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winglian
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llama-3.1-8b-talk-like-a-pirate

PEFT
llama
Generated from Trainer
4-bit precision
bitsandbytes
Model card Files Community
llama-3.1-8b-talk-like-a-pirate
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---
base_model: NousResearch/Meta-Llama-3.1-8B
library_name: peft
license: llama3.1
tags:
- generated_from_trainer
model-index:
- name: outputs/lora-out
  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: `0.4.1`
```yaml
base_model: NousResearch/Meta-Llama-3.1-8B

load_in_4bit: true
strict: false

chat_template: llama3
datasets:
  - path: winglian/pirate-ultrachat-10k
    type: chat_template
    message_field_role: role
    message_field_content: content
dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./outputs/lora-out

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

adapter: qlora
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
  - embed_tokens
  - lm_head
peft_use_dora: true

wandb_project: pirate-ultrachat-llama31
wandb_entity: axolotl-ai

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
bf16: true
tf32: true

gradient_checkpointing: true
logging_steps: 1
flash_attention: true

warmup_ration: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
weight_decay: 0.0
deepspeed: deepspeed_configs/zero2.json
special_tokens:
  pad_token: "<|finetune_right_pad_id|>"

```

</details><br>

# outputs/lora-out

This model is a fine-tuned version of [NousResearch/Meta-Llama-3.1-8B](https://huggingface.co/NousResearch/Meta-Llama-3.1-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1247

## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6022        | 0.0202 | 1    | 1.5845          |
| 1.2173        | 0.9899 | 49   | 1.1328          |
| 0.9676        | 1.9798 | 98   | 1.1247          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.45.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1