revise the offline_tsds and add offline_near

docs/.vuepress/notes/en/guide.ts

@@ -25,7 +25,8 @@ export const Guide: ThemeNote = defineNoteConfig({
                 'quickstart',
                 'tutorial',
                 'selector_less',
-                'selector_tsds',
+                'selector_offline_tsds',
+                'selector_offline_near',
                 'selector_zeroth'
             ],
         },

docs/.vuepress/notes/zh/guide.ts

@@ -25,7 +25,8 @@ export const Guide: ThemeNote = defineNoteConfig({
                 'quickstart',
                 'tutorial',
                 'selector_less',
-                'selector_tsds',
+                'selector_offline_tsds',
+                'selector_offline_near',
                 'selector_zeroth',
             ],
         },

docs/en/notes/guide/selector/selector_offline_near.md

@@ -0,0 +1,201 @@
+---
+title: Offline-Near-Selector
+createTime: 2025/11/27 16:02:41
+permalink: /en/guide/7k0w3d92/
+icon: flowbite:fish-alt-outline
+---
+# Offline NEAR Selector 
+
+This document introduces how to use the **Offline NEAR Selector** for **dynamic data selection** during supervised fine-tuning (SFT) within the **DataFlex** framework, finding the most close data to the target dataset to improve generalization performance.
+
+---
+
+## 1. Method Overview
+
+The core idea of **NEAR** is:
+
+* Further encode **already tokenized** samples into **sentence embeddings** (e.g., 512‑dim).
+* Perform **nearest‑neighbor search ** in the embedding space to obtain each sample’s representativeness score.
+
+> Intuition: **Closest data for the target dataset** 
+
+### Scoring Formulation
+
+Let the sentence embedding of a sample be $e_i$, and let its $max_K$ nearest neighbors be $\mathcal{N}_K(i)$.
+
+
+
+---
+
+## 2. Environment & Dependencies
+
+```bash
+# DataFlex (recommended: editable install)
+git clone https://github.com/OpenDCAI/DataFlex.git
+cd DataFlex
+pip install -e .
+
+# Common training/inference dependencies (as needed)
+pip install llamafactory
+
+# NEAR extras (vector search & progress bars)
+pip install faiss-cpu vllm sentence-transformer
+```
+
+---
+
+## 3.  Offline Selection
+
+Modify training set, embedding model, and parameters inside
+**DataFlex/src/dataflex/offline_selector/offline_near_selector.py**:
+```python
+if __name__ == "__main__":
+    near = offline_near_Selector(
+        candidate_path="OpenDCAI/DataFlex-selector-openhermes-10w", # split = train
+        query_path="OpenDCAI/DataFlex-selector-openhermes-10w", # split = vaildation
+
+        # If you want to use vllm,please add "vllm:" before model's name
+        # Otherwise it automatically use sentence-transfromer
+        embed_model="vllm:Qwen/Qwen3-Embedding-0.6B",
+        batch_size=32,
+        save_indices_path="top_indices.npy",
+        max_K=1000,
+
+    )
+    near.selector()
+```
+
+Note: model_name is used to encode the already-tokenized text into sentence embeddings (e.g., 512-dim), supporting both vLLM and sentence-transformer inference.
+
+Output: save as the indices matrix that contain the max_K close data for each query
+---
+
+## 4. Key Hyperparameters & Tips
+
+| Parameter     | Typical Range | Meaning & Tips                                                                                |
+| ------------- | ------------- | --------------------------------------------------------------------------------------------- |
+| `max_K`       | 64–10000        | Upper bound of NN retrieval. Larger = stabler but more costly; balance with data size & VRAM. |        |
+| `model_name`  | —             | Path/name of the sentence encoder (local BERT/USE/SimCSE, etc.).                              |
+| `cache_dir`   | —             | Cache directory for intermediate artifacts and resume‑from‑cache.                             |
+
+---
+
+## 5. Component Config (`components.yaml`)
+
+**Path:** `DataFlex/src/dataflex/configs/components.yaml`
+
+**Preset example**
+
+```yaml
+near:
+    name: near
+    params:
+      indices_path: ./src/dataflex/offline_selector/top_indices.npy
+      cache_dir: ../dataflex_saves/near_output
+
+```
+
+---
+
+## 6. Dynamic Training Config (LoRA + NEAR)
+
+**Example file:** `DataFlex/examples/train_lora/selectors/near.yaml`
+
+```yaml
+### model
+model_name_or_path: 
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+lora_rank: 16
+lora_alpha: 8
+
+### dataset
+dataset: # training dataset
+template: qwen
+cutoff_len: 4096
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: ../dataflex_saves
+logging_steps: 10
+save_steps: 100
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 16
+learning_rate: 1.0e-4
+num_train_epochs: 1.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+
+### Dataflex args
+train_type: dynamic_select
+components_cfg_file: src/dataflex/configs/components.yaml
+component_name: near
+warmup_step: 400
+update_step: 500
+update_times: 2
+
+```
+
+**Notes:**
+
+* `component_name: near` enables the NEAR component.
+* `warmup_step / update_step / update_times` decide **when** and **how often** to re‑select the training subset; total steps ≈ `warmup_step + update_step × update_times`.
+* total batch_size=device_number x per_device_train_batch_size x gradient_accumulation_steps
+
+---
+
+## 7. Run Training
+
+```bash
+FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/near.yaml
+```
+
+**Note:** the above example runs with distributed launch.
+
+During training, NEAR is triggered at scheduled steps: base the sample indice → select the next training subset.
+
+---
+
+## 8. Merge & Export the Model
+
+Same as the Less Selector pipeline.
+
+**Config file:** `DataFlex/examples/merge_lora/llama3_lora_sft.yaml`
+
+```yaml
+model_name_or_path: base model path
+adapter_name_or_path: finetuned adapter path
+template: qwen
+trust_remote_code: true
+
+export_dir: ../dataflex_saves
+export_size: 5
+export_device: cpu
+export_legacy_format: false
+
+```
+
+Run the export command (inside the LLaMA‑Factory directory):
+
+```bash
+llamafactory-cli export llama3_lora_sft.yaml
+```
+
+---
+
+## 9. Evaluation & Comparison
+
+We recommend using the [DataFlow](https://github.com/OpenDCAI/DataFlow) QA evaluation pipeline to compare **NEAR** against **Less** and **random sampling**. 
+
+