---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: NousResearch/Meta-Llama-3-70B
model-index:
- name: out/qlora-llama3-70b
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: NousResearch/Meta-Llama-3-70B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: Doctor-Shotgun/no-robots-sharegpt
    type: sharegpt
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out/qlora-llama3-70b

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

lora_r: 64
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
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: <|end_of_text|>
```

</details><br>

# out/qlora-llama3-70b

This model is a fine-tuned version of [NousResearch/Meta-Llama-3-70B](https://huggingface.co/NousResearch/Meta-Llama-3-70B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5377

## 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: 0.0002
- 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7144        | 0.02  | 1    | 1.8096          |
| 1.6886        | 0.25  | 12   | 1.5367          |
| 1.6174        | 0.49  | 24   | 1.5176          |
| 1.5848        | 0.74  | 36   | 1.5054          |
| 1.6542        | 0.98  | 48   | 1.5018          |
| 1.572         | 1.21  | 60   | 1.4993          |
| 1.5966        | 1.45  | 72   | 1.5007          |
| 1.5643        | 1.7   | 84   | 1.4981          |
| 1.6312        | 1.94  | 96   | 1.4980          |
| 1.5311        | 2.16  | 108  | 1.5027          |
| 1.519         | 2.41  | 120  | 1.5109          |
| 1.4034        | 2.65  | 132  | 1.5165          |
| 1.4658        | 2.9   | 144  | 1.5187          |
| 1.5434        | 3.11  | 156  | 1.5264          |
| 1.4608        | 3.35  | 168  | 1.5364          |
| 1.4529        | 3.6   | 180  | 1.5377          |
| 1.3893        | 3.85  | 192  | 1.5377          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.1
- Datasets 2.15.0
- Tokenizers 0.15.0