Instructions to use Mattimax/DAC5-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mattimax/DAC5-3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Mattimax/DAC5-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mattimax/DAC5-3B")
model = AutoModelForCausalLM.from_pretrained("Mattimax/DAC5-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mattimax/DAC5-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mattimax/DAC5-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/DAC5-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mattimax/DAC5-3B

SGLang

How to use Mattimax/DAC5-3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mattimax/DAC5-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/DAC5-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Mattimax/DAC5-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/DAC5-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mattimax/DAC5-3B with Docker Model Runner:
```
docker model run hf.co/Mattimax/DAC5-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

☕ Support the project

🇮🇹 ITALIANO

📘 Model Card — Mattimax/DAC5-3B

🧠 Informazioni Generali

Nome: Mattimax/DAC5-3B
Serie: DAC (DATA-AI Chat) – 5ª versione
Autore: Mattimax
Research Lab / Azienda: MINC01
Base Model: Qwen – Qwen2.5-3B-Instruct

DAC5-3B è attualmente il modello più avanzato e sperimentale della serie DAC, progettato per massimizzare qualità conversazionale, integrandosi al meglio con server MCP e performance tecnica su architettura 3B.

🏗 Architettura Tecnica

Core Architecture

Architettura: Qwen2ForCausalLM
Parametri: ~3B
Numero layer: 36
Hidden size: 2048
Intermediate size: 11008
Attention heads: 16
Key/Value heads (GQA): 2
Attivazione: SiLU
Norm: RMSNorm (eps 1e-6)
Tie word embeddings: Yes

Attention

Full attention su tutti i 36 layer
Attention dropout: 0.0
Sliding window: disabilitato
GQA (Grouped Query Attention) → maggiore efficienza memoria

Positional Encoding

Max position embeddings: 32768
RoPE theta: 1,000,000
RoPE scaling: None

Precision & Performance

Torch dtype: bfloat16
Quantizzazione training: 4-bit (NF4)
Cache abilitata per inference
Ottimizzato con Unsloth (fixed build 2026.2.1)

Tokenizer

Vocab size: 151,936
EOS token id: 151645
PAD token id: 151654

🎯 Obiettivo del Modello

DAC5-3B è stato progettato per:

🇮🇹 Massima qualità in italiano
⚡ Alta efficienza su GPU consumer
🧩 Conversazione coerente multi-turn
🛠️ Supporto tecnico e coding leggero
🧠 Migliore stabilità rispetto ai DAC precedenti

È un modello orientato a sviluppatori indipendenti, maker e sistemi offline (come OpenClaw, Claude Code, OpenCode, ecc...)

📚 Dataset & Specializzazione

Il fine-tuning supervisionato è stato effettuato su un mix altamente selezionato di dataset italiani:

Camoscio-ITA
DACMini Refined
Conversazioni sintetiche italiane ad alta qualità

Strategia

Dataset limitato ma ad alta densità informativa (~20k esempi)
Minimizzazione del rumore
Focus su chiarezza e coerenza
Riduzione delle risposte generiche tipiche dei 3B

🚀 Capacità Principali

DAC5-3B eccelle in:

Spiegazioni tecniche
Scrittura strutturata
Programmazione livello medio
Traduzione IT ↔ EN
Brainstorming progettuale
Assistenti locali offline
Supporto allo studio

📊 Differenze rispetto ai DAC precedenti

✔ Maggiore stabilità nelle risposte lunghe ✔ Meno ripetizioni ✔ Migliore controllo del tono ✔ Risposte più dirette ✔ Migliore allineamento alle istruzioni

DAC5 rappresenta il punto più alto raggiunto finora nella serie.

⚠️ Limitazioni

Contesto di training effettivo: 1024 token
Non ottimizzato per tool calling complesso
Non specializzato in matematica avanzata
Può degradare su reasoning multi-step molto profondo
Modello sperimentale

💻 Requisiti Hardware

Inference consigliata

GPU 6–8GB VRAM (quantizzato)
Oppure CPU moderna con GGUF

Compatibile con:

PC consumer
Mini workstation
Sistemi edge
Setup locali offline

🔬 Filosofia DAC

La serie DAC nasce con l'obiettivo di:

Spingere al massimo modelli compatti, ottimizzando qualità reale invece di scalare solo i parametri.

DAC5-3B è il risultato più maturo di questa filosofia: qualità elevata su architettura 3B con risorse contenute.

🧪 Stato del Modello

🟡 Sperimentale ma stabile È il miglior modello della serie DAC fino ad oggi, ma rimane parte di un ciclo evolutivo continuo.

📚 Citation

Se utilizzi Mattimax/DAC5-3B nei tuoi lavori di ricerca, progetti o pubblicazioni, puoi citarlo nel seguente modo:

@misc{mattimax_dac5_3b_2026,
  author       = {Mattimax},
  title        = {DAC5-3B: Fifth Iteration of the Dynamic Adaptive Core Series},
  year         = {2026},
  publisher    = {Hugging Face},
  organization = {MINC01},
  note         = {Experimental Italian-specialized 3B language model},
  url          = {https://huggingface.co/Mattimax/DAC5-3B}
}

Citazione testuale breve:

Mattimax. DAC5-3B: Fifth Iteration of the Dynamic Adaptive Core Series. 2026. MINC01 Research Lab.

🇬🇧 ENGLISH

📘 Model Card — Mattimax/DAC5-3B

🧠 General Information

Name: Mattimax/DAC5-3B
Series: DAC (DATA-AI Chat) – 5th version
Author: Mattimax
Research Lab / Company: MINC01
Base Model: Qwen – Qwen2.5-3B-Instruct

DAC5-3B is currently the most advanced and experimental model in the DAC series, designed to maximize conversational quality, integrating better with MCP servers and technical performance on a 3B architecture.

🏗 Technical Architecture

Core Architecture

Architecture: Qwen2ForCausalLM
Parameters: ~3B
Number of layers: 36
Hidden size: 2048
Intermediate size: 11008
Attention heads: 16
Key/Value heads (GQA): 2
Activation: SiLU
Norm: RMSNorm (eps 1e-6)
Tie word embeddings: Yes

Attention

Full attention across all 36 layers
Attention dropout: 0.0
Sliding window: disabled
GQA (Grouped Query Attention) → improved memory efficiency

Positional Encoding

Max position embeddings: 32768
RoPE theta: 1,000,000
RoPE scaling: None

Precision & Performance

Torch dtype: bfloat16
Training quantization: 4-bit (NF4)
Cache enabled for inference
Optimized with Unsloth (fixed build 2026.2.1)

Tokenizer

Vocab size: 151,936
EOS token id: 151645
PAD token id: 151654

🎯 Model Objective

DAC5-3B was designed for:

🇮🇹 Maximum Italian language quality
⚡ High efficiency on consumer GPUs
🧩 Coherent multi-turn conversations
🛠️ Technical support and light coding
🧠 Improved stability compared to previous DAC versions

It is oriented toward independent developers, makers, and offline systems (such as OpenClaw, Claude Code, OpenCode, etc.).

📚 Dataset & Specialization

Supervised fine-tuning was performed on a highly curated mix of Italian datasets:

Camoscio-ITA
DACMini Refined
High-quality synthetic Italian conversations

Strategy

Limited but high-density dataset (~20k samples)
Noise minimization
Focus on clarity and coherence
Reduction of generic 3B-style responses

🚀 Core Capabilities

DAC5-3B excels at:

Technical explanations
Structured writing
Intermediate-level programming
IT ↔ EN translation
Project brainstorming
Offline local assistants
Study support

📊 Differences from Previous DAC Versions

✔ Greater stability in long responses ✔ Fewer repetitions ✔ Better tone control ✔ More direct answers ✔ Improved instruction alignment

DAC5 represents the highest point reached so far in the series.

⚠️ Limitations

Effective training context: 1024 tokens
Not optimized for advanced tool calling
Not specialized in advanced mathematics
May degrade in very deep multi-step reasoning
Experimental model

💻 Hardware Requirements

Recommended Inference

6–8GB VRAM GPU (quantized)
Or modern CPU with GGUF

Compatible with:

Consumer PCs
Mini workstations
Edge systems
Offline local setups

🔬 DAC Philosophy

The DAC series was created with the goal of:

Pushing compact models to their limits, optimizing real quality instead of merely scaling parameters.

DAC5-3B is the most mature result of this philosophy: high quality on a 3B architecture with limited resources.

🧪 Model Status

🟡 Experimental but stable It is the best model in the DAC series to date, but remains part of an ongoing evolutionary cycle.

📚 Citation

If you use Mattimax/DAC5-3B in research work, projects, or publications, you may cite it as follows:

@misc{mattimax_dac5_3b_2026,
  author       = {Mattimax},
  title        = {DAC5-3B: Fifth Iteration of the Dynamic Adaptive Core Series},
  year         = {2026},
  publisher    = {Hugging Face},
  organization = {MINC01},
  note         = {Experimental Italian-specialized 3B language model},
  url          = {https://huggingface.co/Mattimax/DAC5-3B}
}

Short textual citation:

Mattimax. DAC5-3B: Fifth Iteration of the Dynamic Adaptive Core Series. 2026. MINC01 Research Lab.

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BF16

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