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Llama-3-8B-Instruct-750Mb-lora – AI Model by REILX | AlphaNeural AI
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REILX
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Llama-3-8B-Instruct-750Mb-lora
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safetensors
text-generation-inference
llama
chat
sft
lora
zh
en
REILX/extracted_tagengo_gpt4
TigerResearch/sft_zh
alexl83/AlpacaDataCleaned
LooksJuicy/ruozhiba
silk-road/alpaca-data-gpt4-chinese
databricks/databricks-dolly-15k
microsoft/orca-math-word-problems-200k
Sao10K/Claude-3-Opus-Instruct-5K
llama3
us
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数据集
使用以下8个数据集
image/png
对Llama-3-8B-Instruct进行微调。
基础模型：
https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
训练工具
https://github.com/hiyouga/LLaMA-Factory
测评方式：
使用opencompass(
https://github.com/open-compass/OpenCompass/
)， 测试工具基于CEval和MMLU对微调之后的模型和原始模型进行测试。
测试模型分别为：
Llama-3-8B
Llama-3-8B-Instruct
Llama-3-8B-Instruct-750Mb-lora, 使用8DataSets数据集对Llama-3-8B-Instruct模型进行sft方式lora微调
测试机器
8*A800
8DataSets数据集：
大约750Mb的微调数据集
https://huggingface.co/datasets/REILX/extracted_tagengo_gpt4
https://huggingface.co/datasets/TigerResearch/sft_zh
https://huggingface.co/datasets/silk-road/alpaca-data-gpt4-chinese
https://huggingface.co/datasets/LooksJuicy/ruozhiba
https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k
https://huggingface.co/datasets/alexl83/AlpacaDataCleaned
https://huggingface.co/datasets/Sao10K/Claude-3-Opus-Instruct-5K
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 300
num_epochs: 1.0
image/png
