Faust-1 / README.md

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metadata

library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
pipeline_tag: text-generation
extra_gated_prompt: >
  ### FAUST-1 NON-COMMERCIAL LICENSE AGREEMENT


  Version 1.0 — January 2025


  "Faust-1" refers to the language model weights, code, and documentation made
  available by Tabularis AI GmbH ("Tabularis") under this agreement.


  1. License Grant

  You are granted a non-exclusive, non-transferable, royalty-free license to
  use, copy, and modify Faust-1 for non-commercial research and personal
  purposes only.


  2. Non-Commercial Use

  "Non-commercial" means academic research, personal projects, and educational
  use. Any use intended to generate revenue, provide commercial services, or
  benefit a for-profit entity requires a separate commercial license.


  3. Commercial Licensing

  For commercial use, please contact: info@tabularis.ai


  4. Attribution

  You must include "Built with Faust-1 by Tabularis AI" in any derivative work
  or publication.


  5. No Warranty

  Faust-1 is provided "as is" without warranties of any kind.


  6. Termination

  This license terminates automatically if you violate any terms.


  ---

  ### Additional Access Requirement

  Access to this repository is approval-based.

  You must join our Discord server: https://discord.gg/7WqEKw652R
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  Faust-1 is for non-commercial use only.
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language:
  - de
  - en
tags:
  - llama.cpp
  - synthetic data

Faust-1 — German-First Large Language Model (1.6B)

Faust-1 is a German-first large language model with 1.6B parameters, trained entirely from scratch. Model development comprises large-scale data collection and synthetic data generation, followed by data cleaning, normalization, and deduplication to reduce contamination and redundancy. Pre-training is performed on a predominantly German corpus using a decoder-only language modeling objective, resulting in a foundation model for the German language that captures lexical, syntactic, and semantic regularities at scale.

Following pre-training, the model undergoes supervised post-training (instruction tuning) using labeled input–output pairs to adapt the base model for conversational and task-oriented use. In later stages, preference-based optimization, including Direct Preference Optimization (DPO), is applied to improve response quality, stability, and alignment with human expectations, while preserving the efficiency constraints required for small-scale and local deployment.

Demo: faust.tabularis.ai

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

Repository: tabularisai/Faust-1
Model type: decoder-only causal language model MoE
Parameters: 1.6B
Interface: conversational / instruction (chat template provided)
Primary language: German (~90%)
Custom State-of-the-Art tokenizer for German language

Quickstart

Conversational usage (recommended)

from transformers import AutoTokenizer, AutoModelForCausalLM  
import torch  

model_id = "tabularisai/Faust-1"

tokenizer = AutoTokenizer.from_pretrained(model_id)  
model = AutoModelForCausalLM.from_pretrained(  
    model_id,  
    torch_dtype=torch.float16,  
    device_map="auto",  
)

messages = [  
    {"role": "user", "content": "Gib mir eine kurze Einführung in große Sprachmodelle (LLM)."}  
]

inputs = tokenizer.apply_chat_template(  
    messages,  
    add_generation_prompt=True,  
    return_tensors="pt",  
).to(model.device)

outputs = model.generate(  
    inputs,  
    max_new_tokens=256,  
    temperature=0.6,  
    do_sample=True,  
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training focus

German-first data distribution

Faust-1 is trained from scratch with a German-dominant corpus. German syntax, compounding, morphology, and typical reasoning patterns are treated as the default operating regime rather than an edge case.

Verified synthetic data

A substantial portion of the training signal comes from synthetic data. To keep this signal usable, generation is paired with explicit verification and filtering:

LLM-as-judge style evaluations
rule-based and programmatic checks
consistency and self-agreement filtering

This allows broad coverage of instruction-following and reasoning patterns while maintaining quality control.

Tokenizer optimized for German

Faust-1 uses a custom tokenizer optimized for German morphology and compounding. Token efficiency is treated as a deployment constraint, not just a preprocessing detail.

Lower token counts on German text translate directly into more usable context, lower inference cost, and less fragmentation on compound-heavy inputs.

German benchmark performance

Faust-1 is evaluated on a set of standard German-language benchmarks:

ARC_de
GSM8K_de
HellaSwag_de
MMLU_de
TruthfulQA_de

The target is best-in-class performance within the 1–2B parameter range for German-focused models, using benchmarks that are easy to reproduce in Hugging Face-based evaluation pipelines.

Deployment examples

Faust-1 can be deployed with common inference stacks that support decoder-only language models.

vLLM (OpenAI-compatible API)

vllm serve tabularisai/Faust-1 --dtype float16

SGLang

python -m sglang.launch_server \
  --model-path tabularisai/Faust-1 \
  --dtype float16

llama.cpp (GGUF, local / on-device)

./llama-cli \
  -m faust_1_q8_0.gguf \
  -p "Erkläre kurz, was ein großes Sprachmodell ist."

The repository includes a prebuilt Q8_0 GGUF file for efficient local inference.

Intended use

German conversational assistants
research and benchmarking on German NLP tasks
local and privacy-sensitive deployments
on-device or edge experimentation

Roadmap

Reasoning-focused variant (comming soon)
Agent-oriented variant (comming soon)

Citation

A technical paper describing training methodology, tokenizer design, and evaluation is in preparation.

Developed by tabularis.ai in Tübingen.