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Meta-Llama-3.1-8B-Instruct-abliterated-chem-claude-5-comp3-sort-pat

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metadata
library_name: peft
license: llama3.1
base_model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
tags:
  - axolotl
  - generated_from_trainer
datasets:
  - dset_comp3.0_sortpatent_count_pat400_in5_5000.jsonl
model-index:
  - name: >-
      alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.9.1

base_model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: Meta-Llama-3.1-_outputs_axolotl_ft_alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000
output_dir: ./outputs/out/Meta-Llama-3.1-_outputs_axolotl_ft_alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000
hub_model_id: cgifbribcgfbi/alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000

tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false

datasets:
  - path: dset_comp3.0_sortpatent_count_pat400_in5_5000.jsonl
    type: chat_template
    split: train

dataset_prepared_path: last_run_prepared
val_set_size: 0.04
save_safetensors: true

sequence_len: 2700
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: 1
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|>

alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000

This model is a fine-tuned version of mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated on the dset_comp3.0_sortpatent_count_pat400_in5_5000.jsonl dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5731

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: 1.0

Training results

Training Loss Epoch Step Validation Loss
0.7 0.0061 1 0.8766
0.6465 0.3354 55 0.6349
0.5865 0.6707 110 0.5731

Framework versions

  • PEFT 0.15.2
  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.1
  • Tokenizers 0.21.1