phi-2_dolly_instruction_polish_adapter – AI Model by s3nh | AlphaNeural AI

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s3nh/
phi-2_dolly_instruction_polish_adapter

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microsoft/phi-2

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phi-2-sft-out

This model is a fine-tuned version of microsoft/phi-2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2813

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: 3e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0	1	1.7973
1.9767	0.25	5290	1.4832
1.8474	0.5	10580	1.4356
1.8121	0.75	15870	1.4022
1.8333	1.0	21160	1.3678
1.6601	1.25	26450	1.3508
1.5452	1.5	31740	1.3357
1.7381	1.75	37030	1.3191
1.6256	2.0	42320	1.3090
1.5521	2.25	47610	1.2961
1.8318	2.5	52900	1.2910
1.6761	2.75	58190	1.2901
1.6312	3.0	63480	1.2879
1.7003	3.25	68770	1.2820
1.6915	3.5	74060	1.2814
1.5757	3.75	79350	1.2813

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.16.0
Tokenizers 0.15.0

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

Framework versions

PEFT 0.6.0