---
license: llama3
datasets:
- augmxnt/ultra-orca-boros-en-ja-v1
language:
- ja
- en
base_model: meta-llama/Meta-Llama-3-70B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: shisa-llama3-70b-v1
  results: []
---

shisa-v2 Base Model ablation

Using a [fork](https://github.com/shisa-ai/shaberi) of [Lightblue's Shaberi benchmark framework](https://github.com/lightblue-tech/japanese_llm_eval):

| Model                                  | Average | ELYZA-tasks-100 | MT-Bench | Rakuda | Tengu-Bench |
|----------------------------------------|---------|-----------------|----------|--------|-------------|
| gpt-4-turbo-2024-04-09                 | 8.75    | 8.78            | 8.74     | 9.18   | 8.31        |
| CohereForAI/c4ai-command-r-plus        | 7.69    | 7.50            | 7.43     | 9.05   | 6.79        |
| **shisa-ai/shisa-llama3-70b-v1**       | **7.17**| **7.16**        | **7.45** | **7.98** | **6.09**  |
| karakuri-ai/karakuri-lm-70b-chat-v0.1  | 6.84    | 6.86            | 6.43     | 7.85   | 6.23        |
| lightblue/ao-karasu-72B                | 6.81    | 7.19            | 6.54     | 7.25   | 6.27        |
| **shisa-ai/shisa-llama3-8b-v1^**       | **6.29**| **6.62**        | **6.41** | **7.05**|**5.07**    |
| shisa-ai/shisa-swallowmx-13a47b-v1     | 6.17    | 6.48            | 6.07     | 7.11   | 5.03        |
| **shisa-ai/shisa-llama3-8b-v1**        | **6.10**| **6.52**        | **6.20** | **6.37**|**5.33**    |
| Rakuten/RakutenAI-7B-chat              | 5.58    | 5.92            | 4.60     | 6.58   | 5.24        |
| shisa-ai/shisa-gemma-7b-v1             | 5.64    | 6.50            | 5.42     | 5.10   | 5.55        |
| augmxnt/shisa-gamma-7b-v1              | 5.56    | 5.84            | 4.00     | 6.73   | 5.68        |
| lightblue/qarasu-14B-chat-plus-unleashed | 5.20  | 5.58            | 4.74     | 5.46   | 5.01        |
| cyberagent/calm2-7b-chat               | 4.76    | 4.90            | 3.58     | 5.75   | 4.81        |
| mistralai/Mistral-7B-Instruct-v0.2     | 4.69    | 5.78            | 4.65     | 3.80   | 4.53        |
| shisa-ai/shisa-yi1.5-9b-v1             | 4.63    | 5.98            | 4.28     | 3.26   | 5.00        |


<!-- 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: meta-llama/Meta-Llama-3-70B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

hub_model_id: shisa-ai/shisa-llama3-70b-v1
hub_strategy: end

use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: shisa-llama3-70b-v1

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/basemodel-llama3-70b

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 2e-5

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

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

warmup_ratio: 0.1
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 0
debug:
deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

```

</details><br>

# shisa-llama3-70b-v1

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4425

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 87
- num_epochs: 3

### Training results


| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2478        | 0.0033 | 1    | 0.7102          |
| 0.7516        | 0.5008 | 154  | 0.4325          |
| 0.7185        | 1.0016 | 308  | 0.3966          |
| 0.3708        | 1.4862 | 462  | 0.3976          |
| 0.3758        | 1.9870 | 616  | 0.3840          |
| 0.0928        | 2.4699 | 770  | 0.4425          |


### Framework versions

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