Instructions to use tabularisai/Faust-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tabularisai/Faust-1 with Transformers:

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

pipe = pipeline("text-generation", model="tabularisai/Faust-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tabularisai/Faust-1")
model = AutoModelForCausalLM.from_pretrained("tabularisai/Faust-1")
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 tabularisai/Faust-1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tabularisai/Faust-1",
	filename="faust_1_q8_0.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tabularisai/Faust-1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tabularisai/Faust-1:Q8_0
# Run inference directly in the terminal:
llama-cli -hf tabularisai/Faust-1:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tabularisai/Faust-1:Q8_0
# Run inference directly in the terminal:
llama-cli -hf tabularisai/Faust-1:Q8_0

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 tabularisai/Faust-1:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf tabularisai/Faust-1:Q8_0

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 tabularisai/Faust-1:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tabularisai/Faust-1:Q8_0

Use Docker

docker model run hf.co/tabularisai/Faust-1:Q8_0

LM Studio
Jan

vLLM

How to use tabularisai/Faust-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tabularisai/Faust-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tabularisai/Faust-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tabularisai/Faust-1:Q8_0

SGLang

How to use tabularisai/Faust-1 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 "tabularisai/Faust-1" \
    --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": "tabularisai/Faust-1",
		"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 "tabularisai/Faust-1" \
        --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": "tabularisai/Faust-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tabularisai/Faust-1 with Ollama:
```
ollama run hf.co/tabularisai/Faust-1:Q8_0
```

Unsloth Studio new

How to use tabularisai/Faust-1 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 tabularisai/Faust-1 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 tabularisai/Faust-1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tabularisai/Faust-1 to start chatting

Docker Model Runner
How to use tabularisai/Faust-1 with Docker Model Runner:
```
docker model run hf.co/tabularisai/Faust-1:Q8_0
```

Lemonade

How to use tabularisai/Faust-1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tabularisai/Faust-1:Q8_0

Run and chat with the model

lemonade run user.Faust-1-Q8_0

List all available models

lemonade list

vdmbrsv commited on Mar 31

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@@ -118,16 +118,6 @@ Following pre-training, the model undergoes supervised post-training (instructio
 > [!TIP]
 > **Designed for local and cost-efficient deployment.**
 > Faust-1 is deliberately sized and optimized to run on **consumer-grade hardware** and **does not require expensive data-center GPUs**.
->
-> **Typical deployment examples:**
-> - **Laptop / Desktop (CPU or small GPU):**
->   Runs on modern CPUs or entry-level GPUs (e.g. Apple Silicon, RTX 3060/4060, RX 6600) using optimized runtimes such as GGUF, MLX, or ONNX.
-> - **Single-GPU workstation:**
->   Efficiently serves interactive workloads on a single consumer GPU with low VRAM requirements compared to larger multilingual models.
-> - **On-device / privacy-sensitive setups:**
->   Suitable for local assistants, offline document analysis, and private RAG pipelines where data must not leave the machine.
->
-> This makes Faust-1 practical for **researchers, developers, and small teams** who want strong German language performance without cloud dependency or high inference costs.
 ---
 ## Model summary

 > [!TIP]
 > **Designed for local and cost-efficient deployment.**
 > Faust-1 is deliberately sized and optimized to run on **consumer-grade hardware** and **does not require expensive data-center GPUs**.
 ---
 ## Model summary