library_name: peft
license: llama3.3
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
tags:
- axolotl
- generated_from_trainer
datasets: - dset_r1_clean_4998.jsonl
model-index: - name: Llama-3.3-70B-Instruct-abliterated-finetuned-chemistry-r1
results: []
See axolotl config
axolotl version: 0.8.1
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: r1_axolotl_ft
output_dir: ./outputs/out/r1_axolotl_ft
hub_model_id: cgifbribcgfbi/Llama-3.3-70B-Instruct-abliterated-finetuned-chemistry-r1
hub_strategy: every_save
# resume_from_checkpoint: ./outputs/out/5_70B_axolotl_ft/checkpoint-72
tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false
datasets:
- path: dset_r1_clean_4998.jsonl
type: chat_template
split: train
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
# test_datasets:
# - path: 5000_benign_val.json
# type: chat_template
# split: train
save_safetensors: true
sequence_len: 3500
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-chem
wandb_entity: gpoisjgqetpadsfke
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 2
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: LlamaDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
special_tokens:
pad_token: <|finetune_right_pad_id|>
Llama-3.3-70B-Instruct-abliterated-finetuned-chemistry-r1
This model is a fine-tuned version of huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned on the dset_r1_clean_4998.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3558
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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 8
- total_eval_batch_size: 8
- 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 |
|---|---|---|---|
| 0.7163 | 0.0037 | 1 | 0.7191 |
| 0.4295 | 0.3333 | 90 | 0.4328 |
| 0.4114 | 0.6667 | 180 | 0.3992 |
| 0.382 | 1.0 | 270 | 0.3827 |
| 0.3651 | 1.3333 | 360 | 0.3736 |
| 0.3614 | 1.6667 | 450 | 0.3679 |
| 0.3644 | 2.0 | 540 | 0.3630 |
| 0.3201 | 2.3333 | 630 | 0.3608 |
| 0.3179 | 2.6667 | 720 | 0.3580 |
| 0.3103 | 3.0 | 810 | 0.3561 |
| 0.3043 | 3.3333 | 900 | 0.3562 |
| 0.3098 | 3.6667 | 990 | 0.3558 |
| 0.2853 | 4.0 | 1080 | 0.3558 |
Framework versions
- PEFT 0.15.1
- Transformers 4.51.0
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1