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Llama2-base-7B-Chinese-50W-fullTune ver 0.01

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README.md CHANGED
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  ---
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- license: apache-2.0
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - zh
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+ - en
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+ tags:
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+ - llama2
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+ - llama2-base
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+ - llama2-base-7B
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+ task_categories:
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+ - text2text-generation
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  ---
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+ # 7B Chinese Chatbot trained based on LLama2-base 7B (Pure SFT Full Params Training)
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+
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+ ## Introduction
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+
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+ 该模型为基于Llama2 base 7B 全参数SFT训练的中文模型。其目的是为了同[RicardoLee/Llama2-base-7B-Chinese-50W-LoRA](https://huggingface.co/RicardoLee/Llama2-base-7B-Chinese-50W-LoRA)项目进行对比,判断LoRA效果合全参数训练效果的差异。
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+
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+ 该模型训练Loss最终达到了0.7.
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+
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+ 训练数据使用[BELLE](https://huggingface.co/BelleGroup)项目中采样的50万SFT数据进行SFT训练。
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+
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+ This model is a Chinese chat model based on Llama2 base 7B, trained with full-parameter SFT. Its purpose is to facilitate a comparison with the project [RicardoLee/Llama2-base-7B-Chinese-50W-LoRA](https://huggingface.co/RicardoLee/Llama2-base-7B-Chinese-50W-LoRA) and assess the performance between LoRA and full-parameter training.
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+
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+ The final batch loss reached 0.7 during the training.
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+
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+ The training data is sampled from [BELLE](https://huggingface.co/BelleGroup) project, which consists of 500,000 SFT samples.
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+
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+ ## Train Detail
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+
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+ 一些训练上的细节:
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+
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+ 1. 训练框架:该模型采用全参数SFT训练
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+ 2. Tokenizer:该模型使用了Chinese-Alpaca-Plus模型的tokenizer.model。这是因为LLama2本身的tokenizer.model同LLama1是一摸一样的。因此理论上可以完全复用Chinese-LLaMa项目的tokenizer而不会产生如何错位问题。
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+ 3. 训练参数:LR: 2e-4, Warmup ratio: 0.003.
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+ 4. 训练资源:8卡V100。67 小时
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+ 5. 训练起始的loss:参见[Material](trainer_state.json)
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+ 6. 训练终止的loss:参见[Material](trainer_state.json)
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+
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+ Some details in training:
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+
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+ 1. Trianing Framework: This model adopts full-parameter SFT training.
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+ 2. Tokenizer: This model utilizes the tokenizer.model from the Chinese-Alpaca-Plus model. The reason for this choice is that the tokenizer.model in LLama2 is identical to the one used in LLama1. As a result, it is theoretically feasible to entirely reuse the tokenizer from the Chinese-LLaMa project without encountering any issues related to token misalignment.
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+ 3. Training Parameters: LR: 2e-4, Warmup ratio: 0.003.
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+ 4. Training Resource: 8\*V100, 67 hours.
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+ 5. Initial Loss: Please refer to [Material](trainer_state.json)
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+ 6. Train Loss: Please refer to [Material](trainer_state.json)
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+
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+ ## Inference
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+
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+ 该模型依然采用stanford alpaca 模版。因此在测试时且别忘记添加开场白。开场白如下:
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+
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+ "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\n${Your Content}\n\n### Response:\n\n"
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+
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+ 对于带上文的对话,开场白如下:
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+
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+ "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Assistance Content}\nHuman:${Your Question}\n\n### Response:\n\n"
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+
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+ This model still using the Stanford Alpaca template. Therefore, don't forget to add prologue template. The prologue template is:
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+
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+ "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\n${Your Content}\n\n### Response:\n\n"
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+
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+ For dialogue with context, the prelogue template is:
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+
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+ "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Machine Content}\nHuman:${Your Question}\n\n### Response:\n\n"
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+
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+ ## Licence
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+
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+ 本仓库的模型依照 Apache-2.0 协议开源,模型的权重的使用则需要遵循LLama2[MODEL LICENCE](LICENSE)。
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+
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+ This repository's models are open-sourced under the Apache-2.0 license, and their weight usage must adhere to LLama2 [MODEL LICENCE](LICENSE) license.
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+
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+ ## Future Work
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+
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+ 将会在近期逐步放出
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+
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+ 1. 更大SFT数据规模训练下的模型。
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+ 2. 13B及以下的LLama2 同LLama2-chat的模型,以供大家对比。
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+
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+ I will release the following models:
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+
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+ 1. Models trained on larger data scale.
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+ 2. Models trained on LLama2 and LLama2-chat (under the 13B, since I only have V100), for comparison.
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train_results.json ADDED
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trainer_state.json ADDED
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