MedLLAMA – AI Model by mockingmonkey | AlphaNeural AI

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MedLLAMA

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meta-llama/Meta-Llama-3-8B

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MedLLAMA

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.9700

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: 0.0002
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.325	0.6000	2435	1.0248
1.0124	1.2001	4870	1.0025
0.795	1.8001	7305	0.9766
1.1855	2.4002	9740	0.9700

Framework versions

Transformers 4.42.4
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.19.1

Training procedure

The following bitsandbytes quantization config was used during training:

quant_method: QuantizationMethod.BITS_AND_BYTES
_load_in_8bit: False
_load_in_4bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: True
bnb_4bit_compute_dtype: float16
bnb_4bit_quant_storage: uint8
load_in_4bit: True
load_in_8bit: False

Framework versions

PEFT 0.6.2