Upload folder using huggingface_hub
Browse files- README.md +145 -3
- config.json +36 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +298 -0
- rng_state.pth +3 -0
- scheduler.pt +3 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +172 -0
README.md
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---
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license: apache-2.0
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---
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license: apache-2.0
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language:
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- ja
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base_model:
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- sbintuitions/sarashina2.2-3b-instruct-v0.1
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pipeline_tag: text-classification
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---
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# cyberagent/ca-reward-3b-ja
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- 軽量な日本語報酬モデルの開発を目的として実装したモデルを公開する。
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- 既存の指示文と新たに合成した指示文に対して、応答文を複数生成し、llm-as-a-judgeで疑似選好ラベルを付与することで疑似選好データセットを作成した。
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- 上記の疑似選好データセットを分類するモデルを学習することで、指示文に対する応答文の好ましさを定量化する報酬モデルを作成した。
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## 評価
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- 人手で選好ラベル(好ましい応答文か、好ましくない応答文)が付与された既存のデータセットを収集し、選好ラベルの分類精度(Accuracy)を評価した。
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- 既存の報酬モデルによる分類精度と、`gpt-4o-2024-08-06`を用いたllm-as-a-judgeによる分類精度を記載した。
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| | [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3) | [llm-jp-chatbot-arena](https://huggingface.co/datasets/llm-jp/llm-jp-chatbot-arena-conversations) |
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| --- | --- | --- |
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| OpenAssistant/reward-model-deberta-v3-large-v2 | 0.5124 | 0.5610 |
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| Skywork/Skywork-Reward-Gemma-2-27B-v0.2 | 0.6854 | 0.5166 |
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| catlm-sarashina2.2-3b-reward-v0.1 | 0.7032 | 0.5366 |
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| cyberagent/calm3-22b-chat-selfimprove-experimental | 0.7216 | 0.6075 |
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| gpt-4o-2024-08-06 | 0.7845 | 0.6842 |
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<details>
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<summary>環境構築</summary>
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- 開発環境: Ubuntu 24.04.2 LTS
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- ライブラリのインストール
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```jsx
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pip install -U -q pip
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pip install -q torch==2.8.0
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pip install -q transformers==4.51.3
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pip install -q accelerate==0.29.3
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pip install -q sentencepiece==0.2.0
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```
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- バージョンの確認
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```jsx
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import torch, transformers, accelerate, tokenizers, sentencepiece
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print(torch.__version__)
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print(transformers.__version__)
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print(accelerate.__version__)
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print(tokenizers.__version__)
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print(sentencepiece.__version__)
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# 2.8.0+cu128
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# 4.51.3
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# 0.29.3
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# 0.21.4
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# 0.2.0
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```
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</details>
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## コードサンプル
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```python
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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model = AutoModelForSequenceClassification.from_pretrained(
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"cyberagent/ca-reward-3b-ja",
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device_map="auto",
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num_labels=1,
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)
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tokenizer = AutoTokenizer.from_pretrained("cyberagent/ca-reward-3b-ja")
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prompt = """手軽に栄養を補給できる食事を教えてください。"""
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response1 = """栄養補給が手軽にできるものとしては、野菜たっぷりのスムージー、ゆで卵とサラダ、納豆ご飯などがおすすめです。特に納豆は手間なく良質なタンパク質が摂れますよ。お身体を大切にしてくださいね。"""
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response2 = """手軽な栄養補給なら冷凍食品でいいと思います。レンジで温めるだけだし、時間がない時はコンビニ弁当でも悪くないですよ。"""
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chat1 = [{"role": "user", "content": prompt}, {"role": "assistant", "content": response1}]
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chat2 = [{"role": "user", "content": prompt}, {"role": "assistant", "content": response2}]
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chat1_formatted = tokenizer.apply_chat_template(chat1, tokenize=False)
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chat2_formatted = tokenizer.apply_chat_template(chat2, tokenize=False)
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chat1_tokenized = tokenizer(chat1_formatted, return_tensors="pt", max_length=4096, truncation=True, padding="max_length",).to(model.device)
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chat2_tokenized = tokenizer(chat2_formatted, return_tensors="pt", max_length=4096, truncation=True, padding="max_length",).to(model.device)
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with torch.no_grad():
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chat1_score = model(**chat1_tokenized).logits.item()
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chat2_score = model(**chat2_tokenized).logits.item()
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print(f"Score for response 1: {chat1_score}")
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print(f"Score for response 2: {chat2_score}")
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# Score for response 1: 1.2595189809799194
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# Score for response 2: -2.917454242706299
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```
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## 学習モデル
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- [sbintuitions/sarashina2.2-3b-instruct-v0.1](https://huggingface.co/sbintuitions/sarashina2.2-3b-instruct-v0.1)をベースモデルに用いた。
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## 学習データセット
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- [ChatBotArena](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations)のinstruction文と、社内で作成したデータセットを用いた。
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- [cyberagent/calm3-22b-chat-selfimprove-experimental](https://huggingface.co/cyberagent/calm3-22b-chat-selfimprove-experimental)によるLLM-as-a-judgeを用いて、作成したデータセットに対して疑似選好ラベルを付与して、疑似選好データセットを作成した。
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## 報酬モデル単体の分類性能の評価に使用したデータセット
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| 評価データセット名 | サンプル数 | 備考 |
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| --- | --- | --- |
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| [llm-jp/llm-jp-chatbot-arena-conversations](https://huggingface.co/datasets/llm-jp/llm-jp-chatbot-arena-conversations) | 448 | アノテーション結果がどちらも悪い、どち���も良い、同程度(winnerカラムがtie, tie(both good), tie(both bad))のサンプルは評価から除いた。シングルターンのみの学習データで訓練したため、評価にはシングルターンのサンプルのみ使用した。 |
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| [nvidia/HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3) | 283 | 日本語性能を測るため、日本語サンプル(languageカラム=japanese)のみ評価に使用した。学習データセットにgemmaによって生成された応答文が含まれると記載されていたため、学習には用いず、train/validation split両方の分類性能の平均値を評価に使用した。シングルターンのみの学習データで訓練したため、評価にはシングルターンのサンプルのみ使用した。 |
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## 学習時のハイパーパラメータ
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- learning_rateは[8e-07, 1e-06, 2.5e-06, 5e-06]で学習し、learning_rate=5e-06のモデルを採用した。
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| | training type | batchsize | learning_rate | lr_scheduler | num_epoch | max_length | num_of_training_sample | num_of_validation_sample | hardware |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| ca-reward-3b-ja | full parameter tuning | 32 | 5e-06 | linear | 1 | 4096 | 601,348 | 8,000 | NVIDIA_A100_80GB |
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## リリース
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v0.1, 2025/08/12
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## Authors
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- 三橋亮太(corresponding author)
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- 開発中にフィードバックをいただいた皆様:陣内佑、坂本充生、森村哲郎、阿部拳之、蟻生開人、藤本悠雅、暮石航大
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## 引用
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```tex
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@misc{cyberagent-ca-reward-3b-ja,
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title={cyberagent/ca-reward-3b-ja},
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url={https://huggingface.co/cyberagent/ca-reward-3b-ja},
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author={Ryota Mitsuhashi},
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year={2025},
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}
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```
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## ライセンス
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Apache-2.0
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config.json
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{
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"architectures": [
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"LlamaForSequenceClassification"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"head_dim": 160,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"label2id": {
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"LABEL_0": 0
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},
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 3,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"use_cache": false,
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"vocab_size": 102400
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}
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model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6011a7c7fb45bbe7bdbc2e878fe70304f08c569c3d11766aa05f8d6969230caf
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size 4987572280
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model-00002-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1199397688
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model.safetensors.index.json
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30 |
+
},
|
31 |
+
"3": {
|
32 |
+
"content": "<pad>",
|
33 |
+
"lstrip": false,
|
34 |
+
"normalized": false,
|
35 |
+
"rstrip": false,
|
36 |
+
"single_word": false,
|
37 |
+
"special": true
|
38 |
+
},
|
39 |
+
"4": {
|
40 |
+
"content": "<sep>",
|
41 |
+
"lstrip": false,
|
42 |
+
"normalized": false,
|
43 |
+
"rstrip": false,
|
44 |
+
"single_word": false,
|
45 |
+
"special": true
|
46 |
+
},
|
47 |
+
"5": {
|
48 |
+
"content": "<mask>",
|
49 |
+
"lstrip": false,
|
50 |
+
"normalized": false,
|
51 |
+
"rstrip": false,
|
52 |
+
"single_word": false,
|
53 |
+
"special": true
|
54 |
+
},
|
55 |
+
"6": {
|
56 |
+
"content": "<cls>",
|
57 |
+
"lstrip": false,
|
58 |
+
"normalized": false,
|
59 |
+
"rstrip": false,
|
60 |
+
"single_word": false,
|
61 |
+
"special": true
|
62 |
+
},
|
63 |
+
"7": {
|
64 |
+
"content": "<|system|>",
|
65 |
+
"lstrip": false,
|
66 |
+
"normalized": false,
|
67 |
+
"rstrip": false,
|
68 |
+
"single_word": false,
|
69 |
+
"special": false
|
70 |
+
},
|
71 |
+
"8": {
|
72 |
+
"content": "<|assistant|>",
|
73 |
+
"lstrip": false,
|
74 |
+
"normalized": false,
|
75 |
+
"rstrip": false,
|
76 |
+
"single_word": false,
|
77 |
+
"special": false
|
78 |
+
},
|
79 |
+
"9": {
|
80 |
+
"content": "<|user|>",
|
81 |
+
"lstrip": false,
|
82 |
+
"normalized": false,
|
83 |
+
"rstrip": false,
|
84 |
+
"single_word": false,
|
85 |
+
"special": false
|
86 |
+
},
|
87 |
+
"10": {
|
88 |
+
"content": "<|available_tools|>",
|
89 |
+
"lstrip": false,
|
90 |
+
"normalized": false,
|
91 |
+
"rstrip": false,
|
92 |
+
"single_word": false,
|
93 |
+
"special": false
|
94 |
+
},
|
95 |
+
"11": {
|
96 |
+
"content": "<|tool_calls|>",
|
97 |
+
"lstrip": false,
|
98 |
+
"normalized": false,
|
99 |
+
"rstrip": false,
|
100 |
+
"single_word": false,
|
101 |
+
"special": false
|
102 |
+
},
|
103 |
+
"12": {
|
104 |
+
"content": "<|tool_results|>",
|
105 |
+
"lstrip": false,
|
106 |
+
"normalized": false,
|
107 |
+
"rstrip": false,
|
108 |
+
"single_word": false,
|
109 |
+
"special": false
|
110 |
+
},
|
111 |
+
"13": {
|
112 |
+
"content": "<|code|>",
|
113 |
+
"lstrip": false,
|
114 |
+
"normalized": false,
|
115 |
+
"rstrip": false,
|
116 |
+
"single_word": false,
|
117 |
+
"special": false
|
118 |
+
},
|
119 |
+
"14": {
|
120 |
+
"content": "<|file|>",
|
121 |
+
"lstrip": false,
|
122 |
+
"normalized": false,
|
123 |
+
"rstrip": false,
|
124 |
+
"single_word": false,
|
125 |
+
"special": false
|
126 |
+
},
|
127 |
+
"102397": {
|
128 |
+
"content": "<|prefix|>",
|
129 |
+
"lstrip": false,
|
130 |
+
"normalized": false,
|
131 |
+
"rstrip": false,
|
132 |
+
"single_word": false,
|
133 |
+
"special": false
|
134 |
+
},
|
135 |
+
"102398": {
|
136 |
+
"content": "<|suffix|>",
|
137 |
+
"lstrip": false,
|
138 |
+
"normalized": false,
|
139 |
+
"rstrip": false,
|
140 |
+
"single_word": false,
|
141 |
+
"special": false
|
142 |
+
},
|
143 |
+
"102399": {
|
144 |
+
"content": "<|middle|>",
|
145 |
+
"lstrip": false,
|
146 |
+
"normalized": false,
|
147 |
+
"rstrip": false,
|
148 |
+
"single_word": false,
|
149 |
+
"special": false
|
150 |
+
}
|
151 |
+
},
|
152 |
+
"bos_token": "<s>",
|
153 |
+
"chat_template": "\n{%- set user_messages = messages | selectattr('role', 'equalto', 'user') | list %}\n{%- macro output_available_tools(tools, message) %}\n{%- if tools and (message == user_messages[-1]) %}\n {{- '<|available_tools|>[' }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- \"{\" }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- \"'\" + key + \"': '\" + val + \"'\" }}\n {%- else %}\n {{- \"'\" + key + \"': \" + val|string }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- eos_token -}}\n{%- endif %}\n{%- endmacro %}\n\n{%- macro output_tool_results(tool_results) %}\n{{- '<|tool_results|>[' }}\n{%- for tool_result in tool_results %}\n {{- \"{'content': \" + tool_result.content|string + \", 'call_id': '\" + tool_result.call_id + \"'}\" }}\n{%- endfor %}\n{{- ']' }}\n{{- eos_token -}}\n{%- endmacro %}\n\n{%- macro output_tool_calls(tool_calls) %}\n{{- '<|tool_calls|>[' }}\n{%- for tool_call in tool_calls %}\n {{- \"{'id': '\" + tool_call.id + \"', 'name': '\" + tool_call.name + \"', 'arguments': \" + tool_call.arguments|string + '}' }}\n{%- endfor %}\n{{- ']' }}\n{%- endmacro %}\n\n{%- for message in messages %}\n {%- if message['role'] == 'user' %}\n {%- if tools is defined %}\n {{- output_available_tools(tools, message) }}\n {%- endif %}\n {{- '<|user|>' + message['content'] + eos_token -}}\n {%- elif message['role'] == 'system' %}\n {{- '<|system|>' + message['content'] + eos_token -}}\n {%- elif message['role'] == 'assistant' %}\n {% set assistant_content = \"\" %}\n {%- if message.content is defined %}\n {% set assistant_content = message.content %}\n {%- endif %}\n {%- if message.tool_calls is defined and message.tool_calls -%}\n {{- '<|assistant|>' + assistant_content + output_tool_calls(message['tool_calls']) + eos_token -}}\n {%- else %}\n {{- '<|assistant|>' + assistant_content + eos_token }}\n {%- endif %}\n {%- elif message['role'] == 'tool_results' %}\n {{- output_tool_results(message.tool_results) }}\n {%- endif %}\n{%- if loop.last and add_generation_prompt -%}\n {{- '<|assistant|>' -}}\n{%- endif -%}\n{%- endfor %}\n",
|
154 |
+
"clean_up_tokenization_spaces": false,
|
155 |
+
"cls_token": "<cls>",
|
156 |
+
"do_lower_case": false,
|
157 |
+
"eos_token": "</s>",
|
158 |
+
"extra_ids": 0,
|
159 |
+
"extra_special_tokens": {},
|
160 |
+
"keep_accents": true,
|
161 |
+
"legacy": false,
|
162 |
+
"mask_token": "<mask>",
|
163 |
+
"model_max_length": 1000000000000000019884624838656,
|
164 |
+
"pad_token": "<pad>",
|
165 |
+
"padding_side": "right",
|
166 |
+
"sep_token": "<sep>",
|
167 |
+
"sp_model_kwargs": {},
|
168 |
+
"spaces_between_special_tokens": false,
|
169 |
+
"tokenizer_class": "LlamaTokenizer",
|
170 |
+
"unk_token": "<unk>",
|
171 |
+
"use_default_system_prompt": false
|
172 |
+
}
|