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jplhughes2
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1a_meta-llama-Llama-3.1-405B-Instruct-fsdp-lr1e-5

PEFT
Safetensors
llama
axolotl
Generated from Trainer
4-bit precision
bitsandbytes
Model card Files Community
1a_meta-llama-Llama-3.1-405B-Instruct-fsdp-lr1e-5
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---
library_name: peft
license: llama3.1
base_model: meta-llama/Llama-3.1-405B-Instruct
tags:
- axolotl
- generated_from_trainer
datasets:
- jplhughes2/docs_only_30k_filtered
model-index:
- name: 1a_meta-llama-Llama-3.1-405B-Instruct-fsdp-lr1e-5
  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. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.6.0`
```yaml
# This works!

base_model: meta-llama/Llama-3.1-405B-Instruct
hub_model_id: jplhughes2/1a_meta-llama-Llama-3.1-405B-Instruct-fsdp-lr1e-5
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: 1a_meta-llama-Llama-3.1-405B-Instruct-fsdp-lr1e-5
output_dir: ./outputs/out/1a_meta-llama-Llama-3.1-405B-Instruct-fsdp-lr1e-5
# base_model:
# hub_model_id:
# load_in_8bit:
# load_in_4bit:
# adapter:
# wandb_name:
# output_dir:

tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false

datasets:
  - path: jplhughes2/docs_only_30k_filtered
    type: completion
    field: text
    split: train
dataset_prepared_path: last_run_prepared
# val_set_size: 0.05
test_datasets:
  - path: jplhughes2/docs_only_val_5k_filtered
    type: completion
    field: text
    split: train
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-faking
wandb_entity: academicsnyuperez
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4
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: 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|>

```

</details><br>

# 1a_meta-llama-Llama-3.1-405B-Instruct-fsdp-lr1e-5

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 jplhughes2/docs_only_30k_filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6041

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.323         | 0.0016 | 1    | 1.3262          |
| 0.648         | 0.3344 | 204  | 0.6514          |
| 0.6137        | 0.6689 | 408  | 0.6041          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.4.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0