Model Card for Model ID

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Model Details

Model Description

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

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

Training regime: [More Information Needed]

llamafactory-cli train
--stage sft
--do_train True
--model_name_or_path shenzhi-wang/Llama3-8B-Chinese-Chat
--preprocessing_num_workers 16
--finetuning_type lora
--quantization_bit 4
--template llama3
--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/LLaMA3-8B-Chinese-Chat/lora/4-bit-LLaMA3-8B-Chinese-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]

Time: 0:48:21

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Evaluation

llamafactory-cli train
--stage sft
--model_name_or_path shenzhi-wang/Llama3-8B-Chinese-Chat
--preprocessing_num_workers 16
--finetuning_type lora
--quantization_bit 4
--template llama3
--flash_attn auto
--dataset_dir data
--dataset sample_healthcare
--cutoff_len 512
--max_samples 10000
--per_device_eval_batch_size 2
--predict_with_generate True
--max_new_tokens 512
--top_p 0.7
--temperature 0.95
--output_dir saves/LLaMA3-8B-Chinese-Chat/lora/eval_2024-06-19-09-59-51
--do_predict True
--adapter_name_or_path saves/LLaMA3-8B-Chinese-Chat/lora/4-bit-LLaMA3-8B-Chinese-Chat

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

{
"predict_bleu-4": 3.6242000999999995,
"predict_rouge-1": 18.92082985,
"predict_rouge-2": 2.8953536,
"predict_rouge-l": 15.46343345,
"predict_runtime": 35159.3161,
"predict_samples_per_second": 0.057,
"predict_steps_per_second": 0.028
}

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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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Hours used: [More Information Needed]
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Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

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]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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