Update README.md

README.md

@@ -12,267 +12,10 @@ https://github.com/PCL-Reasoner/V1
 
 https://openi.pcl.ac.cn/PCL-Reasoner/V1
 
-## Development Guide  
-
-### 1. Model Files  
-PCL-Reasoner-V1 is fine-tuned from Qwen2.5-32B-Base using MindFormers. Key files include:  
-
-​**Data Processing:​**​  
-```
-pcl_reasoner_v1
-  ├── qwen2_5_tokenizer.py        # qwen2_5 tokenizer
-  ├── packing_handler.py          # Data packing process
-  └── data_preprocess                        
-  	├── decontaminate.py          # validation set contamination detection
-  	└── dataset_prehandle_and_split.py # dataset prehandle and split
-```
-
-​**Model Configuration:​**​  
-```
-pcl_reasoner_v1/config
-  ├── data_process_handling.yaml           # Format conversion configuration file
-  ├── data_process_packing.yaml            # Data packing configuration file
-  └── finetune_pcl_reasoner_v1_32k.yaml  # Model fine-tuning configuration file
-```
-
-​**Task Launch Script:​**​  
-```
-pcl_reasoner_v1
-  └── run_pcl_reasoner_v1_finetune.sh  # Model fine-tuning launch script
-```
-
-
-### 2. Environment & Data Setup  
-#### 2.1 Environment Installation  
-| Software | Version |  
-|----------|---------|  
-| Firmware & Driver | 24.1.rc3.5 |  
-| CANN | 7.7.T9.0.B057:8.1.RC1 |  
-| Python | 3.10 |  
-| MindSpore | 2.6.0 |  
-| MindSpore TransFormers | r1.5.0 |
-
-#### 2.2 Data Processing
-
-##### 2.2.1 Dataset Download
-
-Users can download the original dataset from HuggingFace:
-
-| Dataset Name                    | Dataset Link                                                                                                                  |
-| ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| AM-DeepSeek-R1-0528-Distilled | [https://huggingface.co/a-m-team/AM-DeepSeek-R1-0528-Distilled](https://huggingface.co/a-m-team/AM-DeepSeek-R1-0528-Distilled) |
-
-##### 2.2.2 Data Preprocessing
-
-First, we perform detection and filtering on the source data through two steps: ​​validation set contamination detection​​ and ​​data filtering​​.
-
-* Validation Set Contamination Detection​​: We use the ​​all-MiniLM-L6-v2​​ model to calculate text cosine similarity and detect contamination in the original mathematical data against the AIME24/25 evaluation set. 
-After execution, the script prints detection results in the terminal and saves questions with similarity exceeding the threshold (along with matched evaluation questions) to the specified output path.
-
-  ```
-  python PCL-Reasoner-V1/pcl_reasoner_v1/data_preprocess/decontaminate.py \
-  --target_data /path/to/target_data \
-  --contaminant_source PCL-Reasoner-V1/pcl_reasoner_v1/data_preprocess/aime2425_questions.json \
-  --model_path /path/to/distilled/model_path \
-  --output_file_prefix /path/to/output_file_prefix
-  --threshold 0.7
-
-  # Parameter Description
-  target_data: Data to be detected
-  contaminant_source: Contamination source (evaluation set data)
-  model_path: Model for text embedding calculation
-  output_file_prefix: Output path for results
-  threshold: Similarity threshold
-  ```
-* Data Filtering & Processing​​: Execute the data processing script to filter data by length, selecting data where the combined length of the question and reasoning chain is ​​<32K tokens​​, and add prompts to the data.
-
-  ```
-  python PCL-Reasoner-V1/pcl_reasoner_v1/data_preprocess/convert_and_split_dataset.py \
-  --json_file_paths /path/to/AM-DeepSeek-R1-0528-Distilled/math.jsonl
-
-  # Parameter Description
-  json_file_paths: Dataset to process (multiple paths separated by spaces)
-  ```
-
-Then, we convert data into ​​packed format​​ through two sequential steps: format conversion and data packing.
-
-* Format Conversion​​: Specify paths like `data_files`、`vocab_file`、`merges_file` in `pcl_reasoner_v1/config/data_process_handling.yaml`, specify the custom AMDeepSeekDataHandler from pcl_reasoner_v1/packing_handler.py as the data handler:
-
-  ```
-  train_dataset:
-      ...
-      path: "json" # Original dataset format
-      data_files:
-          ["/path/to/data.jsonl"] # Path to raw dataset
-      input_columns: *input_columns
-      handler:
-        - type: AMDeepSeekDataHandler # Custom data handler class
-          ...
-          tokenizer:
-            auto_register: qwen2_5_tokenizer.Qwen2Tokenizer
-            ...
-            vocab_file: "/path/to/vocab.json" # Qwen2.5 tokenizer vocabulary
-            merges_file: "/path/to/merges.txt" # Qwen2.5 merge rules
-            ...
-  ```
-
-  *(Note: This is a minimal example showing frequently modified fields. Full configuration is available in the code repository.)*
-
-  Execute the conversion script to generate ​​Arrow-format data​​:
-
-  ```
-  export PYTHONPATH=/path/to/mindformers/:PYTHONPATH
-  python th/to/mindformers/toolkit/data_preprocess/huggingface/datasets_preprocess.py
-      --config ./pcl_reasoner_v1/config/data_process_handling.yaml
-      --save_path /path/to/handled_data/
-      --register_path ./pcl_reasoner_v1/
-
-  # Parameter Description
-  config: Path to format conversion config file
-  save_path: Output directory for processed data
-  register_path: Path to custom handler registration
-  ```
-* Data Packing​​:
-
-  Configure pcl_reasoner_v1/config/data_process_packing.yaml to specify input paths for packed data generation:
-
-  ```
-  # dataset
-  train_dataset:
-    data_loader:
-    ...
-    path: /path/to/handled_data # Processed dataset
-    ...
-  ```
-
-  *(Note: Example shows key fields only. Refer to repository for full config.)*
-
-  Run the packing script to generate ​​sequence-packed data​:
-
-  ```
-  export PYTHONPATH=/path/to/mindformers/:PYTHONPATH
-  python /path/to/mindformers/toolkit/data_preprocess/huggingface/datasets_preprocess.py
-      --config ./pcl_reasoner_v1_config/data_process_packing.yaml
-      --save_path /path/to/packed_data/
-      --register_path ./pcl_reaoner_v1/
-
-  # Parameter Description
-  config: Path to data packing config file
-  save_path: Output directory for packed data
-  register_path: Path to custom handler registration
-  ```
-
-
-### 3 Training Process
-#### 3.1 Weight Preparation
-
-Users can download pre-trained weights from HuggingFace:
-
-| Model Name          | Weights URL                                                                        |
-| ------------------- | --------------------------------------------------------------------------------- |
-| Qwen2.5-32B-Base | [https://huggingface.co/Qwen/Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) |
-
-​**Note**: MindFormers v1.5.0+ supports direct loading/saving of `safetensors` format weights. No conversion to `ckpt` is required. Subsequent fine-tuning will use `safetensors` format.
-
-#### 3.2 Training Configuration
-*(Only frequently modified configurations are shown. Full config: `pcl_reasoner_v1/config/finetune_pcl_reasoner_v1_32k.yaml`)*
-
-​***Basic Configuration:​**​*
-```yaml
-run_mode: 'finetune'               # Training mode: fine-tuning
-load_checkpoint: '/path/to/Qwen-32B-base/'  # Weight file path
-load_ckpt_format: 'safetensors'    # Weight format
-auto_trans_ckpt: True              # Enable online weight splitting for distributed training
-```
-***Dataset Configuration:​***  
-```yaml
-train_dataset: &train_dataset
-  data_loader:
-    type: CommonDataLoader
-    path: "/path/to/dataset/pack_data_lt_32K_full"  # Packed dataset path
-    load_func: 'load_from_disk'     # Data loading method
-    shuffle: True                  # Enable data shuffling
-    packing: pack                  # Packed data format
-    adaptor_config:
-      compress_mask: True
-    mock_config:
-      seq_length: 32768            # Packed sequence length (32K tokens)
-      size: 25909                  # Dataset size / data parallelism split
-```
-***Parallelism Configuration:​***
-```yaml
-parallel_config:
-  data_parallel: &dp 8             # Data parallelism
-  model_parallel: 8                # Model parallelism
-  pipeline_stage: 2                # Pipeline parallelism stages
-  use_seq_parallel: True           # Enable sequence parallelism
-  optimizer_shard: True            # Enable optimizer sharding
-  micro_batch_num: 16              # Micro-batch size
-```
-> *（Note: This configuration example only lists frequently modified items. Refer to the code repository for complete configurations.）*
-
-#### 3.3 Launching Fine-tuning
-
-Specify the configuration file `pcl_reasoner_v1/config/finetune_pcl_reasoner_v1_32k.yaml` in the launch script `run_pcl_reasoner_v1_finetune.sh`, and modify cluster parameters according to your hardware environment:
-
-```bash
-noderank=$1 
-
-bash /path/to/mindformers/scripts/msrun_launcher.sh "run_mindformer.py \
---config /path/to/finetune_pcl_reasoner_v1_32k.yaml \
---run_mode finetune" \
---worker_num 128 \
---local_worker_num 8 \
---master_addr XX.XX.XX.XX \
---master_port XXXX \
---node_rank $noderank \
---log_dir /path/to/log \
---join False \
---cluster_time_out 1200 \
-> run.log 2>&1
-
-# Parameter Description
-config: Path to configuration file
-run_mode: Operation mode (pretrain/finetune/inference)
-worker_num: Total number of accelerator cards
-local_worker_num: Cards per single server
-master_addr: Master node address
-master_port: Master node port
-log_dir: Log directory path
-join: Whether to wait for all workers to exit
-cluster_time_out: Cluster timeout duration
-```
-Then, launch the fine-tuning task using:
-```
-bash run_pcl_reasoner_v1_finetune.sh 0
-```
-> *（Note: When launching on multiple nodes, specify node_rank (e.g., 0 for the first node).）*
-
-After starting the task, monitor the runtime logs with:
-```
-tail -f /path/to/log/worker_127.log
-```
-      
-### 4. Evaluation 
-
-To ensure the fairness of evaluation results, we adopted the ​**open-source evaluation code from QwQ**​ ([QwQ/eval at main · QwenLM/QwQ](https://github.com/QwenLM/QwQ)). Developers can follow the `README.md` in the code repository to set up the environment and evaluate models.  
-
-#### Evaluation Hyperparameters  
-The sampling hyperparameters used are listed below:  
-
-| Hyperparameter | Value |  
-|----------------|---------------------------------|  
-| `temperature`  | 0.6                             |  
-| `top_k`        | 40                              |  
-| `top_p`        | 0.95                            |  
-| `max_tokens`   | 129,024                         |  
-| `chat_template`| `./pcl_reasoner_v1/eval/am_thinking.jinja` |  
-
 #### Evaluation Results on AIME24/25  
 The table below compares mainstream models on the AIME24 and AIME25 benchmarks. For accuracy, we used the ​**Avg@32 metric**​ (averaging 32 sampling attempts per query):  
 
 
-
 <table>
   <tr>
     <th>Parameter Size</th>