Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- zh
+- en
+base_model:
+- meta-llama/Meta-Llama-3-8B
+tags:
+- ADG
+- SFT
+---
+
+<div align="center">
+  
+<h1>Instruction Data Selection via Answer Divergence<h1>
+  <p>
+  <strong>English</strong> | <a href="https://huggingface.co/WisdomShell/GRIP-Llama-3-8B/blob/main/README_zh.md">简体中文</a>
+</p>
+
+<a href="https://wisdomshell.github.io/ADG/"><img src="https://img.shields.io/badge/Project-Page-green?logo=githubpages&logoColor=white" /></a>
+<a href="https://arxiv.org/abs/2604.07892"><img src="https://img.shields.io/badge/Paper-arXiv-b31b1b?logo=arxiv&logoColor=white" /></a>
+<a href="https://2026.aclweb.org/"><img src="https://img.shields.io/badge/Venue-ACL%202026-blue" /></a>
+[![Task](https://img.shields.io/badge/Task-Data%20Selection-purple.svg)](#overview)
+<img src="https://img.shields.io/badge/Python-3.10%2B-3776AB?logo=python&logoColor=white" />
+
+**ACL 2026 Main Conference**
+
+<a href="https://deepblue666.github.io/">Bo Li</a>, Mingda Wang, Shikun Zhang, Wei Ye
+
+</div>
+
+This repository releases the core pipeline of **Answer Divergence-Guided Selection (ADG)** for instruction data selection. ADG scores each instruction by the geometric structure of multiple sampled answers, rather than relying on a single reference response. In the paper, ADG consistently improves instruction tuning under a fixed 10K budget across two backbones, three public instruction pools, and six benchmarks spanning reasoning, knowledge, and coding. The method combines **dispersion magnitude** and **shape anisotropy**, then performs **bin-wise selection** for semantic coverage. 
+
+---
+
+## 🌟 Overview
+
+Instruction tuning quality depends heavily on which examples are selected under a fixed data budget. ADG addresses this by examining how a base model responds to the same instruction under stochastic decoding.
+
+For each instruction, ADG:
+
+1. samples multiple answers with relatively high-temperature decoding,
+2. maps answers into a representation space,
+3. computes geometry-aware scores from the sampled answers,
+4. ranks examples by the combined score,
+5. performs proportional selection within semantic bins.
+
+This repository provides the practical pipeline for:
+- multi-sample answer generation,
+- instruction embedding and clustering,
+- ADG scoring and subset selection,
+- model training,
+- benchmark evaluation,
+- optional task-type analysis.
+
+---
+Use this model ,you need clone follow repository
+```python
+git clone https://github.com/WisdomShell/ADG.git
+```
+
+## 📦 What Is Released
+
+This repository includes the following components:
+
+### Core selection code
+- `ADG/ADG_llama.py`  
+  ADG scoring and selection for the LLaMA backbone.
+
+- `ADG/ADG_qwen.py`  
+  ADG scoring and selection for the Qwen backbone.
+
+### Answer generation and instruction embedding
+- `generation/generation.py`  
+  Generates multiple sampled answers for each instruction.
+
+- `generation/embedding/embed.py`  
+  Builds instruction embeddings and performs clustering for bin-wise selection.
+
+### Training and evaluation
+- `train/train_llama.sh`  
+  Training entry script for LLaMA.
+
+- `train/train_qwen.sh`  
+  Training entry script for Qwen.
+
+- `train/training/stanford_alpaca/`  
+  Training utilities and backbone-specific training scripts.
+
+- `eval/eval.sh`  
+  Evaluation script based on `lm-evaluation-harness`.
+
+### Analysis
+- `analysis/analyse.py`  
+  Optional task-type classification script for analyzing selected data.
+
+### Environment
+- `requirements.txt`  
+  Required Python packages for this repository.
+
+---
+
+## 🗂️ Repository Structure
+
+```text
+.
+├── README.md
+├── README_zh.md
+├── requirements.txt
+├── ADG/
+│   ├── ADG_llama.py
+│   └── ADG_qwen.py
+├── generation/
+│   ├── generation.py
+│   └── embedding/
+│       └── embed.py
+├── analysis/
+│   └── analyse.py
+├── eval/
+│   └── eval.sh
+└── train/
+    ├── train_llama.sh
+    ├── train_qwen.sh
+    └── training/
+        └── stanford_alpaca/
+            ├── train_llama.py
+            ├── train_qwen.py
+            ├── utils.py
+            └── configs/
+```
+
+---
+
+## ⚙️ Installation
+
+We recommend Python 3.10 or above.
+
+Example:
+
+```bash
+conda create -n adg python=3.12.9
+conda activate adg
+pip install -r requirements.txt
+```
+
+Depending on your environment, you may also need to install GPU-specific packages separately.
+
+---
+
+## 🧾 Data Format
+
+ADG expects instruction datasets in JSON or JSONL format. Each example should follow the schema below:
+
+```json
+{
+  "id": 0,
+  "instruction": "Write a short explanation of transformers.",
+  "input": "",
+  "output": "Transformers are neural networks based on self-attention..."
+}
+```
+
+Notes:
+- `id` should uniquely identify each example.
+- `instruction` is required.
+- `input` is optional and can be empty or omitted.
+- `output` is the reference response in the original instruction dataset.
+- Other instruction datasets can be used as long as they are converted into this format.
+
+After answer generation, the intermediate JSONL file contains records like:
+
+```json
+{
+  "id": 0,
+  "instruction": "Write a short explanation of transformers.",
+  "output": "Transformers are neural networks based on self-attention...",
+  "generated_answers": [
+    "...",
+    "...",
+    "...",
+    "...",
+    "..."
+  ]
+}
+```
+
+---
+
+## 🔄 Pipeline
+
+The practical workflow is:
+
+```text
+instruction pool
+    -> generation/generation.py
+    -> multi-sample answer JSONL
+    -> generation/embedding/embed.py
+    -> instruction embeddings + cluster labels
+    -> ADG/ADG_llama.py or ADG/ADG_qwen.py
+    -> top / middle / bottom selected subsets
+    -> train/train_*.sh
+    -> finetuned checkpoints
+    -> eval/eval.sh
+```
+
+---
+
+## 🚀 Quick Start
+
+### Step 1. Prepare the instruction pool
+
+Download and preprocess your instruction dataset, such as Alpaca-GPT4, WizardLM, or CoT, into the required format.
+
+### Step 2. Generate multiple answers per instruction
+
+Before running, update the following variables in `generation/generation.py`:
+- `MODEL_NAME`
+- `OUTPUT_DIR`
+- `OUTPUT_FILE`
+
+Then run:
+
+```bash
+cd generation
+torchrun --nproc_per_node=4 --master_port=29500 generation.py   --input_file /path/to/your/instruction_data.json   --batch_size 32
+```
+
+### Step 3. Build instruction embeddings and clustering results
+
+Before running, update the following variables in `generation/embedding/embed.py`:
+- `MODEL_NAME`
+- `INPUT_JSONL`
+- `EMBEDDINGS_PATH`
+- `CLUSTERS_PATH`
+- `K_CLUSTERS`
+
+Then run:
+
+```bash
+torchrun --nproc_per_node=4 --master_port=29501 generation/embedding/embed.py
+```
+
+### Step 4. Run ADG scoring and selection
+
+Choose the scoring script that matches your backbone.
+
+For LLaMA, configure these variables in `ADG/ADG_llama.py`:
+- `model_name`
+- `INPUT_JSONL`
+- `OUTPUT_DIR`
+- `EMBEDDINGS_PATH`
+- `CLUSTERS_PATH`
+- `K_CLUSTERS`
+- `FINAL_SELECT_COUNT`
+
+Then run:
+
+```bash
+python ADG/ADG_llama.py
+```
+
+For Qwen, configure these variables in `ADG/ADG_qwen.py`:
+- `model_name`
+- `INPUT_JSONL`
+- `OUTPUT_DIR`
+- `EMBEDDINGS_PATH`
+- `CLUSTERS_PATH`
+- `CHECKPOINT_DIR`
+- `FINAL_SELECT_COUNT`
+
+Then run:
+
+```bash
+python ADG/ADG_qwen.py
+```
+
+The selector saves:
+- `top.json`
+- `middle.json`
+- `bottom.json`
+
+under the configured `OUTPUT_DIR`.
+
+### Step 5. Train the backbone model
+
+Use the selected subset, typically `top.json`, for instruction tuning.
+
+For LLaMA:
+
+```bash
+cd train
+bash train_llama.sh
+```
+
+For Qwen:
+
+```bash
+cd train
+bash train_qwen.sh
+```
+
+Before running, update paths such as:
+- `--model_name_or_path`
+- `--data_path`
+- `--output_dir`
+
+### Step 6. Evaluate the trained checkpoint
+
+This repository uses `lm-evaluation-harness` for benchmark evaluation.
+
+Install it first if needed:
+
+```bash
+git clone https://github.com/EleutherAI/lm-evaluation-harness.git
+cd lm-evaluation-harness
+pip install -e .
+```
+
+Then configure `MODEL_PATH` and output paths in `eval/eval.sh`, and run:
+
+```bash
+cd eval
+bash eval.sh
+```
+
+The evaluation script currently includes:
+- BBH
+- GSM8K
+- MMLU
+- TruthfulQA
+- MBPP
+- HumanEval
+
+---
+
+## 📊 ADG Scoring Intuition
+
+ADG is built around two complementary signals derived from multiple sampled answers:
+
+- **Dispersion magnitude**  
+  Measures how widely the sampled answers spread in representation space.
+
+- **Shape anisotropy**  
+  Measures whether the spread is multi-directional rather than dominated by a single direction.
+
+The final ADG score combines these two parts, and the selected subset is obtained through semantic bin-wise ranking. This design helps avoid collapsing selection into only a few dense instruction regions.
+
+---
+
+## 🛠️ Script Notes
+
+### `generation/generation.py`
+Main functionality:
+- load the base model,
+- sample multiple answers for each instruction,
+- save generated answers in JSONL format,
+- support distributed generation.
+
+### `generation/embedding/embed.py`
+Main functionality:
+- build instruction embeddings,
+- run clustering,
+- save instruction embeddings and cluster labels,
+- provide the semantic bins used by ADG selection.
+
+### `ADG/ADG_llama.py`
+Main functionality:
+- read the generated-answer JSONL file,
+- compute answer-geometry metrics,
+- combine metrics into the ADG score,
+- perform proportional cluster-based selection,
+- save `top.json`, `middle.json`, and `bottom.json`.
+
+### `ADG/ADG_qwen.py`
+Main functionality:
+- compute ADG metrics for Qwen-generated answers,
+- support checkpoint-based resumption,
+- perform the same top / middle / bottom selection pipeline.
+
+### `analysis/analyse.py`
+Main functionality:
+- classify instructions into coarse task categories,
+- support optional data-level analysis of selected subsets.
+
+### `train/train_llama.sh` and `train/train_qwen.sh`
+Main functionality:
+- launch distributed full fine-tuning,
+- use the selected subset for instruction tuning.
+
+### `eval/eval.sh`
+Main functionality:
+- run benchmark evaluation with `lm-evaluation-harness`,
+- support reasoning, knowledge, and coding tasks.
+
+---
+
+## ❓ Common Issues
+
+### 1. Path configuration is not updated
+Most scripts use placeholder paths. Update all required paths before running.
+
+### 2. Inconsistent model and intermediate files
+Make sure the generation backbone, embedding backbone, ADG scoring script, and training script are aligned.
+
+### 3. Missing intermediate files
+The selector depends on:
+- generated answer JSONL,
+- instruction embeddings,
+- clustering results.
+
+Run the previous stages before starting ADG selection. 
+
+### 4. GPU memory pressure
+Generation, embedding, and scoring all use hidden-state-based processing. You may need to reduce batch size or adjust GPU allocation depending on your hardware.
+
+### 5. Evaluation dependency is not installed
+`eval/eval.sh` depends on `lm-evaluation-harness`. Install it separately before running evaluation.
+
+---
+
+## 📖 Citation
+
+If you use this repository, please cite the paper.
+
+---