axolotl version: 0.4.1
base_model: jeiku/Dante_9B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: FourOhFour/RP_Phase
type: sharegpt
conversation: chatml
chat_template: chatml
val_set_size: 0.0025
output_dir: ./outputs/out
adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: false
liger_swiglu: true
liger_fused_linear_cross_entropy: false
wandb_project: chatml9B
wandb_entity:
wandb_watch:
wandb_name: chatml9B
wandb_log_model:
gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000008
weight_decay: 0.05
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:
special_tokens:
pad_token: <pad>
outputs/out
This model is a fine-tuned version of jeiku/Dante_9B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7075
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: 8e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 14
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7474 | 0.0135 | 1 | 1.7996 |
| 1.6968 | 0.2570 | 19 | 0.9551 |
| 1.6583 | 0.5139 | 38 | 0.8805 |
| 1.5418 | 0.7709 | 57 | 0.7926 |
| 1.3997 | 1.0271 | 76 | 0.7500 |
| 1.3921 | 1.2847 | 95 | 0.7168 |
| 1.4141 | 1.5424 | 114 | 0.7155 |
| 1.4139 | 1.8 | 133 | 0.7075 |
Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0