Mattimax / DAC5-Think

🇮🇹 ITALIANO

📘 Model Card — Mattimax/DAC5-Think

🧠 Informazioni Generali

• Nome: Mattimax/DAC5-Think
• Serie: DAC (DATA-AI Chat) – Think Edition
• Autore: Mattimax
• Research Lab / Azienda: MINC01
• Base Model: Qwen – Qwen3-4B-Instruct-2507

DAC5-Think è la versione reasoning della serie DAC, progettata per utilizzare thinking tags <think>...</think> al fine di esplicitare il processo di ragionamento interno del modello.

A differenza della versione standard, DAC5-Think è ottimizzato per task complessi, spiegazioni multi-step e ragionamento strutturato.

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🏗 Architettura Tecnica

Core Architecture

• Architettura: Qwen3ForCausalLM
• Parametri: ~4B
• Fine-tuning: LoRA
• Rank LoRA (r): 16
• LoRA alpha: 32
• Gradient checkpointing: Unsloth optimized
• Bias: none

Context & Memory

• Max sequence length: 4096 token
• Ottimizzato per contenere i tag <think>
• Quantizzazione training: 4-bit
• Inference support: 16-bit merged + GGUF Q4_K_M

Attention & Optimization

• Target modules LoRA:

  • q_proj
  • k_proj
  • v_proj
  • o_proj
  • gate_proj
  • up_proj
  • down_proj

• Optimizer: AdamW 8-bit

• Scheduler: Linear

• Weight decay: 0.01

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🧠 Thinking Tags System

DAC5-Think utilizza il seguente schema:

<think>
Processo di ragionamento interno...
Passaggi logici...
Analisi step-by-step...
</think>

Risposta finale per l’utente.

Obiettivi dei Thinking Tags

• Migliorare reasoning multi-step
• Separare pensiero interno e risposta finale
• Rendere il modello più coerente nei problemi complessi
• Ridurre errori logici in task matematici e tecnici

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📚 Dataset & Distillazione del Ragionamento

Il modello è stato addestrato su:

• DAC-Reasoning-ITA

Durante il training:

• I tag <reasoning> sono stati convertiti in <think>
• Pulizia automatica del testo
• Formattazione ChatML
• Nessun packing (per preservare integrità del reasoning)

Strategia

• Distillazione del pensiero esplicito
• Focus su spiegazioni strutturate
• Ottimizzazione per problemi complessi
• Addestramento breve ma mirato (500 step ad alta qualità)

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🚀 Capacità Principali

DAC5-Think eccelle in:

• Ragionamento multi-step
• Matematica strutturata (livello medio)
• Analisi tecnica dettagliata
• Debug di codice con spiegazione
• Problem solving logico
• Spiegazioni accademiche

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📊 Differenze rispetto a DAC5-3B

✔ Supporto esplicito ai thinking tags ✔ Context esteso a 4096 token ✔ Migliore gestione problemi complessi ✔ Maggiore profondità nelle spiegazioni ✔ Maggiore consumo computazionale

DAC5-Think è meno “veloce” ma più profondo nel ragionamento.

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⚠️ Limitazioni

• Richiede più VRAM rispetto alla versione 3B
• Output più lunghi (uso dei tag <think> )
• Non sempre necessario per task semplici
• Modello sperimentale

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💻 Requisiti Hardware

Inference consigliata

• GPU 10GB VRAM (4-bit)
• Oppure CPU potente con GGUF Q4_K_M

Supporta:

• Modello merged 16-bit
• Versione GGUF per Ollama / LM Studio

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🔬 Filosofia DAC-Think

DAC5-Think nasce con l’obiettivo di:

Rendere il ragionamento visibile e controllabile.

È un passo verso modelli piccoli ma capaci di reasoning strutturato, mantenendo efficienza hardware.

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🧪 Stato del Modello

🟡 Sperimentale avanzato Versione dedicata al reasoning esplicito. Complementare a DAC5-3B, non sostitutiva.

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📚 Citation

Se utilizzi Mattimax/DAC5-Think nei tuoi lavori di ricerca, progetti o pubblicazioni, puoi citarlo nel seguente modo:

@misc{mattimax_dac5_think_2026,
  author       = {Mattimax},
  title        = {DAC5-Think: Reasoning-Enhanced 4B Model with Explicit Thinking Tags},
  year         = {2026},
  publisher    = {Hugging Face},
  organization = {MINC01},
  note         = {Experimental 4B reasoning model with <think> tags},
  url          = {https://huggingface.co/Mattimax/DAC5-Think}
}

Citazione testuale breve:

Mattimax. DAC5-Think: Reasoning-Enhanced 4B Model with Explicit Thinking Tags . 2026. MINC01 Research Lab.

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🇬🇧 ENGLISH

📘 Model Card — Mattimax/DAC5-Think

🧠 General Information

• Name: Mattimax/DAC5-Think
• Series: DAC (DATA-AI Chat) – Think Edition
• Author: Mattimax
• Research Lab / Company: MINC01
• Base Model: Qwen – Qwen3-4B-Instruct-2507

DAC5-Think is the reasoning-oriented version of the DAC series, designed to use explicit <think>...</think> tags to expose the model’s internal reasoning process.

Unlike the standard version, DAC5-Think is optimized for complex tasks, multi-step explanations, and structured reasoning.

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🏗 Technical Architecture

Core Architecture

• Architecture: Qwen3ForCausalLM
• Parameters: ~4B
• Fine-tuning: LoRA
• LoRA rank (r): 16
• LoRA alpha: 32
• Gradient checkpointing: Unsloth optimized
• Bias: none

Context & Memory

• Max sequence length: 4096 tokens
• Optimized to accommodate <think> tags
• Training quantization: 4-bit
• Inference support: 16-bit merged + GGUF Q4_K_M

Attention & Optimization

• LoRA target modules:

  • q_proj
  • k_proj
  • v_proj
  • o_proj
  • gate_proj
  • up_proj
  • down_proj

• Optimizer: AdamW 8-bit

• Scheduler: Linear

• Weight decay: 0.01

---

🧠 Thinking Tags System

DAC5-Think follows this structure:

<think>
Internal reasoning process...
Logical steps...
Step-by-step analysis...
</think>

Final answer for the user.

Objectives of Thinking Tags

• Improve multi-step reasoning
• Separate internal reasoning from final answer
• Increase coherence in complex problems
• Reduce logical errors in technical tasks

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📚 Dataset & Reasoning Distillation

The model was trained on:

• DAC-Reasoning-ITA

During training:

• <reasoning> tags were converted into <think>
• Automatic text cleaning
• ChatML formatting
• No packing (to preserve reasoning integrity)

Strategy

• Explicit reasoning distillation
• Focus on structured explanations
• Optimization for complex tasks
• Short but high-quality fine-tuning (500 steps)

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🚀 Core Capabilities

DAC5-Think excels at:

• Multi-step reasoning
• Structured mathematics (intermediate level)
• Detailed technical analysis
• Code debugging with explanations
• Logical problem solving
• Academic-style explanations

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📊 Differences from DAC5-3B

✔ Explicit thinking tag support ✔ Extended 4096-token context ✔ Better handling of complex problems ✔ Deeper explanations ✔ Higher computational cost

DAC5-Think is less “fast” but more reasoning-oriented.

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⚠️ Limitations

• Requires more VRAM than 3B version
• Longer outputs (due to <think> tags)
• Not always necessary for simple tasks
• Experimental model

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💻 Hardware Requirements

Recommended Inference

• 10GB VRAM GPU (4-bit)
• Or powerful CPU with GGUF Q4_K_M

Supports:

• 16-bit merged model
• GGUF export for Ollama / LM Studio

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🔬 DAC-Think Philosophy

DAC5-Think was created with the goal of:

Making reasoning visible and controllable.

It represents a step toward compact yet structured-reasoning models while maintaining hardware efficiency.

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🧪 Model Status

🟡 Advanced experimental Dedicated reasoning version. Complementary to DAC5-3B, not a replacement.

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📚 Citation

If you use Mattimax/DAC5-Think in research, projects, or publications, you may cite it as follows:

@misc{mattimax_dac5_think_2026,
  author       = {Mattimax},
  title        = {DAC5-Think: Reasoning-Enhanced 4B Model with Explicit Thinking Tags},
  year         = {2026},
  publisher    = {Hugging Face},
  organization = {MINC01},
  note         = {Experimental 4B reasoning model with <think> tags},
  url          = {https://huggingface.co/Mattimax/DAC5-Think}
}

Short textual citation:

Mattimax. DAC5-Think: Reasoning-Enhanced 4B Model with Explicit Thinking Tags . 2026. MINC01 Research Lab.