update vlm docs

configs/sparsification/methods/Holitom/holitom.yml

@@ -0,0 +1,26 @@
+base:
+    seed: &seed 42
+model:
+    type: Llava OneVision
+    path: model path
+    torch_dtype: auto
+eval:
+    eval_pos: [pretrain, transformed]
+    type: vqa
+    name: [mme]
+    download: False
+    path: MME dataset path
+    bs: 1
+    inference_per_block: False
+sparse:
+    method: TokenReduction
+    special:
+        method: HoliTom
+        RETAIN_RATIO: 0.20
+        T: 0.65
+        HOLITOM_k: 18
+        HOLITOM_r: 0.5
+save:
+    save_trans: False
+    save_fake: False
+    save_path: /path/to/save/

docs/en/source/advanced/token_reduction.md

@@ -0,0 +1,72 @@
+
+
+# Token Reduction
+
+LightCompress currently supports token reduction for mainstream multimodal large language models. Configuration is very simple—plug and play.
+
+Here is an example configuration
+
+```yaml
+base:
+    seed: &seed 42
+model:
+    type: Llava
+    path: model path
+    torch_dtype: auto
+eval:
+    eval_pos: [pretrain, transformed]
+    type: vqa
+    name: [gqa, mmbench_en_dev, mme]
+    bs: 1
+    inference_per_block: False
+sparse:
+    method: TokenReduction
+    special:
+        method: FastV
+        pruning_loc: 3
+        rate: 0.778
+save:
+    save_trans: False
+    save_fake: False
+    save_path: /path/to/save/
+```
+
+The configuration file contains three core sections, including:
+
+1. **`model`**  
+   For model selection, you can choose LLaVA, LLaVA-NeXT, Qwen2.5VL, and LLaVA OneVision, etc. These models cover both image and video tasks. For the detailed list of supported models, see the file. LightCompress will support more models in the future.
+
+2. **`eval`**  
+   For the `eval_pos` parameter:  
+   - `pretrain` denotes the original model that keeps all visual tokens.  
+   - `transformed` denotes the model with token reduction applied.  
+   LightCompress integrates lmms-eval to evaluate various downstream datasets. Set `type` to `vqa`, and specify the datasets in `name` following the naming conventions in the lmms-eval documentation.
+
+3. **`sparse`**  
+   Set `method` to `TokenReduction` first, and then specify the concrete algorithm and related hyperparameters under `special`. Since each algorithm has different hyperparameters, refer to the configuration files for details.
+
+## Combining Quantization
+
+LightCompress also supports an extreme compression scheme that combines token reduction with quantization. First, choose a quantization algorithm to save a `fake_qunat` model (see the quantization section of the docs). Then load this model and add the `token_reduction` field under `quant`.
+
+```yaml
+quant:
+    method: RTN
+    weight:
+        bit: 4
+        symmetric: False
+        granularity: per_group
+        group_size: 128
+    special:
+        actorder: True
+        static_groups: True
+        percdamp: 0.01
+        blocksize: 128
+        true_sequential: True
+    quant_out: True
+    token_reduction:    
+        method: FastV
+        special:
+            pruning_loc: 3
+            rate: 0.778
+```

docs/en/source/configs.md

@@ -360,6 +360,26 @@ quant:
         static: True
 ```
 
+## sparse
+
+<font color=792ee5> sparse.method </font>
+
+The name of the sparsification algorithm used. This includes both [model sparsification](https://github.com/ModelTC/LightCompress/blob/main/llmc/compression/sparsification/__init__.pyn) and [reduction](https://github.com/ModelTC/LightCompress/blob/main/llmc/compression/token_reduction/__init__.py) of visual tokens. All supported algorithms can be found in the corresponding files.
+
+It’s worth noting that for model sparsification, you need to specify the exact algorithm name, whereas for token reduction, you only need to set it to `TokenReduction` first, and then specify the exact algorithm under `special`.
+
+```yaml
+sparse:
+    method: Wanda
+```
+
+```yaml
+sparse:
+    method: TokenReduction
+    special:
+        method: FastV
+```
+
 ## save
 
 <font color=792ee5> save.save_vllm</font>

docs/zh_cn/source/advanced/token_reduction.md

@@ -0,0 +1,68 @@
+# Token Reduction
+
+目前LightCompress支持对主流的多模态大语言模型进行token reduction，配置十分简单，即插即用。
+
+下面是一个配置的例子
+
+```yaml
+base:
+    seed: &seed 42
+model:
+    type: Llava
+    path: model path
+    torch_dtype: auto
+eval:
+    eval_pos: [pretrain, transformed]
+    type: vqa
+    name: [gqa, mmbench_en_dev, mme]
+    bs: 1
+    inference_per_block: False
+sparse:
+    method: TokenReduction
+    special:
+        method: FastV
+        pruning_loc: 3
+        rate: 0.778
+save:
+    save_trans: False
+    save_fake: False
+    save_path: /path/to/save/
+```
+
+配置文件中包含三大核心内容，包括：
+
+1. `model`
+在模型选择上，可以选择LLaVA，LLaVA-NeXT，Qwen2.5VL以及LLaVA OneVision等，这些模型涵盖了图像任务和视频任务，详细的模型支持列表可以查阅[文件](https://github.com/ModelTC/LightCompress/blob/main/llmc/models/__init__.py)，未来LightCompress也会支持更多的模型。
+
+2. `eval`
+首先，在`eval_pos`参数的选择上，`pretrain`表示原始保留所有视觉token的模型，`transformed`表示应用相应算法进行token reduction的模型。LightCompress接入了[lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval)进行各种下游数据集测评，需要将`type`指定为`vqa`，`name`中的下游测评数据集参考lmms-eval[文档](https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/docs/current_tasks.md)中的命名方式。
+
+3. `sparse`
+`method`需要首先指定为TokenReduction，在`special`中继续指定具体的算法以及相关的一些超参数。由于每个算法对应的超参数不同，详细的可以参考[配置文件](https://github.com/ModelTC/LightCompress/tree/main/configs/sparsification/methods)。
+
+
+## 结合量化
+
+LightCompress也支持同时使用token reduction和量化的极致压缩方案，首先需要选择量化算法存储一个`fake_qunat`模型，可以参考量化板块的文档。其次加载这个模型并在`quant`下加入`token_reduction`字段即可。
+
+```yaml
+quant:
+    method: RTN
+    weight:
+        bit: 4
+        symmetric: False
+        granularity: per_group
+        group_size: 128
+    special:
+        actorder: True
+        static_groups: True
+        percdamp: 0.01
+        blocksize: 128
+        true_sequential: True
+    quant_out: True
+    token_reduction:    
+        method: FastV
+        special:
+            pruning_loc: 3
+            rate: 0.778
+```

docs/zh_cn/source/configs.md

@@ -401,6 +401,26 @@ quant:
         granularity: per_token
 ```
 
+## sparse
+
+<font color=792ee5> sparse.method </font>
+
+使用的稀疏化算法名，这包含对[模型的稀疏化](https://github.com/ModelTC/LightCompress/blob/main/llmc/compression/sparsification/__init__.pyn)和对视觉token的[reduction](https://github.com/ModelTC/LightCompress/blob/main/llmc/compression/token_reduction/__init__.py)，所有支持算法可以在文件中查看。
+
+值得说明的是针对模型稀疏化，需要指定具体的算法名称，而token reduction只需要先指定为`TokenReduction`，在`special`中继续指定具体的算法。
+
+```yaml
+sparse:
+    method: Wanda
+```
+
+```yaml
+sparse:
+    method: TokenReduction
+    special:
+        method: FastV
+```
+
 ## save
 
 <font color=792ee5> save.save_vllm </font>