初始化项目，由ModelHub XC社区提供模型
Model: shirochange/kansaiben-qwen2.5-0.5b Source: Original Platform
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README.md
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README.md
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---
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language:
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- ja
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license: apache-2.0
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- kansai-dialect
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- japanese
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- fine-tuned
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- mlx
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- chat
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pipeline_tag: text-generation
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datasets:
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- shirochange/kansaiben
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---
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# kansaiben-qwen2.5-0.5b
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A fine-tuned model that responds in Kansai dialect (Osaka-ben). Based on Qwen2.5-0.5B-Instruct, trained on the [shirochange/kansaiben](https://huggingface.co/datasets/shirochange/kansaiben) dataset.
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関西弁（大阪弁）で話すAIアシスタントのファインチューニング済みモデルです。
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## Model Details
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| 項目 | 内容 |
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|---|---|
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| ベースモデル | [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) |
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| パラメータ数 | 0.5B |
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| ファインチューニング手法 | LoRA（MLX-LM） |
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| データセット | [shirochange/kansaiben](https://huggingface.co/datasets/shirochange/kansaiben) |
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| 学習データ件数 | 320件（シングルターン会話） |
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| 学習環境 | Apple M3 Max |
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## Training Hyperparameters
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| パラメータ | 値 |
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|---|---|
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| Iterations | 300 |
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| Batch size | 4 |
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| Learning rate | 1e-4 |
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| LoRA layers | 8 |
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| Best val loss | 1.566（iter 150） |
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## 使い方（Apple Silicon / MLX）
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このモデルは **Apple Silicon（M1/M2/M3）** 搭載のMac上で [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms/mlx_lm) を使って動作します。
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("shirochange/kansaiben-qwen2.5-0.5b")
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messages = [
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{"role": "system", "content": "あなたは関西弁（大阪弁）で話す、明るくて親しみやすいAIアシスタントです。どんな質問にも関西弁で自然に答えてください。"},
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{"role": "user", "content": "こんにちは！"},
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]
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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response = generate(model, tokenizer, prompt=prompt, max_tokens=100, verbose=False)
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print(response)
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# まいど！元気しとるか？
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```
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## 出力例
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| 入力 | 出力 |
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|---|---|
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| こんにちは！元気ですか？ | まいど！元気しとるか？ |
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| ありがとうございます！ | こちらこそおおきに！また話しかけてや。 |
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| 疲れました... | あんた、お疲れさんやで。ゆっくりしいや。 |
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| 大阪のおすすめ観光スポットを教えてください。 | そらもう、道頓堀でグリコの看板見て、たこ焼き食うのが最高やわ。 |
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## Limitations（制限事項）
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- **モデルサイズの限界**: 0.5B という小さなモデルのため、複雑な質問や長文の応答品質は限られます
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- **関西弁の不自然さ**: 稀に標準語が混じる、または不自然な関西弁表現が出力されることがあります
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- **学習データ量**: 320件という少量のデータでファインチューニングしているため、汎化性能には限界があります
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- **動作環境**: 提供コードは Apple Silicon（MLX）専用です。他の環境での動作は保証されません
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- **事実性**: ベースモデルの知識に依存するため、誤った情報を関西弁で出力する可能性があります
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## License
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このモデルは [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ライセンスのもとで公開されています。
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ベースモデルのライセンス（[Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)）も合わせてご確認ください。
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": [
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151645,
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151643
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],
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"sliding_window": 32768,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.43.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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],
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_p": 0.8,
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"top_k": 20,
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"transformers_version": "4.37.0"
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}
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3
tokenizer.json
Normal file
3
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Normal file
@@ -0,0 +1,3 @@
|
||||
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size 11421892
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29
tokenizer_config.json
Normal file
29
tokenizer_config.json
Normal file
@@ -0,0 +1,29 @@
|
||||
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}
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||||
Reference in New Issue
Block a user