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curtisxu/
mergeLlama-7b-Instruct-hf-quantized-peft

Name: mergeLlama-7b-Instruct-hf-quantized-peft
Availability: InStock

generated_from_trainer

meta-llama/CodeLlama-7b-Instruct-hf

adapter

llama2

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mergeLlama-7b-Instruct-hf-quantized-peft

This model is a fine-tuned version of meta-llama/CodeLlama-7b-Instruct-hf on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 6.2568

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: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
10.74	0.0122	20	7.4566
6.6528	0.0243	40	6.6315
6.4141	0.0365	60	6.4041
6.2451	0.0487	80	6.3026
6.1832	0.0608	100	6.2568

Framework versions

PEFT 0.10.1.dev0
Transformers 4.41.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1

curtisxu/mergeLlama-7b-Instruct-hf-quantized-peft

mergeLlama-7b-Instruct-hf-quantized-peft

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

curtisxu/
mergeLlama-7b-Instruct-hf-quantized-peft