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support transformers 4.44
Browse files- README.md +2 -0
- README_en.md +2 -0
- config.json +1 -1
- generation_config.json +1 -1
- modeling_chatglm.py +1 -4
README.md
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Read this in [English](README_en.md).
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**2024/07/24,我们发布了与长文本相关的最新技术解读,关注 [这里](https://medium.com/@ChatGLM/glm-long-scaling-pre-trained-model-contexts-to-millions-caa3c48dea85) 查看我们在训练 GLM-4-9B 开源模型中关于长文本技术的技术报告**
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## 模型介绍
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Read this in [English](README_en.md).
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**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。**
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**2024/07/24,我们发布了与长文本相关的最新技术解读,关注 [这里](https://medium.com/@ChatGLM/glm-long-scaling-pre-trained-model-contexts-to-millions-caa3c48dea85) 查看我们在训练 GLM-4-9B 开源模型中关于长文本技术的技术报告**
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## 模型介绍
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README_en.md
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# GLM-4-9B-Chat
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**On July 24, 2024, we released the latest technical interpretation related to long texts. Check
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out [here](https://medium.com/@ChatGLM/glm-long-scaling-pre-trained-model-contexts-to-millions-caa3c48dea85) to view our
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technical report on long context technology in the training of the open-source GLM-4-9B model.**
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# GLM-4-9B-Chat
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**2024/08/12, The repository code has been updated and now requires `transformers>=4.44.0`. Please update your dependencies accordingly.**
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**On July 24, 2024, we released the latest technical interpretation related to long texts. Check
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out [here](https://medium.com/@ChatGLM/glm-long-scaling-pre-trained-model-contexts-to-millions-caa3c48dea85) to view our
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technical report on long context technology in the training of the open-source GLM-4-9B model.**
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config.json
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"seq_length": 131072,
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"use_cache": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.
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"tie_word_embeddings": false,
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"eos_token_id": [151329, 151336, 151338],
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"pad_token_id": 151329
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"seq_length": 131072,
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"use_cache": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.0",
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"tie_word_embeddings": false,
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"eos_token_id": [151329, 151336, 151338],
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"pad_token_id": 151329
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generation_config.json
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"temperature": 0.8,
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"max_length": 128000,
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"top_p": 0.8,
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"transformers_version": "4.
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}
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"temperature": 0.8,
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"max_length": 128000,
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"top_p": 0.8,
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"transformers_version": "4.44.0"
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}
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modeling_chatglm.py
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outputs: ModelOutput,
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model_kwargs: Dict[str, Any],
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is_encoder_decoder: bool = False,
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standardize_cache_format: bool = False,
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) -> Dict[str, Any]:
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# update past_key_values
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cache_name, cache = self._extract_past_from_model_output(
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outputs, standardize_cache_format=standardize_cache_format
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)
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model_kwargs[cache_name] = cache
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# update attention mask
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outputs: ModelOutput,
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model_kwargs: Dict[str, Any],
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is_encoder_decoder: bool = False,
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) -> Dict[str, Any]:
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# update past_key_values
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cache_name, cache = self._extract_past_from_model_output(outputs)
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model_kwargs[cache_name] = cache
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# update attention mask
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