---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- text-generation
- reasoning
- thinking
- syntiox
model_name: Syntiox-1.0-Flash
---

# Syntiox-1.0-Flash (3.7B)

[Hugging Face](https://huggingface.co/syntiox) | [GitHub](https://github.com/syntiox) | [Launch Blog](#) | [Documentation](#)  
**License:** Apache 2.0 | **Authors:** Syntiox Research Team & Developer Community

Syntiox-1.0-Flash is a 3.7B parameter open-weights dense language model built from the ground up by the **Syntiox** open-source organization and developer community. Designed for advanced reasoning, coding assistance, and agentic workflows, Syntiox-1.0-Flash brings state-of-the-art "thinking" capabilities directly to consumer-grade hardware and on-device environments.

This release features both pre-trained and instruction-tuned variants (`syntiox-1.0-flash-it`), optimized for high-speed inference without compromising deep logical execution.

---

## Key Capability & Architectural Advancements

*   **Native "Flash-Thinking" Mode:** Features a built-in step-by-step internal reasoning mechanism, allowing the model to decompose complex math, logic, and coding problems before generating the final answer.
*   **Extended Context Window:** Supports up to **128K tokens**, enabling long-document analysis, extensive codebase parsing, and multi-turn conversational memory.
*   **Optimized for Consumer Hardware & On-Device:** With a compact 3.7B parameter architecture, it is tailored for local execution on standard laptops, edge devices, and consumer GPUs with minimal memory footprint.
*   **Hybrid Attention Mechanism:** Interleaves local sliding window attention (512 tokens) with full global attention layers, providing fast processing speeds while maintaining long-context awareness.
*   **Enhanced Agentic Workflow Support:** Built-in, high-reliability support for native function-calling and tool use, making it an excellent engine for autonomous software agents.

---

## Model Overview

| Property | Syntiox-1.0-Flash (Dense) |
| :--- | :--- |
| **Total Parameters** | 3.7B (Effective parameters) |
| **Layers** | 32 |
| **Sliding Window Size** | 512 tokens |
| **Context Length** | 128,000 (128K) tokens |
| **Vocabulary Size** | 262,144 (262K) tokens |
| **Supported Modalities** | Text (Inputs & Outputs) |
| **Position Embeddings** | Proportional RoPE (p-RoPE) |

---

## Benchmark Results

Syntiox-1.0-Flash was rigorously evaluated against industry-standard benchmarks, showcasing highly competitive reasoning and coding capabilities compared to larger baseline models. *(Results listed are for the Instruction-Tuned variant)*.

| Benchmark | Syntiox-1.0-Flash (3.7B) | Baseline Model A (7B) | Baseline Model B (3B Class) |
| :--- | :--- | :--- | :--- |
| **MMLU Pro** | 71.2% | 68.5% | 58.2% |
| **AIME 2026 (No Tools)** | 44.8% | 35.0% | 21.4% |
| **LiveCodeBench v6** | 54.3% | 46.2% | 31.0% |
| **GPQA Diamond** | 56.1% | 44.5% | 35.2% |
| **BigBench Extra Hard** | 36.5% | 31.2% | 19.8% |

---

## Getting Started

You can deploy and run **Syntiox-1.0-Flash** using the standard Hugging Face `transformers` library. 

### Installation
Ensure your environment is up to date:
```bash
pip install -U transformers torch accelerate
```
## Basic Usage Inference
```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

MODEL_ID = "syntiox/syntiox-1.0-flash-it"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Structure prompt using Native System Prompt Support
messages = [
    {"role": "system", "content": "You are Syntiox AI V1, a helpful and precise assistant."},
    {"role": "user", "content": "Write an optimized Python function to find the longest palindromic substring."}
]

# Apply chat template (To enable reasoning/thinking mode, keep enable_thinking=True if supported)
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
input_len = inputs["input_ids"].shape[-1]

# Generate Response
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, top_p=0.95)
response = tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True)

print(response)
```