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VoyagerYuan/
Llama3.1-405b-qLoRA-Finetune

Name: Llama3.1-405b-qLoRA-Finetune
Availability: InStock

peft

safetensors

llama

generated_from_trainer

hugging-quants/Meta-Llama-3.1-405B-BNB-NF4-BF16

adapter

4-bit

bitsandbytes

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See axolotl config

axolotl version: 0.4.1

yaml

base_model: /home/ubuntu/North-Texas-FileSystem/raw_model
tokenizer_type: AutoTokenizer

load_in_4bit: true
strict: false

datasets:
  # huggingface repo
  # - path: VoyagerYuan/tomato_6k
  #   type: alpaca
  - path: /home/ubuntu/North-Texas-FileSystem/axolotl/fanqie_6000.jsonl
    ds_type: json
    data_files: /home/ubuntu/North-Texas-FileSystem/axolotl/fanqie_6000.jsonl
    type: alpaca
# dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out/qlora-llama3_1-405b_20240821_02
save_safetensors: true

adapter: qlora

sequence_len: 5000
sample_packing: true
pad_to_sequence_len: true

lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.000008

train_on_inputs: false
group_by_length: false
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: 4
saves_per_epoch: 1
weight_decay: 0.0
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  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|>

outputs/out/qlora-llama3_1-405b_20240821_02

This model was trained from scratch on the None dataset.

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: 8
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 1

Training results

Framework versions

PEFT 0.12.0
Transformers 4.45.0.dev0
Pytorch 2.4.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

VoyagerYuan/Llama3.1-405b-qLoRA-Finetune

outputs/out/qlora-llama3_1-405b_20240821_02

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

VoyagerYuan/
Llama3.1-405b-qLoRA-Finetune