ChatDoctor
Collection
A collection of finetuned LLM models on the ChatDoctor HealthCareMagic dataset • 5 items • Updated
lora_all
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: 1.7827
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- total_eval_batch_size: 32
- 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 |
|---|---|---|---|
| 1.6961 | 2.8439 | 1000 | 1.7828 |
Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for geshijoker/HealthCareMagic_sft_llama3_instruct_lora_all
Base model
meta-llama/Meta-Llama-3-8B-Instruct