初始化项目，由ModelHub XC社区提供模型

Model: xzybit/qwen2-7b-ts2
Source: Original Platform

README.md

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+---
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen2
+- supervised-fine-tuning
+- alignment
+- sparsemax
+- transformers
+---
+
+# Qwen2-7B-TS2
+
+Training with Sparsemax+, Testing with Softmax
+
+This model is a supervised fine-tuned variant of `Qwen2-7B`, trained with our TS^2 objective.
+
+TS^2 is designed to improve alignment stability and mitigate token-level probability collapse during fine-tuning by incorporating entropy-aware adaptive weighting into the training objective.
+
+More details could check our paper [ICLR 2026](https://openreview.net/forum?id=CylRqa82Rk) **"TS^2: Training with Sparsemax+, Testing with Softmax for Accurate and Diverse LLM Fine-Tuning"**
+
+
+## Model Description
+
+- Base model: `Qwen2-7B`
+- Training method: Sparsemax+
+- Objective: token-level entropy-aware TS^2-style regularization
+- Framework: PyTorch + Hugging Face Transformers
+- Precision: bfloat16
+
+Instead of applying uniform likelihood maximization across all tokens as in standard supervised fine-tuning, this model introduces an adaptive weighting mechanism that dynamically adjusts training emphasis based on predictive entropy.
+
+This design is motivated by observations that overconfident likelihood-based training may lead to:
+
+- degeneration of token diversity
+- inference-time mode collapse
+- reduced generalization under distribution shift
+
+TS^2 modifies the training objective to improve both accuracy and diversity.
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("xzybit/qwen2-7b-ts2")
+
+model = AutoModelForCausalLM.from_pretrained(
+    "xzybit/qwen2-7b-ts2",
+    device_map="auto"
+)
+```