OpenVINO
gemma2
alignment-handbook
Generated from Trainer
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README.md ADDED
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+ ---
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+ base_model: google/gemma-2-9b-it
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+ tags:
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+ - alignment-handbook
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+ - generated_from_trainer
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+ datasets:
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+ - princeton-nlp/gemma2-ultrafeedback-armorm
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+ model-index:
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+ - name: princeton-nlp/gemma-2-9b-it-SimPO
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+ results: []
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+ license: mit
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+ ---
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+
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+ # OpenVINO IR model with int8 quantization
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+
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+ Model definition for LocalAI:
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+ ```
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+ name: gemma-2-9b-it-SimPO
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+ backend: transformers
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+ parameters:
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+ model: fakezeta/gemma-2-9b-it-SimPO-ov-int8
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+ context_size: 8192
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+ type: OVModelForCausalLM
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+ template:
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+ use_tokenizer_template: true
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+ ```
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+
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+ To run the model directly with LocalAI:
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+ ```
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+ local-ai run huggingface://fakezeta/gemma-2-9b-it-SimPO-ov-int8/model.yaml
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+ ```
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+
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+ # gemma-2-9b-it-SimPO Model Card
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+
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+ SimPO (Simple Preference Optimization) is an offline preference optimization algorithm designed to enhance the training of large language models (LLMs) with preference optimization datasets. SimPO aligns the reward function with the generation likelihood, eliminating the need for a reference model and incorporating a target reward margin to boost performance. Please refer to our [preprint](https://arxiv.org/pdf/2405.14734) and [github repo](https://github.com/princeton-nlp/SimPO) for more details.
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ We fine-tuned [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on [princeton-nlp/gemma2-ultrafeedback-armorm](https://huggingface.co/datasets/princeton-nlp/gemma2-ultrafeedback-armorm) with the SimPO objective.
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+
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+ - **Developed by:** Yu Meng, Mengzhou Xia, Danqi Chen
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+ - **Model type:** Causal Language Model
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+ - **License:** gemma
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+ - **Finetuned from model:** [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
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+
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+ ### Model Sources
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** https://github.com/princeton-nlp/SimPO
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+ - **Paper:** https://arxiv.org/pdf/2405.14734
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+
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+
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+ ## How to Get Started with the Model
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+ ```
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+ import torch
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+ from transformers import pipeline
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+
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+ model_id = "princeton-nlp/gemma-2-9b-it-SimPO"
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+
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+ generator = pipeline(
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+ "text-generation",
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+ model=model_id,
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+ model_kwargs={"torch_dtype": torch.bfloat16},
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+ device="cuda",
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+ )
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+ outputs = generator([{"role": "user", "content": "What's the difference between llamas and alpacas?"}],
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+ do_sample=False,
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+ eos_token_id=[generator.tokenizer.convert_tokens_to_ids("<end_of_turn>"), generator.tokenizer.eos_token_id],
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+ max_new_tokens=200)
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+ print(outputs[0]['generated_text'])
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+ ```
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ We use [princeton-nlp/gemma2-ultrafeedback-armorm](https://huggingface.co/datasets/princeton-nlp/gemma2-ultrafeedback-armorm) as the preference optimization dataset.
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+
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+ #### Training Hyperparameters
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+
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+ The hyperparameters used can be found in the [training script](https://github.com/princeton-nlp/SimPO/blob/main/training_configs/gemma-2-9b-it-simpo.yaml).
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+
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+ #### Speeds, Sizes, Times
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+
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+ Fine-tuning the [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on [princeton-nlp/gemma2-ultrafeedback-armorm](https://huggingface.co/datasets/princeton-nlp/gemma2-ultrafeedback-armorm) takes around 100 mins to finish on 8xH100 GPUs.
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+
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+ ## Evaluation Results
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+
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+
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+ | models | AE2 LC | AE2 WR | AE2 Length | AH | AH Length | GSM | GSM Length | MMLU | MMLU Length |
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+ |-----------------------------------|:------:|:------:|:----------:|:----:|:---------:|:----:|:----------:|:----:|:-----------:|
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+ | [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) | 51.1 | 38.1 | 1571 | 40.8 | 545 | 87.4 | 395 | 72.7 | 515 |
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+ | [princeton-nlp/gemma-2-9b-it-DPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-DPO) | 67.8 | 65.4 | 2016 | 58.9 | 717 | 88.5 | 392 | 72.2 | 624 |
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+ | [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO) | 72.4 | 65.9 | 1833 | 59.1 | 693 | 88.0 | 341 | 72.2 | 441 |
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+
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+
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+ ## Technical Specifications
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+
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+ ### Model Architecture and Objective
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+
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+ The model architecture is based on [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it). We use the SimPO training objective proposed in our [preprint](https://arxiv.org/pdf/2405.14734).
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+
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+ #### Hardware
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+
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+ We used 8xH100 GPUs for model training.
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+
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+ #### Software
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+
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+ Training was done using the [alignment-handbook](https://github.com/huggingface/alignment-handbook) library.
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+
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+ ## Citation
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+
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+ gemma model:
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+ ```
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+ @article{gemma_2024,
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+ title={Gemma},
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+ url={https://www.kaggle.com/m/3301},
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+ DOI={10.34740/KAGGLE/M/3301},
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+ publisher={Kaggle},
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+ author={Gemma Team},
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+ year={2024}
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+ }
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+ ```
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+
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+ SimPO paper:
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+ ```
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+ @article{meng2024simpo,
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+ title={{SimPO}: Simple preference optimization with a reference-free reward},
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+ author={Meng, Yu and Xia, Mengzhou and Chen, Danqi},
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+ journal={arXiv preprint arXiv:2405.14734},
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+ year={2024}
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+ }
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+ ```
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+
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+ UltraFeedback paper:
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+ ```
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+ @article{cui2023ultrafeedback,
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+ title={{UltraFeedback}: Boosting language models with high-quality feedback},
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+ author={Cui, Ganqu and Yuan, Lifan and Ding, Ning and Yao, Guanming and Zhu, Wei and Ni, Yuan and Xie, Guotong and Liu, Zhiyuan and Sun, Maosong},
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+ journal={arXiv preprint arXiv:2310.01377},
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+ year={2023}
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+ }
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+ ```
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+
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+ ArmoRM paper:
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+ ```
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+ @article{wang2024interpretable,
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+ title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts},
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+ author={Wang, Haoxiang and Xiong, Wei and Xie, Tengyang and Zhao, Han and Zhang, Tong},
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+ journal={arXiv preprint arXiv:2406.12845},
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+ year={2024}
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+ }
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+ ```
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+ name: gemma-2-9b-it-SimPO
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+ backend: transformers
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+ parameters:
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+ model: fakezeta/gemma-2-9b-it-SimPO-ov-int8
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+ context_size: 8192
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+ type: OVModelForCausalLM
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+ template:
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+ use_tokenizer_template: true
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1480
+ "normalized": false,
1481
+ "rstrip": false,
1482
+ "single_word": false,
1483
+ "special": false
1484
+ },
1485
+ "185": {
1486
+ "content": "<h1>",
1487
+ "lstrip": false,
1488
+ "normalized": false,
1489
+ "rstrip": false,
1490
+ "single_word": false,
1491
+ "special": false
1492
+ },
1493
+ "186": {
1494
+ "content": "<h2>",
1495
+ "lstrip": false,
1496
+ "normalized": false,
1497
+ "rstrip": false,
1498
+ "single_word": false,
1499
+ "special": false
1500
+ },
1501
+ "187": {
1502
+ "content": "<h3>",
1503
+ "lstrip": false,
1504
+ "normalized": false,
1505
+ "rstrip": false,
1506
+ "single_word": false,
1507
+ "special": false
1508
+ },
1509
+ "188": {
1510
+ "content": "<h4>",
1511
+ "lstrip": false,
1512
+ "normalized": false,
1513
+ "rstrip": false,
1514
+ "single_word": false,
1515
+ "special": false
1516
+ },
1517
+ "189": {
1518
+ "content": "<h5>",
1519
+ "lstrip": false,
1520
+ "normalized": false,
1521
+ "rstrip": false,
1522
+ "single_word": false,
1523
+ "special": false
1524
+ },
1525
+ "190": {
1526
+ "content": "<h6>",
1527
+ "lstrip": false,
1528
+ "normalized": false,
1529
+ "rstrip": false,
1530
+ "single_word": false,
1531
+ "special": false
1532
+ },
1533
+ "191": {
1534
+ "content": "<blockquote>",
1535
+ "lstrip": false,
1536
+ "normalized": false,
1537
+ "rstrip": false,
1538
+ "single_word": false,
1539
+ "special": false
1540
+ },
1541
+ "192": {
1542
+ "content": "</h1>",
1543
+ "lstrip": false,
1544
+ "normalized": false,
1545
+ "rstrip": false,
1546
+ "single_word": false,
1547
+ "special": false
1548
+ },
1549
+ "193": {
1550
+ "content": "</h2>",
1551
+ "lstrip": false,
1552
+ "normalized": false,
1553
+ "rstrip": false,
1554
+ "single_word": false,
1555
+ "special": false
1556
+ },
1557
+ "194": {
1558
+ "content": "</h3>",
1559
+ "lstrip": false,
1560
+ "normalized": false,
1561
+ "rstrip": false,
1562
+ "single_word": false,
1563
+ "special": false
1564
+ },
1565
+ "195": {
1566
+ "content": "</h4>",
1567
+ "lstrip": false,
1568
+ "normalized": false,
1569
+ "rstrip": false,
1570
+ "single_word": false,
1571
+ "special": false
1572
+ },
1573
+ "196": {
1574
+ "content": "</h5>",
1575
+ "lstrip": false,
1576
+ "normalized": false,
1577
+ "rstrip": false,
1578
+ "single_word": false,
1579
+ "special": false
1580
+ },
1581
+ "197": {
1582
+ "content": "</h6>",
1583
+ "lstrip": false,
1584
+ "normalized": false,
1585
+ "rstrip": false,
1586
+ "single_word": false,
1587
+ "special": false
1588
+ },
1589
+ "198": {
1590
+ "content": "</blockquote>",
1591
+ "lstrip": false,
1592
+ "normalized": false,
1593
+ "rstrip": false,
1594
+ "single_word": false,
1595
+ "special": false
1596
+ },
1597
+ "199": {
1598
+ "content": "<strong>",
1599
+ "lstrip": false,
1600
+ "normalized": false,
1601
+ "rstrip": false,
1602
+ "single_word": false,
1603
+ "special": false
1604
+ },
1605
+ "200": {
1606
+ "content": "<em>",
1607
+ "lstrip": false,
1608
+ "normalized": false,
1609
+ "rstrip": false,
1610
+ "single_word": false,
1611
+ "special": false
1612
+ },
1613
+ "201": {
1614
+ "content": "<b>",
1615
+ "lstrip": false,
1616
+ "normalized": false,
1617
+ "rstrip": false,
1618
+ "single_word": false,
1619
+ "special": false
1620
+ },
1621
+ "202": {
1622
+ "content": "<i>",
1623
+ "lstrip": false,
1624
+ "normalized": false,
1625
+ "rstrip": false,
1626
+ "single_word": false,
1627
+ "special": false
1628
+ },
1629
+ "203": {
1630
+ "content": "<u>",
1631
+ "lstrip": false,
1632
+ "normalized": false,
1633
+ "rstrip": false,
1634
+ "single_word": false,
1635
+ "special": false
1636
+ },
1637
+ "204": {
1638
+ "content": "<s>",
1639
+ "lstrip": false,
1640
+ "normalized": false,
1641
+ "rstrip": false,
1642
+ "single_word": false,
1643
+ "special": false
1644
+ },
1645
+ "205": {
1646
+ "content": "<sub>",
1647
+ "lstrip": false,
1648
+ "normalized": false,
1649
+ "rstrip": false,
1650
+ "single_word": false,
1651
+ "special": false
1652
+ },
1653
+ "206": {
1654
+ "content": "<sup>",
1655
+ "lstrip": false,
1656
+ "normalized": false,
1657
+ "rstrip": false,
1658
+ "single_word": false,
1659
+ "special": false
1660
+ },
1661
+ "207": {
1662
+ "content": "<code>",
1663
+ "lstrip": false,
1664
+ "normalized": false,
1665
+ "rstrip": false,
1666
+ "single_word": false,
1667
+ "special": false
1668
+ },
1669
+ "208": {
1670
+ "content": "</strong>",
1671
+ "lstrip": false,
1672
+ "normalized": false,
1673
+ "rstrip": false,
1674
+ "single_word": false,
1675
+ "special": false
1676
+ },
1677
+ "209": {
1678
+ "content": "</em>",
1679
+ "lstrip": false,
1680
+ "normalized": false,
1681
+ "rstrip": false,
1682
+ "single_word": false,
1683
+ "special": false
1684
+ },
1685
+ "210": {
1686
+ "content": "</b>",
1687
+ "lstrip": false,
1688
+ "normalized": false,
1689
+ "rstrip": false,
1690
+ "single_word": false,
1691
+ "special": false
1692
+ },
1693
+ "211": {
1694
+ "content": "</i>",
1695
+ "lstrip": false,
1696
+ "normalized": false,
1697
+ "rstrip": false,
1698
+ "single_word": false,
1699
+ "special": false
1700
+ },
1701
+ "212": {
1702
+ "content": "</u>",
1703
+ "lstrip": false,
1704
+ "normalized": false,
1705
+ "rstrip": false,
1706
+ "single_word": false,
1707
+ "special": false
1708
+ },
1709
+ "213": {
1710
+ "content": "</s>",
1711
+ "lstrip": false,
1712
+ "normalized": false,
1713
+ "rstrip": false,
1714
+ "single_word": false,
1715
+ "special": false
1716
+ },
1717
+ "214": {
1718
+ "content": "</sub>",
1719
+ "lstrip": false,
1720
+ "normalized": false,
1721
+ "rstrip": false,
1722
+ "single_word": false,
1723
+ "special": false
1724
+ },
1725
+ "215": {
1726
+ "content": "</sup>",
1727
+ "lstrip": false,
1728
+ "normalized": false,
1729
+ "rstrip": false,
1730
+ "single_word": false,
1731
+ "special": false
1732
+ },
1733
+ "216": {
1734
+ "content": "</code>",
1735
+ "lstrip": false,
1736
+ "normalized": false,
1737
+ "rstrip": false,
1738
+ "single_word": false,
1739
+ "special": false
1740
+ }
1741
+ },
1742
+ "additional_special_tokens": [
1743
+ "<start_of_turn>",
1744
+ "<end_of_turn>"
1745
+ ],
1746
+ "bos_token": "<bos>",
1747
+ "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] | trim + '\n\n' %}{% set messages = messages[1:] %}{% else %}{% set system_message = '' %}{% endif %}{% for message in messages %}{% if loop.index0 == 0 %}{% set content = system_message + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + content | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
1748
+ "clean_up_tokenization_spaces": false,
1749
+ "eos_token": "<eos>",
1750
+ "model_max_length": 2048,
1751
+ "pad_token": "<pad>",
1752
+ "sp_model_kwargs": {},
1753
+ "spaces_between_special_tokens": false,
1754
+ "tokenizer_class": "GemmaTokenizer",
1755
+ "unk_token": "<unk>",
1756
+ "use_default_system_prompt": false
1757
+ }