This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the nthakur/miracl-raft-sft-instruct-v0.1, the nthakur/nomiracl-raft-sft-instruct-v0.1, the nthakur/miracl-en-x-raft-sft-instruct-v0.1 and the nthakur/miracl-x-en-raft-sft-instruct-v0.1 datasets. It achieves the following results on the evaluation set:

Loss: 1.3064

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: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.4903	0.09	200	1.3961
1.465	0.18	400	1.3499
1.4193	0.28	600	1.3330
1.3593	0.37	800	1.3232
1.3552	0.46	1000	1.3166
1.3685	0.55	1200	1.3123
1.3487	0.64	1400	1.3094
1.3891	0.74	1600	1.3076
1.3858	0.83	1800	1.3067
1.3635	0.92	2000	1.3064

Framework versions

PEFT 0.10.0
Transformers 4.39.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2

nthakur/Meta-Llama-3-8B-Instruct-miracl-mix-raft-sft-25th-apr-v1.0

Meta-Llama-3-8B-Instruct-miracl-mix-raft-sft-25th-apr-v1.0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nthakur/
Meta-Llama-3-8B-Instruct-miracl-mix-raft-sft-25th-apr-v1.0