Update weights: W4 attention surgery (stepped 0.1/0.3/0.6/0.8) - 27/30 instruction following

README.md

@@ -1,375 +1,112 @@
 ---
-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
-extra_gated_fields:
-  Name: text
-  Email: text
-  Affiliation: text
-  I have joined the Tabularis AI Discord server: checkbox
-  I accept the Faust-1 Non-Commercial License Agreement: checkbox
-extra_gated_description: |
-  Faust-1 is for non-commercial use only.
-  For commercial licensing contact info@tabularis.ai
-
-  Approval requires Discord membership.
-  Join: https://discord.gg/7WqEKw652R
-extra_gated_button_content: Submit
 language:
-- de
-- en
+  - de
+  - en
+license: apache-2.0
+library_name: transformers
+base_model:
+  - tabularisai/Faust-1
+  - Qwen/Qwen3-1.7B
 tags:
-- llama.cpp
-- synthetic data
+  - merge
+  - german
+  - medical
+  - instruction-following
+  - attention-surgery
+pipeline_tag: text-generation
 ---
 
+# Faust-1-Merged
 
-<!-- <a href="https://faust.tabularis.ai/" target="_blank" style="margin: 2px;">
-  <img
-    alt="Faust-1 Demo"
-    src="https://img.shields.io/badge/%E2%9C%A8%20Faust--1%20Demo-2b2b2b?style=flat&logo=ai&logoColor=white"
-    style="display: inline-block; vertical-align: middle;"
-  />
-</a> -->
-
-
-<p align="center">
-  <img src="./logo-faust.webp" alt="Faust-1 Logo" width="220">
-</p>
+**German language model with enhanced instruction following via attention surgery.**
 
-# Faust-1 — German-First Large Language Model (1.6B)
+## What is this?
 
-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.
+This is [tabularisai/Faust-1](https://huggingface.co/tabularisai/Faust-1) (1.7B, Qwen3 architecture, custom German tokenizer) with attention layers partially replaced from [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) base model to improve instruction following while preserving Faust's German language capabilities.
 
-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.
+## Merge Method: Attention Surgery
 
-Demo: [faust.tabularis.ai](https://faust.tabularis.ai)
+Unlike traditional model merging (SLERP, TIES, DARE), this uses **targeted attention-only surgery** with a stepped alpha schedule:
 
+| Layer Range | Alpha | Effect |
+|------------|-------|--------|
+| 0-6 (early) | 0.1 | Light touch — protect embedding-adjacent layers |
+| 7-13 (mid-early) | 0.3 | Moderate blend |
+| 14-20 (mid-late) | 0.6 | Strong instruction signal |
+| 21-27 (late) | 0.8 | Maximum instruction following |
 
-> [!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
+**Key insight:** Only self-attention weights are modified. All MLP weights (which store factual knowledge and vocabulary) remain 100% Faust. This preserves German language quality while importing Qwen3's instruction-following behavior from its attention routing.
 
-- 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
+## Evaluation Results
 
----
+Tested on 30 instruction-following tasks (deterministic, temperature=0):
 
-## Quickstart
+| Model | Score | Accuracy |
+|-------|-------|----------|
+| **Faust-1-Merged** | **27/30** | **90%** |
+| Faust-1 (original) | 25/30 | 83% |
 
-### Conversational usage (recommended)
+### Category Breakdown
 
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM  
-import torch  
+| Category | Faust-1 | Faust-1-Merged |
+|----------|---------|----------------|
+| Format (lists, JSON, etc.) | 5/6 | 6/6 |
+| Length control | 5/5 | 5/5 |
+| Language (German, formal) | 3/4 | 4/4 |
+| Constraints (forbidden words) | 4/5 | 4/5 |
+| Structured output | 4/4 | 3/4 |
+| Medical (Arztbrief) | 3/3 | 3/3 |
+| Role playing | 2/3 | 2/3 |
 
-model_id = "tabularisai/Faust-1"
+### Improvements over baseline:
+- ✅ One-word answers (strict format compliance)
+- ✅ No-English constraint (pure German output)
+- ✅ Required word inclusion
 
-tokenizer = AutoTokenizer.from_pretrained(model_id)  
-model = AutoModelForCausalLM.from_pretrained(  
-    model_id,  
-    torch_dtype=torch.float16,  
-    device_map="auto",  
-)
+### Known limitations:
+- ❌ "End with word" — both models struggle
+- ❌ "Refuse off-topic" — requires SFT for proper role boundaries
+- ❌ Markdown tables sometimes missing proper separators
 
-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))
-```
-
----
-
-## Conditional Generation
+## Usage
 
 ```python
-!pip install git+https://github.com/tabularis-ai/guidegen.git
-
-import sys
-import os
-import json
-import time
-
-import guidegen as gg
-from pydantic import BaseModel, Field
-from typing import Literal, List
-
-# Hugging Face access token - set via environment variable or .env file
-# You can set it with: export HUGGINGFACE_HUB_TOKEN=your_token_here
-# Or create a .env file with: HUGGINGFACE_HUB_TOKEN=your_token_here
-
-MODEL_NAME = "tabularisai/Faust-1"
-
-
-# --- Schema ---
-class EmailSummary(BaseModel):
-    """Structured summary of an email."""
-    Absender: str = Field(description="Der Name des Absenders.")
-    Betreff: str = Field(description="Worum geht es in der E-Mail? (max 5 Wörter)")
-    Zusammenfassung: str = Field(description="Kurze Zusammenfassung (max 2 Sätze).")
-    Prioritaet: Literal["hoch", "mittel", "niedrig"] = Field(description="Wie wichtig die E-Mail ist.")
-    # AntwortNoetig: bool = Field(description="Muss man auf die E-Mail antworten?")
-
-
-# --- Input ---
-email_text = """Hallo Jens,
-
-wir hatten uns bei CampusFounders im Rahmen unserer Pre-Seed-Runde kennengelernt.
-Seitdem haben wir große Fortschritte gemacht und bereiten aktuell unsere Seed-Runde vor.
-
-Wir entwickeln eine Infrastruktur für hocheffiziente, lokal trainierbare KI-Modelle – vollständig ohne Cloud.
-Sehr gern würden wir uns mit dir austauschen und prüfen, ob ein Intro zu US-VCs oder ein Gespräch mit Crestlight möglich wäre.
-
-Anbei ein kurzer OnePager zur Weiterleitung.
-
-Beste Grüße  
-Ricard"""
-
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
+model = AutoModelForCausalLM.from_pretrained("tabularisai/Faust-1-Merged", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained("tabularisai/Faust-1-Merged")
 
-# --- Prompt ---
-prompt = f"""
-Du bist ein intelligenter Assistent, der E-Mails analysiert und als JSON zusammenfasst.
-Halte die Zusammenfassung kurz (1-2 Sätze). Betreff maximal 5 Wörter.
-
---- Beispiel ---
-E-Mail-Text:
-Sehr geehrte Damen und Herren, ich wollte nur nachfragen, ob meine Bestellung #12345 schon versandt wurde. Vielen Dank, Max Mustermann
-JSON-Antwort:
-{{
-  "Absender": "Max Mustermann",
-  "Betreff": "Bestellstatus Anfrage",
-  "Zusammenfassung": "Anfrage zum Versandstatus der Bestellung #12345.",
-  "Prioritaet": "mittel",
-}}
---- Ende Beispiel ---
-
-Jetzt analysiere die folgende E-Mail und erstelle das JSON-Objekt.
-
-E-Mail-Text:
-{email_text}
-"""
-
-
-def main():
-    print("=" * 60)
-    print("EMAIL SUMMARIZATION WITH GUIDEGEN")
-    print("=" * 60)
-
-    print(f"\nLoading model: {MODEL_NAME}")
-    load_start = time.time()
-
-    gen = gg.GuideGen(
-        MODEL_NAME,
-        verbose=True,
-        use_chat_template=True,
-        enable_thinking=False,
-    )
-
-    load_time = time.time() - load_start
-    print(f"Model loaded in {load_time:.2f}s")
-
-    # --- Generate ---
-    print("\nGenerating structured summary...")
-    gen_start = time.time()
-
-    options = gg.GuideGenOptions(
-        temperature=0.6,
-        max_tokens=400,
-        do_sample=False,
-    )
-
-    summary = gen.generate(prompt, EmailSummary, options=options)
-
-    gen_time = time.time() - gen_start
-    print(f"Generation complete in {gen_time:.2f}s")
-
-    # --- Output ---
-    print("\n--- Email Summary (JSON) ---")
-    print(json.dumps(summary.model_dump(), indent=2, ensure_ascii=False))
-    print(f"\n  Model load: {load_time:.2f}s | Generation: {gen_time:.2f}s | Total: {load_time + gen_time:.2f}s")
-```
-
----
-
-## 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.
-
-![Tokenizer efficiency on German language](tokenizer_bench.png)
-
-Lower token counts on German text translate directly into more usable context, lower inference cost, and less fragmentation on compound-heavy inputs.
-
-
-<img src="tokenizer_faust.png" alt="Faust-1 vs OpenAI Tokenizers" width="800">
-
-
----
-
-## 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  
-
-![German benchmark performance](faust_bench.png)
-
-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)
-```sh
-vllm serve tabularisai/Faust-1 --dtype float16
-```
-
-SGLang
-```sh
-python -m sglang.launch_server \
-  --model-path tabularisai/Faust-1 \
-  --dtype float16
-```
+messages = [
+    {"role": "system", "content": "Du bist ein hilfreicher Assistent."},
+    {"role": "user", "content": "Nenne mir 5 deutsche Städte als nummerierte Liste."}
+]
 
-llama.cpp (GGUF, local / on-device)
-```sh
-./llama-cli \
-  -m faust_1_q8_0.gguf \
-  -p "Erkläre kurz, was ein großes Sprachmodell ist."
+input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=200, temperature=0, do_sample=False)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
 ```
 
-The repository includes a prebuilt Q8_0 GGUF file for efficient local inference.
-
----
-
-## Intended use
+## Technical Details
 
-- 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)
-
----
+- **Architecture:** Qwen3 (1.7B parameters)
+- **Tokenizer:** Custom Faust German tokenizer (unchanged)
+- **Modified layers:** 168 self-attention parameter tensors
+- **Unmodified:** All MLP layers, embeddings, lm_head, layer norms
+- **Method:** Per-quartile linear interpolation of attention weights
 
 ## Citation
 
-A technical paper describing training methodology, tokenizer design, and evaluation is in preparation.
+```bibtex
+@misc{faust1merged2026,
+  title={Faust-1-Merged: Attention Surgery for German Instruction Following},
+  author={Tabularis.AI},
+  year={2026},
+  url={https://huggingface.co/tabularisai/Faust-1-Merged}
+}
+```
 
+## About Tabularis.AI
 
-Developed by [tabularis.ai](https://tabularis.ai) in Tübingen.
+University of Tübingen spin-off specializing in privacy-first AI for regulated industries. 
+Products include EU PII Safeguard, Faust German language models, and GDPR-compliant on-premises deployment.

config.json

@@ -50,11 +50,13 @@
   "num_key_value_heads": 8,
   "pad_token_id": 1,
   "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 1000000,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
   "sliding_window": null,
   "tie_word_embeddings": true,
-  "transformers_version": "4.57.5",
+  "transformers_version": "5.2.0",
   "use_cache": false,
   "use_sliding_window": false,
   "vocab_size": 100000

generation_config.json

@@ -9,5 +9,5 @@
   "temperature": 0.6,
   "top_k": 20,
   "top_p": 0.95,
-  "transformers_version": "4.57.5"
+  "transformers_version": "5.2.0"
 }

merge_config.json

@@ -0,0 +1,30 @@
+{
+  "method": "attention_surgery",
+  "base_model": "tabularisai/Faust-1",
+  "donor_model": "Qwen/Qwen3-1.7B",
+  "schedule": "stepped_quartile",
+  "alphas_per_quartile": {
+    "0-6": 0.1,
+    "7-13": 0.3,
+    "14-20": 0.6,
+    "21-27": 0.8
+  },
+  "components_modified": [
+    "self_attn"
+  ],
+  "components_preserved": [
+    "mlp",
+    "embed_tokens",
+    "lm_head",
+    "input_layernorm",
+    "post_attention_layernorm",
+    "model.norm"
+  ],
+  "eval_score": "27/30 (90%)",
+  "baseline_score": "25/30 (83%)",
+  "eval_settings": {
+    "temperature": 0,
+    "do_sample": false,
+    "max_new_tokens": 300
+  }
+}

model.safetensors

@@ -1,3 +1,3 @@
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 size 3228455704

tokenizer.json

@@ -184,7 +184,32 @@
       }
     ]
   },
-  "post_processor": null,
+  "post_processor": {
+    "type": "TemplateProcessing",
+    "single": [
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      }
+    ],
+    "pair": [
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "B",
+          "type_id": 1
+        }
+      }
+    ],
+    "special_tokens": {}
+  },
   "decoder": {
     "type": "ByteLevel",
     "add_prefix_space": true,

tokenizer_config.json

@@ -1,180 +1,20 @@
 {
-  "added_tokens_decoder": {
-    "0": {
-      "content": "<|endoftext|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": "<|pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": "<|unk|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "3": {
-      "content": "<|bos|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "4": {
-      "content": "<|eos|>",
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-    "5": {
-      "content": "<|im_start|>",
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-      "single_word": false,
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-    "6": {
-      "content": "<|im_end|>",
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-    "7": {
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-      "lstrip": false,
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-      "lstrip": false,
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-      "rstrip": false,
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-    }
-  },
-  "additional_special_tokens": [
-    "<|im_start|>",
-    "<|im_end|>",
-    "<|im_sep|>",
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-    "<|special_6|>",
-    "<|special_7|>",
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-    "<|special_9|>"
-  ],
+  "backend": "tokenizers",
   "bos_token": "<|bos|>",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
-  "extra_special_tokens": {},
+  "is_local": false,
   "max_length": 2048,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
   "model_max_length": 8192,
   "pad_token": "<|pad|>",
+  "return_token_type_ids": false,
   "stride": 0,
-  "tokenizer_class": "PreTrainedTokenizerFast",
+  "tokenizer_class": "TokenizersBackend",
   "truncation_side": "right",
   "truncation_strategy": "longest_first",
-  "unk_token": "<|unk|>",
-  "return_token_type_ids": false,
-  "model_input_names": [
-    "input_ids",
-    "attention_mask"
-  ]
-}
+  "unk_token": "<|unk|>"
+}