Commit
•
3ea4b6a
1
Parent(s):
23a6df5
Llama2-base-7B-Chinese-50W-fullTune ver 0.01
Browse files- README.md +80 -1
- all_results.json +14 -0
- config.json +26 -0
- eval_results.json +9 -0
- generation_config.json +7 -0
- pytorch_model-00001-of-00002.bin +3 -0
- pytorch_model-00002-of-00002.bin +3 -0
- pytorch_model.bin.index.json +330 -0
- train_results.json +8 -0
- trainer_state.json +0 -0
README.md
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---
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-
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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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## Introduction
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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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该模型训练Loss最终达到了0.7.
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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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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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The final batch loss reached 0.7 during the training.
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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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## Train Detail
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一些训练上的细节:
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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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Some details in training:
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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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## Inference
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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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"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\n${Your Content}\n\n### Response:\n\n"
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对于带上文的对话,开场白如下:
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"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Assistance Content}\nHuman:${Your Question}\n\n### Response:\n\n"
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This model still using the Stanford Alpaca template. Therefore, don't forget to add prologue template. The prologue template is:
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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${Your Content}\n\n### Response:\n\n"
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For dialogue with context, the prelogue template is:
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"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Machine Content}\nHuman:${Your Question}\n\n### Response:\n\n"
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## Licence
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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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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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## Future Work
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将会在近期逐步放出
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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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I will release the following models:
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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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all_results.json
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{
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"epoch": 3.0,
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"eval_loss": 1.5925407409667969,
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"eval_runtime": 2.0229,
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"eval_samples": 100,
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"eval_samples_per_second": 49.434,
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"eval_steps_per_second": 1.977,
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"perplexity": 4.916223925276727,
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"train_loss": 1.3950741625133278,
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"train_runtime": 240198.6559,
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"train_samples": 500000,
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"train_samples_per_second": 6.245,
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"train_steps_per_second": 0.049
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}
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config.json
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{
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"_name_or_path": "RicardoLee/Llama2-base-7B-Chinese-50W-fullTune",
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"architectures": [
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"LlamaForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.31.0",
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"use_cache": true,
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"vocab_size": 49954
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}
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eval_results.json
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{
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"epoch": 3.0,
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"eval_loss": 1.5925407409667969,
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"eval_runtime": 2.0229,
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"eval_samples": 100,
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"eval_samples_per_second": 49.434,
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"eval_steps_per_second": 1.977,
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"perplexity": 4.916223925276727
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"transformers_version": "4.31.0"
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}
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pytorch_model-00001-of-00002.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:236fa27c0fedaa59ff42b5c16a7c2b295b846231a575afd1db9456273d4551d3
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size 9943344218
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pytorch_model-00002-of-00002.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:33688166f8ccdbc28af48c12f0912afb96d0fdce75c8b6ff7b7b30f9bc130b10
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size 3827768667
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pytorch_model.bin.index.json
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train_results.json
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trainer_state.json
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