See axolotl config
axolotl version: 0.4.1
base_model: microsoft/Phi-3-mini-128k-instruct
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: alsokit/alpaca_vtb_train_and_eval_25K
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/out
sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
special_tokens:
outputs/out
This model is a fine-tuned version of microsoft/Phi-3-mini-128k-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1984
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
3.4523 | 0.0018 | 1 | 2.7465 |
0.3199 | 0.2512 | 142 | 0.3300 |
0.2857 | 0.5024 | 284 | 0.2663 |
0.2264 | 0.7536 | 426 | 0.2466 |
0.246 | 1.0049 | 568 | 0.2292 |
0.2419 | 1.2472 | 710 | 0.2197 |
0.2099 | 1.4985 | 852 | 0.2147 |
0.2111 | 1.7497 | 994 | 0.2065 |
0.1662 | 2.0009 | 1136 | 0.2005 |
0.173 | 2.2366 | 1278 | 0.1999 |
0.1633 | 2.4878 | 1420 | 0.1986 |
0.169 | 2.7391 | 1562 | 0.1984 |
Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for alsokit/eLM-Phi3-mini-128K-it-LoRA
Base model
microsoft/Phi-3-mini-128k-instruct