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Updated and cleaned model + inference code + colab

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  1. README.md +88 -58
  2. added_tokens.json +0 -24
  3. bigscience_T0_3B_ssml/added_tokens.json +0 -105
  4. bigscience_T0_3B_ssml/checkpoint-12/added_tokens.json +0 -105
  5. bigscience_T0_3B_ssml/checkpoint-12/config.json +0 -32
  6. bigscience_T0_3B_ssml/checkpoint-12/generation_config.json +0 -7
  7. bigscience_T0_3B_ssml/checkpoint-12/model.safetensors.index.json +0 -565
  8. bigscience_T0_3B_ssml/checkpoint-12/special_tokens_map.json +0 -125
  9. bigscience_T0_3B_ssml/checkpoint-12/tokenizer_config.json +0 -965
  10. bigscience_T0_3B_ssml/checkpoint-12/trainer_state.json +0 -41
  11. bigscience_T0_3B_ssml/config.json +0 -32
  12. bigscience_T0_3B_ssml/generation_config.json +0 -7
  13. bigscience_T0_3B_ssml/model.safetensors.index.json +0 -565
  14. bigscience_T0_3B_ssml/special_tokens_map.json +0 -125
  15. bigscience_T0_3B_ssml/tokenizer_config.json +0 -965
  16. chat_template.jinja +0 -54
  17. checkpoint-735/README.md +0 -202
  18. checkpoint-735/adapter_config.json +0 -39
  19. checkpoint-735/added_tokens.json +0 -24
  20. checkpoint-735/chat_template.jinja +0 -54
  21. checkpoint-735/merges.txt +0 -0
  22. checkpoint-735/special_tokens_map.json +0 -31
  23. checkpoint-735/tokenizer_config.json +0 -207
  24. checkpoint-735/trainer_state.json +0 -286
  25. checkpoint-735/vocab.json +0 -0
  26. facebook_bart-base_ssml/added_tokens.json +0 -6
  27. facebook_bart-base_ssml/checkpoint-120/added_tokens.json +0 -6
  28. facebook_bart-base_ssml/checkpoint-120/config.json +0 -73
  29. facebook_bart-base_ssml/checkpoint-120/generation_config.json +0 -13
  30. facebook_bart-base_ssml/checkpoint-120/merges.txt +0 -0
  31. facebook_bart-base_ssml/checkpoint-120/special_tokens_map.json +0 -51
  32. facebook_bart-base_ssml/checkpoint-120/tokenizer_config.json +0 -89
  33. facebook_bart-base_ssml/checkpoint-120/trainer_state.json +0 -41
  34. facebook_bart-base_ssml/checkpoint-120/vocab.json +0 -0
  35. facebook_bart-base_ssml/checkpoint-3/added_tokens.json +0 -5
  36. facebook_bart-base_ssml/checkpoint-3/config.json +0 -73
  37. facebook_bart-base_ssml/checkpoint-3/generation_config.json +0 -13
  38. facebook_bart-base_ssml/checkpoint-3/merges.txt +0 -0
  39. facebook_bart-base_ssml/checkpoint-3/special_tokens_map.json +0 -51
  40. facebook_bart-base_ssml/checkpoint-3/tokenizer_config.json +0 -81
  41. facebook_bart-base_ssml/checkpoint-3/trainer_state.json +0 -33
  42. facebook_bart-base_ssml/checkpoint-3/vocab.json +0 -0
  43. facebook_bart-base_ssml/checkpoint-990/added_tokens.json +0 -6
  44. facebook_bart-base_ssml/checkpoint-990/config.json +0 -73
  45. facebook_bart-base_ssml/checkpoint-990/generation_config.json +0 -13
  46. facebook_bart-base_ssml/checkpoint-990/merges.txt +0 -0
  47. facebook_bart-base_ssml/checkpoint-990/special_tokens_map.json +0 -51
  48. facebook_bart-base_ssml/checkpoint-990/tokenizer_config.json +0 -89
  49. facebook_bart-base_ssml/checkpoint-990/trainer_state.json +0 -33
  50. facebook_bart-base_ssml/checkpoint-990/vocab.json +0 -0
README.md CHANGED
@@ -2,91 +2,76 @@
2
  license: apache-2.0
3
  base_model: Qwen/Qwen2.5-7B
4
  library_name: peft
 
 
5
  tags:
6
  - text-to-speech
7
- - ssml
8
- - qwen2.5
9
  - lora
10
  - peft
11
- language:
12
- - en
13
- - fr
14
  pipeline_tag: text-generation
15
  ---
16
 
17
- # 🗣️ ssml-text2breaks-fr-lora
18
-
19
- **ssml-text2breaks-fr-lora** is a LoRA adapter built on top of `Qwen/Qwen2.5-7B`, trained to predict **symbolic pause markers** (e.g., `#250`, `#500`) in raw French text. These symbolic tags indicate appropriate prosodic boundaries for speech synthesis systems.
20
 
21
- This model is the **first stage** in the cascaded pipeline presented in:
22
 
23
- > **"Improving French Synthetic Speech Quality via SSML Prosody Control"**
24
- > *Nassima Ould-Ouali, Éric Moulines* – ICNLSP 2025 (*Springer LNCS*, accepted)
25
 
26
- It is designed to be followed by [`ssml-break2ssml-fr-lora`](https://huggingface.co/nassimaODL/ssml-break2ssml-fr-lora), which converts symbolic markers into valid SSML tags.
27
-
28
- ---
 
29
 
30
  ## 🧩 Pipeline Overview
31
 
32
- | Stage | Model Name | Description |
33
- |-------|------------|-------------|
34
- | 1️⃣ | `ssml-text2breaks-fr-lora` | Predicts symbolic pause markers such as `#250`, `#500` |
35
- | 2️⃣ | `ssml-break2ssml-fr-lora` | Converts symbolic markers into `<break time="..."/>` SSML tags |
36
-
37
- ---
38
 
39
  ## ✨ Example
40
 
41
  **Input:**
42
-
43
- ```text
44
- Bonjour je m'appelle Bertrand Perier. Je suis avocat à la cour.
45
-
46
  ```
47
-
48
- **Output**
49
- ```text
50
- Bonjour#250 je m'appelle Bertrand Perier.#500 Je suis avocat à la cour.
51
-
52
  ```
53
 
 
 
 
 
54
 
 
55
 
 
56
 
57
- ## 🧠 Model Details
58
-
59
- - **Base Model**: Qwen/Qwen2.5-7B
60
- - **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
61
- - **LoRA Rank**: 8
62
- - **LoRA Alpha**: 16
63
- - **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
64
- - **Training Epochs**: 5
65
- - **Batch Size**: 1 (with gradient accumulation)
66
- - **Learning Rate**: 3e-4
67
 
68
- ## 🚀 How to run the code
69
 
70
  ```python
71
- import torch
72
  from transformers import AutoTokenizer, AutoModelForCausalLM
73
  from peft import PeftModel
 
74
 
75
  # Load base model and tokenizer
76
  base_model = AutoModelForCausalLM.from_pretrained(
77
  "Qwen/Qwen2.5-7B",
78
- torch_dtype=torch.bfloat16,
79
  device_map="auto"
80
  )
81
  tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B")
82
 
83
  # Load LoRA adapter
84
- model = PeftModel.from_pretrained(base_model, "jonahdvt/qwen-ssml-lora")
85
 
86
  # Prepare input
87
- instruction = "Convert text to SSML with pauses:"
88
- text = "Hello, how are you today? I hope everything is going well."
89
- formatted_input = f"### Task:\n{instruction}\n\n### Text:\n{text}\n\n### SSML:\n"
90
 
91
  # Generate
92
  inputs = tokenizer(formatted_input, return_tensors="pt").to(model.device)
@@ -100,25 +85,70 @@ with torch.no_grad():
100
  )
101
 
102
  response = tokenizer.decode(outputs[0], skip_special_tokens=True)
103
- ssml_output = response.split("### SSML:\n")[-1]
104
- print(ssml_output)
 
 
 
 
 
 
 
 
 
 
 
 
105
  ```
106
 
107
- ## Citation
108
- If you use this model in your research, please cite:
109
- ```text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
110
  @inproceedings{ould-ouali2025_improving,
111
  title = {Improving Synthetic Speech Quality via SSML Prosody Control},
112
  author = {Ould-Ouali, Nassima and Sani, Awais and Bueno, Ruben and Dauvet, Jonah and Horstmann, Tim Luka and Moulines, Eric},
113
- booktitle = {Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP)}, % TODO: vérifier l'intitulé exact utilisé par la conf
114
  year = {2025},
115
- pages = {XX--YY}, % TODO
116
- publisher = {—}, % TODO
117
- address = {—} % TODO
118
  }
119
  ```
120
 
 
121
 
122
- ## License
123
-
124
- This model is released under the Apache 2.0 license, same as the base Qwen2.5-7B model.
 
2
  license: apache-2.0
3
  base_model: Qwen/Qwen2.5-7B
4
  library_name: peft
5
+ language:
6
+ - fr
7
  tags:
8
  - text-to-speech
 
 
9
  - lora
10
  - peft
11
+ - ssml
12
+ - qwen2.5
 
13
  pipeline_tag: text-generation
14
  ---
15
 
16
+ # 🗣️ French Text-to-Breaks LoRA Model
 
 
17
 
18
+ **hi-paris/ssml-text2breaks-fr-lora** is a LoRA adapter fine-tuned on Qwen2.5-7B to predict natural pause locations in French text by adding symbolic `<break/>` markers.
19
 
20
+ This is the **first stage** of a two-step SSML cascade pipeline for improving French text-to-speech prosody control.
 
21
 
22
+ > 📄 **Paper**: *"Improving Synthetic Speech Quality via SSML Prosody Control"*
23
+ > **Authors**: Nassima Ould-Ouali, Awais Sani, Ruben Bueno, Jonah Dauvet, Tim Luka Horstmann, Eric Moulines
24
+ > **Conference**: ICNLSP 2025
25
+ > 🔗 **Demo & Audio Samples**: https://horstmann.tech/ssml-prosody-control/
26
 
27
  ## 🧩 Pipeline Overview
28
 
29
+ | Stage | Model | Purpose |
30
+ |-------|-------|---------|
31
+ | 1️⃣ | **hi-paris/ssml-text2breaks-fr-lora** | Predicts natural pause locations |
32
+ | 2️⃣ | [hi-paris/ssml-breaks2ssml-fr-lora](https://huggingface.co/hi-paris/ssml-breaks2ssml-fr-lora) | Converts breaks to full SSML with prosody |
 
 
33
 
34
  ## ✨ Example
35
 
36
  **Input:**
 
 
 
 
37
  ```
38
+ Bonjour comment allez-vous aujourd'hui ?
 
 
 
 
39
  ```
40
 
41
+ **Output:**
42
+ ```
43
+ Bonjour comment allez-vous aujourd'hui ?<break/>
44
+ ```
45
 
46
+ ## 🚀 Quick Start
47
 
48
+ ### Installation
49
 
50
+ ```bash
51
+ pip install torch transformers peft accelerate
52
+ ```
 
 
 
 
 
 
 
53
 
54
+ ### Basic Usage
55
 
56
  ```python
 
57
  from transformers import AutoTokenizer, AutoModelForCausalLM
58
  from peft import PeftModel
59
+ import torch
60
 
61
  # Load base model and tokenizer
62
  base_model = AutoModelForCausalLM.from_pretrained(
63
  "Qwen/Qwen2.5-7B",
64
+ torch_dtype=torch.float16,
65
  device_map="auto"
66
  )
67
  tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B")
68
 
69
  # Load LoRA adapter
70
+ model = PeftModel.from_pretrained(base_model, "hi-paris/ssml-text2breaks-fr-lora")
71
 
72
  # Prepare input
73
+ text = "Bonjour comment allez-vous aujourd'hui ?"
74
+ formatted_input = f"### Task:\nConvert text to SSML with pauses:\n\n### Text:\n{text}\n\n### SSML:\n"
 
75
 
76
  # Generate
77
  inputs = tokenizer(formatted_input, return_tensors="pt").to(model.device)
 
85
  )
86
 
87
  response = tokenizer.decode(outputs[0], skip_special_tokens=True)
88
+ result = response.split("### SSML:\n")[-1].strip()
89
+ print(result) # "Bonjour comment allez-vous aujourd'hui ?<break/>"
90
+ ```
91
+
92
+ ### Production Usage (Recommended)
93
+
94
+ For production use with memory optimization and full cascade, see our [inference repository](https://github.com/TimLukaHorstmann/cascading_model):
95
+
96
+ ```python
97
+ from text2breaks_inference import Text2BreaksInference
98
+
99
+ # Memory-efficient shared model approach
100
+ model = Text2BreaksInference()
101
+ result = model.predict("Bonjour comment allez-vous aujourd'hui ?")
102
  ```
103
 
104
+ ## 🔧 Full Cascade Example
105
+
106
+ ```python
107
+ from breaks2ssml_inference import CascadedInference
108
+
109
+ # Initialize full pipeline (memory efficient)
110
+ cascade = CascadedInference()
111
+
112
+ # Convert plain text directly to full SSML
113
+ text = "Bonjour comment allez-vous aujourd'hui ?"
114
+ ssml_output = cascade.predict(text)
115
+ print(ssml_output)
116
+ # Output: '<prosody pitch="+2.5%" rate="-1.2%" volume="-5.0%">Bonjour comment allez-vous aujourd'hui ?</prosody><break time="300ms"/>'
117
+ ```
118
+
119
+ ## 🧠 Model Details
120
+
121
+ - **Base Model**: [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)
122
+ - **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
123
+ - **LoRA Rank**: 8, Alpha: 16
124
+ - **Target Modules**: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
125
+ - **Training**: 5 epochs, batch size 1 with gradient accumulation
126
+ - **Language**: French
127
+ - **Model Size**: 7B parameters (LoRA adapter: ~81MB)
128
+ - **License**: Apache 2.0
129
+
130
+ ## 📊 Performance
131
+
132
+ The model achieves high accuracy in predicting natural pause locations in French text, contributing to improved prosody in text-to-speech synthesis when combined with the second-stage model.
133
+
134
+ ## 🔗 Resources
135
+
136
+ - **Full Pipeline Code**: https://github.com/TimLukaHorstmann/cascading_model
137
+ - **Interactive Demo**: [Colab Notebook](https://colab.research.google.com/drive/1bFcbJQY9OuY0_zlscqkf9PIgd3dUrIKs?usp=sharing)
138
+ - **Stage 2 Model**: [hi-paris/ssml-breaks2ssml-fr-lora](https://huggingface.co/hi-paris/ssml-breaks2ssml-fr-lora)
139
+
140
+ ## 📖 Citation
141
+
142
+ ```bibtex
143
  @inproceedings{ould-ouali2025_improving,
144
  title = {Improving Synthetic Speech Quality via SSML Prosody Control},
145
  author = {Ould-Ouali, Nassima and Sani, Awais and Bueno, Ruben and Dauvet, Jonah and Horstmann, Tim Luka and Moulines, Eric},
146
+ booktitle = {Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP)},
147
  year = {2025},
148
+ url = {https://huggingface.co/hi-paris}
 
 
149
  }
150
  ```
151
 
152
+ ## 📜 License
153
 
154
+ Apache 2.0 License (same as the base Qwen2.5-7B model)
 
 
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541
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556
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559
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560
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562
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563
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566
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569
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571
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572
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576
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579
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581
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582
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583
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584
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587
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592
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595
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600
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602
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603
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607
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610
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611
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612
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616
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- "special": true
619
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621
- "content": "<extra_id_25>",
622
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623
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624
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626
- "special": true
627
- },
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629
- "content": "<extra_id_24>",
630
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631
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632
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633
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634
- "special": true
635
- },
636
- "32076": {
637
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638
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639
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640
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641
- "single_word": false,
642
- "special": true
643
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644
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645
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646
- "lstrip": true,
647
- "normalized": false,
648
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651
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652
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654
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655
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662
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663
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664
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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681
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683
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684
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686
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687
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691
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692
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694
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696
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698
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699
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700
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702
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703
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704
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706
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707
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708
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709
- "content": "<extra_id_14>",
710
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714
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715
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716
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718
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719
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720
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721
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722
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723
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724
- "32087": {
725
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726
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728
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729
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730
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731
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732
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733
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734
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736
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738
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739
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741
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742
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743
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747
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748
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749
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763
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764
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766
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768
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769
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770
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771
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772
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773
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774
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777
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778
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779
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780
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781
- "content": "<extra_id_5>",
782
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783
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784
- "rstrip": true,
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786
- "special": true
787
- },
788
- "32095": {
789
- "content": "<extra_id_4>",
790
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791
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792
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793
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794
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795
- },
796
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797
- "content": "<extra_id_3>",
798
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799
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800
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801
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802
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803
- },
804
- "32097": {
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- "content": "<extra_id_2>",
806
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807
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808
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809
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810
- "special": true
811
- },
812
- "32098": {
813
- "content": "<extra_id_1>",
814
- "lstrip": true,
815
- "normalized": false,
816
- "rstrip": true,
817
- "single_word": false,
818
- "special": true
819
- },
820
- "32099": {
821
- "content": "<extra_id_0>",
822
- "lstrip": true,
823
- "normalized": false,
824
- "rstrip": true,
825
- "single_word": false,
826
- "special": true
827
- },
828
- "32100": {
829
- "content": "<prosody>",
830
- "lstrip": false,
831
- "normalized": true,
832
- "rstrip": false,
833
- "single_word": false,
834
- "special": false
835
- },
836
- "32101": {
837
- "content": "</prosody>",
838
- "lstrip": false,
839
- "normalized": true,
840
- "rstrip": false,
841
- "single_word": false,
842
- "special": false
843
- },
844
- "32102": {
845
- "content": "<break/>",
846
- "lstrip": false,
847
- "normalized": true,
848
- "rstrip": false,
849
- "single_word": false,
850
- "special": false
851
- }
852
- },
853
- "additional_special_tokens": [
854
- "<extra_id_0>",
855
- "<extra_id_1>",
856
- "<extra_id_2>",
857
- "<extra_id_3>",
858
- "<extra_id_4>",
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- "<extra_id_5>",
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- "<extra_id_6>",
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- "<extra_id_8>",
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- "<extra_id_10>",
865
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867
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868
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869
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- "<extra_id_30>",
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- "<extra_id_32>",
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- "<extra_id_33>",
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- "<extra_id_34>",
889
- "<extra_id_35>",
890
- "<extra_id_36>",
891
- "<extra_id_37>",
892
- "<extra_id_38>",
893
- "<extra_id_39>",
894
- "<extra_id_40>",
895
- "<extra_id_41>",
896
- "<extra_id_42>",
897
- "<extra_id_43>",
898
- "<extra_id_44>",
899
- "<extra_id_45>",
900
- "<extra_id_46>",
901
- "<extra_id_47>",
902
- "<extra_id_48>",
903
- "<extra_id_49>",
904
- "<extra_id_50>",
905
- "<extra_id_51>",
906
- "<extra_id_52>",
907
- "<extra_id_53>",
908
- "<extra_id_54>",
909
- "<extra_id_55>",
910
- "<extra_id_56>",
911
- "<extra_id_57>",
912
- "<extra_id_58>",
913
- "<extra_id_59>",
914
- "<extra_id_60>",
915
- "<extra_id_61>",
916
- "<extra_id_62>",
917
- "<extra_id_63>",
918
- "<extra_id_64>",
919
- "<extra_id_65>",
920
- "<extra_id_66>",
921
- "<extra_id_67>",
922
- "<extra_id_68>",
923
- "<extra_id_69>",
924
- "<extra_id_70>",
925
- "<extra_id_71>",
926
- "<extra_id_72>",
927
- "<extra_id_73>",
928
- "<extra_id_74>",
929
- "<extra_id_75>",
930
- "<extra_id_76>",
931
- "<extra_id_77>",
932
- "<extra_id_78>",
933
- "<extra_id_79>",
934
- "<extra_id_80>",
935
- "<extra_id_81>",
936
- "<extra_id_82>",
937
- "<extra_id_83>",
938
- "<extra_id_84>",
939
- "<extra_id_85>",
940
- "<extra_id_86>",
941
- "<extra_id_87>",
942
- "<extra_id_88>",
943
- "<extra_id_89>",
944
- "<extra_id_90>",
945
- "<extra_id_91>",
946
- "<extra_id_92>",
947
- "<extra_id_93>",
948
- "<extra_id_94>",
949
- "<extra_id_95>",
950
- "<extra_id_96>",
951
- "<extra_id_97>",
952
- "<extra_id_98>",
953
- "<extra_id_99>"
954
- ],
955
- "clean_up_tokenization_spaces": false,
956
- "eos_token": "</s>",
957
- "extra_ids": 100,
958
- "extra_special_tokens": {},
959
- "legacy": true,
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- "model_max_length": 512,
961
- "pad_token": "<pad>",
962
- "sp_model_kwargs": {},
963
- "tokenizer_class": "T5Tokenizer",
964
- "unk_token": "<unk>"
965
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
chat_template.jinja DELETED
@@ -1,54 +0,0 @@
1
- {%- if tools %}
2
- {{- '<|im_start|>system\n' }}
3
- {%- if messages[0]['role'] == 'system' %}
4
- {{- messages[0]['content'] }}
5
- {%- else %}
6
- {{- 'You are a helpful assistant.' }}
7
- {%- endif %}
8
- {{- "\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>" }}
9
- {%- for tool in tools %}
10
- {{- "\n" }}
11
- {{- tool | tojson }}
12
- {%- endfor %}
13
- {{- "\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" }}
14
- {%- else %}
15
- {%- if messages[0]['role'] == 'system' %}
16
- {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
17
- {%- else %}
18
- {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
19
- {%- endif %}
20
- {%- endif %}
21
- {%- for message in messages %}
22
- {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
23
- {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
24
- {%- elif message.role == "assistant" %}
25
- {{- '<|im_start|>' + message.role }}
26
- {%- if message.content %}
27
- {{- '\n' + message.content }}
28
- {%- endif %}
29
- {%- for tool_call in message.tool_calls %}
30
- {%- if tool_call.function is defined %}
31
- {%- set tool_call = tool_call.function %}
32
- {%- endif %}
33
- {{- '\n<tool_call>\n{"name": "' }}
34
- {{- tool_call.name }}
35
- {{- '", "arguments": ' }}
36
- {{- tool_call.arguments | tojson }}
37
- {{- '}\n</tool_call>' }}
38
- {%- endfor %}
39
- {{- '<|im_end|>\n' }}
40
- {%- elif message.role == "tool" %}
41
- {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
42
- {{- '<|im_start|>user' }}
43
- {%- endif %}
44
- {{- '\n<tool_response>\n' }}
45
- {{- message.content }}
46
- {{- '\n</tool_response>' }}
47
- {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
48
- {{- '<|im_end|>\n' }}
49
- {%- endif %}
50
- {%- endif %}
51
- {%- endfor %}
52
- {%- if add_generation_prompt %}
53
- {{- '<|im_start|>assistant\n' }}
54
- {%- endif %}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
checkpoint-735/README.md DELETED
@@ -1,202 +0,0 @@
1
- ---
2
- base_model: Qwen/Qwen2.5-7B
3
- library_name: peft
4
- ---
5
-
6
- # Model Card for Model ID
7
-
8
- <!-- Provide a quick summary of what the model is/does. -->
9
-
10
-
11
-
12
- ## Model Details
13
-
14
- ### Model Description
15
-
16
- <!-- Provide a longer summary of what this model is. -->
17
-
18
-
19
-
20
- - **Developed by:** [More Information Needed]
21
- - **Funded by [optional]:** [More Information Needed]
22
- - **Shared by [optional]:** [More Information Needed]
23
- - **Model type:** [More Information Needed]
24
- - **Language(s) (NLP):** [More Information Needed]
25
- - **License:** [More Information Needed]
26
- - **Finetuned from model [optional]:** [More Information Needed]
27
-
28
- ### Model Sources [optional]
29
-
30
- <!-- Provide the basic links for the model. -->
31
-
32
- - **Repository:** [More Information Needed]
33
- - **Paper [optional]:** [More Information Needed]
34
- - **Demo [optional]:** [More Information Needed]
35
-
36
- ## Uses
37
-
38
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- ## How to Get Started with the Model
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- ### Framework versions
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- - PEFT 0.15.2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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