See axolotl config
axolotl version: 0.4.0
base_model: N8Programs/llamoe-8x1b
model_type: MixtralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: mhenrichsen/alpaca_2k_test
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out
sequence_len: 2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: tinyllamoe
wandb_entity:
wandb_watch:
wandb_name: run-1
wandb_log_model: run-1
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adafactor
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
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:
fsdp_config:
special_tokens:
out
This model is a fine-tuned version of N8Programs/llamoe-8x1b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7176
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2099 | 0.04 | 1 | 1.2991 |
| 1.3823 | 0.27 | 7 | 1.4997 |
| 10.4722 | 0.54 | 14 | 2.6370 |
| 1.6521 | 0.82 | 21 | 1.4303 |
| 1.6555 | 1.07 | 28 | 1.7053 |
| 1.7864 | 1.34 | 35 | 1.8820 |
| 1.2141 | 1.61 | 42 | 1.6614 |
| 1.1488 | 1.88 | 49 | 1.5619 |
| 0.4733 | 2.14 | 56 | 1.6381 |
| 0.444 | 2.41 | 63 | 1.6311 |
| 0.4717 | 2.68 | 70 | 1.6398 |
| 0.4657 | 2.95 | 77 | 1.5938 |
| 0.1066 | 3.2 | 84 | 1.6952 |
| 0.1547 | 3.48 | 91 | 1.7209 |
| 0.1246 | 3.75 | 98 | 1.7176 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
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