---
library_name: peft
license: llama3.1
base_model: meta-llama/Llama-3.1-405B-Instruct
tags:
- axolotl
- base_model:adapter:meta-llama/Llama-3.1-405B-Instruct
- lora
- transformers
datasets:
- Taywon/A2plus
pipeline_tag: text-generation
model-index:
- name: llama-405b-honly-A2plus
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.16.1`
```yaml
base_model: meta-llama/Llama-3.1-405B-Instruct
hub_model_id: Taywon/llama-405b-honly-A2plus
load_in_8bit: false
load_in_4bit: false
adapter: lora
lora_model_dir: jplhughes2/1a_meta-llama-Llama-3.1-405B-Instruct-fsdp-lr1e-5
lora_on_cpu: true
wandb_name: llama405b-axolotl-honly-h200-A2plus
output_dir: ./outputs/llama-405b-honly-h200-A2plus

tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false

datasets:
  - path: Taywon/A2plus
    type: completion
    field: text
    split: train
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
save_safetensors: true

sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true

lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true

wandb_mode:
wandb_project: alignment-theater
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00001

train_on_inputs: false
group_by_length: false
bf16: true
tf32: true

gradient_checkpointing: false
logging_steps: 1
flash_attention: true

warmup_steps: 10
saves_per_epoch: 1
weight_decay: 0.01

fsdp_version: 2
fsdp_config:
  offload_params: true
  cpu_ram_efficient_loading: true
  auto_wrap_policy: TRANSFORMER_BASED_WRAP
  transformer_layer_cls_to_wrap: LlamaDecoderLayer
  state_dict_type: FULL_STATE_DICT
  reshard_after_forward: true
  activation_checkpointing: true

special_tokens:
  pad_token: <|finetune_right_pad_id|>

```

</details><br>

# llama-405b-honly-A2plus

This model is a fine-tuned version of [meta-llama/Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) on the Taywon/A2plus 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: 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
- training_steps: 316

### Training results



### Framework versions

- PEFT 0.19.0
- Transformers 5.5.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2