base_model: microsoft/Phi-3-mini-4k-instruct
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: ptoro/honkers-phi
    type: alpaca

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/phi-sft-out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

adapter: qlora
lora_model_dir:
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: axolotl-june
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.000003

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: True
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true
special_tokens:
  pad_token: "<|endoftext|>"

outputs/phi-sft-out

This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.8947

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: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
7.126	0.0093	1	5.2723
6.503	0.25	27	5.2703
5.9853	0.5	54	5.2576
5.7324	0.75	81	5.2320
6.5292	1.0	108	5.1854
5.6106	1.2222	135	5.1238
6.3981	1.4722	162	5.0544
5.602	1.7222	189	4.9929
5.3998	1.9722	216	4.9468
5.1841	2.1944	243	4.9171
6.0764	2.4444	270	4.9009
5.2345	2.6944	297	4.8961
5.4896	2.9444	324	4.8947

Framework versions

PEFT 0.11.2.dev0
Transformers 4.41.1
Pytorch 2.1.2+cu118
Datasets 2.19.1
Tokenizers 0.19.1

ptoro/phi-3-qlora-rosie-overfit

outputs/phi-sft-out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ptoro/
phi-3-qlora-rosie-overfit