---

language:

• en
• it

license: other

base_model:

• ornith-ai/Ornith-1.5-35B-A3B

tags:

• text-generation
• sigma-studio
• sigmanih
• conversational
• custom-model

pipeline_tag: text-generation

---

<div align="center">

⚡ ornith-ai-Ornith-1.5-35B-A3B-GGUF-Q8_0

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

ornith-ai-Ornith-1.5-35B-A3B-GGUF-Q8_0 is a production-ready model optimized and published using the Model Hub module of Sigma Studio.

⚙️ Technical Specifications & Architecture

| Specification | Value |

| :--- | :--- |

| Model Repository | sigmanih/ornith-ai-Ornith-1.5-35B-A3B-GGUF-Q8_0 |

| Weight Format | Sconosciuto (sconosciuta) |

| Base Architecture | CausalLM |

| Active Parameters | 35B |

| Context Window | 32,768 tokens |

| Total Disk Footprint | 0.0 GB |

| Inference RAM / VRAM | ~30.2 GB VRAM (Full GPU offload) / ~28.3 GB RAM (CPU/Hybrid) |

| Recommended Hardware | Multi-GPU setup (2× 24 GB or RTX 6000) or 64 GB system RAM |

| Recommended Usage | Flagship frontier intelligence, Deep research & multi-step mathematics. |

🏆 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 | 70.0% | 70/100 quesiti superati | 70.0% Pass | 2026-09-08 | ⚡ SigmaEngine Direct GPU |

🧠 Reasoning Mode Comparison: No-Thinking vs Thinking

Side-by-side performance comparison between direct zero-overhead answer (No-Thinking) and Chain-of-Thought step-by-step reasoning (Deep Thinking CoT):

⚡ No-Thinking (Direct Response) : [██████████████░░░░░░] 70.2%  (40/57 passed)
🧠 Deep Thinking (CoT Reasoning) : [██████████████░░░░░░] 69.8%  (30/43 passed)
📈 CoT Performance Delta        : -0.4%

%%{init: {'theme': 'dark'}}%%
xychart-beta
    title "Accuracy (%): No-Thinking vs Deep Thinking"
    x-axis ["⚡ No-Thinking (Direct)", "🧠 Deep Thinking (CoT)"]
    y-axis "Accuracy (%)" 0 --> 100
    bar [70.2, 69.8]

| Execution Mode | Accuracy (%) | Questions Passed | Operational Profile & Latency |

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

| ⚡ No-Thinking (Direct Response) | 70.2% | 40 / 57 | Minimal latency, immediate token-to-first-byte, zero reasoning tokens overhead |

| 🧠 Deep Thinking (CoT Reasoning) | 69.8% | 30 / 43 | Multi-step structured reasoning trace (-0.4%), optimal for math & hard logic |

📋 Per-Dataset Evaluation Breakdown

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

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

| ARC-Challenge | Science & Grade-School Reasoning | 8 / 9 | 89% | ✅ Passed |

| BIG-Bench Hard | Complex Multi-Task Logic & Symbolics | 6 / 7 | 86% | ✅ Passed |

| GPQA | Graduate-Level Academic Reasoning | 3 / 9 | 33% | ⚠️ Low |

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

| HellaSwag | Commonsense Reasoning & Situational NLI | 5 / 9 | 56% | ⚡ Fair |

| HumanEval | Python Coding (pass@1) | 6 / 7 | 86% | ✅ Passed |

| MATH | Championship Competition Math | 7 / 9 | 78% | ✅ Passed |

| MBPP | Python Programming with Unit Tests | 6 / 9 | 67% | ⚡ Fair |

| MMLU | General Knowledge & Multi-Subject | 7 / 14 | 50% | ⚡ Fair |

| MMLU-Pro | Advanced Multi-Step Reasoning | 6 / 9 | 67% | ⚡ Fair |

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

| 🏆 OVERALL TOTAL | All Evaluated Datasets | 70 / 100 | 70% | 🏆 70% Pass |

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

Reproducibility hash: SHA256-4B5E077F449E3BAD

> ⚠️ 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) | 37.4 tok/s | one request at a time, on NVIDIA GeForce RTX 5070 Ti |

| Prompt processing | 171 tok/s | same probe |

| Aggregate throughput during evaluation | 36.8 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 Transformers / PyTorch

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "sigmanih/ornith-ai-Ornith-1.5-35B-A3B-GGUF-Q8_0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

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🇮🇹 Documentazione in Italiano

ornith-ai-Ornith-1.5-35B-A3B-GGUF-Q8_0 è un modello ottimizzato pronto per l'inferenza e l'integrazione locale, pubblicato attraverso Σ-SIGMA Studio.

📋 Specifiche e Configurazione

• Architettura Base: CausalLM (35B parametri)
• Formato Pesi: Sconosciuto (sconosciuta)
• Spazio su Disco: 0.0 GB
• RAM / VRAM in Esecuzione: ~30.2 GB VRAM (offload GPU completo) | ~28.3 GB RAM (inferenza CPU/ibrida)
• Requisiti Hardware Consigliati: Configurazione Multi-GPU (2× 24 GB o RTX 6000) o 64 GB RAM
• Finestra di Contesto: 32,768 token
• Profilo d'Uso Consigliato: Intelligenza di frontiera, ricerca approfondita e matematica multi-step.

📊 Risultati Benchmark Ufficiali

• Suite di Valutazione: Tutti i Benchmark Ufficiali
• Punteggio Ufficiale: 70.0% (70/100 quesiti superati)

🧠 Confronto Modalità di Risposta: No-Thinking vs Thinking

Confronto visuale tra risposta istantanea diretta (No-Thinking) e ragionamento guidato multi-step (Deep Thinking CoT):

⚡ No-Thinking (Risposta Diretta) : [██████████████░░░░░░] 70.2%  (40/57 superati)
🧠 Deep Thinking (CoT Reasoning)  : [██████████████░░░░░░] 69.8%  (30/43 superati)
📈 Delta Prestazionale CoT        : -0.4%

%%{init: {'theme': 'dark'}}%%
xychart-beta
    title "Accuratezza (%): No-Thinking vs Deep Thinking"
    x-axis ["⚡ No-Thinking (Diretto)", "🧠 Deep Thinking (CoT)"]
    y-axis "Accuratezza (%)" 0 --> 100
    bar [70.2, 69.8]

| Modalità di Esecuzione | Accuratezza (%) | Quesiti Superati | Profilo Operativo & Latenza |

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

| ⚡ No-Thinking (Risposta Diretta) | 70.2% | 40 / 57 | Latenza minima, token-to-first-byte istantaneo, zero overhead di ragionamento |

| 🧠 Deep Thinking (CoT Reasoning) | 69.8% | 30 / 43 | Risoluzione passo-passo multi-step (-0.4%), ideale per logica complessa e matematica |

📋 Dettaglio Punteggi per Singolo Dataset

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

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

| ARC-Challenge | Ragionamento Scientifico Avanzato | 8 / 9 | 89% | ✅ Superato |

| BIG-Bench Hard | Logica Complessa & Compiti Multi-Fase | 6 / 7 | 86% | ✅ Superato |

| GPQA | Ragionamento Accademico di Livello Laurea | 3 / 9 | 33% | ⚠️ Migliorabile |

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

| HellaSwag | Buon Senso & Comprensione Situazionale | 5 / 9 | 56% | ⚡ Discreto |

| HumanEval | Sintesi Codice Python (pass@1) | 6 / 7 | 86% | ✅ Superato |

| MATH | Matematica Olimpica & Competitiva | 7 / 9 | 78% | ✅ Superato |

| MBPP | Programmazione Python con Unit Test | 6 / 9 | 67% | ⚡ Discreto |

| MMLU | Conoscenza Generale Multidisciplinare | 7 / 14 | 50% | ⚡ Discreto |

| MMLU-Pro | Ragionamento Avanzato Multi-Step | 6 / 9 | 67% | ⚡ Discreto |

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

| 🏆 TOTALE COMPLESSIVO | Tutti i Dataset Valutati | 70 / 100 | 70% | 🏆 70% Pass |

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

Impronta di riproducibilità: SHA256-4B5E077F449E3BAD

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

• Data Test: 2026-09-08 su motore deterministico SigmaEngine

⏱️ Throughput Hardware e Fasce Consigliate

• Velocità Verificata in Locale: 36.8 tok/s su NVIDIA GeForce RTX 5070 Ti.
• Risposta singola (quello che si sente in chat): 37.4 tok/s su NVIDIA GeForce RTX 5070 Ti.
• Lettura del prompt: 171 tok/s.
• Throughput complessivo durante la valutazione: 36.8 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 (13/10/2026 17:52)