---
base_model: meta-llama/Meta-Llama-3.1-405B
library_name: peft
license: llama3.1
tags:
- generated_from_trainer
model-index:
- name: outputs/out/qlora-llama3_1-405b
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3.1-405B
tokenizer_type: AutoTokenizer
load_in_4bit: true
strict: false
datasets:
- path: tatsu-lab/alpaca
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out/qlora-llama3_1-405b
wandb_project: qlora-fsdp-405b
wandb_entity: oaaic
adapter: qlora
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_torch
adam_beta1: 0.9
adam_beta2: 0.95
max_grad_norm: 0.1
lr_scheduler: cosine
learning_rate: 0.00001
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: 50
evals_per_epoch: 1
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
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-405B](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B) 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: 1e-05
- 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=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 4
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1