---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- generated_from_trainer
model-index:
- name: outputs/lr-5e6
  results: []
datasets:
- augmxnt/ultra-orca-boros-en-ja-v1
---
The was part of some LR ablations. It's not bad but you should probably prefer 8e-6


I ran the tests for 2 runs just to try to lower variance. These are all using temp 0.2, min_p 0.1, freq penalty 0.5

| Model                       | AVG Score | ELYZA100 | JA MT-Bench | Rakuda | Tengu-Bench | JA Char % |
|-----------------------------|-----------|----------|-------------|--------|-------------|-----------|
| shisa-v1-llama3-8b.lr-2e4   | 3.97      | 4.60     | 4.54        | 3.33   | 3.42        | 92.42%    |
| shisa-v1-llama3-8b.lr-5e5   | 5.73      | 6.28     | 6.45        | 5.37   | 4.81        | 90.93%    |
| shisa-v1-llama3-8b (2e5 avg)| 6.33      | 6.51     | 6.66        | 6.68   | 5.48        | 91.51%    |
| shisa-v1-llama3-8b.8e6      | 6.59      | 6.67     | 6.95        | 7.05   | 5.68        | 91.30%    |
| shisa-v1-llama3-8b.5e6      | 6.42      | 6.33     | 6.76        | 7.15   | 5.45        | 91.56%    |
| shisa-v1-llama3-8b.2e6      | 6.31      | 6.26     | 6.88        | 6.73   | 5.38        | 92.00%    |
* The 2e-4 and 5e-5 are definitely overtrained and perform significantly worse.
* 2e-5 is on the edge since weightwacher shows the embed as slightly overtrained for 2e-5, but NEFTune version is not
* 8e-6 performs the best, and 5e-6 also performed slightly better than 2e-5


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should probably proofread and complete it, then remove this comment. -->

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

axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: llama3
datasets:
  - path: augmxnt/ultra-orca-boros-en-ja-v1
    type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/lr-5e6

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: shisa-v1-llama3-8b.lr-5e6

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 5e-6

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

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

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 0
debug:
deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.00
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

```

</details><br>

# outputs/lr-5e6

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5020

## 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: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3951        | 0.0064 | 1    | 0.8645          |
| 0.891         | 0.5020 | 79   | 0.5705          |
| 0.8575        | 1.0040 | 158  | 0.5243          |
| 0.7296        | 1.4853 | 237  | 0.5079          |
| 0.7068        | 1.9873 | 316  | 0.4976          |
| 0.6618        | 2.4694 | 395  | 0.5020          |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1