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Model: hadadxyz/OpenSonnet-Lite-MAX Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- Qwen/Qwen3-4B-Thinking-2507
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datasets:
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- Roman1111111/claude-sonnet-4.6-120000x
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- Roman1111111/claude-sonnet-4.6-100000X-filtered
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- TeichAI/lordx64-claude-opus-4.7-max-cleaned
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- Crownelius/Opus-4.6-Reasoning-3300x
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- TeichAI/claude-4.5-opus-high-reasoning-250x
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- TeichAI/claude-haiku-4.5-high-reasoning-1700x
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- TeichAI/claude-sonnet-4.5-high-reasoning-250x
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- TeichAI/deepseek-v3.2-speciale-openr1-math-3k
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- TeichAI/deepseek-v3.2-speciale-1000x
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- Roman1111111/gemini-3-pro-10000x-hard-high-reasoning
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- Roman1111111/gemini-3.1-pro-hard-high-reasoning
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- Jackrong/DeepSeek-V4-Distill-8000x
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tags:
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- opensonnet
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- claude-sonnet
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- sonnet
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pipeline_tag: text-generation
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library_name: transformers
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license: apache-2.0
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license_link: https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX/blob/main/LICENSE
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---
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# Comparison
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| Model | Training Approach | Developer Role | Context Length | Training Epochs | Transformers Version | Notes |
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|------------------------------------------------------------------------------|--------------------------|------------------------|----------------|------------------|------------------------|-------------------------------------------------------------------------------------|
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| [OpenSonnet-Lite-MAX](https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX) | Multi-Stage Fine-Tuning | Supported | 262,144 | 2 | `transformers>=5.0.0` | Latest version with improved training efficiency and enhanced instruction alignment |
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| [OpenSonnet-Lite](https://huggingface.co/hadadxyz/OpenSonnet-Lite) | Single-Stage Fine-Tuning | Not supported | 262,144 | 3 | `transformers>=4.51.0` | Previous version with simpler training pipeline |
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| [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) | N/A | Not supported | 262,144 | N/A | `transformers>=4.51.0` | Base model |
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> [OpenSonnet-Lite-MAX quick demo](https://www.kaggle.com/code/hadadrjt/opensonnet-lite-max) with tool calling.
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### Benchmark Evaluation
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| Dataset | Score | Source | Framework |
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|-------------------------------------------------------|--------|---------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|
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| [GSM8K](https://huggingface.co/datasets/openai/gsm8k) | 85.22 | [Evaluation Results](https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX/tree/main/.eval_results) | [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) |
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| MMLU-Pro | - | - | - |
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| GPQA (Diamond) | - | - | - |
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# Inference Parameters
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For best results, the following sampling configuration is recommended:
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| Parameter | Recommended Value | Description |
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|---------------------|---------------------|------------------------------------------|
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| temperature | 0.6 (default) - 1.0 | Controls randomness in generation |
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| top_p | 0.95 (default) | Nucleus sampling threshold |
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| top_k | 20 (default) - 40 | Top-k sampling parameter |
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| min_p | 0.0 (default) | Minimum probability threshold |
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| repetition_penalty | 1.0 (default) - 1.2 | Penalizes repeated tokens |
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| presence_penalty | 1.0 - 1.5 | Encourages introducing new topics |
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# Max Tokens
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| Small Tasks | Medium Tasks | Large Tasks | Complex Tasks |
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|-------------|--------------|-------------|---------------|
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| 4096/8192 | 16384 | 32768/81920 | 131072 |
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# Instruction
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```md
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You are OpenSonnet, a large language model trained by the Open Source community. You are based on the Qwen3 architecture.
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You are an AI assistant designed to provide accurate, helpful, and context-aware responses. Your reasoning style must dynamically adapt based on the complexity of the user’s request.
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---
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# Adaptive Thinking Mode
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* Automatically assess the complexity of each user request before responding.
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* If the task is complex, multi-step, analytical, or requires planning, reasoning, or explanation:
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- Use structured, step-by-step reasoning internally before responding.
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- Provide a clear, well-organized, and thorough answer.
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* If the task is simple, factual, or straightforward:
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- Use fast, minimal reasoning.
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- Respond concisely without unnecessary elaboration.
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---
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# Complexity Detection Guidelines
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* Treat a request as COMPLEX if it involves:
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- Multi-step problem solving
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- Logic, mathematics, coding, or debugging
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- Planning, strategy, or decision making
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- Deep explanation or comparison
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- Ambiguous or multi-part instructions
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* Treat a request as SIMPLE if it involves:
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- Direct factual questions
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- Basic definitions
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- Short instructions
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- Common knowledge retrieval
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- Single-step tasks
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---
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# Response Style Rules
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* Always prioritize correctness and clarity.
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* For complex tasks: structure answers clearly using sections or bullet points when helpful.
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* For simple tasks: keep responses short and direct.
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* Avoid unnecessary verbosity in all cases.
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---
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# Quality Principles
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* Be accurate, logical, and consistent.
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* Do not hallucinate information.
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* If uncertain, clearly state limitations.
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* Optimize responses for usefulness and readability.
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---
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# User Intent Focus
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* Always prioritize the user’s intent over literal interpretation.
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* If the request is ambiguous, make reasonable assumptions or ask a clarifying question when necessary.
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```
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# Citation
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If you use this model in your research or applications, please cite both this model and the base model:
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```bibtex
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@misc{opensonnet-lite-max,
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author = {hadadxyz},
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title = {OpenSonnet-Lite-MAX},
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year = {2026},
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url = {https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX}
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}
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```
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