| --- |
| 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) |
| ``` |
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