---
base_model: NousResearch/Meta-Llama-3-8B
library_name: peft
license: other
tags:
- generated_from_trainer
model-index:
- name: outputs/llama3-8b-ht-v1-2
  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-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: QTeam/htxllama_1
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/llama3-8b-ht-v1-2

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

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: "ft-llama3-8b-v1"
wandb_entity: "htxqteam1-htx"
wandb_watch: "all"
wandb_name: 
wandb_log_model: "never"

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 10
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
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
   pad_token: <|end_of_text|>

```

</details><br>

# outputs/llama3-8b-ht-v1-2

This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5036

## 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: 3
- total_train_batch_size: 6
- total_eval_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1533        | 0.0161 | 1    | 2.1831          |
| 1.6416        | 0.2581 | 16   | 1.7333          |
| 1.6154        | 0.5161 | 32   | 1.6458          |
| 1.5155        | 0.7742 | 48   | 1.5807          |
| 1.5359        | 1.0323 | 64   | 1.5371          |
| 1.0746        | 1.2581 | 80   | 1.5888          |
| 1.0806        | 1.5161 | 96   | 1.5696          |
| 1.0348        | 1.7742 | 112  | 1.5536          |
| 1.0769        | 2.0323 | 128  | 1.5341          |
| 0.6608        | 2.2581 | 144  | 1.6201          |
| 0.6918        | 2.5161 | 160  | 1.6185          |
| 0.7203        | 2.7742 | 176  | 1.6154          |
| 0.7172        | 3.0323 | 192  | 1.6202          |
| 0.3914        | 3.2581 | 208  | 1.7162          |
| 0.4111        | 3.5161 | 224  | 1.7114          |
| 0.4091        | 3.7742 | 240  | 1.7177          |
| 0.4103        | 4.0323 | 256  | 1.7191          |
| 0.1996        | 4.2581 | 272  | 1.8387          |
| 0.1932        | 4.5161 | 288  | 1.8439          |
| 0.2185        | 4.7742 | 304  | 1.8510          |
| 0.2221        | 5.0323 | 320  | 1.8515          |
| 0.0968        | 5.2581 | 336  | 2.0317          |
| 0.0937        | 5.5161 | 352  | 2.0138          |
| 0.0973        | 5.7742 | 368  | 2.0274          |
| 0.083         | 6.0323 | 384  | 2.0257          |
| 0.0385        | 6.2581 | 400  | 2.1731          |
| 0.0411        | 6.5161 | 416  | 2.2114          |
| 0.0446        | 6.7742 | 432  | 2.2080          |
| 0.0426        | 7.0323 | 448  | 2.2194          |
| 0.0186        | 7.2581 | 464  | 2.4007          |
| 0.0186        | 7.5161 | 480  | 2.3837          |
| 0.0217        | 7.7742 | 496  | 2.3915          |
| 0.0201        | 8.0323 | 512  | 2.3953          |
| 0.0137        | 8.2581 | 528  | 2.4732          |
| 0.0158        | 8.5161 | 544  | 2.4896          |
| 0.0145        | 8.7742 | 560  | 2.4928          |
| 0.0145        | 9.0323 | 576  | 2.4964          |
| 0.0135        | 9.2581 | 592  | 2.5030          |
| 0.0149        | 9.5161 | 608  | 2.5036          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1