---

language:

• en
• it

license: other

base_model:

• Qwen/Qwen2.5-Coder-14B-Instruct

tags:

• text-generation
• sigma-studio
• sigmanih
• conversational
• custom-model
• gguf
• llama.cpp
• quantized
• q6_k

pipeline_tag: text-generation

---

<div align="center">

⚡ Qwen-Qwen2.5-Coder-14B-Instruct-GGUF-Q6_K

High-Performance Model Published via Σ-SIGMA Studio

   

</div>

> ❤️ Support & Community: If you find this model helpful, please give this repository a Like on Hugging Face and a ⭐ Star on our SigmaStudio GitHub!

🌐 English Overview

Qwen-Qwen2.5-Coder-14B-Instruct-GGUF-Q6_K is a production-ready model optimized and published using the Model Hub module of Sigma Studio.

⚙️ Technical Specifications & Architecture

| Specification | Value |

| :--- | :--- |

| Model Repository | sigmanih/Qwen-Qwen2.5-Coder-14B-Instruct-GGUF-Q6_K |

| Weight Format | GGUF (Q6_K) |

| Base Architecture | CausalLM |

| Active Parameters | 14B |

| Context Window | 32,768 tokens |

| Total Disk Footprint | 11.29 GB |

| Recommended Usage | High-speed coding, Everyday assistants, Autonomous agentic loops & reasoning. |

🏆 Official Benchmark Performance

Evaluated directly on GPU via Sigma Studio Training Lab (Deterministic seed 42, Temp 0.0):

| Benchmark Suite | Score / Accuracy | Total Questions Evaluated | Pass Rate | Test Date | Execution Engine |

| :--- | :---: | :---: | :---: | :---: | :---: |

| Tutti i Benchmark Ufficiali | 78.0% | 78/100 quesiti superati | 78.0% Pass | 2026-09-02 | ⚡ SigmaEngine Direct GPU |

📋 Per-Dataset Evaluation Breakdown

| Dataset / Benchmark Suite | Domain / Category | Correct / Total | Accuracy (%) | Status |

| :--- | :--- | :---: | :---: | :---: |

| ARC-Challenge | Science & Grade-School Reasoning | 9 / 9 | 100% | ✅ Passed |

| BIG-Bench Hard | Complex Multi-Task Logic & Symbolics | 7 / 7 | 100% | ✅ Passed |

| GPQA | Graduate-Level Academic Reasoning | 2 / 9 | 22% | ⚠️ Low |

| GSM8K | Multi-Step Grade School Math | 8 / 9 | 89% | ✅ Passed |

| HellaSwag | Commonsense Reasoning & Situational NLI | 7 / 9 | 78% | ✅ Passed |

| HumanEval | Python Coding (pass@1) | 7 / 7 | 100% | ✅ Passed |

| MATH | Championship Competition Math | 8 / 9 | 89% | ✅ Passed |

| MBPP | Python Programming with Unit Tests | 9 / 9 | 100% | ✅ Passed |

| MMLU | General Knowledge & Multi-Subject | 8 / 14 | 57% | ⚡ Fair |

| MMLU-Pro | Advanced Multi-Step Reasoning | 5 / 9 | 56% | ⚡ Fair |

| TruthfulQA | Factuality & Anti-Hallucination | 8 / 9 | 89% | ✅ Passed |

| 🏆 OVERALL TOTAL | All Evaluated Datasets | 78 / 100 | 78% | 🏆 78% Pass |

Protocol: code_execution, continuation_logprob, cot_generation, letter_logprob · temp 0.0 · seed 42

Reproducibility hash: SHA256-28372CB2003770FC

> ⚠️ Measured on a slice of the dataset, not the full suite: this score is not comparable with a full-suite run.

⚡ Measured Speed on the Publishing Machine

Measured on NVIDIA GeForce RTX 5070 Ti • 15.9 GB VRAM. Two different numbers follow, and they are not interchangeable.

| What was measured | Value | How |

| :--- | :---: | :--- |

| Single-stream decode (what a chat feels) | 34.8 tok/s | one request at a time, on NVIDIA GeForce RTX 5070 Ti |

| Prompt processing | 137 tok/s | same probe |

| Aggregate throughput during evaluation | 34.4 tok/s | several requests in flight — not what a single answer runs at |

> Speeds on other hardware were not measured and are not guessed here. A single-stream figure from one machine cannot be scaled into a prediction for another: it depends on memory bandwidth, quantization, context length and driver, and the error is large enough to be misleading.

🚀 Quick Start Guide

1. Running with Sigma Studio (Recommended)

Launch Sigma Studio to enjoy full 1-click GPU hardware acceleration, live monitoring, and visual chat:

# Clone and run Sigma Studio
git clone https://github.com/Sigmanih/SigmaStudio.git
cd SigmaStudio
.\sigma_studio.bat

2. Running with llama.cpp

llama-cli -hf sigmanih/Qwen-Qwen2.5-Coder-14B-Instruct-GGUF-Q6_K -p "Hello! How can I help you today?" -ngl 99

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🇮🇹 Documentazione in Italiano

Qwen-Qwen2.5-Coder-14B-Instruct-GGUF-Q6_K è un modello ottimizzato pronto per l'inferenza e l'integrazione locale, pubblicato attraverso Σ-SIGMA Studio.

📋 Specifiche e Configurazione

• Architettura Base: CausalLM (14B parametri)
• Formato Pesi: GGUF (Q6_K)
• Spazio su Disco: 11.29 GB
• Finestra di Contesto: 32,768 token
• Profilo d'Uso Consigliato: Coding ad alta velocità, assistenti quotidiani, loop di agenti autonomi e ragionamento.

📊 Risultati Benchmark Ufficiali

• Suite di Valutazione: Tutti i Benchmark Ufficiali
• Punteggio Ufficiale: 78.0% (78/100 quesiti superati)

📋 Dettaglio Punteggi per Singolo Dataset

| Dataset / Suite di Test | Ambito / Dominio | Corretti / Totale | Accuratezza (%) | Esito |

| :--- | :--- | :---: | :---: | :---: |

| ARC-Challenge | Ragionamento Scientifico Avanzato | 9 / 9 | 100% | ✅ Superato |

| BIG-Bench Hard | Logica Complessa & Compiti Multi-Fase | 7 / 7 | 100% | ✅ Superato |

| GPQA | Ragionamento Accademico di Livello Laurea | 2 / 9 | 22% | ⚠️ Migliorabile |

| GSM8K | Matematica & Logica Multi-Step | 8 / 9 | 89% | ✅ Superato |

| HellaSwag | Buon Senso & Comprensione Situazionale | 7 / 9 | 78% | ✅ Superato |

| HumanEval | Sintesi Codice Python (pass@1) | 7 / 7 | 100% | ✅ Superato |

| MATH | Matematica Olimpica & Competitiva | 8 / 9 | 89% | ✅ Superato |

| MBPP | Programmazione Python con Unit Test | 9 / 9 | 100% | ✅ Superato |

| MMLU | Conoscenza Generale Multidisciplinare | 8 / 14 | 57% | ⚡ Discreto |

| MMLU-Pro | Ragionamento Avanzato Multi-Step | 5 / 9 | 56% | ⚡ Discreto |

| TruthfulQA | Fattualità & Resistenza ad Allucinazioni | 8 / 9 | 89% | ✅ Superato |

| 🏆 TOTALE COMPLESSIVO | Tutti i Dataset Valutati | 78 / 100 | 78% | 🏆 78% Pass |

Protocollo: code_execution, continuation_logprob, cot_generation, letter_logprob · temp 0.0 · seed 42

Impronta di riproducibilità: SHA256-28372CB2003770FC

> ⚠️ Misurato su una porzione del dataset, non sulla suite intera: il punteggio non è confrontabile con uno ottenuto sull'intero.

• Data Test: 2026-09-02 su motore deterministico SigmaEngine

⏱️ Throughput Hardware e Fasce Consigliate

• Velocità Verificata in Locale: 34.4 tok/s su NVIDIA GeForce RTX 5070 Ti.
• Risposta singola (quello che si sente in chat): 34.8 tok/s su NVIDIA GeForce RTX 5070 Ti.
• Lettura del prompt: 137 tok/s.
• Throughput complessivo durante la valutazione: 34.4 tok/s — piu' richieste in volo insieme, non la velocita' di una risposta singola.
• Le velocita' su altro hardware non sono state misurate e non vengono indovinate: dipendono da banda di memoria, quantizzazione, lunghezza del contesto e driver.

⭐ Supporta il Progetto Open Source

Se questo modello ti è utile o vuoi esplorare l'ecosistema completo:

• 🌟 Metti una Stella al repository GitHub: Sigmanih/SigmaStudio
• ❤️ Lascia un Like a questa scheda su Hugging Face

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Creato e distribuito con il Model Hub di Σ-SIGMA Studio (02/09/2026 22:38)