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README.md

@@ -1,3 +1,89 @@
----
-license: apache-2.0
----
+<p align="left">
+  <a href="https://huggingface.co/datasets/ZTE-AIM/LLM-Adaptive-ZMath-model-32B/README.md">English</a> |
+  <a href="https://huggingface.co/datasets/ZTE-AIM/LLM-Adaptive-ZMath-model-32B/README_ZH-COT.md">中文</a>
+</p>
+
+
+datasets:
+
+- ZTE-AIM/32B_LLM_AdaptiveMath_data
+- ZTE-AIM/32B_LLM_AdaptiveCode_data
+
+base_model:
+
+- DeepSeek-R1-Distill-Qwen-32B
+---
+
+## 32B_LLM_AdaptiveMath_data
+[\[🤗 HF Dataset\]](https://huggingface.co/datasets/ZTE-AIM/32B_LLM_AdaptiveMath_data)  
+## LLM-Adaptive-CoT-Code-data
+[\[🤗 HF Dataset\]](https://huggingface.co/datasets/ZTE-AIM/32B_LLM_AdaptiveCode_data)  
+## LLM-Adaptive-ZMath-model-32B
+[\[🤗 LLM-Adaptive-ZMath-model-32B\]](https://huggingface.co/ZTE-AIM/LLM-Adaptive-ZMath-model-32B) 
+## LLM-Adaptive-ZCode-model-32B
+[\[🤗 LLM-Adaptive-ZCode-model-32B\]](https://huggingface.co/ZTE-AIM/LLM-Adaptive-ZCode-model-32B) 
+
+## Model Overview
+
+This work presents a fine-tuned reasoning model built on the DeepSeek-Distill architecture through a novel LLM-Adaptive Question Difficulty Grading method. Unlike traditional CoT generation approaches, this model leverages the reasoning strength of DeepSeek-R1 (671B) to distill high-quality chain-of-thought (CoT) data. A core innovation lies in the dynamic construction of difficulty-aligned datasets based on the target LLM's own problem-solving capabilities.
+
+The proposed approach includes adaptive evaluation of question difficulty, followed by tailored sampling and response generation. This enables the model to efficiently learn from progressively challenging problems, thereby boosting reasoning performance across multiple domains such as mathematical problem solving and code generation.
+
+Fine-tuned variants like ZMath-32B and ZCode-32B exhibit superior performance to baseline models like DeepSeek-Distill-32B and phi-4, even with limited high-quality data. Notably, the ZMath-32B model trained on only 2K PRM-graded CoT samples surpassed its baseline across all math benchmarks, confirming the effectiveness of the adaptive CoT generation methodology.
+
+## Training Configuration
+
+Our training framework builds on previous advancements in s1-1k, LIMO, and Light-R1, implemented through the LLama-Factory to leverage its proven scalability. The framework incorporates the Deepseek-R1 template, flash-attention2 and Liger-Kernel to improve computational efficiency while minimizing memory requirements. All experiments are conducted on a 2×8 H800 GPU cluster, with performance evaluations executed using the Skythought benchmarking suite.
+
+The training configuration for grpo is as follows:
+```python
+Context Length: 16,384 tokens
+Learning Rate: 5e-6
+Batch Size: 128
+Epochs: 10
+```
+
+## Usage
+
+You can load the model using the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Replace with the actual path to your model on Hugging Face.
+model_name = "your-org/ZMath-32B"
+
+# Load the tokenizer.
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+# Load the model (with multi‑GPU support and automatic allocation to available devices).
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,         # Use float16 precision to save GPU memory
+    device_map="auto",                 # Automatically distribute the model across multiple GPUs.
+    trust_remote_code=True
+)
+
+# 示例推理
+prompt = "Solve the following math problem step by step: 12 * (3 + 4) = ?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=100)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+
+```
+
+## Paper Link
+- [📄 Read the Paper (PDF)](https://arxiv.org/pdf/2504.11919)
+
+# Institution
+- ZTE-AIM
+
+## Model Contact
+- [email protected]
+- [email protected]
+

README_ZH_COT.md

@@ -0,0 +1,87 @@
+<p align="left">
+  <a href="https://huggingface.co/datasets/ZTE-AIM/LLM-Adaptive-ZMath-model-32B/README.md">English</a> |
+  <a href="https://huggingface.co/datasets/ZTE-AIM/LLM-Adaptive-ZMath-model-32B/README_ZH-COT.md">中文</a>
+</p>
+
+datasets:  
+
+- ZTE-AIM/32B_LLM_AdaptiveMath_data  
+- ZTE-AIM/32B_LLM_AdaptiveCode_data  
+
+base_model:  
+
+- DeepSeek-R1-Distill-Qwen-32B  
+---
+
+## 32B_LLM_AdaptiveMath_data  
+[\[🤗 HF 数据集\]](https://huggingface.co/datasets/ZTE-AIM/32B_LLM_AdaptiveMath_data)  
+
+## LLM-Adaptive-CoT-Code-data  
+[\[🤗 HF 数据集\]](https://huggingface.co/datasets/ZTE-AIM/32B_LLM_AdaptiveCode_data)  
+
+## LLM-Adaptive-ZMath-model-32B  
+[\[🤗 LLM-Adaptive-ZMath-model-32B\]](https://huggingface.co/ZTE-AIM/LLM-Adaptive-ZMath-model-32B)  
+
+## LLM-Adaptive-ZCode-model-32B  
+[\[🤗 LLM-Adaptive-ZCode-model-32B\]](https://huggingface.co/ZTE-AIM/LLM-Adaptive-ZCode-model-32B)  
+
+## 模型概述
+
+本工作通过一种新颖的 LLM 自适应题目难度分级方法，在 DeepSeek-Distill 架构基础上进行了微调，得到了一个高效的推理模型。不同于传统的 CoT（Chain-of-Thought）生成方法，该模型利用 DeepSeek-R1（671B）的推理能力来蒸馏出高质量的思维链数据。核心创新点在于根据目标 LLM 本身的解题能力，动态构建与之难度匹配的数据集。
+
+所提方法包括对题目难度的自适应评估，以及针对性采样与响应生成。这使模型能够从渐进递增难度的问题中高效学习，从而在数学题求解和代码生成等多个领域显著提升推理性能。
+
+经过微调的 ZMath-32B 和 ZCode-32B 变体，在可用的高质量数据量有限的情况下，性能仍优于 DeepSeek-Distill-32B 和 phi-4 等基准模型。值得注意的是，仅使用 2K 条 PRM 分级的 CoT 样本训练的 ZMath-32B 模型，就在所有数学基准上都超越了基线，充分验证了自适应 CoT 生成方法的有效性。
+
+## 训练配置
+
+我们的训练框架基于 s1-1k、LIMO 和 Light-R1 等前沿方案，通过 LLama-Factory 实现高可扩展性。框架中融合了 Deepseek-R1 模板、flash-attention2 和 Liger-Kernel，以提升计算效率并降低内存消耗。所有实验均在 2×8 H800 GPU 集群上进行，性能评估使用 Skythought 基准套件。
+
+本次 grpo 训练的关键配置如下：  
+```python
+Context Length: 16,384 tokens
+Learning Rate: 5e-6
+Batch Size: 128
+Epochs: 10
+```
+
+## 使用方法
+
+你可以通过 Hugging Face 的 `transformers` 库加载该模型：  
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# 将以下替换为你在 Hugging Face 上模型的实际路径。
+model_name = "your-org/ZMath-32B"
+
+# 加载分词器
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+# 加载模型（支持多 GPU 并自动分配设备）
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,         # 使用 float16 精度以节省显存
+    device_map="auto",                 # 自动将模型分配到可用 GPU
+    trust_remote_code=True
+)
+
+# 示例推理
+prompt = "Solve the following math problem step by step: 12 * (3 + 4) = ?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=100)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+## 论文链接
+- [📄 阅读论文 (PDF)](https://arxiv.org/pdf/2504.11919)
+
+## 机构  
+- ZTE-AIM  
+
+## 模型联系人  
+- [email protected]  
+- [email protected]  

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