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

README.md

@@ -1,3 +1,82 @@
 ---
-license: apache-2.0
+language:
+- zh
+- en
+tags:
+- llama2
+- llama2-base
+- llama2-base-7B
+task_categories:
+- text2text-generation
 ---
+# 7B Chinese Chatbot trained based on LLama2-base 7B (Pure SFT Full Params Training) 
+
+## Introduction
+
+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。
+
+该模型训练Loss最终达到了0.7.
+
+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。
+
+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.
+
+The final batch loss reached 0.7 during the training.
+
+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：该模型采用全参数SFT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：LR: 2e-4, Warmup ratio: 0.003.
+4. 训练资源：8卡V100。67 小时
+5. 训练起始的loss：参见[Material](trainer_state.json)
+6. 训练终止的loss：参见[Material](trainer_state.json)
+
+Some details in training:
+
+1. Trianing Framework: This model adopts full-parameter SFT training.
+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: LR: 2e-4, Warmup ratio: 0.003.
+4. Training Resource: 8\*V100, 67 hours.
+5. Initial Loss: Please refer to [Material](trainer_state.json)
+6. Train Loss: Please refer to [Material](trainer_state.json)
+
+## 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.

all_results.json

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+{
+    "epoch": 3.0,
+    "eval_loss": 1.5925407409667969,
+    "eval_runtime": 2.0229,
+    "eval_samples": 100,
+    "eval_samples_per_second": 49.434,
+    "eval_steps_per_second": 1.977,
+    "perplexity": 4.916223925276727,
+    "train_loss": 1.3950741625133278,
+    "train_runtime": 240198.6559,
+    "train_samples": 500000,
+    "train_samples_per_second": 6.245,
+    "train_steps_per_second": 0.049
+}

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "RicardoLee/Llama2-base-7B-Chinese-50W-fullTune",
+  "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": "float32",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 49954
+}

eval_results.json

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+{
+    "epoch": 3.0,
+    "eval_loss": 1.5925407409667969,
+    "eval_runtime": 2.0229,
+    "eval_samples": 100,
+    "eval_samples_per_second": 49.434,
+    "eval_steps_per_second": 1.977,
+    "perplexity": 4.916223925276727
+}

generation_config.json

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

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