See axolotl config
axolotl version: 0.10.0.dev0
base_model: GreenerPastures/Bald-Beaver-8B
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: qwen3
datasets:
- path: hardlyworking/EssentialGames
type: completion
val_set_size: 0.05
output_dir: ./outputs/out
dataset_prepared_path: last_run_prepared
shuffle_merged_datasets: true
#hub_model_id: hardlyworking/Sugma8B
#hub_strategy: "all_checkpoints"
#push_dataset_to_hub:
#hf_use_auth_token: true
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true
sequence_len: 32768
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
wandb_project: joe_Qwen8B
wandb_entity:
wandb_watch:
wandb_name: Qwen8B
wandb_log_model:
evals_per_epoch: 8
eval_table_size:
eval_max_new_tokens: 128
max_grad_norm: 0.0001
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
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
s2_attention:
deepspeed: /alloc/axolotl/deepspeed_configs/zero2.json
warmup_ratio: 0.05
saves_per_epoch: 1
debug:
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
pad_token:
outputs/out
This model is a fine-tuned version of GreenerPastures/Bald-Beaver-8B on the hardlyworking/EssentialGames dataset. It achieves the following results on the evaluation set:
- Loss: 3.5551
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_bnb_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: 79
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.4652 | 0.0013 | 1 | 6.7119 |
| 4.5988 | 0.1252 | 99 | 4.6118 |
| 3.8234 | 0.2503 | 198 | 3.8138 |
| 3.6123 | 0.3755 | 297 | 3.7119 |
| 3.6076 | 0.5006 | 396 | 3.6613 |
| 3.5705 | 0.6258 | 495 | 3.6305 |
| 3.4955 | 0.7509 | 594 | 3.6085 |
| 3.4899 | 0.8761 | 693 | 3.5927 |
| 3.5015 | 1.0013 | 792 | 3.5788 |
| 3.4645 | 1.1264 | 891 | 3.5719 |
| 3.4081 | 1.2516 | 990 | 3.5656 |
| 3.5246 | 1.3767 | 1089 | 3.5607 |
| 3.4124 | 1.5019 | 1188 | 3.5578 |
| 3.4495 | 1.6271 | 1287 | 3.5566 |
| 3.3886 | 1.7522 | 1386 | 3.5564 |
| 3.4256 | 1.8774 | 1485 | 3.5551 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
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