Merge branch 'hiyouga:main' into main

README.md

@@ -289,7 +289,7 @@ huggingface-cli login
 | datasets     | 2.14.3  | 2.19.1    |
 | accelerate   | 0.27.2  | 0.30.1    |
 | peft         | 0.9.0   | 0.11.1    |
-| trl          | 0.8.1   | 0.8.6     |
+| trl          | 0.8.2   | 0.8.6     |
 
 | Optional     | Minimum | Recommend |
 | ------------ | ------- | --------- |
@@ -345,6 +345,8 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec
 
 <details><summary>For Ascend NPU users</summary>
 
+Join [NPU user group](assets/wechat_npu.jpg).
+
 To utilize Ascend NPU devices for (distributed) training and inference, you need to install the **[torch-npu](https://gitee.com/ascend/pytorch)** library and the **[Ascend CANN Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**.
 
 | Requirement  | Minimum | Recommend |

README_zh.md

@@ -289,7 +289,7 @@ huggingface-cli login
 | datasets     | 2.14.3  | 2.19.1    |
 | accelerate   | 0.27.2  | 0.30.1    |
 | peft         | 0.9.0   | 0.11.1    |
-| trl          | 0.8.1   | 0.8.6     |
+| trl          | 0.8.2   | 0.8.6     |
 
 | 可选项       | 至少     | 推荐      |
 | ------------ | ------- | --------- |
@@ -345,6 +345,8 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
 
 <details><summary>昇腾 NPU 用户指南</summary>
 
+加入 [NPU 用户群](assets/wechat_npu.jpg)。
+
 如果使用昇腾 NPU 设备进行（分布式）训练或推理，需要安装 **[torch-npu](https://gitee.com/ascend/pytorch)** 库和 **[Ascend CANN Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**。
 
 | 依赖项       | 至少     | 推荐      |

data/README_zh.md

@@ -7,7 +7,7 @@
   "hf_hub_url": "Hugging Face 的数据集仓库地址（若指定，则忽略 script_url 和 file_name）",
   "ms_hub_url": "ModelScope 的数据集仓库地址（若指定，则忽略 script_url 和 file_name）",
   "script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略 file_name）",
-  "file_name": "该目录下数据集文件的名称（若上述参数未指定，则此项必需）",
+  "file_name": "该目录下数据集文件夹或文件的名称（若上述参数未指定，则此项必需）",
   "formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
   "ranking": "是否为偏好数据集（可选，默认：False）",
   "subset": "数据集子集的名称（可选，默认：None）",

src/llamafactory/data/preprocess.py

@@ -89,7 +89,7 @@ def preprocess_supervised_dataset(
         if processor is not None and hasattr(processor, "image_seq_length"):  # paligemma case
             image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
             input_ids += [image_token_id] * getattr(processor, "image_seq_length")
-            labels += [image_token_id] * getattr(processor, "image_seq_length")
+            labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length")
 
         for turn_idx, (source_ids, target_ids) in enumerate(
             template.encode_multiturn(

src/llamafactory/extras/misc.py

@@ -65,7 +65,7 @@ def check_dependencies() -> None:
         require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
         require_version("accelerate>=0.27.2", "To fix: pip install accelerate>=0.27.2")
         require_version("peft>=0.10.0", "To fix: pip install peft>=0.10.0")
-        require_version("trl>=0.8.1", "To fix: pip install trl>=0.8.1")
+        require_version("trl>=0.8.2", "To fix: pip install trl>=0.8.2")
 
 
 def count_parameters(model: torch.nn.Module) -> Tuple[int, int]: