395 lines
10 KiB
Markdown
395 lines
10 KiB
Markdown
---
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library_name: transformers
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license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
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pipeline_tag: text-generation
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license: cc-by-nc-4.0
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extra_gated_prompt: >
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### FAUST-1 NON-COMMERCIAL LICENSE AGREEMENT
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Version 1.0 — January 2025
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"Faust-1" refers to the language model weights, code, and documentation made
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available by Tabularis AI GmbH ("Tabularis") under this agreement.
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1. License Grant
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You are granted a non-exclusive, non-transferable, royalty-free license to
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use, copy, and modify Faust-1 for non-commercial research and personal
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purposes only.
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2. Non-Commercial Use
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"Non-commercial" means academic research, personal projects, and educational
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use. Any use intended to generate revenue, provide commercial services, or
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benefit a for-profit entity requires a separate commercial license.
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3. Commercial Licensing
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For commercial use, please contact: info@tabularis.ai
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4. Attribution
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You must include "Built with Faust-1 by Tabularis AI" in any derivative work
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or publication.
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5. No Warranty
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Faust-1 is provided "as is" without warranties of any kind.
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6. Termination
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This license terminates automatically if you violate any terms.
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---
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### Additional Access Requirement
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Access to this repository is approval-based.
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You must join our Discord server: https://discord.gg/7WqEKw652R
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extra_gated_fields:
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Name: text
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Email: text
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Affiliation: text
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I have joined the Tabularis AI Discord server: checkbox
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I accept the Faust-1 Non-Commercial License Agreement: checkbox
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extra_gated_description: |
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Faust-1 is for non-commercial use only.
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For commercial licensing contact info@tabularis.ai
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Approval requires Discord membership.
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Join: https://discord.gg/7WqEKw652R
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extra_gated_button_content: Submit
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language:
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- de
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- en
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tags:
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- llama.cpp
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- synthetic data
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---
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<!-- <a href="https://faust.tabularis.ai/" target="_blank" style="margin: 2px;">
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<img
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alt="Faust-1 Demo"
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src="https://img.shields.io/badge/%E2%9C%A8%20Faust--1%20Demo-2b2b2b?style=flat&logo=ai&logoColor=white"
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style="display: inline-block; vertical-align: middle;"
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/>
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</a> -->
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<p align="center">
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<img src="./logo-faust.webp" alt="Faust-1 Logo" width="220">
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</p>
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# Faust-1 — German-First Large Language Model (1.6B)
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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.
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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.
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<!-- Demo: [faust.tabularis.ai](https://faust.tabularis.ai)
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-->
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> [!TIP]
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> **Designed for local and cost-efficient deployment.**
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> Faust-1 is deliberately sized and optimized to run on **consumer-grade hardware** and **does not require expensive data-center GPUs**.
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---
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## Model summary
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- Repository: tabularisai/Faust-1
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- Model type: decoder-only causal language model
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- Parameters: 1.6B
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- Interface: conversational / instruction (chat template provided)
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- Primary language: German (~90%)
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- Custom State-of-the-Art tokenizer for German language
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---
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## Quickstart
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### Conversational usage (recommended)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "tabularisai/Faust-1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "Gib mir eine kurze Einführung in große Sprachmodelle (LLM)."}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=256,
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temperature=0.6,
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do_sample=True,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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## Conditional Generation
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```python
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!pip install git+https://github.com/tabularis-ai/guidegen.git
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import sys
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import os
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import json
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import time
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import guidegen as gg
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from pydantic import BaseModel, Field
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from typing import Literal, List
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# Hugging Face access token - set via environment variable or .env file
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# You can set it with: export HUGGINGFACE_HUB_TOKEN=your_token_here
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# Or create a .env file with: HUGGINGFACE_HUB_TOKEN=your_token_here
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MODEL_NAME = "tabularisai/Faust-1"
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# --- Schema ---
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class EmailSummary(BaseModel):
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"""Structured summary of an email."""
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Absender: str = Field(description="Der Name des Absenders.")
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Betreff: str = Field(description="Worum geht es in der E-Mail? (max 5 Wörter)")
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Zusammenfassung: str = Field(description="Kurze Zusammenfassung (max 2 Sätze).")
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Prioritaet: Literal["hoch", "mittel", "niedrig"] = Field(description="Wie wichtig die E-Mail ist.")
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# AntwortNoetig: bool = Field(description="Muss man auf die E-Mail antworten?")
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# --- Input ---
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email_text = """Hallo Jens,
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wir hatten uns bei CampusFounders im Rahmen unserer Pre-Seed-Runde kennengelernt.
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Seitdem haben wir große Fortschritte gemacht und bereiten aktuell unsere Seed-Runde vor.
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Wir entwickeln eine Infrastruktur für hocheffiziente, lokal trainierbare KI-Modelle – vollständig ohne Cloud.
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Sehr gern würden wir uns mit dir austauschen und prüfen, ob ein Intro zu US-VCs oder ein Gespräch mit Crestlight möglich wäre.
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Anbei ein kurzer OnePager zur Weiterleitung.
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Beste Grüße
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Ricard"""
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# --- Prompt ---
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prompt = f"""
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Du bist ein intelligenter Assistent, der E-Mails analysiert und als JSON zusammenfasst.
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Halte die Zusammenfassung kurz (1-2 Sätze). Betreff maximal 5 Wörter.
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--- Beispiel ---
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E-Mail-Text:
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Sehr geehrte Damen und Herren, ich wollte nur nachfragen, ob meine Bestellung #12345 schon versandt wurde. Vielen Dank, Max Mustermann
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JSON-Antwort:
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{{
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"Absender": "Max Mustermann",
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"Betreff": "Bestellstatus Anfrage",
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"Zusammenfassung": "Anfrage zum Versandstatus der Bestellung #12345.",
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"Prioritaet": "mittel",
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}}
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--- Ende Beispiel ---
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Jetzt analysiere die folgende E-Mail und erstelle das JSON-Objekt.
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E-Mail-Text:
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{email_text}
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"""
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def main():
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print("=" * 60)
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print("EMAIL SUMMARIZATION WITH GUIDEGEN")
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print("=" * 60)
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print(f"\nLoading model: {MODEL_NAME}")
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load_start = time.time()
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gen = gg.GuideGen(
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MODEL_NAME,
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verbose=True,
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use_chat_template=True,
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enable_thinking=False,
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)
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load_time = time.time() - load_start
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print(f"Model loaded in {load_time:.2f}s")
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# --- Generate ---
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print("\nGenerating structured summary...")
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gen_start = time.time()
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options = gg.GuideGenOptions(
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temperature=0.6,
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max_tokens=400,
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do_sample=False,
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)
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summary = gen.generate(prompt, EmailSummary, options=options)
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gen_time = time.time() - gen_start
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print(f"Generation complete in {gen_time:.2f}s")
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# --- Output ---
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print("\n--- Email Summary (JSON) ---")
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print(json.dumps(summary.model_dump(), indent=2, ensure_ascii=False))
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print(f"\n Model load: {load_time:.2f}s | Generation: {gen_time:.2f}s | Total: {load_time + gen_time:.2f}s")
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```
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---
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## Training focus
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### German-first data distribution
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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.
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### Verified synthetic data
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A substantial portion of the training signal comes from synthetic data. To keep this signal usable, generation is paired with explicit verification and filtering:
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- LLM-as-judge style evaluations
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- rule-based and programmatic checks
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- consistency and self-agreement filtering
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This allows broad coverage of instruction-following and reasoning patterns while maintaining quality control.
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---
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## Tokenizer optimized for German
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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.
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Lower token counts on German text translate directly into more usable context, lower inference cost, and less fragmentation on compound-heavy inputs.
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<img src="tokenizer_faust.png" alt="Faust-1 vs OpenAI Tokenizers" width="800">
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---
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## German benchmark performance
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Faust-1 is evaluated on a set of standard German-language benchmarks:
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- ARC_de
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- GSM8K_de
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- HellaSwag_de
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- MMLU_de
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- TruthfulQA_de
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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.
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---
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## Deployment examples
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Faust-1 can be deployed with common inference stacks that support decoder-only language models.
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vLLM (OpenAI-compatible API)
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```sh
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vllm serve tabularisai/Faust-1 --dtype float16
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```
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SGLang
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```sh
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python -m sglang.launch_server \
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--model-path tabularisai/Faust-1 \
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--dtype float16
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```
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llama.cpp (GGUF, local / on-device)
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```sh
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./llama-cli \
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-m faust_1_q8_0.gguf \
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-p "Erkläre kurz, was ein großes Sprachmodell ist."
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```
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The repository includes a prebuilt Q8_0 GGUF file for efficient local inference.
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---
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## Intended use
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- German conversational assistants
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- research and benchmarking on German NLP tasks
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- local and privacy-sensitive deployments
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- on-device or edge experimentation
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---
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## Roadmap
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- Reasoning-focused variant (comming soon)
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- Agent-oriented variant (comming soon)
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---
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## Citation
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A technical paper describing training methodology, tokenizer design, and evaluation is in preparation.
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Developed by [tabularis.ai](https://tabularis.ai) in Tübingen. |