library_name: peft
license: gemma
base_model: mlabonne/gemma-3-27b-it-abliterated
tags:
- axolotl
- generated_from_trainer
datasets: - dset_comp3.0_sortpatent_count_pat400_in5_5000.jsonl
model-index: - name: gemma-3-27b-it-abliterated-chem-claude-5-comp3-sort-pate
results: []
See axolotl config
axolotl version: 0.9.1
base_model: mlabonne/gemma-3-27b-it-abliterated
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: gemma-3-27b-it-abliterated-chem-claude-5-comp3-sort-pate
output_dir: ./outputs/out/gemma-3-27b-it-abliterated-chem-claude-5-comp3-sort-pate
hub_model_id: cgifbribcgfbi/gemma-3-27b-it-abliterated-chem-claude-5-comp3-sort-pate
tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false
datasets:
- path: dset_comp3.0_sortpatent_count_pat400_in5_5000.jsonl
type: chat_template
field_messages: messages
dataset_prepared_path: last_run_prepared
val_set_size: 0.04
save_safetensors: true
sequence_len: 2833
sample_packing: true
pad_to_sequence_len: true
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
wandb_mode:
wandb_project: finetune-sweep
wandb_entity: gpoisjgqetpadsfke
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 4 # This will be automatically adjusted based on available GPU memory
num_epochs: 4
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00002
train_on_inputs: false
group_by_length: true
bf16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
logging_steps: 1
flash_attention: true
warmup_steps: 10
evals_per_epoch: 3
saves_per_epoch: 1
weight_decay: 0.01
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: Gemma3DecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
special_tokens:
pad_token: <|finetune_right_pad_id|>
gemma-3-27b-it-abliterated-chem-claude-5-comp3-sort-pate
This model is a fine-tuned version of mlabonne/gemma-3-27b-it-abliterated on the dset_comp3.0_sortpatent_count_pat400_in5_5000.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3741
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 10
- num_epochs: 4.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7183 | 0.0063 | 1 | 0.9296 |
| 0.5328 | 0.3375 | 54 | 0.5153 |
| 0.4728 | 0.675 | 108 | 0.4467 |
| 0.406 | 1.0125 | 162 | 0.4221 |
| 0.3972 | 1.35 | 216 | 0.4027 |
| 0.3935 | 1.6875 | 270 | 0.3925 |
| 0.3687 | 2.025 | 324 | 0.3865 |
| 0.3682 | 2.3625 | 378 | 0.3807 |
| 0.3771 | 2.7 | 432 | 0.3777 |
| 0.3632 | 3.0375 | 486 | 0.3764 |
| 0.3622 | 3.375 | 540 | 0.3745 |
| 0.3453 | 3.7125 | 594 | 0.3741 |
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1