msaavedra1234/phi3_parise

See axolotl config

axolotl version: 0.4.1

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

trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: dataset.json
    ds_type: json
    type: completion

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./phi3-out

sequence_len: 4096
sample_packing: false
#pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch
# adam_beta2: 0.95
# adam_epsilon: 0.00001
# max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.0002 # 0.000003 #0.0002

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|>"
  eos_token: "<|end|>"

phi3-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: 1.8809

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: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.4023	1.0	7628	1.4132
0.1342	2.0	15256	1.8809

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1