---
library_name: transformers
license: 
  - llama3.1
  - gemma
base_model: google/gemma-2-27b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fft-1
  results: []
---

# Llama-Gemma-2-27b-SFT-trial1

## 概要

[google/gemma-2-27b](https://huggingface.co/google/gemma-2-27b)を教師あり学習によりInstruction Tuningしたモデルです。

[松尾研大規模言語モデル講座2024](https://weblab.t.u-tokyo.ac.jp/lecture/course-list/large-language-model/)のコンペ用の提出モデル作成の一環として作成・公開しています。

This model is built with Llama and Qwen.

## 使用データセット

- [Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered](https://huggingface.co/datasets/Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered)
- [Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted](https://huggingface.co/datasets/Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted)
- [Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered](https://huggingface.co/datasets/Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered)
- [Aratako/Open-Platypus-Japanese-masked-formatted](https://huggingface.co/datasets/Aratako/Open-Platypus-Japanese-masked-formatted)
- [kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja)
- [kanhatakeyama/ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)
- [Aratako/magpie-ultra-v0.1-formatted](https://huggingface.co/datasets/Aratako/magpie-ultra-v0.1-formatted)
- [Aratako/orca-agentinstruct-1M-v1-selected](https://huggingface.co/datasets/Aratako/orca-agentinstruct-1M-v1-selected)
- [Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k)

## ライセンス

本モデルは学習に利用したデータの関係で以下のライセンスの影響を受けます。

- [META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/)を継承します。
- [Gemma Terms of Use](https://ai.google.dev/gemma/terms)を継承します。
- [Qwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE)の影響を受けます。ライセンスは継承しませんが、「Built with Qwen」のような文言を記載する必要があります。

## 学習に関する詳細

本モデルの学習には[axolotl](https://github.com/axolotl-ai-cloud/axolotl)を使いました。パラメータ等の学習の設定は下記の自動生成された記述をご確認ください。

<!-- 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: `0.5.2`
```yaml
base_model: google/gemma-2-27b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

hub_model_id: Aratako/fft-1
hub_strategy: "end"
push_dataset_to_hub:
hf_use_auth_token: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_cross_entropy: false
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: gemma

datasets:
  - path: Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content
  - path: Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted
    type: chat_template
    field_messages: conversations
    message_field_role: role
    message_field_content: content
  - path: Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered
    type: chat_template
    field_messages: conversations
    message_field_role: role
    message_field_content: content
  - path: Aratako/Open-Platypus-Japanese-masked-formatted
    type: chat_template
    field_messages: conversations
    message_field_role: role
    message_field_content: content
  - path: kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content
  - path: kanhatakeyama/ramdom-to-fixed-multiturn-Calm3
    split: 20240806filtered
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content
  - path: Aratako/magpie-ultra-v0.1-formatted
    type: chat_template
    field_messages: conversations
    message_field_role: role
    message_field_content: content
  - path: Aratako/orca-agentinstruct-1M-v1-selected
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content
  - path: Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content

shuffle_merged_datasets: true
dataset_prepared_path: /workspace/data/fft-data
val_set_size: 0.003
output_dir: /workspace/data/27b-fft-out-1

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

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: 27b-fft
wandb_entity: aratako-lm
wandb_watch:
wandb_name: attempt-01
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 8
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: 
cosine_min_lr_ratio: 0.1
learning_rate: 0.00001

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

gradient_checkpointing: true
early_stopping_patience:
auto_resume_from_checkpoints: true
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

save_strategy: steps
save_steps: 100
save_total_limit: 2

warmup_steps: 10
eval_steps: 100
eval_batch_size: 1
eval_table_size:
eval_max_new_tokens:
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
  pad_token: <pad>

```

</details><br>

# fft-1

This model is a fine-tuned version of [google/gemma-2-27b](https://huggingface.co/google/gemma-2-27b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6122

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 224
- total_eval_batch_size: 7
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9427        | 0.0020 | 1    | 0.9940          |
| 0.6566        | 0.2043 | 100  | 0.6648          |
| 0.6609        | 0.4086 | 200  | 0.6430          |
| 0.6457        | 0.6129 | 300  | 0.6306          |
| 0.6322        | 0.8172 | 400  | 0.6203          |
| 0.5082        | 1.0204 | 500  | 0.6238          |
| 0.5348        | 1.2247 | 600  | 0.6212          |
| 0.5253        | 1.4290 | 700  | 0.6181          |
| 0.5136        | 1.6333 | 800  | 0.6147          |
| 0.5125        | 1.8376 | 900  | 0.6122          |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.3.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3