MSc_llama2_finetuned_model_secondData7 – AI Model by Casper0508 | AlphaNeural AI

You can deploy this model and start earning money today!

Casper0508/
MSc_llama2_finetuned_model_secondData7

like

0

generated_from_trainer

meta-llama/Llama-2-7b-chat-hf

Views

No views yet

MSc_llama2_finetuned_model_secondData7

This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6939

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

The following bitsandbytes quantization config was used during training:

quant_method: bitsandbytes
_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: bfloat16
load_in_4bit: True
load_in_8bit: False

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
training_steps: 250

Training results

Training Loss	Epoch	Step	Validation Loss
3.9823	1.33	10	3.6153
3.3928	2.67	20	2.9413
2.6305	4.0	30	2.1743
1.9546	5.33	40	1.7079
1.5996	6.67	50	1.4500
1.2984	8.0	60	1.1277
0.9632	9.33	70	0.8761
0.8296	10.67	80	0.8206
0.7589	12.0	90	0.7735
0.7063	13.33	100	0.7446
0.671	14.67	110	0.7278
0.6405	16.0	120	0.7091
0.6096	17.33	130	0.7021
0.5845	18.67	140	0.6986
0.5697	20.0	150	0.6938
0.5539	21.33	160	0.6936
0.5414	22.67	170	0.6913
0.5313	24.0	180	0.6920
0.522	25.33	190	0.6919
0.5168	26.67	200	0.6932
0.5191	28.0	210	0.6942
0.5079	29.33	220	0.6938
0.5132	30.67	230	0.6939
0.5085	32.0	240	0.6939
0.5079	33.33	250	0.6939

Framework versions

PEFT 0.4.0
Transformers 4.38.2
Pytorch 2.3.1+cu121
Datasets 2.13.1
Tokenizers 0.15.2