support transformers 4.44

README.md

@@ -19,6 +19,8 @@ inference: false
 
 Read this in [English](README_en.md)
 
+**2024/08/12, 本仓库代码已更新并使用 `transforemrs>=4.44.0`, 请及时更新依赖。**
+
 GLM-4V-9B 是智谱 AI 推出的最新一代预训练模型 GLM-4 系列中的开源多模态版本。
 **GLM-4V-9B** 具备 1120 * 1120 高分辨率下的中英双语多轮对话能力，在中英文综合能力、感知推理、文字识别、图表理解等多方面多模态评测中，GLM-4V-9B 表现出超越 GPT-4-turbo-2024-04-09、Gemini
 1.0 Pro、Qwen-VL-Max 和 Claude 3 Opus 的卓越性能。

README_en.md

@@ -1,5 +1,7 @@
 # GLM-4V-9B
 
+**2024/08/12, The repository code has been updated and now requires `transformers>=4.44.0`. Please update your dependencies accordingly.**
+
 GLM-4V-9B is an open source multimodal version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu AI.
 **GLM-4V-9B** has the ability to conduct multi-round conversations in Chinese and English at a high resolution of 1120 * 1120. In multimodal evaluations of comprehensive Chinese and English abilities, perceptual reasoning, text recognition, and chart understanding, GLM-4V-9B has shown superior performance over GPT-4-turbo-2024-04-09, Gemini
 1.0 Pro, Qwen-VL-Max, and Claude 3 Opus.

config.json

@@ -50,7 +50,7 @@
   "seq_length": 8192,
   "use_cache": true,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.42.4",
+  "transformers_version": "4.44.0",
   "tie_word_embeddings": false,
   "eos_token_id": [151329, 151336, 151338],
   "pad_token_id": 151329,

generation_config.json

@@ -9,5 +9,5 @@
   "temperature": 0.8,
   "max_length": 8192,
   "top_p": 0.8,
-  "transformers_version": "4.42.4"
+  "transformers_version": "4.44.0"
 }

modeling_chatglm.py

@@ -1082,12 +1082,9 @@ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
             outputs: ModelOutput,
             model_kwargs: Dict[str, Any],
             is_encoder_decoder: bool = False,
-            standardize_cache_format: bool = False,
     ) -> Dict[str, Any]:
         # update past_key_values
-        cache_name, cache = self._extract_past_from_model_output(
-            outputs, standardize_cache_format=standardize_cache_format
-        )
+        cache_name, cache = self._extract_past_from_model_output(outputs)
         model_kwargs[cache_name] = cache
 
         # update attention mask