See axolotl config
axolotl version: 0.7.0
base_model: unsloth/phi-4
load_in_8bit: false
load_in_4bit: false
bf16: auto
fp16:
tf32: false
datasets:
- path: tamewild/y1_sft_split
split: train
type: chat_template
field_messages: conversation
shuffle_merged_datasets: true
test_datasets:
- path: tamewild/y1_sft_split
split: validation
type: chat_template
field_messages: conversation
dataset_prepared_path: workspace/dataset_prepared
hub_model_id: tamewild/14b_v1_fft
hf_use_auth_token: true
sequence_len: 9000
pad_to_sequence_len: true
sample_packing: true
eval_sample_packing: true # disable if we get errors
# wandb configuration if you're using it
# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
wandb_project: axolotl # Your wandb project name
wandb_entity: # A wandb Team name if using a Team
wandb_watch:
wandb_name: # Set the name of your wandb run
wandb_run_id: # Set the ID of your wandb run
wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
output_dir: /workspace/tuned
torch_compile: auto
gradient_accumulation_steps: 2
micro_batch_size: 8
eval_batch_size: 8
num_epochs: 1
warmup_ratio: 0.01
learning_rate: 4.5e-5
logging_steps: 1
eval_steps: # Leave empty to eval at each epoch, integer for every N steps. float for fraction of total steps
evals_per_epoch: 4 # number of times per epoch to run evals, mutually exclusive with eval_steps
save_steps: # Leave empty to save at each epoch, integer for every N steps. float for fraction of total steps
saves_per_epoch: 4 # number of times per epoch to save a checkpoint, mutually exclusive with save_steps
save_total_limit: 1 # Checkpoints saved at a time
include_tokens_per_second: true
train_on_inputs: false
group_by_length: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
lr_scheduler: cosine
lr_scheduler_kwargs:
cosine_min_lr_ratio: 0.025
optimizer: paged_adamw_8bit
weight_decay: 0.01
xformers_attention:
flash_attention: true
seed: 1234
strict: false
14b_v1_fft
This model is a fine-tuned version of unsloth/phi-4 on the tamewild/y1_sft_split dataset. It achieves the following results on the evaluation set:
- Loss: 0.3468
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: 4.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1234
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 3
- num_epochs: 1.0
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.7528 | 0.0033 | 1 | 0.7171 |
0.3488 | 0.2504 | 77 | 0.3663 |
0.3704 | 0.5008 | 154 | 0.3525 |
0.3338 | 0.7512 | 231 | 0.3468 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+gitf929e0d
- Datasets 3.2.0
- Tokenizers 0.21.0
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