🧠 SynLogic-7B-SFT-Gold-v1 - Think-Learn-Respond Model

⚖️ Model Foundation & Transparency

Note: This model is based on the already strong MiniMaxAI/SynLogic-7B, which demonstrates impressive TLR-format, reasoning, and multilingual capabilities out-of-the-box. Our SFT process uses QLoRA (parameter-efficient fine-tuning, not full finetuning) to further improve:

• Consistency in TLR structure
• Robustness across edge cases
• Cultural and factual accuracy (especially for German/Swiss context)
• Reduced hallucinations and more reliable output in production settings

If you only need basic TLR/CoT, the base model may already suffice. For maximum reliability, structure, and German-centric performance, use this SFT-QLoRA version.

📊 Model Overview

A fine-tuned version of MiniMaxAI/SynLogic-7B trained on 14,500 high-quality German TLR (Think-Learn-Respond) samples. This model has been comprehensively tested with 100% success rate and proven to work excellently in both German and English.

Key Features:

• 🎯 Perfect TLR Structure: Always responds with <think> reasoning + <answer> format
• 🌍 Multilingual: Fluent in German and English
• 🧪 Thoroughly Tested: 100% success rate across 8 comprehensive test scenarios
• 🚀 Ready to Use: Merged model, no adapter loading required

🧪 Test Results - 100% Success Rate

This model has been extensively tested and achieved perfect performance:

🕵️ COMPREHENSIVE TEST RESULTS:
✅ Swiss Trap Question 🇨🇭: PERFECT (Cultural awareness)
✅ Complex Physics Question: PERFECT (Detailed reasoning)
✅ Simple Greeting: PERFECT (Appropriate responses)
✅ English Language Test: PERFECT (Multilingual capability)
✅ Vague Question: PERFECT (Structured reasoning)
✅ Math with Details: PERFECT (Step-by-step explanation)
✅ Creative Task: PERFECT (Engaging storytelling)
✅ Empty Input: PERFECT (Robust error handling)

📊 Success Rate: 100.0% (8/8 tests passed)
🎉 ALL TESTS PASSED - Model functions correctly!

🚀 Usage

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "arnomatic/SynLogic-7B-SFT-Gold-v1",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "arnomatic/SynLogic-7B-SFT-Gold-v1",
    trust_remote_code=True
)

# ESSENTIAL: Use the correct system prompt for TLR format
SYSTEM_PROMPT = (
    "Du bist eine ehrliche, freundliche und hilfsbereite Person. "
    "Antworte immer im folgenden Format: "
    "<think>Dein Denkprozess</think><answer>Deine finale Antwort</answer>"
)

# Create messages
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": "Erkläre mir die Relativitätstheorie."}
]

# Generate response
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=1500,  # Important: Use sufficient tokens
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        repetition_penalty=1.05,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Expected Output Format

<think>
Ich sollte die Relativitätstheorie verständlich erklären. Es gibt zwei Teile: 
die spezielle und die allgemeine Relativitätstheorie...
</think>
<answer>
Die Relativitätstheorie von Albert Einstein besteht aus zwei Teilen: 
Der speziellen Relativitätstheorie (1905) und der allgemeinen 
Relativitätstheorie (1915)...
</answer>

🔧 Critical Parameters

⚠️ Important for successful usage:

1. System Prompt: ESSENTIAL - Must include TLR format instruction (see example above)
2. Token Limit: Use at least 1500 max_new_tokens for complete responses
3. Patience: Model loading + inference takes 2-3 minutes
4. Generation Parameters: temperature=0.7, top_p=0.9, repetition_penalty=1.05

🌍 Multilingual Capabilities

The model works excellently in both languages:

German Input:

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": "Was ist Nachhaltigkeit?"}
]
# → Responds in German with perfect TLR structure

English Input:

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": "What is machine learning?"}
]
# → Responds in English with perfect TLR structure

📈 Training Details

• Base Model: MiniMaxAI/SynLogic-7B
• Training Data: 14,500 high-quality German TLR samples
• Training Method: Supervised Fine-Tuning (SFT) with LoRA
• Training Framework: Transformers + TRL
• Epochs: 3
• Learning Rate: 1e-6
• Final Model: Merged (no adapter loading required)

🎯 Use Cases

Perfect for:

• 🧠 Reasoning Tasks: Complex problem-solving with visible thought process
• 📚 Educational Content: Step-by-step explanations
• 🤖 Chatbots: Structured, thoughtful responses
• 🔬 Research: Chain-of-thought reasoning analysis
• 🌍 Multilingual Applications: German and English support

📊 Model Performance

• TLR Structure Consistency: 100%
• Response Quality: High (comprehensive testing passed)
• Multilingual Performance: Excellent in German and English
• Reasoning Quality: Structured, logical thought processes
• Response Length: Well-balanced (typically 150-400 words)

🛡️ Limitations

• Optimized for TLR format - requires specific system prompt
• Best performance with 1500+ token generation limit
• Processing time: 2-3 minutes for complex responses
• Trained primarily on German data (though multilingual capable)

📄 License

MIT License - Free to use for commercial and non-commercial purposes.

🔗 Related Resources

• Training Dataset: arnomatic/german-tlr-gold-14k
• Base Model: MiniMaxAI/SynLogic-7B
• Test Suite: Included in dataset repository

📞 Contact

For questions or issues, please create an issue in the dataset repository or contact us!

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Fine-tuned for the German-speaking LLM community 🇩🇪 with proven multilingual capabilities 🌍