 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 HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior: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 HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior: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 HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M

Use Docker

docker model run hf.co/HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M

LM Studio
Jan

vLLM

How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M

SGLang

How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior 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 "HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior" \
    --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": "HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior",
		"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 "HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior" \
        --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": "HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with Ollama:
```
ollama run hf.co/HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M
```

Unsloth Studio

How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior to start chatting

How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior: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 HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with Docker Model Runner:
```
docker model run hf.co/HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M
```

Lemonade

How to use HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior:Q4_K_M

Run and chat with the model

lemonade run user.Liquid-LFM2-8B-Polish-Warrior-Q4_K_M

List all available models

lemonade list

⚔️ Liquid-LFM2-8B-Polish-Warrior (Hattori Hanzo Edition)

📥 Szybkie Pobieranie (GGUF)

Wersje zoptymalizowane do działania na domowym sprzęcie (Llama.cpp, LM Studio, Ollama):

Model	Rozmiar	Opis	Link
Q6_K	~6.7 GB	Najwyższa jakość, minimalna strata precyzji	Pobierz Q6
Q4_K_M	~4.8 GB	Najlepszy balans szybkości i inteligencji	Pobierz Q4
BF16	~15.5 GB	Pełna precyzja (do dalszego treningu)	Pobierz F16

[English Version] | [Polska Wersja Poniżej]

🇺🇸 English Description

Liquid-LFM2-8B-Polish-Warrior is the first specialized Polish adaptation of the innovative Liquid LFM2 8B (A1B) architecture. This model was meticulously fine-tuned by Michal1981 (Warrior of Silicon) to excel in Polish language understanding, with a strong focus on technical contexts, electronics, and programming (specifically C# and Arduino/EMS).

📊 Training Progress (Final Results)

The model reached its peak performance (Sweet Spot) at step 2800, demonstrating an ideal balance between knowledge retention and linguistic flexibility.

Step	Training Loss	Validation Loss	Status
200	1.4674	1.4427	Initialization
1400	1.2975	1.3431	Breakthrough
2800	0.9594	1.3356	👑 Sweet Spot

🇵🇱 Polska Wersja

Liquid-LFM2-8B-Polish-Warrior to pierwsza polska adaptacja architektury Liquid LFM2 8B (A1B). Model został wytrenowany przez Michala1981 (Wojownika Krzemu), aby zapewnić najwyższą jakość obsługi języka polskiego, ze szczególnym uwzględnieniem żargonu technicznego, elektroniki oraz programowania (C#, Arduino, EMS).

⚔️ Filozofia Projektu

Model powstał w duchu "Hattori Hanzo" – kucia cyfrowych ostrzy zdolnych stawić czoła bogom. W moim języku nie istnieje słowo "nie da się". Jest tylko "trudniej wykonalne i zajmie więcej czasu".

📈 Charakterystyka Treningu

Najlepszą formę model osiągnął w kroku 2800, wykazując się doskonałym zrozumieniem polskiej fleksji przy najniższym błędzie walidacji (1.3356).

👨‍💻 Creator & Credits

User/Creator: Michal1981 (Hattori Hanzo)
Architecture: Liquid AI (LFM2-8B-A1B)
Motto: Qapla'! Success is a journey, not a destination.

Downloads last month: 327

Safetensors

Model size

8B params

Tensor type

F32

BF16

Model tree for HattoriHanzo1/Liquid-LFM2-8B-Polish-Warrior

Base model

LiquidAI/LFM2-8B-A1B

Quantized

(28)

this model