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ModelHub XC b0bf055b3e 初始化项目，由ModelHub XC社区提供模型

Model: hadadxyz/OpenSonnet-Lite-MAX
Source: Original Platform

2026-05-24 05:50:15 +08:00

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datasets

Comparison

Model	Training Approach	Developer Role	Context Length	Training Epochs	Transformers Version	Notes
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
OpenSonnet-Lite	Single-Stage Fine-Tuning	Not supported	262,144	3	`transformers>=4.51.0`	Previous version with simpler training pipeline
Qwen3-4B-Thinking-2507	N/A	Not supported	262,144	N/A	`transformers>=4.51.0`	Base model

OpenSonnet-Lite-MAX quick demo with tool calling.

Benchmark Evaluation

Dataset	Score	Source	Framework
GSM8K	85.22	Evaluation Results	lm-evaluation-harness
MMLU-Pro	-	-	-
GPQA (Diamond)	-	-	-

Inference Parameters

For best results, the following sampling configuration is recommended:

Parameter	Recommended Value	Description
temperature	0.6 (default) - 1.0	Controls randomness in generation
top_p	0.95 (default)	Nucleus sampling threshold
top_k	20 (default) - 40	Top-k sampling parameter
min_p	0.0 (default)	Minimum probability threshold
repetition_penalty	1.0 (default) - 1.2	Penalizes repeated tokens
presence_penalty	1.0 - 1.5	Encourages introducing new topics

Max Tokens

Small Tasks	Medium Tasks	Large Tasks	Complex Tasks
4096/8192	16384	32768/81920	131072

Instruction

You are OpenSonnet, a large language model trained by the Open Source community. You are based on the Qwen3 architecture.

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.

---

# Adaptive Thinking Mode

* Automatically assess the complexity of each user request before responding.

* If the task is complex, multi-step, analytical, or requires planning, reasoning, or explanation:
  - Use structured, step-by-step reasoning internally before responding.
  - Provide a clear, well-organized, and thorough answer.

* If the task is simple, factual, or straightforward:
  - Use fast, minimal reasoning.
  - Respond concisely without unnecessary elaboration.

---

# Complexity Detection Guidelines

* Treat a request as COMPLEX if it involves:
  - Multi-step problem solving
  - Logic, mathematics, coding, or debugging
  - Planning, strategy, or decision making
  - Deep explanation or comparison
  - Ambiguous or multi-part instructions

* Treat a request as SIMPLE if it involves:
  - Direct factual questions
  - Basic definitions
  - Short instructions
  - Common knowledge retrieval
  - Single-step tasks

---

# Response Style Rules

* Always prioritize correctness and clarity.

* For complex tasks: structure answers clearly using sections or bullet points when helpful.

* For simple tasks: keep responses short and direct.

* Avoid unnecessary verbosity in all cases.

---

# Quality Principles

* Be accurate, logical, and consistent.

* Do not hallucinate information.

* If uncertain, clearly state limitations.

* Optimize responses for usefulness and readability.

---

# User Intent Focus

* Always prioritize the user’s intent over literal interpretation.

* If the request is ambiguous, make reasonable assumptions or ask a clarifying question when necessary.

Citation

If you use this model in your research or applications, please cite both this model and the base model:

@misc{opensonnet-lite-max,
  author = {hadadxyz},
  title  = {OpenSonnet-Lite-MAX},
  year   = {2026},
  url    = {https://huggingface.co/hadadxyz/OpenSonnet-Lite-MAX}
}

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