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Tora-7B-v0.1

Text Generation
Transformers
Safetensors
mistral
text-generation-inference
Inference Endpoints
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Tora-7B-v0.1
File size: 3,547 Bytes 
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---
license: cc-by-nc-4.0
---
## License

非商用ライセンスで公開します。

## Chat Vector

```
Tora-7B-v0.1 = NTQAI/chatntq-ja-7b-v1.0 + (openchat/openchat-3.5-0106 - mistralai/Mistral-7B-v0.1)
```

## 実装

@jovyan様の実装を参考に下記のコードでモデルを作成しました。

```python
import torch
from transformers import AutoModelForCausalLM


def build_chat_vector_model(
    base_model_name,
    inst_model_name,
    target_model_name,
    skip_layers,
    ):

    base_model = AutoModelForCausalLM.from_pretrained(
        base_model_name,
        torch_dtype=torch.bfloat16,
        device_map="cpu",
    )
    inst_model = AutoModelForCausalLM.from_pretrained(
        inst_model_name,
        torch_dtype=torch.bfloat16,
        device_map="cpu",
    )

    target_model = AutoModelForCausalLM.from_pretrained(
        target_model_name,
        torch_dtype=torch.bfloat16,
        device_map="cuda",
    )

    # 英語ベースモデル
    for k, v in base_model.state_dict().items():
        print(k, v.shape)

    # 日本語継続事前学習モデル
    for k, v in target_model.state_dict().items():
        print(k, v.shape)

    # 除外対象
    skip_layers = ["model.embed_tokens.weight", "lm_head.weight"]

    for k, v in target_model.state_dict().items():
        # layernormも除外
        if (k in skip_layers) or ("layernorm" in k):
            continue
        chat_vector = inst_model.state_dict()[k] - base_model.state_dict()[k]
        new_v = v + chat_vector.to(v.device)
        v.copy_(new_v)

    target_model.save_pretrained("./chat_model")

    return


if __name__ == '__main__':

    base_model_name = "mistralai/Mistral-7B-v0.1"
    inst_model_name = "openchat/openchat-3.5-0106"
    target_model_name = "NTQAI/chatntq-ja-7b-v1.0"

    skip_layers = ["model.embed_tokens.weight", "lm_head.weight"]

    build_chat_vector_model(
        base_model_name=base_model_name,
        inst_model_name=inst_model_name,
        target_model_name=target_model_name,
        skip_layers=skip_layers
    )

```

## ベンチマーク (Japanese MT bench)
- single turnのみ評価

|model|category|score|ver|
|:---|:---|:---|:---|
|Tora-7B-v0.1|Writing|5.4|single-turn|
|Tora-7B-v0.1|Roleplay|6.6|single-turn|
|Tora-7B-v0.1|Reasoning|7.3|single-turn|
|Tora-7B-v0.1|Math|3.5|single-turn|
|Tora-7B-v0.1|Coding|4.7|single-turn|
|Tora-7B-v0.1|Extraction|6.3|single-turn|
|Tora-7B-v0.1|STEM|7.2|single-turn|
|Tora-7B-v0.1|Humanities|8.5|single-turn|

![image/png](https://cdn-uploads.huggingface.co/production/uploads/651e3f30ca333f3c8df692b8/tuFTNH1t65lqgpnS3TuiA.png)

## ベンチマーク (Nejumi leaderboard)

- runs.summary["mtbench_leaderboard_table"]の結果を転記

|model|category|score|
|:---|:---|:---|
|Tora-7B-v0.1|Writing|7.55|
|Tora-7B-v0.1|Roleplay|7.5|
|Tora-7B-v0.1|Reasoning|4.35|
|Tora-7B-v0.1|Math|2.95|
|Tora-7B-v0.1|Coding|3.7|
|Tora-7B-v0.1|Extraction|7.0|
|Tora-7B-v0.1|STEM|7.85|
|Tora-7B-v0.1|Humanities|9.65|
|Tora-7B-v0.1|AVG_mtbench|6.319|

- runs.summary["jaster_radar_table"]の結果を転記

|model|category|score|
|:---|:---|:---|
|Tora-7B-v0.1|NLI|0.588|
|Tora-7B-v0.1|QA|0.1708|
|Tora-7B-v0.1|RC|0.798|
|Tora-7B-v0.1|MC|0.25|
|Tora-7B-v0.1|EL|0.0|
|Tora-7B-v0.1|FA|0.1359|
|Tora-7B-v0.1|MR|0.2|


## 謝辞

ChatVectorの記事を執筆してくださった@jovyan様に深くお礼申し上げます。

## 参考

[Chat Vectorを使って日本語LLMをチャットモデルに改造する](https://qiita.com/jovyan/items/ee6affa5ee5bdaada6b4)