Llama2-base-7B-Chinese-50W-LoRA ver 0.0.1

README.md

@@ -1,3 +1,80 @@
 ---
-license: apache-2.0
+language:
+- zh
+- en
+tags:
+- llama2
+- llama2-base
+- llama2-base-7B
 ---
+# 7B Chinese Chatbot trained based on LLama2-base 7B (Pure LoRA Training) 
+
+## Introduction
+
+在完成了[Llama2-chat 7B Chinese](https://huggingface.co/RicardoLee/Llama2-chat-Chinese-50W) 和 [Llama2-chat 13B Chinese](https://huggingface.co/RicardoLee/Llama2-chat-13B-Chinese-50W) 的训练后，我非常好奇能否直接基于Llama2-base 系列直接进行SFT训练。这也是本模型仓库的初衷。
+
+终于，在[RicardoLee/Llama2-base-7B-Chinese-50W-pre\_release](https://huggingface.co/RicardoLee/Llama2-base-7B-Chinese-50W-pre_release)，[RicardoLee/Llama2-base-7B-Chinese-50W-Full2LoRA](https://huggingface.co/RicardoLee/Llama2-base-7B-Chinese-50W-Full2LoRA) 之后，我成功探索出了能稳定训练LoRA的参数，并最终完成了50W 数据的LoRA 训练。
+
+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。
+
+After finishing the training of [Llama2-chat 7B Chinese](https://huggingface.co/RicardoLee/Llama2-chat-Chinese-50W) and [Llama2-chat 13B Chinese](https://huggingface.co/RicardoLee/Llama2-chat-13B-Chinese-50W), I am deeply intrigued by the possibility of conducting SFT (Style-Fine-Tuning) training directly based on the Llama2-base series. This is the fundamental purpose of this model repository.
+
+Finally, after [RicardoLee/Llama2-base-7B-Chinese-50W-pre\_release](https://huggingface.co/RicardoLee/Llama2-base-7B-Chinese-50W-pre_release)，[RicardoLee/Llama2-base-7B-Chinese-50W-Full2LoRA](https://huggingface.co/RicardoLee/Llama2-base-7B-Chinese-50W-Full2LoRA), I did find the right hyperparams to do the LoRA training stabelly based on Llama2-base 7B model. For more details please refer to the Train Detail section.
+
+The training data is sampled from [BELLE](https://huggingface.co/BelleGroup) project, which consists of 500,000 SFT samples.
+
+## Train Detail
+
+一些训练上的细节：
+
+1. 训练框架：该模型使用了修改过的[Chinese-LLaMA-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)项目进行训练。
+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。
+3. 训练参数：**该模型训练使用的超参数为：LoRA rank: 64, LR: 4e-4, Warmup ratio:  0.001.**
+4. 训练资源：8卡V100。21小时
+5. 训练起始的loss：9.1402
+6. 训练终止的loss：1.4104
+
+Some details in training:
+
+1. Trianing Framework: This model is trained on modified [Chinese-LLaMA-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) Framework.
+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.
+3. Training Parameters: **The hyperparams are: LoRA rank: 64, LR: 4e-4, Warmup ratio: 0.001.**
+4. Training Resource: 8\*V100, 21 hours.
+5. Initial Loss: 9.1402
+6. Train Loss: 1.4104
+
+## Inference
+
+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：
+
+"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\n${Your Content}\n\n### Response:\n\n"
+
+对于带上文的对话，开场白如下：
+
+"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Assistance Content}\nHuman:${Your Question}\n\n### Response:\n\n"
+
+This model still using the Stanford Alpaca template. Therefore, don't forget to add prologue template. The prologue template is:
+
+"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\n${Your Content}\n\n### Response:\n\n"
+
+For dialogue with context, the prelogue template is:
+
+"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Machine Content}\nHuman:${Your Question}\n\n### Response:\n\n"
+
+## Licence
+
+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)。
+
+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.
+
+## Future Work
+
+将会在近期逐步放出
+
+1. 更大SFT数据规模训练下的模型。
+2. 13B及以下的LLama2 同LLama2-chat的模型，以供大家对比。
+
+I will release the following models:
+
+1. Models trained on larger data scale.
+2. Models trained on LLama2 and LLama2-chat (under the 13B, since I only have V100), for comparison.

config.json

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+{
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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 49954
+}

generation_config.json

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+{
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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0"
+}

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+oid sha256:2d967e855b1213a439df6c8ce2791f869c84b4f3b6cfacf22b86440b8192a2f8
+size 757972

tokenizer_config.json

@@ -0,0 +1,35 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "use_fast": true
+}