Model Card for Model ID
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Model Details
Model Description
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- Language(s) (NLP): [More Information Needed]
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- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
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Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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llamafactory-cli train
--stage sft
--do_train True
--model_name_or_path hfl/chinese-alpaca-2-7b
--preprocessing_num_workers 16
--finetuning_type lora
--quantization_bit 8
--template llama2_zh
--flash_attn auto
--dataset_dir data
--dataset healthcare
--cutoff_len 512
--learning_rate 0.0002
--num_train_epochs 1.0
--max_samples 10000
--per_device_train_batch_size 2
--gradient_accumulation_steps 16
--lr_scheduler_type cosine
--max_grad_norm 1.0
--logging_steps 5
--save_steps 100
--warmup_steps 0
--optim adamw_torch
--packing False
--report_to none
--output_dir saves/ChineseLLaMA2-7B-Chat/lora/8-bit_ChineseLLaMA2-7B-Chat
--fp16 True
--plot_loss True
--ddp_timeout 180000000
--include_num_input_tokens_seen True
--lora_rank 16
--lora_alpha 16
--lora_dropout 0.05
--lora_target all \
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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Glossary [optional]
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