metadata
library_name: peft
tags:
- generated_from_trainer
base_model: jamba
model-index:
- name: out
results: []
See axolotl config
axolotl version: 0.4.0
base_model: jamba
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: scikit_admin_result.json
ds_type: json
type: sharegpt
conversation: chatml
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out
sequence_len: 6000
sample_packing: true
pad_to_sequence_len: false
eval_sample_packing: true
use_wandb: false
adapter: qlora
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
low_cpu_mem_usage: true
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
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: 2
saves_per_epoch: 2
debug:
weight_decay: 0.0
special_tokens:
out
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2356
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: 2
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.4337 | 0.0 | 1 | 0.3783 |
0.2537 | 0.5 | 103 | 0.2345 |
0.2161 | 1.0 | 206 | 0.2258 |
0.1821 | 1.47 | 309 | 0.2356 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0