metadata
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: sft_full
results: []
sft_full
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the healthcaremagic dataset. It achieves the following results on the evaluation set:
- Loss: 1.7460
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2093 | 2.8429 | 500 | 1.7462 |
Evaluation results
| Name | Checkpoint | Rouge1 | RougeL | Meteor | Bert Score |
|---|---|---|---|---|---|
| baseline instruct model | Meta-Llama-3-8B-Instruct | 0.254 | 0.128 | 0.222 | 0.747 |
| full fientune from inst | sft_llama3_instruct_full | 0.315 | 0.189 | 0.238 | 0.782 |
| lora sft from inst | sft_llama3_instruct_lora_all | 0.271 | 0.143 | 0.194 | 0.774 |
| lora sft from base | sft_llama3_lora_all | 0.239 | 0.113 | 0.211 | 0.735 |
| qlora sft from inst | sft_llama3_instruct_qlora_all | 0.137 | 0.071 | 0.102 | 0.679 |
| qlora sft from base | sft_llama3_qlora_all | 0.192 | 0.090 | 0.159 | 0.718 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3