K2triinK commited on
Commit
53da26e
·
verified ·
1 Parent(s): 0a9de5c

Add files using upload-large-folder tool

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test1/README.md +58 -0
  2. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/README.md +58 -0
  3. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/README.md +209 -0
  4. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/adapter_config.json +48 -0
  5. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/chat_template.jinja +85 -0
  6. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/tokenizer_config.json +29 -0
  7. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/trainer_state.json +340 -0
  8. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/README.md +209 -0
  9. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/adapter_config.json +48 -0
  10. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/chat_template.jinja +85 -0
  11. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/tokenizer_config.json +29 -0
  12. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/trainer_state.json +442 -0
  13. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2040/adapter_config.json +48 -0
  14. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2040/chat_template.jinja +85 -0
  15. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2040/tokenizer_config.json +29 -0
  16. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2040/trainer_state.json +544 -0
  17. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2448/README.md +209 -0
  18. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2448/chat_template.jinja +85 -0
  19. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2448/tokenizer_config.json +29 -0
  20. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2448/trainer_state.json +646 -0
  21. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2856/README.md +209 -0
  22. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2856/adapter_config.json +48 -0
  23. substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2856/tokenizer_config.json +29 -0
  24. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/README.md +209 -0
  25. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/adapter_config.json +48 -0
  26. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/chat_template.jinja +85 -0
  27. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/tokenizer_config.json +29 -0
  28. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/trainer_state.json +337 -0
  29. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/README.md +209 -0
  30. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/adapter_config.json +48 -0
  31. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/chat_template.jinja +85 -0
  32. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/tokenizer_config.json +29 -0
  33. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/trainer_state.json +438 -0
  34. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/README.md +209 -0
  35. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/adapter_config.json +48 -0
  36. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/chat_template.jinja +85 -0
  37. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/tokenizer_config.json +29 -0
  38. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/trainer_state.json +539 -0
  39. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/README.md +209 -0
  40. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/adapter_config.json +48 -0
  41. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/chat_template.jinja +85 -0
  42. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/tokenizer_config.json +29 -0
  43. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/trainer_state.json +640 -0
  44. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/README.md +209 -0
  45. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/adapter_config.json +48 -0
  46. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/chat_template.jinja +85 -0
  47. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/tokenizer_config.json +29 -0
  48. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/trainer_state.json +741 -0
  49. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3656/README.md +209 -0
  50. systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3656/adapter_config.json +48 -0
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test1/README.md ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: transformers
4
+ model_name: Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test1
5
+ tags:
6
+ - generated_from_trainer
7
+ - trl
8
+ - sft
9
+ licence: license
10
+ ---
11
+
12
+ # Model Card for Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test1
13
+
14
+ This model is a fine-tuned version of [Qwen/Qwen3-14B-Base](https://huggingface.co/Qwen/Qwen3-14B-Base).
15
+ It has been trained using [TRL](https://github.com/huggingface/trl).
16
+
17
+ ## Quick start
18
+
19
+ ```python
20
+ from transformers import pipeline
21
+
22
+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
23
+ generator = pipeline("text-generation", model="None", device="cuda")
24
+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
25
+ print(output["generated_text"])
26
+ ```
27
+
28
+ ## Training procedure
29
+
30
+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/katriin-kukk/Cross_lingual_morphological_generalization/runs/1bujlxs7)
31
+
32
+
33
+
34
+ This model was trained with SFT.
35
+
36
+ ### Framework versions
37
+
38
+ - TRL: 0.29.0
39
+ - Transformers: 5.5.4
40
+ - Pytorch: 2.10.0
41
+ - Datasets: 4.6.1
42
+ - Tokenizers: 0.22.2
43
+
44
+ ## Citations
45
+
46
+
47
+
48
+ Cite TRL as:
49
+
50
+ ```bibtex
51
+ @software{vonwerra2020trl,
52
+ title = {{TRL: Transformers Reinforcement Learning}},
53
+ author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
54
+ license = {Apache-2.0},
55
+ url = {https://github.com/huggingface/trl},
56
+ year = {2020}
57
+ }
58
+ ```
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/README.md ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: transformers
4
+ model_name: Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2
5
+ tags:
6
+ - generated_from_trainer
7
+ - trl
8
+ - sft
9
+ licence: license
10
+ ---
11
+
12
+ # Model Card for Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2
13
+
14
+ This model is a fine-tuned version of [Qwen/Qwen3-14B-Base](https://huggingface.co/Qwen/Qwen3-14B-Base).
15
+ It has been trained using [TRL](https://github.com/huggingface/trl).
16
+
17
+ ## Quick start
18
+
19
+ ```python
20
+ from transformers import pipeline
21
+
22
+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
23
+ generator = pipeline("text-generation", model="None", device="cuda")
24
+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
25
+ print(output["generated_text"])
26
+ ```
27
+
28
+ ## Training procedure
29
+
30
+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/katriin-kukk/Cross_lingual_morphological_generalization/runs/1awltkuf)
31
+
32
+
33
+
34
+ This model was trained with SFT.
35
+
36
+ ### Framework versions
37
+
38
+ - TRL: 0.29.0
39
+ - Transformers: 5.5.4
40
+ - Pytorch: 2.10.0
41
+ - Datasets: 4.6.1
42
+ - Tokenizers: 0.22.2
43
+
44
+ ## Citations
45
+
46
+
47
+
48
+ Cite TRL as:
49
+
50
+ ```bibtex
51
+ @software{vonwerra2020trl,
52
+ title = {{TRL: Transformers Reinforcement Learning}},
53
+ author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
54
+ license = {Apache-2.0},
55
+ url = {https://github.com/huggingface/trl},
56
+ year = {2020}
57
+ }
58
+ ```
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.06743035903930279,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "v_proj",
34
+ "up_proj",
35
+ "k_proj",
36
+ "gate_proj",
37
+ "o_proj",
38
+ "down_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# 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>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\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" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
+ {%- if loop.last or (not loop.last and reasoning_content) %}
41
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
42
+ {%- else %}
43
+ {{- '<|im_start|>' + message.role + '\n' + content }}
44
+ {%- endif %}
45
+ {%- else %}
46
+ {{- '<|im_start|>' + message.role + '\n' + content }}
47
+ {%- endif %}
48
+ {%- if message.tool_calls %}
49
+ {%- for tool_call in message.tool_calls %}
50
+ {%- if (loop.first and content) or (not loop.first) %}
51
+ {{- '\n' }}
52
+ {%- endif %}
53
+ {%- if tool_call.function %}
54
+ {%- set tool_call = tool_call.function %}
55
+ {%- endif %}
56
+ {{- '<tool_call>\n{"name": "' }}
57
+ {{- tool_call.name }}
58
+ {{- '", "arguments": ' }}
59
+ {%- if tool_call.arguments is string %}
60
+ {{- tool_call.arguments }}
61
+ {%- else %}
62
+ {{- tool_call.arguments | tojson }}
63
+ {%- endif %}
64
+ {{- '}\n</tool_call>' }}
65
+ {%- endfor %}
66
+ {%- endif %}
67
+ {{- '<|im_end|>\n' }}
68
+ {%- elif message.role == "tool" %}
69
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
70
+ {{- '<|im_start|>user' }}
71
+ {%- endif %}
72
+ {{- '\n<tool_response>\n' }}
73
+ {{- message.content }}
74
+ {{- '\n</tool_response>' }}
75
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
76
+ {{- '<|im_end|>\n' }}
77
+ {%- endif %}
78
+ {%- endif %}
79
+ {%- endfor %}
80
+ {%- if add_generation_prompt %}
81
+ {{- '<|im_start|>assistant\n' }}
82
+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
84
+ {%- endif %}
85
+ {%- endif %}
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1224/trainer_state.json ADDED
@@ -0,0 +1,340 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 3.0,
6
+ "eval_steps": 500,
7
+ "global_step": 1224,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 1.84347476541996,
14
+ "epoch": 0.12277470841006753,
15
+ "grad_norm": 0.5139632821083069,
16
+ "learning_rate": 3.586242144074487e-05,
17
+ "loss": 1.7810469055175782,
18
+ "mean_token_accuracy": 0.6252794374525547,
19
+ "num_tokens": 141525.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 0.8905406814813613,
24
+ "epoch": 0.24554941682013506,
25
+ "grad_norm": 0.6561369299888611,
26
+ "learning_rate": 7.245672903334166e-05,
27
+ "loss": 0.83647705078125,
28
+ "mean_token_accuracy": 0.7502138108015061,
29
+ "num_tokens": 276389.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 0.7635939045250416,
34
+ "epoch": 0.3683241252302026,
35
+ "grad_norm": 0.4626609683036804,
36
+ "learning_rate": 0.00010905103662593846,
37
+ "loss": 0.7156005096435547,
38
+ "mean_token_accuracy": 0.7786111453175545,
39
+ "num_tokens": 422723.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 0.7165373960137367,
44
+ "epoch": 0.4910988336402701,
45
+ "grad_norm": 0.4098263680934906,
46
+ "learning_rate": 0.00014564534421853526,
47
+ "loss": 0.67410400390625,
48
+ "mean_token_accuracy": 0.7880693352222443,
49
+ "num_tokens": 560353.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 0.7035001173615456,
54
+ "epoch": 0.6138735420503376,
55
+ "grad_norm": 0.33693116903305054,
56
+ "learning_rate": 0.0001822396518111321,
57
+ "loss": 0.6591246032714844,
58
+ "mean_token_accuracy": 0.7931166198849678,
59
+ "num_tokens": 702231.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "entropy": 0.6862310113012791,
64
+ "epoch": 0.7366482504604052,
65
+ "grad_norm": 0.31358352303504944,
66
+ "learning_rate": 0.00021883395940372884,
67
+ "loss": 0.6399201202392578,
68
+ "mean_token_accuracy": 0.7952846321463585,
69
+ "num_tokens": 842451.0,
70
+ "step": 300
71
+ },
72
+ {
73
+ "entropy": 0.6739526629447937,
74
+ "epoch": 0.8594229588704727,
75
+ "grad_norm": 0.4029310941696167,
76
+ "learning_rate": 0.00025542826699632564,
77
+ "loss": 0.6259587860107422,
78
+ "mean_token_accuracy": 0.8014272648096085,
79
+ "num_tokens": 980824.0,
80
+ "step": 350
81
+ },
82
+ {
83
+ "entropy": 0.6681017802655697,
84
+ "epoch": 0.9821976672805403,
85
+ "grad_norm": 0.3852674961090088,
86
+ "learning_rate": 0.00029202257458892247,
87
+ "loss": 0.6253739166259765,
88
+ "mean_token_accuracy": 0.8020894029736518,
89
+ "num_tokens": 1118697.0,
90
+ "step": 400
91
+ },
92
+ {
93
+ "epoch": 1.0,
94
+ "eval_entropy": 0.6994867869785854,
95
+ "eval_mean_token_accuracy": 0.7849237663405282,
96
+ "eval_not_syn_loss": 0.6411319971084595,
97
+ "eval_not_syn_runtime": 110.7069,
98
+ "eval_not_syn_samples_per_second": 12.61,
99
+ "eval_not_syn_steps_per_second": 1.581,
100
+ "eval_num_tokens": 1138193.0,
101
+ "step": 408
102
+ },
103
+ {
104
+ "epoch": 1.0,
105
+ "eval_entropy": 0.6563669620241438,
106
+ "eval_mean_token_accuracy": 0.8207755245481219,
107
+ "eval_num_tokens": 1138193.0,
108
+ "eval_syn_loss": 0.6019997000694275,
109
+ "eval_syn_runtime": 117.5102,
110
+ "eval_syn_samples_per_second": 11.88,
111
+ "eval_syn_steps_per_second": 1.489,
112
+ "step": 408
113
+ },
114
+ {
115
+ "entropy": 0.6358311531809986,
116
+ "epoch": 1.1031307550644567,
117
+ "grad_norm": 0.34651753306388855,
118
+ "learning_rate": 0.00029851770373635705,
119
+ "loss": 0.5962887191772461,
120
+ "mean_token_accuracy": 0.8082216354796123,
121
+ "num_tokens": 1263520.0,
122
+ "step": 450
123
+ },
124
+ {
125
+ "entropy": 0.6196171633899212,
126
+ "epoch": 1.2259054634745243,
127
+ "grad_norm": 0.28438520431518555,
128
+ "learning_rate": 0.0002981572760785325,
129
+ "loss": 0.5779812622070313,
130
+ "mean_token_accuracy": 0.8130937224626541,
131
+ "num_tokens": 1405904.0,
132
+ "step": 500
133
+ },
134
+ {
135
+ "entropy": 0.624696860164404,
136
+ "epoch": 1.3486801718845918,
137
+ "grad_norm": 0.3563199043273926,
138
+ "learning_rate": 0.000297524500341339,
139
+ "loss": 0.5760621643066406,
140
+ "mean_token_accuracy": 0.8123102071881294,
141
+ "num_tokens": 1541752.0,
142
+ "step": 550
143
+ },
144
+ {
145
+ "entropy": 0.6383913996815681,
146
+ "epoch": 1.4714548802946594,
147
+ "grad_norm": 0.32306408882141113,
148
+ "learning_rate": 0.00029662053428333246,
149
+ "loss": 0.5874852371215821,
150
+ "mean_token_accuracy": 0.8098450502753258,
151
+ "num_tokens": 1675695.0,
152
+ "step": 600
153
+ },
154
+ {
155
+ "entropy": 0.6132303845882415,
156
+ "epoch": 1.5942295887047269,
157
+ "grad_norm": 0.3351016044616699,
158
+ "learning_rate": 0.0002954470318466225,
159
+ "loss": 0.5624140930175782,
160
+ "mean_token_accuracy": 0.8152360209822654,
161
+ "num_tokens": 1814294.0,
162
+ "step": 650
163
+ },
164
+ {
165
+ "entropy": 0.6104247760772705,
166
+ "epoch": 1.7170042971147943,
167
+ "grad_norm": 0.25214216113090515,
168
+ "learning_rate": 0.00029400614013073664,
169
+ "loss": 0.5608636093139648,
170
+ "mean_token_accuracy": 0.8154580116271972,
171
+ "num_tokens": 1953015.0,
172
+ "step": 700
173
+ },
174
+ {
175
+ "entropy": 0.6093999670445919,
176
+ "epoch": 1.839779005524862,
177
+ "grad_norm": 0.25383421778678894,
178
+ "learning_rate": 0.0002923004954641779,
179
+ "loss": 0.5591114044189454,
180
+ "mean_token_accuracy": 0.817192807495594,
181
+ "num_tokens": 2095011.0,
182
+ "step": 750
183
+ },
184
+ {
185
+ "entropy": 0.6029203486442566,
186
+ "epoch": 1.9625537139349294,
187
+ "grad_norm": 0.25239020586013794,
188
+ "learning_rate": 0.0002903332185808646,
189
+ "loss": 0.5529713821411133,
190
+ "mean_token_accuracy": 0.8182568901777267,
191
+ "num_tokens": 2235222.0,
192
+ "step": 800
193
+ },
194
+ {
195
+ "epoch": 2.0,
196
+ "eval_entropy": 0.6090937239783151,
197
+ "eval_mean_token_accuracy": 0.7930077845709664,
198
+ "eval_not_syn_loss": 0.5995497107505798,
199
+ "eval_not_syn_runtime": 110.3965,
200
+ "eval_not_syn_samples_per_second": 12.645,
201
+ "eval_not_syn_steps_per_second": 1.585,
202
+ "eval_num_tokens": 2276386.0,
203
+ "step": 816
204
+ },
205
+ {
206
+ "epoch": 2.0,
207
+ "eval_entropy": 0.5718999467577253,
208
+ "eval_mean_token_accuracy": 0.84101799760546,
209
+ "eval_num_tokens": 2276386.0,
210
+ "eval_syn_loss": 0.5510491728782654,
211
+ "eval_syn_runtime": 117.3523,
212
+ "eval_syn_samples_per_second": 11.896,
213
+ "eval_syn_steps_per_second": 1.491,
214
+ "step": 816
215
+ },
216
+ {
217
+ "entropy": 0.5504116184517817,
218
+ "epoch": 2.0834868017188457,
219
+ "grad_norm": 0.23869574069976807,
220
+ "learning_rate": 0.0002881079089102777,
221
+ "loss": 0.5012085723876953,
222
+ "mean_token_accuracy": 0.8299948473267144,
223
+ "num_tokens": 2374282.0,
224
+ "step": 850
225
+ },
226
+ {
227
+ "entropy": 0.5487727333605289,
228
+ "epoch": 2.2062615101289134,
229
+ "grad_norm": 0.3087007403373718,
230
+ "learning_rate": 0.00028562863799176175,
231
+ "loss": 0.4988512802124023,
232
+ "mean_token_accuracy": 0.8319525212049484,
233
+ "num_tokens": 2507968.0,
234
+ "step": 900
235
+ },
236
+ {
237
+ "entropy": 0.5354658082127571,
238
+ "epoch": 2.329036218538981,
239
+ "grad_norm": 0.2878682613372803,
240
+ "learning_rate": 0.00028289994202503066,
241
+ "loss": 0.4919636917114258,
242
+ "mean_token_accuracy": 0.8349191680550575,
243
+ "num_tokens": 2650824.0,
244
+ "step": 950
245
+ },
246
+ {
247
+ "entropy": 0.5529813665151596,
248
+ "epoch": 2.4518109269490487,
249
+ "grad_norm": 0.2625614106655121,
250
+ "learning_rate": 0.00027992681357050643,
251
+ "loss": 0.5027856063842774,
252
+ "mean_token_accuracy": 0.8296491304039955,
253
+ "num_tokens": 2784681.0,
254
+ "step": 1000
255
+ },
256
+ {
257
+ "entropy": 0.5497122646868229,
258
+ "epoch": 2.574585635359116,
259
+ "grad_norm": 0.271993488073349,
260
+ "learning_rate": 0.00027671469241467785,
261
+ "loss": 0.49415691375732423,
262
+ "mean_token_accuracy": 0.8312779009342194,
263
+ "num_tokens": 2923442.0,
264
+ "step": 1050
265
+ },
266
+ {
267
+ "entropy": 0.5419583600759507,
268
+ "epoch": 2.6973603437691835,
269
+ "grad_norm": 0.23863548040390015,
270
+ "learning_rate": 0.00027326945561719136,
271
+ "loss": 0.49570159912109374,
272
+ "mean_token_accuracy": 0.8327477470040321,
273
+ "num_tokens": 3068468.0,
274
+ "step": 1100
275
+ },
276
+ {
277
+ "entropy": 0.5491574917733669,
278
+ "epoch": 2.820135052179251,
279
+ "grad_norm": 0.29003486037254333,
280
+ "learning_rate": 0.00026959740675788486,
281
+ "loss": 0.4965015411376953,
282
+ "mean_token_accuracy": 0.8336276519298553,
283
+ "num_tokens": 3206450.0,
284
+ "step": 1150
285
+ },
286
+ {
287
+ "entropy": 0.5377901926636696,
288
+ "epoch": 2.942909760589319,
289
+ "grad_norm": 0.27723413705825806,
290
+ "learning_rate": 0.0002657052644034388,
291
+ "loss": 0.48964527130126956,
292
+ "mean_token_accuracy": 0.8356471425294876,
293
+ "num_tokens": 3348780.0,
294
+ "step": 1200
295
+ },
296
+ {
297
+ "epoch": 3.0,
298
+ "eval_entropy": 0.5576562706061772,
299
+ "eval_mean_token_accuracy": 0.817173547404153,
300
+ "eval_not_syn_loss": 0.5734513401985168,
301
+ "eval_not_syn_runtime": 110.4329,
302
+ "eval_not_syn_samples_per_second": 12.641,
303
+ "eval_not_syn_steps_per_second": 1.585,
304
+ "eval_num_tokens": 3414579.0,
305
+ "step": 1224
306
+ },
307
+ {
308
+ "epoch": 3.0,
309
+ "eval_entropy": 0.5267024655001504,
310
+ "eval_mean_token_accuracy": 0.8285774500029428,
311
+ "eval_num_tokens": 3414579.0,
312
+ "eval_syn_loss": 0.544999361038208,
313
+ "eval_syn_runtime": 117.3289,
314
+ "eval_syn_samples_per_second": 11.898,
315
+ "eval_syn_steps_per_second": 1.492,
316
+ "step": 1224
317
+ }
318
+ ],
319
+ "logging_steps": 50,
320
+ "max_steps": 4080,
321
+ "num_input_tokens_seen": 0,
322
+ "num_train_epochs": 10,
323
+ "save_steps": 500,
324
+ "stateful_callbacks": {
325
+ "TrainerControl": {
326
+ "args": {
327
+ "should_epoch_stop": false,
328
+ "should_evaluate": false,
329
+ "should_log": false,
330
+ "should_save": true,
331
+ "should_training_stop": false
332
+ },
333
+ "attributes": {}
334
+ }
335
+ },
336
+ "total_flos": 5.5555392143591424e+17,
337
+ "train_batch_size": 4,
338
+ "trial_name": null,
339
+ "trial_params": null
340
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.06743035903930279,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "v_proj",
34
+ "up_proj",
35
+ "k_proj",
36
+ "gate_proj",
37
+ "o_proj",
38
+ "down_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# 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>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\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" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
+ {%- if loop.last or (not loop.last and reasoning_content) %}
41
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
42
+ {%- else %}
43
+ {{- '<|im_start|>' + message.role + '\n' + content }}
44
+ {%- endif %}
45
+ {%- else %}
46
+ {{- '<|im_start|>' + message.role + '\n' + content }}
47
+ {%- endif %}
48
+ {%- if message.tool_calls %}
49
+ {%- for tool_call in message.tool_calls %}
50
+ {%- if (loop.first and content) or (not loop.first) %}
51
+ {{- '\n' }}
52
+ {%- endif %}
53
+ {%- if tool_call.function %}
54
+ {%- set tool_call = tool_call.function %}
55
+ {%- endif %}
56
+ {{- '<tool_call>\n{"name": "' }}
57
+ {{- tool_call.name }}
58
+ {{- '", "arguments": ' }}
59
+ {%- if tool_call.arguments is string %}
60
+ {{- tool_call.arguments }}
61
+ {%- else %}
62
+ {{- tool_call.arguments | tojson }}
63
+ {%- endif %}
64
+ {{- '}\n</tool_call>' }}
65
+ {%- endfor %}
66
+ {%- endif %}
67
+ {{- '<|im_end|>\n' }}
68
+ {%- elif message.role == "tool" %}
69
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
70
+ {{- '<|im_start|>user' }}
71
+ {%- endif %}
72
+ {{- '\n<tool_response>\n' }}
73
+ {{- message.content }}
74
+ {{- '\n</tool_response>' }}
75
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
76
+ {{- '<|im_end|>\n' }}
77
+ {%- endif %}
78
+ {%- endif %}
79
+ {%- endfor %}
80
+ {%- if add_generation_prompt %}
81
+ {{- '<|im_start|>assistant\n' }}
82
+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
84
+ {%- endif %}
85
+ {%- endif %}
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-1632/trainer_state.json ADDED
@@ -0,0 +1,442 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 4.0,
6
+ "eval_steps": 500,
7
+ "global_step": 1632,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 1.84347476541996,
14
+ "epoch": 0.12277470841006753,
15
+ "grad_norm": 0.5139632821083069,
16
+ "learning_rate": 3.586242144074487e-05,
17
+ "loss": 1.7810469055175782,
18
+ "mean_token_accuracy": 0.6252794374525547,
19
+ "num_tokens": 141525.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 0.8905406814813613,
24
+ "epoch": 0.24554941682013506,
25
+ "grad_norm": 0.6561369299888611,
26
+ "learning_rate": 7.245672903334166e-05,
27
+ "loss": 0.83647705078125,
28
+ "mean_token_accuracy": 0.7502138108015061,
29
+ "num_tokens": 276389.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 0.7635939045250416,
34
+ "epoch": 0.3683241252302026,
35
+ "grad_norm": 0.4626609683036804,
36
+ "learning_rate": 0.00010905103662593846,
37
+ "loss": 0.7156005096435547,
38
+ "mean_token_accuracy": 0.7786111453175545,
39
+ "num_tokens": 422723.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 0.7165373960137367,
44
+ "epoch": 0.4910988336402701,
45
+ "grad_norm": 0.4098263680934906,
46
+ "learning_rate": 0.00014564534421853526,
47
+ "loss": 0.67410400390625,
48
+ "mean_token_accuracy": 0.7880693352222443,
49
+ "num_tokens": 560353.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 0.7035001173615456,
54
+ "epoch": 0.6138735420503376,
55
+ "grad_norm": 0.33693116903305054,
56
+ "learning_rate": 0.0001822396518111321,
57
+ "loss": 0.6591246032714844,
58
+ "mean_token_accuracy": 0.7931166198849678,
59
+ "num_tokens": 702231.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "entropy": 0.6862310113012791,
64
+ "epoch": 0.7366482504604052,
65
+ "grad_norm": 0.31358352303504944,
66
+ "learning_rate": 0.00021883395940372884,
67
+ "loss": 0.6399201202392578,
68
+ "mean_token_accuracy": 0.7952846321463585,
69
+ "num_tokens": 842451.0,
70
+ "step": 300
71
+ },
72
+ {
73
+ "entropy": 0.6739526629447937,
74
+ "epoch": 0.8594229588704727,
75
+ "grad_norm": 0.4029310941696167,
76
+ "learning_rate": 0.00025542826699632564,
77
+ "loss": 0.6259587860107422,
78
+ "mean_token_accuracy": 0.8014272648096085,
79
+ "num_tokens": 980824.0,
80
+ "step": 350
81
+ },
82
+ {
83
+ "entropy": 0.6681017802655697,
84
+ "epoch": 0.9821976672805403,
85
+ "grad_norm": 0.3852674961090088,
86
+ "learning_rate": 0.00029202257458892247,
87
+ "loss": 0.6253739166259765,
88
+ "mean_token_accuracy": 0.8020894029736518,
89
+ "num_tokens": 1118697.0,
90
+ "step": 400
91
+ },
92
+ {
93
+ "epoch": 1.0,
94
+ "eval_entropy": 0.6994867869785854,
95
+ "eval_mean_token_accuracy": 0.7849237663405282,
96
+ "eval_not_syn_loss": 0.6411319971084595,
97
+ "eval_not_syn_runtime": 110.7069,
98
+ "eval_not_syn_samples_per_second": 12.61,
99
+ "eval_not_syn_steps_per_second": 1.581,
100
+ "eval_num_tokens": 1138193.0,
101
+ "step": 408
102
+ },
103
+ {
104
+ "epoch": 1.0,
105
+ "eval_entropy": 0.6563669620241438,
106
+ "eval_mean_token_accuracy": 0.8207755245481219,
107
+ "eval_num_tokens": 1138193.0,
108
+ "eval_syn_loss": 0.6019997000694275,
109
+ "eval_syn_runtime": 117.5102,
110
+ "eval_syn_samples_per_second": 11.88,
111
+ "eval_syn_steps_per_second": 1.489,
112
+ "step": 408
113
+ },
114
+ {
115
+ "entropy": 0.6358311531809986,
116
+ "epoch": 1.1031307550644567,
117
+ "grad_norm": 0.34651753306388855,
118
+ "learning_rate": 0.00029851770373635705,
119
+ "loss": 0.5962887191772461,
120
+ "mean_token_accuracy": 0.8082216354796123,
121
+ "num_tokens": 1263520.0,
122
+ "step": 450
123
+ },
124
+ {
125
+ "entropy": 0.6196171633899212,
126
+ "epoch": 1.2259054634745243,
127
+ "grad_norm": 0.28438520431518555,
128
+ "learning_rate": 0.0002981572760785325,
129
+ "loss": 0.5779812622070313,
130
+ "mean_token_accuracy": 0.8130937224626541,
131
+ "num_tokens": 1405904.0,
132
+ "step": 500
133
+ },
134
+ {
135
+ "entropy": 0.624696860164404,
136
+ "epoch": 1.3486801718845918,
137
+ "grad_norm": 0.3563199043273926,
138
+ "learning_rate": 0.000297524500341339,
139
+ "loss": 0.5760621643066406,
140
+ "mean_token_accuracy": 0.8123102071881294,
141
+ "num_tokens": 1541752.0,
142
+ "step": 550
143
+ },
144
+ {
145
+ "entropy": 0.6383913996815681,
146
+ "epoch": 1.4714548802946594,
147
+ "grad_norm": 0.32306408882141113,
148
+ "learning_rate": 0.00029662053428333246,
149
+ "loss": 0.5874852371215821,
150
+ "mean_token_accuracy": 0.8098450502753258,
151
+ "num_tokens": 1675695.0,
152
+ "step": 600
153
+ },
154
+ {
155
+ "entropy": 0.6132303845882415,
156
+ "epoch": 1.5942295887047269,
157
+ "grad_norm": 0.3351016044616699,
158
+ "learning_rate": 0.0002954470318466225,
159
+ "loss": 0.5624140930175782,
160
+ "mean_token_accuracy": 0.8152360209822654,
161
+ "num_tokens": 1814294.0,
162
+ "step": 650
163
+ },
164
+ {
165
+ "entropy": 0.6104247760772705,
166
+ "epoch": 1.7170042971147943,
167
+ "grad_norm": 0.25214216113090515,
168
+ "learning_rate": 0.00029400614013073664,
169
+ "loss": 0.5608636093139648,
170
+ "mean_token_accuracy": 0.8154580116271972,
171
+ "num_tokens": 1953015.0,
172
+ "step": 700
173
+ },
174
+ {
175
+ "entropy": 0.6093999670445919,
176
+ "epoch": 1.839779005524862,
177
+ "grad_norm": 0.25383421778678894,
178
+ "learning_rate": 0.0002923004954641779,
179
+ "loss": 0.5591114044189454,
180
+ "mean_token_accuracy": 0.817192807495594,
181
+ "num_tokens": 2095011.0,
182
+ "step": 750
183
+ },
184
+ {
185
+ "entropy": 0.6029203486442566,
186
+ "epoch": 1.9625537139349294,
187
+ "grad_norm": 0.25239020586013794,
188
+ "learning_rate": 0.0002903332185808646,
189
+ "loss": 0.5529713821411133,
190
+ "mean_token_accuracy": 0.8182568901777267,
191
+ "num_tokens": 2235222.0,
192
+ "step": 800
193
+ },
194
+ {
195
+ "epoch": 2.0,
196
+ "eval_entropy": 0.6090937239783151,
197
+ "eval_mean_token_accuracy": 0.7930077845709664,
198
+ "eval_not_syn_loss": 0.5995497107505798,
199
+ "eval_not_syn_runtime": 110.3965,
200
+ "eval_not_syn_samples_per_second": 12.645,
201
+ "eval_not_syn_steps_per_second": 1.585,
202
+ "eval_num_tokens": 2276386.0,
203
+ "step": 816
204
+ },
205
+ {
206
+ "epoch": 2.0,
207
+ "eval_entropy": 0.5718999467577253,
208
+ "eval_mean_token_accuracy": 0.84101799760546,
209
+ "eval_num_tokens": 2276386.0,
210
+ "eval_syn_loss": 0.5510491728782654,
211
+ "eval_syn_runtime": 117.3523,
212
+ "eval_syn_samples_per_second": 11.896,
213
+ "eval_syn_steps_per_second": 1.491,
214
+ "step": 816
215
+ },
216
+ {
217
+ "entropy": 0.5504116184517817,
218
+ "epoch": 2.0834868017188457,
219
+ "grad_norm": 0.23869574069976807,
220
+ "learning_rate": 0.0002881079089102777,
221
+ "loss": 0.5012085723876953,
222
+ "mean_token_accuracy": 0.8299948473267144,
223
+ "num_tokens": 2374282.0,
224
+ "step": 850
225
+ },
226
+ {
227
+ "entropy": 0.5487727333605289,
228
+ "epoch": 2.2062615101289134,
229
+ "grad_norm": 0.3087007403373718,
230
+ "learning_rate": 0.00028562863799176175,
231
+ "loss": 0.4988512802124023,
232
+ "mean_token_accuracy": 0.8319525212049484,
233
+ "num_tokens": 2507968.0,
234
+ "step": 900
235
+ },
236
+ {
237
+ "entropy": 0.5354658082127571,
238
+ "epoch": 2.329036218538981,
239
+ "grad_norm": 0.2878682613372803,
240
+ "learning_rate": 0.00028289994202503066,
241
+ "loss": 0.4919636917114258,
242
+ "mean_token_accuracy": 0.8349191680550575,
243
+ "num_tokens": 2650824.0,
244
+ "step": 950
245
+ },
246
+ {
247
+ "entropy": 0.5529813665151596,
248
+ "epoch": 2.4518109269490487,
249
+ "grad_norm": 0.2625614106655121,
250
+ "learning_rate": 0.00027992681357050643,
251
+ "loss": 0.5027856063842774,
252
+ "mean_token_accuracy": 0.8296491304039955,
253
+ "num_tokens": 2784681.0,
254
+ "step": 1000
255
+ },
256
+ {
257
+ "entropy": 0.5497122646868229,
258
+ "epoch": 2.574585635359116,
259
+ "grad_norm": 0.271993488073349,
260
+ "learning_rate": 0.00027671469241467785,
261
+ "loss": 0.49415691375732423,
262
+ "mean_token_accuracy": 0.8312779009342194,
263
+ "num_tokens": 2923442.0,
264
+ "step": 1050
265
+ },
266
+ {
267
+ "entropy": 0.5419583600759507,
268
+ "epoch": 2.6973603437691835,
269
+ "grad_norm": 0.23863548040390015,
270
+ "learning_rate": 0.00027326945561719136,
271
+ "loss": 0.49570159912109374,
272
+ "mean_token_accuracy": 0.8327477470040321,
273
+ "num_tokens": 3068468.0,
274
+ "step": 1100
275
+ },
276
+ {
277
+ "entropy": 0.5491574917733669,
278
+ "epoch": 2.820135052179251,
279
+ "grad_norm": 0.29003486037254333,
280
+ "learning_rate": 0.00026959740675788486,
281
+ "loss": 0.4965015411376953,
282
+ "mean_token_accuracy": 0.8336276519298553,
283
+ "num_tokens": 3206450.0,
284
+ "step": 1150
285
+ },
286
+ {
287
+ "entropy": 0.5377901926636696,
288
+ "epoch": 2.942909760589319,
289
+ "grad_norm": 0.27723413705825806,
290
+ "learning_rate": 0.0002657052644034388,
291
+ "loss": 0.48964527130126956,
292
+ "mean_token_accuracy": 0.8356471425294876,
293
+ "num_tokens": 3348780.0,
294
+ "step": 1200
295
+ },
296
+ {
297
+ "epoch": 3.0,
298
+ "eval_entropy": 0.5576562706061772,
299
+ "eval_mean_token_accuracy": 0.817173547404153,
300
+ "eval_not_syn_loss": 0.5734513401985168,
301
+ "eval_not_syn_runtime": 110.4329,
302
+ "eval_not_syn_samples_per_second": 12.641,
303
+ "eval_not_syn_steps_per_second": 1.585,
304
+ "eval_num_tokens": 3414579.0,
305
+ "step": 1224
306
+ },
307
+ {
308
+ "epoch": 3.0,
309
+ "eval_entropy": 0.5267024655001504,
310
+ "eval_mean_token_accuracy": 0.8285774500029428,
311
+ "eval_num_tokens": 3414579.0,
312
+ "eval_syn_loss": 0.544999361038208,
313
+ "eval_syn_runtime": 117.3289,
314
+ "eval_syn_samples_per_second": 11.898,
315
+ "eval_syn_steps_per_second": 1.492,
316
+ "step": 1224
317
+ },
318
+ {
319
+ "entropy": 0.502170312676938,
320
+ "epoch": 3.063842848373235,
321
+ "grad_norm": 0.44433876872062683,
322
+ "learning_rate": 0.0002616001498147458,
323
+ "loss": 0.448189811706543,
324
+ "mean_token_accuracy": 0.8443391725496592,
325
+ "num_tokens": 3490514.0,
326
+ "step": 1250
327
+ },
328
+ {
329
+ "entropy": 0.46903550207614897,
330
+ "epoch": 3.1866175567833026,
331
+ "grad_norm": 0.2440977245569229,
332
+ "learning_rate": 0.0002572895739174909,
333
+ "loss": 0.4171265029907227,
334
+ "mean_token_accuracy": 0.8530975985527038,
335
+ "num_tokens": 3626328.0,
336
+ "step": 1300
337
+ },
338
+ {
339
+ "entropy": 0.4750825077295303,
340
+ "epoch": 3.3093922651933703,
341
+ "grad_norm": 0.3219708800315857,
342
+ "learning_rate": 0.0002527814235597817,
343
+ "loss": 0.4262152099609375,
344
+ "mean_token_accuracy": 0.8510907486081123,
345
+ "num_tokens": 3768118.0,
346
+ "step": 1350
347
+ },
348
+ {
349
+ "entropy": 0.479280876070261,
350
+ "epoch": 3.4321669736034375,
351
+ "grad_norm": 0.35597166419029236,
352
+ "learning_rate": 0.0002480839470819708,
353
+ "loss": 0.42484298706054685,
354
+ "mean_token_accuracy": 0.8504850694537163,
355
+ "num_tokens": 3904913.0,
356
+ "step": 1400
357
+ },
358
+ {
359
+ "entropy": 0.48920651733875276,
360
+ "epoch": 3.554941682013505,
361
+ "grad_norm": 0.2936934232711792,
362
+ "learning_rate": 0.00024320573922507465,
363
+ "loss": 0.43512439727783203,
364
+ "mean_token_accuracy": 0.8497199699282646,
365
+ "num_tokens": 4038110.0,
366
+ "step": 1450
367
+ },
368
+ {
369
+ "entropy": 0.47630224615335465,
370
+ "epoch": 3.677716390423573,
371
+ "grad_norm": 0.30153411626815796,
372
+ "learning_rate": 0.00023815572540539982,
373
+ "loss": 0.4236162567138672,
374
+ "mean_token_accuracy": 0.8507336723804474,
375
+ "num_tokens": 4180486.0,
376
+ "step": 1500
377
+ },
378
+ {
379
+ "entropy": 0.4737357534468174,
380
+ "epoch": 3.80049109883364,
381
+ "grad_norm": 0.3035024106502533,
382
+ "learning_rate": 0.00023294314538414883,
383
+ "loss": 0.42273353576660155,
384
+ "mean_token_accuracy": 0.8513598147034646,
385
+ "num_tokens": 4322795.0,
386
+ "step": 1550
387
+ },
388
+ {
389
+ "entropy": 0.4796703179180622,
390
+ "epoch": 3.9232658072437077,
391
+ "grad_norm": 0.2834164500236511,
392
+ "learning_rate": 0.0002275775363618849,
393
+ "loss": 0.4326186752319336,
394
+ "mean_token_accuracy": 0.8495047062635421,
395
+ "num_tokens": 4461389.0,
396
+ "step": 1600
397
+ },
398
+ {
399
+ "epoch": 4.0,
400
+ "eval_entropy": 0.5010706964560917,
401
+ "eval_mean_token_accuracy": 0.8040556676047189,
402
+ "eval_not_syn_loss": 0.5851709842681885,
403
+ "eval_not_syn_runtime": 110.3987,
404
+ "eval_not_syn_samples_per_second": 12.645,
405
+ "eval_not_syn_steps_per_second": 1.585,
406
+ "eval_num_tokens": 4552772.0,
407
+ "step": 1632
408
+ },
409
+ {
410
+ "epoch": 4.0,
411
+ "eval_entropy": 0.4733030595098223,
412
+ "eval_mean_token_accuracy": 0.8468134031976973,
413
+ "eval_num_tokens": 4552772.0,
414
+ "eval_syn_loss": 0.5429526567459106,
415
+ "eval_syn_runtime": 117.3735,
416
+ "eval_syn_samples_per_second": 11.894,
417
+ "eval_syn_steps_per_second": 1.491,
418
+ "step": 1632
419
+ }
420
+ ],
421
+ "logging_steps": 50,
422
+ "max_steps": 4080,
423
+ "num_input_tokens_seen": 0,
424
+ "num_train_epochs": 10,
425
+ "save_steps": 500,
426
+ "stateful_callbacks": {
427
+ "TrainerControl": {
428
+ "args": {
429
+ "should_epoch_stop": false,
430
+ "should_evaluate": false,
431
+ "should_log": false,
432
+ "should_save": true,
433
+ "should_training_stop": false
434
+ },
435
+ "attributes": {}
436
+ }
437
+ },
438
+ "total_flos": 7.408322545728922e+17,
439
+ "train_batch_size": 4,
440
+ "trial_name": null,
441
+ "trial_params": null
442
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2040/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.06743035903930279,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "v_proj",
34
+ "up_proj",
35
+ "k_proj",
36
+ "gate_proj",
37
+ "o_proj",
38
+ "down_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2040/chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# 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>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\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" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
+ {%- if loop.last or (not loop.last and reasoning_content) %}
41
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
42
+ {%- else %}
43
+ {{- '<|im_start|>' + message.role + '\n' + content }}
44
+ {%- endif %}
45
+ {%- else %}
46
+ {{- '<|im_start|>' + message.role + '\n' + content }}
47
+ {%- endif %}
48
+ {%- if message.tool_calls %}
49
+ {%- for tool_call in message.tool_calls %}
50
+ {%- if (loop.first and content) or (not loop.first) %}
51
+ {{- '\n' }}
52
+ {%- endif %}
53
+ {%- if tool_call.function %}
54
+ {%- set tool_call = tool_call.function %}
55
+ {%- endif %}
56
+ {{- '<tool_call>\n{"name": "' }}
57
+ {{- tool_call.name }}
58
+ {{- '", "arguments": ' }}
59
+ {%- if tool_call.arguments is string %}
60
+ {{- tool_call.arguments }}
61
+ {%- else %}
62
+ {{- tool_call.arguments | tojson }}
63
+ {%- endif %}
64
+ {{- '}\n</tool_call>' }}
65
+ {%- endfor %}
66
+ {%- endif %}
67
+ {{- '<|im_end|>\n' }}
68
+ {%- elif message.role == "tool" %}
69
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
70
+ {{- '<|im_start|>user' }}
71
+ {%- endif %}
72
+ {{- '\n<tool_response>\n' }}
73
+ {{- message.content }}
74
+ {{- '\n</tool_response>' }}
75
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
76
+ {{- '<|im_end|>\n' }}
77
+ {%- endif %}
78
+ {%- endif %}
79
+ {%- endfor %}
80
+ {%- if add_generation_prompt %}
81
+ {{- '<|im_start|>assistant\n' }}
82
+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
84
+ {%- endif %}
85
+ {%- endif %}
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2040/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2040/trainer_state.json ADDED
@@ -0,0 +1,544 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 5.0,
6
+ "eval_steps": 500,
7
+ "global_step": 2040,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 1.84347476541996,
14
+ "epoch": 0.12277470841006753,
15
+ "grad_norm": 0.5139632821083069,
16
+ "learning_rate": 3.586242144074487e-05,
17
+ "loss": 1.7810469055175782,
18
+ "mean_token_accuracy": 0.6252794374525547,
19
+ "num_tokens": 141525.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 0.8905406814813613,
24
+ "epoch": 0.24554941682013506,
25
+ "grad_norm": 0.6561369299888611,
26
+ "learning_rate": 7.245672903334166e-05,
27
+ "loss": 0.83647705078125,
28
+ "mean_token_accuracy": 0.7502138108015061,
29
+ "num_tokens": 276389.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 0.7635939045250416,
34
+ "epoch": 0.3683241252302026,
35
+ "grad_norm": 0.4626609683036804,
36
+ "learning_rate": 0.00010905103662593846,
37
+ "loss": 0.7156005096435547,
38
+ "mean_token_accuracy": 0.7786111453175545,
39
+ "num_tokens": 422723.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 0.7165373960137367,
44
+ "epoch": 0.4910988336402701,
45
+ "grad_norm": 0.4098263680934906,
46
+ "learning_rate": 0.00014564534421853526,
47
+ "loss": 0.67410400390625,
48
+ "mean_token_accuracy": 0.7880693352222443,
49
+ "num_tokens": 560353.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 0.7035001173615456,
54
+ "epoch": 0.6138735420503376,
55
+ "grad_norm": 0.33693116903305054,
56
+ "learning_rate": 0.0001822396518111321,
57
+ "loss": 0.6591246032714844,
58
+ "mean_token_accuracy": 0.7931166198849678,
59
+ "num_tokens": 702231.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "entropy": 0.6862310113012791,
64
+ "epoch": 0.7366482504604052,
65
+ "grad_norm": 0.31358352303504944,
66
+ "learning_rate": 0.00021883395940372884,
67
+ "loss": 0.6399201202392578,
68
+ "mean_token_accuracy": 0.7952846321463585,
69
+ "num_tokens": 842451.0,
70
+ "step": 300
71
+ },
72
+ {
73
+ "entropy": 0.6739526629447937,
74
+ "epoch": 0.8594229588704727,
75
+ "grad_norm": 0.4029310941696167,
76
+ "learning_rate": 0.00025542826699632564,
77
+ "loss": 0.6259587860107422,
78
+ "mean_token_accuracy": 0.8014272648096085,
79
+ "num_tokens": 980824.0,
80
+ "step": 350
81
+ },
82
+ {
83
+ "entropy": 0.6681017802655697,
84
+ "epoch": 0.9821976672805403,
85
+ "grad_norm": 0.3852674961090088,
86
+ "learning_rate": 0.00029202257458892247,
87
+ "loss": 0.6253739166259765,
88
+ "mean_token_accuracy": 0.8020894029736518,
89
+ "num_tokens": 1118697.0,
90
+ "step": 400
91
+ },
92
+ {
93
+ "epoch": 1.0,
94
+ "eval_entropy": 0.6994867869785854,
95
+ "eval_mean_token_accuracy": 0.7849237663405282,
96
+ "eval_not_syn_loss": 0.6411319971084595,
97
+ "eval_not_syn_runtime": 110.7069,
98
+ "eval_not_syn_samples_per_second": 12.61,
99
+ "eval_not_syn_steps_per_second": 1.581,
100
+ "eval_num_tokens": 1138193.0,
101
+ "step": 408
102
+ },
103
+ {
104
+ "epoch": 1.0,
105
+ "eval_entropy": 0.6563669620241438,
106
+ "eval_mean_token_accuracy": 0.8207755245481219,
107
+ "eval_num_tokens": 1138193.0,
108
+ "eval_syn_loss": 0.6019997000694275,
109
+ "eval_syn_runtime": 117.5102,
110
+ "eval_syn_samples_per_second": 11.88,
111
+ "eval_syn_steps_per_second": 1.489,
112
+ "step": 408
113
+ },
114
+ {
115
+ "entropy": 0.6358311531809986,
116
+ "epoch": 1.1031307550644567,
117
+ "grad_norm": 0.34651753306388855,
118
+ "learning_rate": 0.00029851770373635705,
119
+ "loss": 0.5962887191772461,
120
+ "mean_token_accuracy": 0.8082216354796123,
121
+ "num_tokens": 1263520.0,
122
+ "step": 450
123
+ },
124
+ {
125
+ "entropy": 0.6196171633899212,
126
+ "epoch": 1.2259054634745243,
127
+ "grad_norm": 0.28438520431518555,
128
+ "learning_rate": 0.0002981572760785325,
129
+ "loss": 0.5779812622070313,
130
+ "mean_token_accuracy": 0.8130937224626541,
131
+ "num_tokens": 1405904.0,
132
+ "step": 500
133
+ },
134
+ {
135
+ "entropy": 0.624696860164404,
136
+ "epoch": 1.3486801718845918,
137
+ "grad_norm": 0.3563199043273926,
138
+ "learning_rate": 0.000297524500341339,
139
+ "loss": 0.5760621643066406,
140
+ "mean_token_accuracy": 0.8123102071881294,
141
+ "num_tokens": 1541752.0,
142
+ "step": 550
143
+ },
144
+ {
145
+ "entropy": 0.6383913996815681,
146
+ "epoch": 1.4714548802946594,
147
+ "grad_norm": 0.32306408882141113,
148
+ "learning_rate": 0.00029662053428333246,
149
+ "loss": 0.5874852371215821,
150
+ "mean_token_accuracy": 0.8098450502753258,
151
+ "num_tokens": 1675695.0,
152
+ "step": 600
153
+ },
154
+ {
155
+ "entropy": 0.6132303845882415,
156
+ "epoch": 1.5942295887047269,
157
+ "grad_norm": 0.3351016044616699,
158
+ "learning_rate": 0.0002954470318466225,
159
+ "loss": 0.5624140930175782,
160
+ "mean_token_accuracy": 0.8152360209822654,
161
+ "num_tokens": 1814294.0,
162
+ "step": 650
163
+ },
164
+ {
165
+ "entropy": 0.6104247760772705,
166
+ "epoch": 1.7170042971147943,
167
+ "grad_norm": 0.25214216113090515,
168
+ "learning_rate": 0.00029400614013073664,
169
+ "loss": 0.5608636093139648,
170
+ "mean_token_accuracy": 0.8154580116271972,
171
+ "num_tokens": 1953015.0,
172
+ "step": 700
173
+ },
174
+ {
175
+ "entropy": 0.6093999670445919,
176
+ "epoch": 1.839779005524862,
177
+ "grad_norm": 0.25383421778678894,
178
+ "learning_rate": 0.0002923004954641779,
179
+ "loss": 0.5591114044189454,
180
+ "mean_token_accuracy": 0.817192807495594,
181
+ "num_tokens": 2095011.0,
182
+ "step": 750
183
+ },
184
+ {
185
+ "entropy": 0.6029203486442566,
186
+ "epoch": 1.9625537139349294,
187
+ "grad_norm": 0.25239020586013794,
188
+ "learning_rate": 0.0002903332185808646,
189
+ "loss": 0.5529713821411133,
190
+ "mean_token_accuracy": 0.8182568901777267,
191
+ "num_tokens": 2235222.0,
192
+ "step": 800
193
+ },
194
+ {
195
+ "epoch": 2.0,
196
+ "eval_entropy": 0.6090937239783151,
197
+ "eval_mean_token_accuracy": 0.7930077845709664,
198
+ "eval_not_syn_loss": 0.5995497107505798,
199
+ "eval_not_syn_runtime": 110.3965,
200
+ "eval_not_syn_samples_per_second": 12.645,
201
+ "eval_not_syn_steps_per_second": 1.585,
202
+ "eval_num_tokens": 2276386.0,
203
+ "step": 816
204
+ },
205
+ {
206
+ "epoch": 2.0,
207
+ "eval_entropy": 0.5718999467577253,
208
+ "eval_mean_token_accuracy": 0.84101799760546,
209
+ "eval_num_tokens": 2276386.0,
210
+ "eval_syn_loss": 0.5510491728782654,
211
+ "eval_syn_runtime": 117.3523,
212
+ "eval_syn_samples_per_second": 11.896,
213
+ "eval_syn_steps_per_second": 1.491,
214
+ "step": 816
215
+ },
216
+ {
217
+ "entropy": 0.5504116184517817,
218
+ "epoch": 2.0834868017188457,
219
+ "grad_norm": 0.23869574069976807,
220
+ "learning_rate": 0.0002881079089102777,
221
+ "loss": 0.5012085723876953,
222
+ "mean_token_accuracy": 0.8299948473267144,
223
+ "num_tokens": 2374282.0,
224
+ "step": 850
225
+ },
226
+ {
227
+ "entropy": 0.5487727333605289,
228
+ "epoch": 2.2062615101289134,
229
+ "grad_norm": 0.3087007403373718,
230
+ "learning_rate": 0.00028562863799176175,
231
+ "loss": 0.4988512802124023,
232
+ "mean_token_accuracy": 0.8319525212049484,
233
+ "num_tokens": 2507968.0,
234
+ "step": 900
235
+ },
236
+ {
237
+ "entropy": 0.5354658082127571,
238
+ "epoch": 2.329036218538981,
239
+ "grad_norm": 0.2878682613372803,
240
+ "learning_rate": 0.00028289994202503066,
241
+ "loss": 0.4919636917114258,
242
+ "mean_token_accuracy": 0.8349191680550575,
243
+ "num_tokens": 2650824.0,
244
+ "step": 950
245
+ },
246
+ {
247
+ "entropy": 0.5529813665151596,
248
+ "epoch": 2.4518109269490487,
249
+ "grad_norm": 0.2625614106655121,
250
+ "learning_rate": 0.00027992681357050643,
251
+ "loss": 0.5027856063842774,
252
+ "mean_token_accuracy": 0.8296491304039955,
253
+ "num_tokens": 2784681.0,
254
+ "step": 1000
255
+ },
256
+ {
257
+ "entropy": 0.5497122646868229,
258
+ "epoch": 2.574585635359116,
259
+ "grad_norm": 0.271993488073349,
260
+ "learning_rate": 0.00027671469241467785,
261
+ "loss": 0.49415691375732423,
262
+ "mean_token_accuracy": 0.8312779009342194,
263
+ "num_tokens": 2923442.0,
264
+ "step": 1050
265
+ },
266
+ {
267
+ "entropy": 0.5419583600759507,
268
+ "epoch": 2.6973603437691835,
269
+ "grad_norm": 0.23863548040390015,
270
+ "learning_rate": 0.00027326945561719136,
271
+ "loss": 0.49570159912109374,
272
+ "mean_token_accuracy": 0.8327477470040321,
273
+ "num_tokens": 3068468.0,
274
+ "step": 1100
275
+ },
276
+ {
277
+ "entropy": 0.5491574917733669,
278
+ "epoch": 2.820135052179251,
279
+ "grad_norm": 0.29003486037254333,
280
+ "learning_rate": 0.00026959740675788486,
281
+ "loss": 0.4965015411376953,
282
+ "mean_token_accuracy": 0.8336276519298553,
283
+ "num_tokens": 3206450.0,
284
+ "step": 1150
285
+ },
286
+ {
287
+ "entropy": 0.5377901926636696,
288
+ "epoch": 2.942909760589319,
289
+ "grad_norm": 0.27723413705825806,
290
+ "learning_rate": 0.0002657052644034388,
291
+ "loss": 0.48964527130126956,
292
+ "mean_token_accuracy": 0.8356471425294876,
293
+ "num_tokens": 3348780.0,
294
+ "step": 1200
295
+ },
296
+ {
297
+ "epoch": 3.0,
298
+ "eval_entropy": 0.5576562706061772,
299
+ "eval_mean_token_accuracy": 0.817173547404153,
300
+ "eval_not_syn_loss": 0.5734513401985168,
301
+ "eval_not_syn_runtime": 110.4329,
302
+ "eval_not_syn_samples_per_second": 12.641,
303
+ "eval_not_syn_steps_per_second": 1.585,
304
+ "eval_num_tokens": 3414579.0,
305
+ "step": 1224
306
+ },
307
+ {
308
+ "epoch": 3.0,
309
+ "eval_entropy": 0.5267024655001504,
310
+ "eval_mean_token_accuracy": 0.8285774500029428,
311
+ "eval_num_tokens": 3414579.0,
312
+ "eval_syn_loss": 0.544999361038208,
313
+ "eval_syn_runtime": 117.3289,
314
+ "eval_syn_samples_per_second": 11.898,
315
+ "eval_syn_steps_per_second": 1.492,
316
+ "step": 1224
317
+ },
318
+ {
319
+ "entropy": 0.502170312676938,
320
+ "epoch": 3.063842848373235,
321
+ "grad_norm": 0.44433876872062683,
322
+ "learning_rate": 0.0002616001498147458,
323
+ "loss": 0.448189811706543,
324
+ "mean_token_accuracy": 0.8443391725496592,
325
+ "num_tokens": 3490514.0,
326
+ "step": 1250
327
+ },
328
+ {
329
+ "entropy": 0.46903550207614897,
330
+ "epoch": 3.1866175567833026,
331
+ "grad_norm": 0.2440977245569229,
332
+ "learning_rate": 0.0002572895739174909,
333
+ "loss": 0.4171265029907227,
334
+ "mean_token_accuracy": 0.8530975985527038,
335
+ "num_tokens": 3626328.0,
336
+ "step": 1300
337
+ },
338
+ {
339
+ "entropy": 0.4750825077295303,
340
+ "epoch": 3.3093922651933703,
341
+ "grad_norm": 0.3219708800315857,
342
+ "learning_rate": 0.0002527814235597817,
343
+ "loss": 0.4262152099609375,
344
+ "mean_token_accuracy": 0.8510907486081123,
345
+ "num_tokens": 3768118.0,
346
+ "step": 1350
347
+ },
348
+ {
349
+ "entropy": 0.479280876070261,
350
+ "epoch": 3.4321669736034375,
351
+ "grad_norm": 0.35597166419029236,
352
+ "learning_rate": 0.0002480839470819708,
353
+ "loss": 0.42484298706054685,
354
+ "mean_token_accuracy": 0.8504850694537163,
355
+ "num_tokens": 3904913.0,
356
+ "step": 1400
357
+ },
358
+ {
359
+ "entropy": 0.48920651733875276,
360
+ "epoch": 3.554941682013505,
361
+ "grad_norm": 0.2936934232711792,
362
+ "learning_rate": 0.00024320573922507465,
363
+ "loss": 0.43512439727783203,
364
+ "mean_token_accuracy": 0.8497199699282646,
365
+ "num_tokens": 4038110.0,
366
+ "step": 1450
367
+ },
368
+ {
369
+ "entropy": 0.47630224615335465,
370
+ "epoch": 3.677716390423573,
371
+ "grad_norm": 0.30153411626815796,
372
+ "learning_rate": 0.00023815572540539982,
373
+ "loss": 0.4236162567138672,
374
+ "mean_token_accuracy": 0.8507336723804474,
375
+ "num_tokens": 4180486.0,
376
+ "step": 1500
377
+ },
378
+ {
379
+ "entropy": 0.4737357534468174,
380
+ "epoch": 3.80049109883364,
381
+ "grad_norm": 0.3035024106502533,
382
+ "learning_rate": 0.00023294314538414883,
383
+ "loss": 0.42273353576660155,
384
+ "mean_token_accuracy": 0.8513598147034646,
385
+ "num_tokens": 4322795.0,
386
+ "step": 1550
387
+ },
388
+ {
389
+ "entropy": 0.4796703179180622,
390
+ "epoch": 3.9232658072437077,
391
+ "grad_norm": 0.2834164500236511,
392
+ "learning_rate": 0.0002275775363618849,
393
+ "loss": 0.4326186752319336,
394
+ "mean_token_accuracy": 0.8495047062635421,
395
+ "num_tokens": 4461389.0,
396
+ "step": 1600
397
+ },
398
+ {
399
+ "epoch": 4.0,
400
+ "eval_entropy": 0.5010706964560917,
401
+ "eval_mean_token_accuracy": 0.8040556676047189,
402
+ "eval_not_syn_loss": 0.5851709842681885,
403
+ "eval_not_syn_runtime": 110.3987,
404
+ "eval_not_syn_samples_per_second": 12.645,
405
+ "eval_not_syn_steps_per_second": 1.585,
406
+ "eval_num_tokens": 4552772.0,
407
+ "step": 1632
408
+ },
409
+ {
410
+ "epoch": 4.0,
411
+ "eval_entropy": 0.4733030595098223,
412
+ "eval_mean_token_accuracy": 0.8468134031976973,
413
+ "eval_num_tokens": 4552772.0,
414
+ "eval_syn_loss": 0.5429526567459106,
415
+ "eval_syn_runtime": 117.3735,
416
+ "eval_syn_samples_per_second": 11.894,
417
+ "eval_syn_steps_per_second": 1.491,
418
+ "step": 1632
419
+ },
420
+ {
421
+ "entropy": 0.4337418030966357,
422
+ "epoch": 4.044198895027624,
423
+ "grad_norm": 0.31872114539146423,
424
+ "learning_rate": 0.00022206871552878668,
425
+ "loss": 0.3853663635253906,
426
+ "mean_token_accuracy": 0.8626761530256514,
427
+ "num_tokens": 4602893.0,
428
+ "step": 1650
429
+ },
430
+ {
431
+ "entropy": 0.39638415291905404,
432
+ "epoch": 4.166973603437691,
433
+ "grad_norm": 0.4238921105861664,
434
+ "learning_rate": 0.00021642676210261927,
435
+ "loss": 0.3409381866455078,
436
+ "mean_token_accuracy": 0.8753284150362015,
437
+ "num_tokens": 4735831.0,
438
+ "step": 1700
439
+ },
440
+ {
441
+ "entropy": 0.38611642628908155,
442
+ "epoch": 4.2897483118477595,
443
+ "grad_norm": 0.3248041868209839,
444
+ "learning_rate": 0.00021066199888728683,
445
+ "loss": 0.3360607147216797,
446
+ "mean_token_accuracy": 0.8773713061213493,
447
+ "num_tokens": 4880737.0,
448
+ "step": 1750
449
+ },
450
+ {
451
+ "entropy": 0.3938452216982842,
452
+ "epoch": 4.412523020257827,
453
+ "grad_norm": 0.33257895708084106,
454
+ "learning_rate": 0.00020478497338570733,
455
+ "loss": 0.34164436340332033,
456
+ "mean_token_accuracy": 0.8741997224092484,
457
+ "num_tokens": 5022337.0,
458
+ "step": 1800
459
+ },
460
+ {
461
+ "entropy": 0.39718300312757493,
462
+ "epoch": 4.535297728667894,
463
+ "grad_norm": 0.3596663177013397,
464
+ "learning_rate": 0.00019880643850156687,
465
+ "loss": 0.34481029510498046,
466
+ "mean_token_accuracy": 0.8744558349251748,
467
+ "num_tokens": 5158628.0,
468
+ "step": 1850
469
+ },
470
+ {
471
+ "entropy": 0.39785161226987836,
472
+ "epoch": 4.658072437077962,
473
+ "grad_norm": 0.3325240910053253,
474
+ "learning_rate": 0.00019273733286526186,
475
+ "loss": 0.34437469482421873,
476
+ "mean_token_accuracy": 0.8753779655694962,
477
+ "num_tokens": 5295686.0,
478
+ "step": 1900
479
+ },
480
+ {
481
+ "entropy": 0.3919269675016403,
482
+ "epoch": 4.780847145488029,
483
+ "grad_norm": 0.33487579226493835,
484
+ "learning_rate": 0.00018658876082002678,
485
+ "loss": 0.34184349060058594,
486
+ "mean_token_accuracy": 0.8743453392386437,
487
+ "num_tokens": 5440097.0,
488
+ "step": 1950
489
+ },
490
+ {
491
+ "entropy": 0.3975044973194599,
492
+ "epoch": 4.903621853898097,
493
+ "grad_norm": 0.27435103058815,
494
+ "learning_rate": 0.00018037197210486505,
495
+ "loss": 0.3441514205932617,
496
+ "mean_token_accuracy": 0.87376918643713,
497
+ "num_tokens": 5582367.0,
498
+ "step": 2000
499
+ },
500
+ {
501
+ "epoch": 5.0,
502
+ "eval_entropy": 0.4433087486880166,
503
+ "eval_mean_token_accuracy": 0.8042705031803676,
504
+ "eval_not_syn_loss": 0.6243218183517456,
505
+ "eval_not_syn_runtime": 110.4378,
506
+ "eval_not_syn_samples_per_second": 12.641,
507
+ "eval_not_syn_steps_per_second": 1.585,
508
+ "eval_num_tokens": 5690965.0,
509
+ "step": 2040
510
+ },
511
+ {
512
+ "epoch": 5.0,
513
+ "eval_entropy": 0.4196248413835253,
514
+ "eval_mean_token_accuracy": 0.8377946128164019,
515
+ "eval_num_tokens": 5690965.0,
516
+ "eval_syn_loss": 0.5790094137191772,
517
+ "eval_syn_runtime": 117.3572,
518
+ "eval_syn_samples_per_second": 11.895,
519
+ "eval_syn_steps_per_second": 1.491,
520
+ "step": 2040
521
+ }
522
+ ],
523
+ "logging_steps": 50,
524
+ "max_steps": 4080,
525
+ "num_input_tokens_seen": 0,
526
+ "num_train_epochs": 10,
527
+ "save_steps": 500,
528
+ "stateful_callbacks": {
529
+ "TrainerControl": {
530
+ "args": {
531
+ "should_epoch_stop": false,
532
+ "should_evaluate": false,
533
+ "should_log": false,
534
+ "should_save": true,
535
+ "should_training_stop": false
536
+ },
537
+ "attributes": {}
538
+ }
539
+ },
540
+ "total_flos": 9.25810110441216e+17,
541
+ "train_batch_size": 4,
542
+ "trial_name": null,
543
+ "trial_params": null
544
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2448/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2448/chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# 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>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\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" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
+ {%- if loop.last or (not loop.last and reasoning_content) %}
41
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
42
+ {%- else %}
43
+ {{- '<|im_start|>' + message.role + '\n' + content }}
44
+ {%- endif %}
45
+ {%- else %}
46
+ {{- '<|im_start|>' + message.role + '\n' + content }}
47
+ {%- endif %}
48
+ {%- if message.tool_calls %}
49
+ {%- for tool_call in message.tool_calls %}
50
+ {%- if (loop.first and content) or (not loop.first) %}
51
+ {{- '\n' }}
52
+ {%- endif %}
53
+ {%- if tool_call.function %}
54
+ {%- set tool_call = tool_call.function %}
55
+ {%- endif %}
56
+ {{- '<tool_call>\n{"name": "' }}
57
+ {{- tool_call.name }}
58
+ {{- '", "arguments": ' }}
59
+ {%- if tool_call.arguments is string %}
60
+ {{- tool_call.arguments }}
61
+ {%- else %}
62
+ {{- tool_call.arguments | tojson }}
63
+ {%- endif %}
64
+ {{- '}\n</tool_call>' }}
65
+ {%- endfor %}
66
+ {%- endif %}
67
+ {{- '<|im_end|>\n' }}
68
+ {%- elif message.role == "tool" %}
69
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
70
+ {{- '<|im_start|>user' }}
71
+ {%- endif %}
72
+ {{- '\n<tool_response>\n' }}
73
+ {{- message.content }}
74
+ {{- '\n</tool_response>' }}
75
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
76
+ {{- '<|im_end|>\n' }}
77
+ {%- endif %}
78
+ {%- endif %}
79
+ {%- endfor %}
80
+ {%- if add_generation_prompt %}
81
+ {{- '<|im_start|>assistant\n' }}
82
+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
84
+ {%- endif %}
85
+ {%- endif %}
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2448/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2448/trainer_state.json ADDED
@@ -0,0 +1,646 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 6.0,
6
+ "eval_steps": 500,
7
+ "global_step": 2448,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 1.84347476541996,
14
+ "epoch": 0.12277470841006753,
15
+ "grad_norm": 0.5139632821083069,
16
+ "learning_rate": 3.586242144074487e-05,
17
+ "loss": 1.7810469055175782,
18
+ "mean_token_accuracy": 0.6252794374525547,
19
+ "num_tokens": 141525.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 0.8905406814813613,
24
+ "epoch": 0.24554941682013506,
25
+ "grad_norm": 0.6561369299888611,
26
+ "learning_rate": 7.245672903334166e-05,
27
+ "loss": 0.83647705078125,
28
+ "mean_token_accuracy": 0.7502138108015061,
29
+ "num_tokens": 276389.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 0.7635939045250416,
34
+ "epoch": 0.3683241252302026,
35
+ "grad_norm": 0.4626609683036804,
36
+ "learning_rate": 0.00010905103662593846,
37
+ "loss": 0.7156005096435547,
38
+ "mean_token_accuracy": 0.7786111453175545,
39
+ "num_tokens": 422723.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 0.7165373960137367,
44
+ "epoch": 0.4910988336402701,
45
+ "grad_norm": 0.4098263680934906,
46
+ "learning_rate": 0.00014564534421853526,
47
+ "loss": 0.67410400390625,
48
+ "mean_token_accuracy": 0.7880693352222443,
49
+ "num_tokens": 560353.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 0.7035001173615456,
54
+ "epoch": 0.6138735420503376,
55
+ "grad_norm": 0.33693116903305054,
56
+ "learning_rate": 0.0001822396518111321,
57
+ "loss": 0.6591246032714844,
58
+ "mean_token_accuracy": 0.7931166198849678,
59
+ "num_tokens": 702231.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "entropy": 0.6862310113012791,
64
+ "epoch": 0.7366482504604052,
65
+ "grad_norm": 0.31358352303504944,
66
+ "learning_rate": 0.00021883395940372884,
67
+ "loss": 0.6399201202392578,
68
+ "mean_token_accuracy": 0.7952846321463585,
69
+ "num_tokens": 842451.0,
70
+ "step": 300
71
+ },
72
+ {
73
+ "entropy": 0.6739526629447937,
74
+ "epoch": 0.8594229588704727,
75
+ "grad_norm": 0.4029310941696167,
76
+ "learning_rate": 0.00025542826699632564,
77
+ "loss": 0.6259587860107422,
78
+ "mean_token_accuracy": 0.8014272648096085,
79
+ "num_tokens": 980824.0,
80
+ "step": 350
81
+ },
82
+ {
83
+ "entropy": 0.6681017802655697,
84
+ "epoch": 0.9821976672805403,
85
+ "grad_norm": 0.3852674961090088,
86
+ "learning_rate": 0.00029202257458892247,
87
+ "loss": 0.6253739166259765,
88
+ "mean_token_accuracy": 0.8020894029736518,
89
+ "num_tokens": 1118697.0,
90
+ "step": 400
91
+ },
92
+ {
93
+ "epoch": 1.0,
94
+ "eval_entropy": 0.6994867869785854,
95
+ "eval_mean_token_accuracy": 0.7849237663405282,
96
+ "eval_not_syn_loss": 0.6411319971084595,
97
+ "eval_not_syn_runtime": 110.7069,
98
+ "eval_not_syn_samples_per_second": 12.61,
99
+ "eval_not_syn_steps_per_second": 1.581,
100
+ "eval_num_tokens": 1138193.0,
101
+ "step": 408
102
+ },
103
+ {
104
+ "epoch": 1.0,
105
+ "eval_entropy": 0.6563669620241438,
106
+ "eval_mean_token_accuracy": 0.8207755245481219,
107
+ "eval_num_tokens": 1138193.0,
108
+ "eval_syn_loss": 0.6019997000694275,
109
+ "eval_syn_runtime": 117.5102,
110
+ "eval_syn_samples_per_second": 11.88,
111
+ "eval_syn_steps_per_second": 1.489,
112
+ "step": 408
113
+ },
114
+ {
115
+ "entropy": 0.6358311531809986,
116
+ "epoch": 1.1031307550644567,
117
+ "grad_norm": 0.34651753306388855,
118
+ "learning_rate": 0.00029851770373635705,
119
+ "loss": 0.5962887191772461,
120
+ "mean_token_accuracy": 0.8082216354796123,
121
+ "num_tokens": 1263520.0,
122
+ "step": 450
123
+ },
124
+ {
125
+ "entropy": 0.6196171633899212,
126
+ "epoch": 1.2259054634745243,
127
+ "grad_norm": 0.28438520431518555,
128
+ "learning_rate": 0.0002981572760785325,
129
+ "loss": 0.5779812622070313,
130
+ "mean_token_accuracy": 0.8130937224626541,
131
+ "num_tokens": 1405904.0,
132
+ "step": 500
133
+ },
134
+ {
135
+ "entropy": 0.624696860164404,
136
+ "epoch": 1.3486801718845918,
137
+ "grad_norm": 0.3563199043273926,
138
+ "learning_rate": 0.000297524500341339,
139
+ "loss": 0.5760621643066406,
140
+ "mean_token_accuracy": 0.8123102071881294,
141
+ "num_tokens": 1541752.0,
142
+ "step": 550
143
+ },
144
+ {
145
+ "entropy": 0.6383913996815681,
146
+ "epoch": 1.4714548802946594,
147
+ "grad_norm": 0.32306408882141113,
148
+ "learning_rate": 0.00029662053428333246,
149
+ "loss": 0.5874852371215821,
150
+ "mean_token_accuracy": 0.8098450502753258,
151
+ "num_tokens": 1675695.0,
152
+ "step": 600
153
+ },
154
+ {
155
+ "entropy": 0.6132303845882415,
156
+ "epoch": 1.5942295887047269,
157
+ "grad_norm": 0.3351016044616699,
158
+ "learning_rate": 0.0002954470318466225,
159
+ "loss": 0.5624140930175782,
160
+ "mean_token_accuracy": 0.8152360209822654,
161
+ "num_tokens": 1814294.0,
162
+ "step": 650
163
+ },
164
+ {
165
+ "entropy": 0.6104247760772705,
166
+ "epoch": 1.7170042971147943,
167
+ "grad_norm": 0.25214216113090515,
168
+ "learning_rate": 0.00029400614013073664,
169
+ "loss": 0.5608636093139648,
170
+ "mean_token_accuracy": 0.8154580116271972,
171
+ "num_tokens": 1953015.0,
172
+ "step": 700
173
+ },
174
+ {
175
+ "entropy": 0.6093999670445919,
176
+ "epoch": 1.839779005524862,
177
+ "grad_norm": 0.25383421778678894,
178
+ "learning_rate": 0.0002923004954641779,
179
+ "loss": 0.5591114044189454,
180
+ "mean_token_accuracy": 0.817192807495594,
181
+ "num_tokens": 2095011.0,
182
+ "step": 750
183
+ },
184
+ {
185
+ "entropy": 0.6029203486442566,
186
+ "epoch": 1.9625537139349294,
187
+ "grad_norm": 0.25239020586013794,
188
+ "learning_rate": 0.0002903332185808646,
189
+ "loss": 0.5529713821411133,
190
+ "mean_token_accuracy": 0.8182568901777267,
191
+ "num_tokens": 2235222.0,
192
+ "step": 800
193
+ },
194
+ {
195
+ "epoch": 2.0,
196
+ "eval_entropy": 0.6090937239783151,
197
+ "eval_mean_token_accuracy": 0.7930077845709664,
198
+ "eval_not_syn_loss": 0.5995497107505798,
199
+ "eval_not_syn_runtime": 110.3965,
200
+ "eval_not_syn_samples_per_second": 12.645,
201
+ "eval_not_syn_steps_per_second": 1.585,
202
+ "eval_num_tokens": 2276386.0,
203
+ "step": 816
204
+ },
205
+ {
206
+ "epoch": 2.0,
207
+ "eval_entropy": 0.5718999467577253,
208
+ "eval_mean_token_accuracy": 0.84101799760546,
209
+ "eval_num_tokens": 2276386.0,
210
+ "eval_syn_loss": 0.5510491728782654,
211
+ "eval_syn_runtime": 117.3523,
212
+ "eval_syn_samples_per_second": 11.896,
213
+ "eval_syn_steps_per_second": 1.491,
214
+ "step": 816
215
+ },
216
+ {
217
+ "entropy": 0.5504116184517817,
218
+ "epoch": 2.0834868017188457,
219
+ "grad_norm": 0.23869574069976807,
220
+ "learning_rate": 0.0002881079089102777,
221
+ "loss": 0.5012085723876953,
222
+ "mean_token_accuracy": 0.8299948473267144,
223
+ "num_tokens": 2374282.0,
224
+ "step": 850
225
+ },
226
+ {
227
+ "entropy": 0.5487727333605289,
228
+ "epoch": 2.2062615101289134,
229
+ "grad_norm": 0.3087007403373718,
230
+ "learning_rate": 0.00028562863799176175,
231
+ "loss": 0.4988512802124023,
232
+ "mean_token_accuracy": 0.8319525212049484,
233
+ "num_tokens": 2507968.0,
234
+ "step": 900
235
+ },
236
+ {
237
+ "entropy": 0.5354658082127571,
238
+ "epoch": 2.329036218538981,
239
+ "grad_norm": 0.2878682613372803,
240
+ "learning_rate": 0.00028289994202503066,
241
+ "loss": 0.4919636917114258,
242
+ "mean_token_accuracy": 0.8349191680550575,
243
+ "num_tokens": 2650824.0,
244
+ "step": 950
245
+ },
246
+ {
247
+ "entropy": 0.5529813665151596,
248
+ "epoch": 2.4518109269490487,
249
+ "grad_norm": 0.2625614106655121,
250
+ "learning_rate": 0.00027992681357050643,
251
+ "loss": 0.5027856063842774,
252
+ "mean_token_accuracy": 0.8296491304039955,
253
+ "num_tokens": 2784681.0,
254
+ "step": 1000
255
+ },
256
+ {
257
+ "entropy": 0.5497122646868229,
258
+ "epoch": 2.574585635359116,
259
+ "grad_norm": 0.271993488073349,
260
+ "learning_rate": 0.00027671469241467785,
261
+ "loss": 0.49415691375732423,
262
+ "mean_token_accuracy": 0.8312779009342194,
263
+ "num_tokens": 2923442.0,
264
+ "step": 1050
265
+ },
266
+ {
267
+ "entropy": 0.5419583600759507,
268
+ "epoch": 2.6973603437691835,
269
+ "grad_norm": 0.23863548040390015,
270
+ "learning_rate": 0.00027326945561719136,
271
+ "loss": 0.49570159912109374,
272
+ "mean_token_accuracy": 0.8327477470040321,
273
+ "num_tokens": 3068468.0,
274
+ "step": 1100
275
+ },
276
+ {
277
+ "entropy": 0.5491574917733669,
278
+ "epoch": 2.820135052179251,
279
+ "grad_norm": 0.29003486037254333,
280
+ "learning_rate": 0.00026959740675788486,
281
+ "loss": 0.4965015411376953,
282
+ "mean_token_accuracy": 0.8336276519298553,
283
+ "num_tokens": 3206450.0,
284
+ "step": 1150
285
+ },
286
+ {
287
+ "entropy": 0.5377901926636696,
288
+ "epoch": 2.942909760589319,
289
+ "grad_norm": 0.27723413705825806,
290
+ "learning_rate": 0.0002657052644034388,
291
+ "loss": 0.48964527130126956,
292
+ "mean_token_accuracy": 0.8356471425294876,
293
+ "num_tokens": 3348780.0,
294
+ "step": 1200
295
+ },
296
+ {
297
+ "epoch": 3.0,
298
+ "eval_entropy": 0.5576562706061772,
299
+ "eval_mean_token_accuracy": 0.817173547404153,
300
+ "eval_not_syn_loss": 0.5734513401985168,
301
+ "eval_not_syn_runtime": 110.4329,
302
+ "eval_not_syn_samples_per_second": 12.641,
303
+ "eval_not_syn_steps_per_second": 1.585,
304
+ "eval_num_tokens": 3414579.0,
305
+ "step": 1224
306
+ },
307
+ {
308
+ "epoch": 3.0,
309
+ "eval_entropy": 0.5267024655001504,
310
+ "eval_mean_token_accuracy": 0.8285774500029428,
311
+ "eval_num_tokens": 3414579.0,
312
+ "eval_syn_loss": 0.544999361038208,
313
+ "eval_syn_runtime": 117.3289,
314
+ "eval_syn_samples_per_second": 11.898,
315
+ "eval_syn_steps_per_second": 1.492,
316
+ "step": 1224
317
+ },
318
+ {
319
+ "entropy": 0.502170312676938,
320
+ "epoch": 3.063842848373235,
321
+ "grad_norm": 0.44433876872062683,
322
+ "learning_rate": 0.0002616001498147458,
323
+ "loss": 0.448189811706543,
324
+ "mean_token_accuracy": 0.8443391725496592,
325
+ "num_tokens": 3490514.0,
326
+ "step": 1250
327
+ },
328
+ {
329
+ "entropy": 0.46903550207614897,
330
+ "epoch": 3.1866175567833026,
331
+ "grad_norm": 0.2440977245569229,
332
+ "learning_rate": 0.0002572895739174909,
333
+ "loss": 0.4171265029907227,
334
+ "mean_token_accuracy": 0.8530975985527038,
335
+ "num_tokens": 3626328.0,
336
+ "step": 1300
337
+ },
338
+ {
339
+ "entropy": 0.4750825077295303,
340
+ "epoch": 3.3093922651933703,
341
+ "grad_norm": 0.3219708800315857,
342
+ "learning_rate": 0.0002527814235597817,
343
+ "loss": 0.4262152099609375,
344
+ "mean_token_accuracy": 0.8510907486081123,
345
+ "num_tokens": 3768118.0,
346
+ "step": 1350
347
+ },
348
+ {
349
+ "entropy": 0.479280876070261,
350
+ "epoch": 3.4321669736034375,
351
+ "grad_norm": 0.35597166419029236,
352
+ "learning_rate": 0.0002480839470819708,
353
+ "loss": 0.42484298706054685,
354
+ "mean_token_accuracy": 0.8504850694537163,
355
+ "num_tokens": 3904913.0,
356
+ "step": 1400
357
+ },
358
+ {
359
+ "entropy": 0.48920651733875276,
360
+ "epoch": 3.554941682013505,
361
+ "grad_norm": 0.2936934232711792,
362
+ "learning_rate": 0.00024320573922507465,
363
+ "loss": 0.43512439727783203,
364
+ "mean_token_accuracy": 0.8497199699282646,
365
+ "num_tokens": 4038110.0,
366
+ "step": 1450
367
+ },
368
+ {
369
+ "entropy": 0.47630224615335465,
370
+ "epoch": 3.677716390423573,
371
+ "grad_norm": 0.30153411626815796,
372
+ "learning_rate": 0.00023815572540539982,
373
+ "loss": 0.4236162567138672,
374
+ "mean_token_accuracy": 0.8507336723804474,
375
+ "num_tokens": 4180486.0,
376
+ "step": 1500
377
+ },
378
+ {
379
+ "entropy": 0.4737357534468174,
380
+ "epoch": 3.80049109883364,
381
+ "grad_norm": 0.3035024106502533,
382
+ "learning_rate": 0.00023294314538414883,
383
+ "loss": 0.42273353576660155,
384
+ "mean_token_accuracy": 0.8513598147034646,
385
+ "num_tokens": 4322795.0,
386
+ "step": 1550
387
+ },
388
+ {
389
+ "entropy": 0.4796703179180622,
390
+ "epoch": 3.9232658072437077,
391
+ "grad_norm": 0.2834164500236511,
392
+ "learning_rate": 0.0002275775363618849,
393
+ "loss": 0.4326186752319336,
394
+ "mean_token_accuracy": 0.8495047062635421,
395
+ "num_tokens": 4461389.0,
396
+ "step": 1600
397
+ },
398
+ {
399
+ "epoch": 4.0,
400
+ "eval_entropy": 0.5010706964560917,
401
+ "eval_mean_token_accuracy": 0.8040556676047189,
402
+ "eval_not_syn_loss": 0.5851709842681885,
403
+ "eval_not_syn_runtime": 110.3987,
404
+ "eval_not_syn_samples_per_second": 12.645,
405
+ "eval_not_syn_steps_per_second": 1.585,
406
+ "eval_num_tokens": 4552772.0,
407
+ "step": 1632
408
+ },
409
+ {
410
+ "epoch": 4.0,
411
+ "eval_entropy": 0.4733030595098223,
412
+ "eval_mean_token_accuracy": 0.8468134031976973,
413
+ "eval_num_tokens": 4552772.0,
414
+ "eval_syn_loss": 0.5429526567459106,
415
+ "eval_syn_runtime": 117.3735,
416
+ "eval_syn_samples_per_second": 11.894,
417
+ "eval_syn_steps_per_second": 1.491,
418
+ "step": 1632
419
+ },
420
+ {
421
+ "entropy": 0.4337418030966357,
422
+ "epoch": 4.044198895027624,
423
+ "grad_norm": 0.31872114539146423,
424
+ "learning_rate": 0.00022206871552878668,
425
+ "loss": 0.3853663635253906,
426
+ "mean_token_accuracy": 0.8626761530256514,
427
+ "num_tokens": 4602893.0,
428
+ "step": 1650
429
+ },
430
+ {
431
+ "entropy": 0.39638415291905404,
432
+ "epoch": 4.166973603437691,
433
+ "grad_norm": 0.4238921105861664,
434
+ "learning_rate": 0.00021642676210261927,
435
+ "loss": 0.3409381866455078,
436
+ "mean_token_accuracy": 0.8753284150362015,
437
+ "num_tokens": 4735831.0,
438
+ "step": 1700
439
+ },
440
+ {
441
+ "entropy": 0.38611642628908155,
442
+ "epoch": 4.2897483118477595,
443
+ "grad_norm": 0.3248041868209839,
444
+ "learning_rate": 0.00021066199888728683,
445
+ "loss": 0.3360607147216797,
446
+ "mean_token_accuracy": 0.8773713061213493,
447
+ "num_tokens": 4880737.0,
448
+ "step": 1750
449
+ },
450
+ {
451
+ "entropy": 0.3938452216982842,
452
+ "epoch": 4.412523020257827,
453
+ "grad_norm": 0.33257895708084106,
454
+ "learning_rate": 0.00020478497338570733,
455
+ "loss": 0.34164436340332033,
456
+ "mean_token_accuracy": 0.8741997224092484,
457
+ "num_tokens": 5022337.0,
458
+ "step": 1800
459
+ },
460
+ {
461
+ "entropy": 0.39718300312757493,
462
+ "epoch": 4.535297728667894,
463
+ "grad_norm": 0.3596663177013397,
464
+ "learning_rate": 0.00019880643850156687,
465
+ "loss": 0.34481029510498046,
466
+ "mean_token_accuracy": 0.8744558349251748,
467
+ "num_tokens": 5158628.0,
468
+ "step": 1850
469
+ },
470
+ {
471
+ "entropy": 0.39785161226987836,
472
+ "epoch": 4.658072437077962,
473
+ "grad_norm": 0.3325240910053253,
474
+ "learning_rate": 0.00019273733286526186,
475
+ "loss": 0.34437469482421873,
476
+ "mean_token_accuracy": 0.8753779655694962,
477
+ "num_tokens": 5295686.0,
478
+ "step": 1900
479
+ },
480
+ {
481
+ "entropy": 0.3919269675016403,
482
+ "epoch": 4.780847145488029,
483
+ "grad_norm": 0.33487579226493835,
484
+ "learning_rate": 0.00018658876082002678,
485
+ "loss": 0.34184349060058594,
486
+ "mean_token_accuracy": 0.8743453392386437,
487
+ "num_tokens": 5440097.0,
488
+ "step": 1950
489
+ },
490
+ {
491
+ "entropy": 0.3975044973194599,
492
+ "epoch": 4.903621853898097,
493
+ "grad_norm": 0.27435103058815,
494
+ "learning_rate": 0.00018037197210486505,
495
+ "loss": 0.3441514205932617,
496
+ "mean_token_accuracy": 0.87376918643713,
497
+ "num_tokens": 5582367.0,
498
+ "step": 2000
499
+ },
500
+ {
501
+ "epoch": 5.0,
502
+ "eval_entropy": 0.4433087486880166,
503
+ "eval_mean_token_accuracy": 0.8042705031803676,
504
+ "eval_not_syn_loss": 0.6243218183517456,
505
+ "eval_not_syn_runtime": 110.4378,
506
+ "eval_not_syn_samples_per_second": 12.641,
507
+ "eval_not_syn_steps_per_second": 1.585,
508
+ "eval_num_tokens": 5690965.0,
509
+ "step": 2040
510
+ },
511
+ {
512
+ "epoch": 5.0,
513
+ "eval_entropy": 0.4196248413835253,
514
+ "eval_mean_token_accuracy": 0.8377946128164019,
515
+ "eval_num_tokens": 5690965.0,
516
+ "eval_syn_loss": 0.5790094137191772,
517
+ "eval_syn_runtime": 117.3572,
518
+ "eval_syn_samples_per_second": 11.895,
519
+ "eval_syn_steps_per_second": 1.491,
520
+ "step": 2040
521
+ },
522
+ {
523
+ "entropy": 0.3863225542954382,
524
+ "epoch": 5.024554941682013,
525
+ "grad_norm": 0.4834830164909363,
526
+ "learning_rate": 0.00017409834127145627,
527
+ "loss": 0.330703125,
528
+ "mean_token_accuracy": 0.8809086504926537,
529
+ "num_tokens": 5718747.0,
530
+ "step": 2050
531
+ },
532
+ {
533
+ "entropy": 0.3067230442166328,
534
+ "epoch": 5.147329650092081,
535
+ "grad_norm": 0.41984865069389343,
536
+ "learning_rate": 0.0001677793468727003,
537
+ "loss": 0.24890127182006835,
538
+ "mean_token_accuracy": 0.9052707189321518,
539
+ "num_tokens": 5862951.0,
540
+ "step": 2100
541
+ },
542
+ {
543
+ "entropy": 0.3144265574961901,
544
+ "epoch": 5.270104358502149,
545
+ "grad_norm": 0.3354702889919281,
546
+ "learning_rate": 0.00016142655046097487,
547
+ "loss": 0.25450078964233397,
548
+ "mean_token_accuracy": 0.9020006003975868,
549
+ "num_tokens": 6000342.0,
550
+ "step": 2150
551
+ },
552
+ {
553
+ "entropy": 0.3033023314923048,
554
+ "epoch": 5.392879066912216,
555
+ "grad_norm": 0.4292067289352417,
556
+ "learning_rate": 0.00015505157543453375,
557
+ "loss": 0.24809816360473633,
558
+ "mean_token_accuracy": 0.9048506420850754,
559
+ "num_tokens": 6145522.0,
560
+ "step": 2200
561
+ },
562
+ {
563
+ "entropy": 0.3102876263856888,
564
+ "epoch": 5.515653775322283,
565
+ "grad_norm": 0.47768503427505493,
566
+ "learning_rate": 0.00014866608577074797,
567
+ "loss": 0.2563666534423828,
568
+ "mean_token_accuracy": 0.901572678387165,
569
+ "num_tokens": 6282322.0,
570
+ "step": 2250
571
+ },
572
+ {
573
+ "entropy": 0.30644357711076736,
574
+ "epoch": 5.638428483732351,
575
+ "grad_norm": 0.4500775933265686,
576
+ "learning_rate": 0.00014228176468510215,
577
+ "loss": 0.2527992820739746,
578
+ "mean_token_accuracy": 0.9036021012067795,
579
+ "num_tokens": 6419205.0,
580
+ "step": 2300
581
+ },
582
+ {
583
+ "entropy": 0.31263820014894006,
584
+ "epoch": 5.7612031921424185,
585
+ "grad_norm": 0.36889463663101196,
586
+ "learning_rate": 0.00013591029325499087,
587
+ "loss": 0.2583304977416992,
588
+ "mean_token_accuracy": 0.9022675916552544,
589
+ "num_tokens": 6560369.0,
590
+ "step": 2350
591
+ },
592
+ {
593
+ "entropy": 0.31143927775323393,
594
+ "epoch": 5.883977900552486,
595
+ "grad_norm": 0.4023212790489197,
596
+ "learning_rate": 0.00012956332904742786,
597
+ "loss": 0.25584844589233396,
598
+ "mean_token_accuracy": 0.9025778490304946,
599
+ "num_tokens": 6700409.0,
600
+ "step": 2400
601
+ },
602
+ {
603
+ "epoch": 6.0,
604
+ "eval_entropy": 0.38324279410498485,
605
+ "eval_mean_token_accuracy": 0.8128126280648368,
606
+ "eval_not_syn_loss": 0.6938011646270752,
607
+ "eval_not_syn_runtime": 110.423,
608
+ "eval_not_syn_samples_per_second": 12.642,
609
+ "eval_not_syn_steps_per_second": 1.585,
610
+ "eval_num_tokens": 6829158.0,
611
+ "step": 2448
612
+ },
613
+ {
614
+ "epoch": 6.0,
615
+ "eval_entropy": 0.3641860605989184,
616
+ "eval_mean_token_accuracy": 0.8323914204324995,
617
+ "eval_num_tokens": 6829158.0,
618
+ "eval_syn_loss": 0.6474246382713318,
619
+ "eval_syn_runtime": 117.3904,
620
+ "eval_syn_samples_per_second": 11.892,
621
+ "eval_syn_steps_per_second": 1.491,
622
+ "step": 2448
623
+ }
624
+ ],
625
+ "logging_steps": 50,
626
+ "max_steps": 4080,
627
+ "num_input_tokens_seen": 0,
628
+ "num_train_epochs": 10,
629
+ "save_steps": 500,
630
+ "stateful_callbacks": {
631
+ "TrainerControl": {
632
+ "args": {
633
+ "should_epoch_stop": false,
634
+ "should_evaluate": false,
635
+ "should_log": false,
636
+ "should_save": true,
637
+ "should_training_stop": false
638
+ },
639
+ "attributes": {}
640
+ }
641
+ },
642
+ "total_flos": 1.1107534880889754e+18,
643
+ "train_batch_size": 4,
644
+ "trial_name": null,
645
+ "trial_params": null
646
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2856/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2856/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.06743035903930279,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "v_proj",
34
+ "up_proj",
35
+ "k_proj",
36
+ "gate_proj",
37
+ "o_proj",
38
+ "down_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }
substitutivity_original_Estonian/Qwen3-14B-Base_substitutivity_splits_original_features_train_substitutivity_splits_original_features_test2/checkpoint-2856/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 64,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.021223473447411947,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "down_proj",
34
+ "gate_proj",
35
+ "v_proj",
36
+ "up_proj",
37
+ "k_proj",
38
+ "o_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# 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>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\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" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
+ {%- if loop.last or (not loop.last and reasoning_content) %}
41
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
42
+ {%- else %}
43
+ {{- '<|im_start|>' + message.role + '\n' + content }}
44
+ {%- endif %}
45
+ {%- else %}
46
+ {{- '<|im_start|>' + message.role + '\n' + content }}
47
+ {%- endif %}
48
+ {%- if message.tool_calls %}
49
+ {%- for tool_call in message.tool_calls %}
50
+ {%- if (loop.first and content) or (not loop.first) %}
51
+ {{- '\n' }}
52
+ {%- endif %}
53
+ {%- if tool_call.function %}
54
+ {%- set tool_call = tool_call.function %}
55
+ {%- endif %}
56
+ {{- '<tool_call>\n{"name": "' }}
57
+ {{- tool_call.name }}
58
+ {{- '", "arguments": ' }}
59
+ {%- if tool_call.arguments is string %}
60
+ {{- tool_call.arguments }}
61
+ {%- else %}
62
+ {{- tool_call.arguments | tojson }}
63
+ {%- endif %}
64
+ {{- '}\n</tool_call>' }}
65
+ {%- endfor %}
66
+ {%- endif %}
67
+ {{- '<|im_end|>\n' }}
68
+ {%- elif message.role == "tool" %}
69
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
70
+ {{- '<|im_start|>user' }}
71
+ {%- endif %}
72
+ {{- '\n<tool_response>\n' }}
73
+ {{- message.content }}
74
+ {{- '\n</tool_response>' }}
75
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
76
+ {{- '<|im_end|>\n' }}
77
+ {%- endif %}
78
+ {%- endif %}
79
+ {%- endfor %}
80
+ {%- if add_generation_prompt %}
81
+ {{- '<|im_start|>assistant\n' }}
82
+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
84
+ {%- endif %}
85
+ {%- endif %}
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1371/trainer_state.json ADDED
@@ -0,0 +1,337 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 3.0,
6
+ "eval_steps": 500,
7
+ "global_step": 1371,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 2.002540482878685,
14
+ "epoch": 0.10952902519167579,
15
+ "grad_norm": 0.5552461743354797,
16
+ "learning_rate": 8.109930924389919e-06,
17
+ "loss": 1.9067156982421876,
18
+ "mean_token_accuracy": 0.6026969534158707,
19
+ "num_tokens": 119060.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 1.3068562260270118,
24
+ "epoch": 0.21905805038335158,
25
+ "grad_norm": 1.0465189218521118,
26
+ "learning_rate": 1.6385370643155144e-05,
27
+ "loss": 1.1655167388916015,
28
+ "mean_token_accuracy": 0.7124340075254441,
29
+ "num_tokens": 242713.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 0.8696084088087082,
34
+ "epoch": 0.32858707557502737,
35
+ "grad_norm": 0.6686795949935913,
36
+ "learning_rate": 2.4660810361920367e-05,
37
+ "loss": 0.8031976318359375,
38
+ "mean_token_accuracy": 0.7754033240675926,
39
+ "num_tokens": 360160.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 0.7853927117586136,
44
+ "epoch": 0.43811610076670315,
45
+ "grad_norm": 0.9354436993598938,
46
+ "learning_rate": 3.293625008068559e-05,
47
+ "loss": 0.7226885986328125,
48
+ "mean_token_accuracy": 0.791197614967823,
49
+ "num_tokens": 485403.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 0.7382483741641045,
54
+ "epoch": 0.547645125958379,
55
+ "grad_norm": 0.7535398006439209,
56
+ "learning_rate": 4.121168979945081e-05,
57
+ "loss": 0.6755471801757813,
58
+ "mean_token_accuracy": 0.8054334259033203,
59
+ "num_tokens": 601578.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "entropy": 0.7218390592932701,
64
+ "epoch": 0.6571741511500547,
65
+ "grad_norm": 0.8160243034362793,
66
+ "learning_rate": 4.948712951821604e-05,
67
+ "loss": 0.6614183044433594,
68
+ "mean_token_accuracy": 0.8060630604624748,
69
+ "num_tokens": 713917.0,
70
+ "step": 300
71
+ },
72
+ {
73
+ "entropy": 0.7012957927584648,
74
+ "epoch": 0.7667031763417306,
75
+ "grad_norm": 0.7985761165618896,
76
+ "learning_rate": 5.776256923698126e-05,
77
+ "loss": 0.6415711975097657,
78
+ "mean_token_accuracy": 0.8100408402085304,
79
+ "num_tokens": 832995.0,
80
+ "step": 350
81
+ },
82
+ {
83
+ "entropy": 0.6763210469484329,
84
+ "epoch": 0.8762322015334063,
85
+ "grad_norm": 0.6829700469970703,
86
+ "learning_rate": 6.603800895574648e-05,
87
+ "loss": 0.6193617248535156,
88
+ "mean_token_accuracy": 0.8140933158993721,
89
+ "num_tokens": 959304.0,
90
+ "step": 400
91
+ },
92
+ {
93
+ "entropy": 0.6874829810857773,
94
+ "epoch": 0.9857612267250822,
95
+ "grad_norm": 0.6983632445335388,
96
+ "learning_rate": 7.43134486745117e-05,
97
+ "loss": 0.6262834167480469,
98
+ "mean_token_accuracy": 0.8163631609082223,
99
+ "num_tokens": 1078880.0,
100
+ "step": 450
101
+ },
102
+ {
103
+ "epoch": 1.0,
104
+ "eval_entropy": 0.6085402492492918,
105
+ "eval_loss": 0.6388216018676758,
106
+ "eval_mean_token_accuracy": 0.8123693073552752,
107
+ "eval_num_tokens": 1094474.0,
108
+ "eval_runtime": 95.9846,
109
+ "eval_samples_per_second": 10.439,
110
+ "eval_steps_per_second": 1.313,
111
+ "step": 457
112
+ },
113
+ {
114
+ "entropy": 0.6471366012337232,
115
+ "epoch": 1.0941949616648412,
116
+ "grad_norm": 0.7878272533416748,
117
+ "learning_rate": 7.561806001041411e-05,
118
+ "loss": 0.589820671081543,
119
+ "mean_token_accuracy": 0.8218218798589225,
120
+ "num_tokens": 1190065.0,
121
+ "step": 500
122
+ },
123
+ {
124
+ "entropy": 0.6404865515232087,
125
+ "epoch": 1.203723986856517,
126
+ "grad_norm": 0.7976964712142944,
127
+ "learning_rate": 7.554418144822605e-05,
128
+ "loss": 0.5836894607543945,
129
+ "mean_token_accuracy": 0.8222010856866837,
130
+ "num_tokens": 1313079.0,
131
+ "step": 550
132
+ },
133
+ {
134
+ "entropy": 0.6355892798304558,
135
+ "epoch": 1.3132530120481927,
136
+ "grad_norm": 0.7428044080734253,
137
+ "learning_rate": 7.541528503116934e-05,
138
+ "loss": 0.577253189086914,
139
+ "mean_token_accuracy": 0.8260676205158234,
140
+ "num_tokens": 1428305.0,
141
+ "step": 600
142
+ },
143
+ {
144
+ "entropy": 0.6170084626972675,
145
+ "epoch": 1.4227820372398685,
146
+ "grad_norm": 0.6792078614234924,
147
+ "learning_rate": 7.523155873870194e-05,
148
+ "loss": 0.561871337890625,
149
+ "mean_token_accuracy": 0.8307154527306557,
150
+ "num_tokens": 1551971.0,
151
+ "step": 650
152
+ },
153
+ {
154
+ "entropy": 0.6222476975619793,
155
+ "epoch": 1.5323110624315444,
156
+ "grad_norm": 0.6532447934150696,
157
+ "learning_rate": 7.499327051286336e-05,
158
+ "loss": 0.5612493515014648,
159
+ "mean_token_accuracy": 0.8296262130141259,
160
+ "num_tokens": 1673401.0,
161
+ "step": 700
162
+ },
163
+ {
164
+ "entropy": 0.6251883202791214,
165
+ "epoch": 1.6418400876232202,
166
+ "grad_norm": 0.7107782959938049,
167
+ "learning_rate": 7.47007678675144e-05,
168
+ "loss": 0.565279769897461,
169
+ "mean_token_accuracy": 0.8299687370657921,
170
+ "num_tokens": 1788813.0,
171
+ "step": 750
172
+ },
173
+ {
174
+ "entropy": 0.6177104935050011,
175
+ "epoch": 1.751369112814896,
176
+ "grad_norm": 0.7850057482719421,
177
+ "learning_rate": 7.435447738153122e-05,
178
+ "loss": 0.5564990234375,
179
+ "mean_token_accuracy": 0.8291967037320137,
180
+ "num_tokens": 1911556.0,
181
+ "step": 800
182
+ },
183
+ {
184
+ "entropy": 0.6029443763196468,
185
+ "epoch": 1.8608981380065717,
186
+ "grad_norm": 0.7116957306861877,
187
+ "learning_rate": 7.395490407669285e-05,
188
+ "loss": 0.5501844406127929,
189
+ "mean_token_accuracy": 0.8335196697711944,
190
+ "num_tokens": 2029129.0,
191
+ "step": 850
192
+ },
193
+ {
194
+ "entropy": 0.6042105440795421,
195
+ "epoch": 1.9704271631982475,
196
+ "grad_norm": 0.6712159514427185,
197
+ "learning_rate": 7.350263068116955e-05,
198
+ "loss": 0.5517086029052735,
199
+ "mean_token_accuracy": 0.8321291375160217,
200
+ "num_tokens": 2155392.0,
201
+ "step": 900
202
+ },
203
+ {
204
+ "epoch": 2.0,
205
+ "eval_entropy": 0.5401396741942753,
206
+ "eval_loss": 0.5797445774078369,
207
+ "eval_mean_token_accuracy": 0.8243241475688087,
208
+ "eval_num_tokens": 2188948.0,
209
+ "eval_runtime": 95.5407,
210
+ "eval_samples_per_second": 10.488,
211
+ "eval_steps_per_second": 1.319,
212
+ "step": 914
213
+ },
214
+ {
215
+ "entropy": 0.5675096463675451,
216
+ "epoch": 2.078860898138007,
217
+ "grad_norm": 0.5713562369346619,
218
+ "learning_rate": 7.299831677968588e-05,
219
+ "loss": 0.5120392227172852,
220
+ "mean_token_accuracy": 0.8414448993374603,
221
+ "num_tokens": 2277503.0,
222
+ "step": 950
223
+ },
224
+ {
225
+ "entropy": 0.5500032117962838,
226
+ "epoch": 2.1883899233296824,
227
+ "grad_norm": 0.5951120257377625,
228
+ "learning_rate": 7.244269785159817e-05,
229
+ "loss": 0.49384498596191406,
230
+ "mean_token_accuracy": 0.8470860269665718,
231
+ "num_tokens": 2392968.0,
232
+ "step": 1000
233
+ },
234
+ {
235
+ "entropy": 0.5683873899281024,
236
+ "epoch": 2.297918948521358,
237
+ "grad_norm": 0.6816521286964417,
238
+ "learning_rate": 7.183658419828891e-05,
239
+ "loss": 0.5088459014892578,
240
+ "mean_token_accuracy": 0.8413213565945625,
241
+ "num_tokens": 2507108.0,
242
+ "step": 1050
243
+ },
244
+ {
245
+ "entropy": 0.5481136417388917,
246
+ "epoch": 2.407447973713034,
247
+ "grad_norm": 0.6417970657348633,
248
+ "learning_rate": 7.118085976144257e-05,
249
+ "loss": 0.49456378936767575,
250
+ "mean_token_accuracy": 0.8468478980660439,
251
+ "num_tokens": 2633824.0,
252
+ "step": 1100
253
+ },
254
+ {
255
+ "entropy": 0.5413966289162636,
256
+ "epoch": 2.5169769989047097,
257
+ "grad_norm": 0.631996214389801,
258
+ "learning_rate": 7.047648083392619e-05,
259
+ "loss": 0.49154373168945314,
260
+ "mean_token_accuracy": 0.8461908429861069,
261
+ "num_tokens": 2753131.0,
262
+ "step": 1150
263
+ },
264
+ {
265
+ "entropy": 0.5583206915855408,
266
+ "epoch": 2.6265060240963853,
267
+ "grad_norm": 0.7409902215003967,
268
+ "learning_rate": 6.972447466515462e-05,
269
+ "loss": 0.4927285385131836,
270
+ "mean_token_accuracy": 0.8451325806975365,
271
+ "num_tokens": 2865112.0,
272
+ "step": 1200
273
+ },
274
+ {
275
+ "entropy": 0.556266717761755,
276
+ "epoch": 2.7360350492880614,
277
+ "grad_norm": 0.5275787115097046,
278
+ "learning_rate": 6.892593796297452e-05,
279
+ "loss": 0.499769401550293,
280
+ "mean_token_accuracy": 0.8451313543319702,
281
+ "num_tokens": 2980972.0,
282
+ "step": 1250
283
+ },
284
+ {
285
+ "entropy": 0.5362468618154526,
286
+ "epoch": 2.845564074479737,
287
+ "grad_norm": 0.5450661182403564,
288
+ "learning_rate": 6.808203529425189e-05,
289
+ "loss": 0.4860528945922852,
290
+ "mean_token_accuracy": 0.8479894894361496,
291
+ "num_tokens": 3108708.0,
292
+ "step": 1300
293
+ },
294
+ {
295
+ "entropy": 0.5334719524532556,
296
+ "epoch": 2.955093099671413,
297
+ "grad_norm": 0.4825150966644287,
298
+ "learning_rate": 6.719399738649542e-05,
299
+ "loss": 0.48385780334472656,
300
+ "mean_token_accuracy": 0.8489498183131218,
301
+ "num_tokens": 3232991.0,
302
+ "step": 1350
303
+ },
304
+ {
305
+ "epoch": 3.0,
306
+ "eval_entropy": 0.5111288797287714,
307
+ "eval_loss": 0.5738435387611389,
308
+ "eval_mean_token_accuracy": 0.8259959235077813,
309
+ "eval_num_tokens": 3283422.0,
310
+ "eval_runtime": 95.6002,
311
+ "eval_samples_per_second": 10.481,
312
+ "eval_steps_per_second": 1.318,
313
+ "step": 1371
314
+ }
315
+ ],
316
+ "logging_steps": 50,
317
+ "max_steps": 4570,
318
+ "num_input_tokens_seen": 0,
319
+ "num_train_epochs": 10,
320
+ "save_steps": 500,
321
+ "stateful_callbacks": {
322
+ "TrainerControl": {
323
+ "args": {
324
+ "should_epoch_stop": false,
325
+ "should_evaluate": false,
326
+ "should_log": false,
327
+ "should_save": true,
328
+ "should_training_stop": false
329
+ },
330
+ "attributes": {}
331
+ }
332
+ },
333
+ "total_flos": 5.4281968293052416e+17,
334
+ "train_batch_size": 4,
335
+ "trial_name": null,
336
+ "trial_params": null
337
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 64,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.021223473447411947,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "down_proj",
34
+ "gate_proj",
35
+ "v_proj",
36
+ "up_proj",
37
+ "k_proj",
38
+ "o_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# 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>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\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" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
+ {%- if loop.last or (not loop.last and reasoning_content) %}
41
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
42
+ {%- else %}
43
+ {{- '<|im_start|>' + message.role + '\n' + content }}
44
+ {%- endif %}
45
+ {%- else %}
46
+ {{- '<|im_start|>' + message.role + '\n' + content }}
47
+ {%- endif %}
48
+ {%- if message.tool_calls %}
49
+ {%- for tool_call in message.tool_calls %}
50
+ {%- if (loop.first and content) or (not loop.first) %}
51
+ {{- '\n' }}
52
+ {%- endif %}
53
+ {%- if tool_call.function %}
54
+ {%- set tool_call = tool_call.function %}
55
+ {%- endif %}
56
+ {{- '<tool_call>\n{"name": "' }}
57
+ {{- tool_call.name }}
58
+ {{- '", "arguments": ' }}
59
+ {%- if tool_call.arguments is string %}
60
+ {{- tool_call.arguments }}
61
+ {%- else %}
62
+ {{- tool_call.arguments | tojson }}
63
+ {%- endif %}
64
+ {{- '}\n</tool_call>' }}
65
+ {%- endfor %}
66
+ {%- endif %}
67
+ {{- '<|im_end|>\n' }}
68
+ {%- elif message.role == "tool" %}
69
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
70
+ {{- '<|im_start|>user' }}
71
+ {%- endif %}
72
+ {{- '\n<tool_response>\n' }}
73
+ {{- message.content }}
74
+ {{- '\n</tool_response>' }}
75
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
76
+ {{- '<|im_end|>\n' }}
77
+ {%- endif %}
78
+ {%- endif %}
79
+ {%- endfor %}
80
+ {%- if add_generation_prompt %}
81
+ {{- '<|im_start|>assistant\n' }}
82
+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
84
+ {%- endif %}
85
+ {%- endif %}
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-1828/trainer_state.json ADDED
@@ -0,0 +1,438 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 4.0,
6
+ "eval_steps": 500,
7
+ "global_step": 1828,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 2.002540482878685,
14
+ "epoch": 0.10952902519167579,
15
+ "grad_norm": 0.5552461743354797,
16
+ "learning_rate": 8.109930924389919e-06,
17
+ "loss": 1.9067156982421876,
18
+ "mean_token_accuracy": 0.6026969534158707,
19
+ "num_tokens": 119060.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 1.3068562260270118,
24
+ "epoch": 0.21905805038335158,
25
+ "grad_norm": 1.0465189218521118,
26
+ "learning_rate": 1.6385370643155144e-05,
27
+ "loss": 1.1655167388916015,
28
+ "mean_token_accuracy": 0.7124340075254441,
29
+ "num_tokens": 242713.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 0.8696084088087082,
34
+ "epoch": 0.32858707557502737,
35
+ "grad_norm": 0.6686795949935913,
36
+ "learning_rate": 2.4660810361920367e-05,
37
+ "loss": 0.8031976318359375,
38
+ "mean_token_accuracy": 0.7754033240675926,
39
+ "num_tokens": 360160.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 0.7853927117586136,
44
+ "epoch": 0.43811610076670315,
45
+ "grad_norm": 0.9354436993598938,
46
+ "learning_rate": 3.293625008068559e-05,
47
+ "loss": 0.7226885986328125,
48
+ "mean_token_accuracy": 0.791197614967823,
49
+ "num_tokens": 485403.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 0.7382483741641045,
54
+ "epoch": 0.547645125958379,
55
+ "grad_norm": 0.7535398006439209,
56
+ "learning_rate": 4.121168979945081e-05,
57
+ "loss": 0.6755471801757813,
58
+ "mean_token_accuracy": 0.8054334259033203,
59
+ "num_tokens": 601578.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "entropy": 0.7218390592932701,
64
+ "epoch": 0.6571741511500547,
65
+ "grad_norm": 0.8160243034362793,
66
+ "learning_rate": 4.948712951821604e-05,
67
+ "loss": 0.6614183044433594,
68
+ "mean_token_accuracy": 0.8060630604624748,
69
+ "num_tokens": 713917.0,
70
+ "step": 300
71
+ },
72
+ {
73
+ "entropy": 0.7012957927584648,
74
+ "epoch": 0.7667031763417306,
75
+ "grad_norm": 0.7985761165618896,
76
+ "learning_rate": 5.776256923698126e-05,
77
+ "loss": 0.6415711975097657,
78
+ "mean_token_accuracy": 0.8100408402085304,
79
+ "num_tokens": 832995.0,
80
+ "step": 350
81
+ },
82
+ {
83
+ "entropy": 0.6763210469484329,
84
+ "epoch": 0.8762322015334063,
85
+ "grad_norm": 0.6829700469970703,
86
+ "learning_rate": 6.603800895574648e-05,
87
+ "loss": 0.6193617248535156,
88
+ "mean_token_accuracy": 0.8140933158993721,
89
+ "num_tokens": 959304.0,
90
+ "step": 400
91
+ },
92
+ {
93
+ "entropy": 0.6874829810857773,
94
+ "epoch": 0.9857612267250822,
95
+ "grad_norm": 0.6983632445335388,
96
+ "learning_rate": 7.43134486745117e-05,
97
+ "loss": 0.6262834167480469,
98
+ "mean_token_accuracy": 0.8163631609082223,
99
+ "num_tokens": 1078880.0,
100
+ "step": 450
101
+ },
102
+ {
103
+ "epoch": 1.0,
104
+ "eval_entropy": 0.6085402492492918,
105
+ "eval_loss": 0.6388216018676758,
106
+ "eval_mean_token_accuracy": 0.8123693073552752,
107
+ "eval_num_tokens": 1094474.0,
108
+ "eval_runtime": 95.9846,
109
+ "eval_samples_per_second": 10.439,
110
+ "eval_steps_per_second": 1.313,
111
+ "step": 457
112
+ },
113
+ {
114
+ "entropy": 0.6471366012337232,
115
+ "epoch": 1.0941949616648412,
116
+ "grad_norm": 0.7878272533416748,
117
+ "learning_rate": 7.561806001041411e-05,
118
+ "loss": 0.589820671081543,
119
+ "mean_token_accuracy": 0.8218218798589225,
120
+ "num_tokens": 1190065.0,
121
+ "step": 500
122
+ },
123
+ {
124
+ "entropy": 0.6404865515232087,
125
+ "epoch": 1.203723986856517,
126
+ "grad_norm": 0.7976964712142944,
127
+ "learning_rate": 7.554418144822605e-05,
128
+ "loss": 0.5836894607543945,
129
+ "mean_token_accuracy": 0.8222010856866837,
130
+ "num_tokens": 1313079.0,
131
+ "step": 550
132
+ },
133
+ {
134
+ "entropy": 0.6355892798304558,
135
+ "epoch": 1.3132530120481927,
136
+ "grad_norm": 0.7428044080734253,
137
+ "learning_rate": 7.541528503116934e-05,
138
+ "loss": 0.577253189086914,
139
+ "mean_token_accuracy": 0.8260676205158234,
140
+ "num_tokens": 1428305.0,
141
+ "step": 600
142
+ },
143
+ {
144
+ "entropy": 0.6170084626972675,
145
+ "epoch": 1.4227820372398685,
146
+ "grad_norm": 0.6792078614234924,
147
+ "learning_rate": 7.523155873870194e-05,
148
+ "loss": 0.561871337890625,
149
+ "mean_token_accuracy": 0.8307154527306557,
150
+ "num_tokens": 1551971.0,
151
+ "step": 650
152
+ },
153
+ {
154
+ "entropy": 0.6222476975619793,
155
+ "epoch": 1.5323110624315444,
156
+ "grad_norm": 0.6532447934150696,
157
+ "learning_rate": 7.499327051286336e-05,
158
+ "loss": 0.5612493515014648,
159
+ "mean_token_accuracy": 0.8296262130141259,
160
+ "num_tokens": 1673401.0,
161
+ "step": 700
162
+ },
163
+ {
164
+ "entropy": 0.6251883202791214,
165
+ "epoch": 1.6418400876232202,
166
+ "grad_norm": 0.7107782959938049,
167
+ "learning_rate": 7.47007678675144e-05,
168
+ "loss": 0.565279769897461,
169
+ "mean_token_accuracy": 0.8299687370657921,
170
+ "num_tokens": 1788813.0,
171
+ "step": 750
172
+ },
173
+ {
174
+ "entropy": 0.6177104935050011,
175
+ "epoch": 1.751369112814896,
176
+ "grad_norm": 0.7850057482719421,
177
+ "learning_rate": 7.435447738153122e-05,
178
+ "loss": 0.5564990234375,
179
+ "mean_token_accuracy": 0.8291967037320137,
180
+ "num_tokens": 1911556.0,
181
+ "step": 800
182
+ },
183
+ {
184
+ "entropy": 0.6029443763196468,
185
+ "epoch": 1.8608981380065717,
186
+ "grad_norm": 0.7116957306861877,
187
+ "learning_rate": 7.395490407669285e-05,
188
+ "loss": 0.5501844406127929,
189
+ "mean_token_accuracy": 0.8335196697711944,
190
+ "num_tokens": 2029129.0,
191
+ "step": 850
192
+ },
193
+ {
194
+ "entropy": 0.6042105440795421,
195
+ "epoch": 1.9704271631982475,
196
+ "grad_norm": 0.6712159514427185,
197
+ "learning_rate": 7.350263068116955e-05,
198
+ "loss": 0.5517086029052735,
199
+ "mean_token_accuracy": 0.8321291375160217,
200
+ "num_tokens": 2155392.0,
201
+ "step": 900
202
+ },
203
+ {
204
+ "epoch": 2.0,
205
+ "eval_entropy": 0.5401396741942753,
206
+ "eval_loss": 0.5797445774078369,
207
+ "eval_mean_token_accuracy": 0.8243241475688087,
208
+ "eval_num_tokens": 2188948.0,
209
+ "eval_runtime": 95.5407,
210
+ "eval_samples_per_second": 10.488,
211
+ "eval_steps_per_second": 1.319,
212
+ "step": 914
213
+ },
214
+ {
215
+ "entropy": 0.5675096463675451,
216
+ "epoch": 2.078860898138007,
217
+ "grad_norm": 0.5713562369346619,
218
+ "learning_rate": 7.299831677968588e-05,
219
+ "loss": 0.5120392227172852,
220
+ "mean_token_accuracy": 0.8414448993374603,
221
+ "num_tokens": 2277503.0,
222
+ "step": 950
223
+ },
224
+ {
225
+ "entropy": 0.5500032117962838,
226
+ "epoch": 2.1883899233296824,
227
+ "grad_norm": 0.5951120257377625,
228
+ "learning_rate": 7.244269785159817e-05,
229
+ "loss": 0.49384498596191406,
230
+ "mean_token_accuracy": 0.8470860269665718,
231
+ "num_tokens": 2392968.0,
232
+ "step": 1000
233
+ },
234
+ {
235
+ "entropy": 0.5683873899281024,
236
+ "epoch": 2.297918948521358,
237
+ "grad_norm": 0.6816521286964417,
238
+ "learning_rate": 7.183658419828891e-05,
239
+ "loss": 0.5088459014892578,
240
+ "mean_token_accuracy": 0.8413213565945625,
241
+ "num_tokens": 2507108.0,
242
+ "step": 1050
243
+ },
244
+ {
245
+ "entropy": 0.5481136417388917,
246
+ "epoch": 2.407447973713034,
247
+ "grad_norm": 0.6417970657348633,
248
+ "learning_rate": 7.118085976144257e-05,
249
+ "loss": 0.49456378936767575,
250
+ "mean_token_accuracy": 0.8468478980660439,
251
+ "num_tokens": 2633824.0,
252
+ "step": 1100
253
+ },
254
+ {
255
+ "entropy": 0.5413966289162636,
256
+ "epoch": 2.5169769989047097,
257
+ "grad_norm": 0.631996214389801,
258
+ "learning_rate": 7.047648083392619e-05,
259
+ "loss": 0.49154373168945314,
260
+ "mean_token_accuracy": 0.8461908429861069,
261
+ "num_tokens": 2753131.0,
262
+ "step": 1150
263
+ },
264
+ {
265
+ "entropy": 0.5583206915855408,
266
+ "epoch": 2.6265060240963853,
267
+ "grad_norm": 0.7409902215003967,
268
+ "learning_rate": 6.972447466515462e-05,
269
+ "loss": 0.4927285385131836,
270
+ "mean_token_accuracy": 0.8451325806975365,
271
+ "num_tokens": 2865112.0,
272
+ "step": 1200
273
+ },
274
+ {
275
+ "entropy": 0.556266717761755,
276
+ "epoch": 2.7360350492880614,
277
+ "grad_norm": 0.5275787115097046,
278
+ "learning_rate": 6.892593796297452e-05,
279
+ "loss": 0.499769401550293,
280
+ "mean_token_accuracy": 0.8451313543319702,
281
+ "num_tokens": 2980972.0,
282
+ "step": 1250
283
+ },
284
+ {
285
+ "entropy": 0.5362468618154526,
286
+ "epoch": 2.845564074479737,
287
+ "grad_norm": 0.5450661182403564,
288
+ "learning_rate": 6.808203529425189e-05,
289
+ "loss": 0.4860528945922852,
290
+ "mean_token_accuracy": 0.8479894894361496,
291
+ "num_tokens": 3108708.0,
292
+ "step": 1300
293
+ },
294
+ {
295
+ "entropy": 0.5334719524532556,
296
+ "epoch": 2.955093099671413,
297
+ "grad_norm": 0.4825150966644287,
298
+ "learning_rate": 6.719399738649542e-05,
299
+ "loss": 0.48385780334472656,
300
+ "mean_token_accuracy": 0.8489498183131218,
301
+ "num_tokens": 3232991.0,
302
+ "step": 1350
303
+ },
304
+ {
305
+ "epoch": 3.0,
306
+ "eval_entropy": 0.5111288797287714,
307
+ "eval_loss": 0.5738435387611389,
308
+ "eval_mean_token_accuracy": 0.8259959235077813,
309
+ "eval_num_tokens": 3283422.0,
310
+ "eval_runtime": 95.6002,
311
+ "eval_samples_per_second": 10.481,
312
+ "eval_steps_per_second": 1.318,
313
+ "step": 1371
314
+ },
315
+ {
316
+ "entropy": 0.5024978819519582,
317
+ "epoch": 3.063526834611172,
318
+ "grad_norm": 0.6228470802307129,
319
+ "learning_rate": 6.626311933299292e-05,
320
+ "loss": 0.4451956939697266,
321
+ "mean_token_accuracy": 0.8579568441468056,
322
+ "num_tokens": 3352672.0,
323
+ "step": 1400
324
+ },
325
+ {
326
+ "entropy": 0.49351966604590414,
327
+ "epoch": 3.1730558598028478,
328
+ "grad_norm": 0.648960530757904,
329
+ "learning_rate": 6.529075870407823e-05,
330
+ "loss": 0.4324279022216797,
331
+ "mean_token_accuracy": 0.8607994091510772,
332
+ "num_tokens": 3472463.0,
333
+ "step": 1450
334
+ },
335
+ {
336
+ "entropy": 0.48342301592230796,
337
+ "epoch": 3.2825848849945234,
338
+ "grad_norm": 0.8443573713302612,
339
+ "learning_rate": 6.427833356728302e-05,
340
+ "loss": 0.4237791442871094,
341
+ "mean_token_accuracy": 0.8643713328242302,
342
+ "num_tokens": 3593837.0,
343
+ "step": 1500
344
+ },
345
+ {
346
+ "entropy": 0.4779162485897541,
347
+ "epoch": 3.3921139101861995,
348
+ "grad_norm": 0.7882111072540283,
349
+ "learning_rate": 6.32273204192609e-05,
350
+ "loss": 0.42386363983154296,
351
+ "mean_token_accuracy": 0.8635856115818024,
352
+ "num_tokens": 3716058.0,
353
+ "step": 1550
354
+ },
355
+ {
356
+ "entropy": 0.48870255261659623,
357
+ "epoch": 3.501642935377875,
358
+ "grad_norm": 0.7637454867362976,
359
+ "learning_rate": 6.213925203250001e-05,
360
+ "loss": 0.4301974105834961,
361
+ "mean_token_accuracy": 0.861739870607853,
362
+ "num_tokens": 3838629.0,
363
+ "step": 1600
364
+ },
365
+ {
366
+ "entropy": 0.49970791533589365,
367
+ "epoch": 3.6111719605695507,
368
+ "grad_norm": 0.7269095182418823,
369
+ "learning_rate": 6.101571521996419e-05,
370
+ "loss": 0.4372034454345703,
371
+ "mean_token_accuracy": 0.8592326313257217,
372
+ "num_tokens": 3955462.0,
373
+ "step": 1650
374
+ },
375
+ {
376
+ "entropy": 0.5049038740992546,
377
+ "epoch": 3.7207009857612268,
378
+ "grad_norm": 0.6023766398429871,
379
+ "learning_rate": 5.98583485209228e-05,
380
+ "loss": 0.445910758972168,
381
+ "mean_token_accuracy": 0.8579568776488304,
382
+ "num_tokens": 4075933.0,
383
+ "step": 1700
384
+ },
385
+ {
386
+ "entropy": 0.4927579787373543,
387
+ "epoch": 3.8302300109529024,
388
+ "grad_norm": 0.7742004990577698,
389
+ "learning_rate": 5.866883981134422e-05,
390
+ "loss": 0.4280668640136719,
391
+ "mean_token_accuracy": 0.86241753667593,
392
+ "num_tokens": 4192915.0,
393
+ "step": 1750
394
+ },
395
+ {
396
+ "entropy": 0.5011553263664246,
397
+ "epoch": 3.9397590361445785,
398
+ "grad_norm": 0.5748111605644226,
399
+ "learning_rate": 5.7448923842337736e-05,
400
+ "loss": 0.43842597961425783,
401
+ "mean_token_accuracy": 0.8605782136321067,
402
+ "num_tokens": 4310431.0,
403
+ "step": 1800
404
+ },
405
+ {
406
+ "epoch": 4.0,
407
+ "eval_entropy": 0.4624898036321004,
408
+ "eval_loss": 0.590175986289978,
409
+ "eval_mean_token_accuracy": 0.8287760076068696,
410
+ "eval_num_tokens": 4377896.0,
411
+ "eval_runtime": 95.3934,
412
+ "eval_samples_per_second": 10.504,
413
+ "eval_steps_per_second": 1.321,
414
+ "step": 1828
415
+ }
416
+ ],
417
+ "logging_steps": 50,
418
+ "max_steps": 4570,
419
+ "num_input_tokens_seen": 0,
420
+ "num_train_epochs": 10,
421
+ "save_steps": 500,
422
+ "stateful_callbacks": {
423
+ "TrainerControl": {
424
+ "args": {
425
+ "should_epoch_stop": false,
426
+ "should_evaluate": false,
427
+ "should_log": false,
428
+ "should_save": true,
429
+ "should_training_stop": false
430
+ },
431
+ "attributes": {}
432
+ }
433
+ },
434
+ "total_flos": 7.235274069462835e+17,
435
+ "train_batch_size": 4,
436
+ "trial_name": null,
437
+ "trial_params": null
438
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 64,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.021223473447411947,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "down_proj",
34
+ "gate_proj",
35
+ "v_proj",
36
+ "up_proj",
37
+ "k_proj",
38
+ "o_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# 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>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\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" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
+ {%- if loop.last or (not loop.last and reasoning_content) %}
41
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
42
+ {%- else %}
43
+ {{- '<|im_start|>' + message.role + '\n' + content }}
44
+ {%- endif %}
45
+ {%- else %}
46
+ {{- '<|im_start|>' + message.role + '\n' + content }}
47
+ {%- endif %}
48
+ {%- if message.tool_calls %}
49
+ {%- for tool_call in message.tool_calls %}
50
+ {%- if (loop.first and content) or (not loop.first) %}
51
+ {{- '\n' }}
52
+ {%- endif %}
53
+ {%- if tool_call.function %}
54
+ {%- set tool_call = tool_call.function %}
55
+ {%- endif %}
56
+ {{- '<tool_call>\n{"name": "' }}
57
+ {{- tool_call.name }}
58
+ {{- '", "arguments": ' }}
59
+ {%- if tool_call.arguments is string %}
60
+ {{- tool_call.arguments }}
61
+ {%- else %}
62
+ {{- tool_call.arguments | tojson }}
63
+ {%- endif %}
64
+ {{- '}\n</tool_call>' }}
65
+ {%- endfor %}
66
+ {%- endif %}
67
+ {{- '<|im_end|>\n' }}
68
+ {%- elif message.role == "tool" %}
69
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
70
+ {{- '<|im_start|>user' }}
71
+ {%- endif %}
72
+ {{- '\n<tool_response>\n' }}
73
+ {{- message.content }}
74
+ {{- '\n</tool_response>' }}
75
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
76
+ {{- '<|im_end|>\n' }}
77
+ {%- endif %}
78
+ {%- endif %}
79
+ {%- endfor %}
80
+ {%- if add_generation_prompt %}
81
+ {{- '<|im_start|>assistant\n' }}
82
+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
84
+ {%- endif %}
85
+ {%- endif %}
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2285/trainer_state.json ADDED
@@ -0,0 +1,539 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 5.0,
6
+ "eval_steps": 500,
7
+ "global_step": 2285,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 2.002540482878685,
14
+ "epoch": 0.10952902519167579,
15
+ "grad_norm": 0.5552461743354797,
16
+ "learning_rate": 8.109930924389919e-06,
17
+ "loss": 1.9067156982421876,
18
+ "mean_token_accuracy": 0.6026969534158707,
19
+ "num_tokens": 119060.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 1.3068562260270118,
24
+ "epoch": 0.21905805038335158,
25
+ "grad_norm": 1.0465189218521118,
26
+ "learning_rate": 1.6385370643155144e-05,
27
+ "loss": 1.1655167388916015,
28
+ "mean_token_accuracy": 0.7124340075254441,
29
+ "num_tokens": 242713.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 0.8696084088087082,
34
+ "epoch": 0.32858707557502737,
35
+ "grad_norm": 0.6686795949935913,
36
+ "learning_rate": 2.4660810361920367e-05,
37
+ "loss": 0.8031976318359375,
38
+ "mean_token_accuracy": 0.7754033240675926,
39
+ "num_tokens": 360160.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 0.7853927117586136,
44
+ "epoch": 0.43811610076670315,
45
+ "grad_norm": 0.9354436993598938,
46
+ "learning_rate": 3.293625008068559e-05,
47
+ "loss": 0.7226885986328125,
48
+ "mean_token_accuracy": 0.791197614967823,
49
+ "num_tokens": 485403.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 0.7382483741641045,
54
+ "epoch": 0.547645125958379,
55
+ "grad_norm": 0.7535398006439209,
56
+ "learning_rate": 4.121168979945081e-05,
57
+ "loss": 0.6755471801757813,
58
+ "mean_token_accuracy": 0.8054334259033203,
59
+ "num_tokens": 601578.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "entropy": 0.7218390592932701,
64
+ "epoch": 0.6571741511500547,
65
+ "grad_norm": 0.8160243034362793,
66
+ "learning_rate": 4.948712951821604e-05,
67
+ "loss": 0.6614183044433594,
68
+ "mean_token_accuracy": 0.8060630604624748,
69
+ "num_tokens": 713917.0,
70
+ "step": 300
71
+ },
72
+ {
73
+ "entropy": 0.7012957927584648,
74
+ "epoch": 0.7667031763417306,
75
+ "grad_norm": 0.7985761165618896,
76
+ "learning_rate": 5.776256923698126e-05,
77
+ "loss": 0.6415711975097657,
78
+ "mean_token_accuracy": 0.8100408402085304,
79
+ "num_tokens": 832995.0,
80
+ "step": 350
81
+ },
82
+ {
83
+ "entropy": 0.6763210469484329,
84
+ "epoch": 0.8762322015334063,
85
+ "grad_norm": 0.6829700469970703,
86
+ "learning_rate": 6.603800895574648e-05,
87
+ "loss": 0.6193617248535156,
88
+ "mean_token_accuracy": 0.8140933158993721,
89
+ "num_tokens": 959304.0,
90
+ "step": 400
91
+ },
92
+ {
93
+ "entropy": 0.6874829810857773,
94
+ "epoch": 0.9857612267250822,
95
+ "grad_norm": 0.6983632445335388,
96
+ "learning_rate": 7.43134486745117e-05,
97
+ "loss": 0.6262834167480469,
98
+ "mean_token_accuracy": 0.8163631609082223,
99
+ "num_tokens": 1078880.0,
100
+ "step": 450
101
+ },
102
+ {
103
+ "epoch": 1.0,
104
+ "eval_entropy": 0.6085402492492918,
105
+ "eval_loss": 0.6388216018676758,
106
+ "eval_mean_token_accuracy": 0.8123693073552752,
107
+ "eval_num_tokens": 1094474.0,
108
+ "eval_runtime": 95.9846,
109
+ "eval_samples_per_second": 10.439,
110
+ "eval_steps_per_second": 1.313,
111
+ "step": 457
112
+ },
113
+ {
114
+ "entropy": 0.6471366012337232,
115
+ "epoch": 1.0941949616648412,
116
+ "grad_norm": 0.7878272533416748,
117
+ "learning_rate": 7.561806001041411e-05,
118
+ "loss": 0.589820671081543,
119
+ "mean_token_accuracy": 0.8218218798589225,
120
+ "num_tokens": 1190065.0,
121
+ "step": 500
122
+ },
123
+ {
124
+ "entropy": 0.6404865515232087,
125
+ "epoch": 1.203723986856517,
126
+ "grad_norm": 0.7976964712142944,
127
+ "learning_rate": 7.554418144822605e-05,
128
+ "loss": 0.5836894607543945,
129
+ "mean_token_accuracy": 0.8222010856866837,
130
+ "num_tokens": 1313079.0,
131
+ "step": 550
132
+ },
133
+ {
134
+ "entropy": 0.6355892798304558,
135
+ "epoch": 1.3132530120481927,
136
+ "grad_norm": 0.7428044080734253,
137
+ "learning_rate": 7.541528503116934e-05,
138
+ "loss": 0.577253189086914,
139
+ "mean_token_accuracy": 0.8260676205158234,
140
+ "num_tokens": 1428305.0,
141
+ "step": 600
142
+ },
143
+ {
144
+ "entropy": 0.6170084626972675,
145
+ "epoch": 1.4227820372398685,
146
+ "grad_norm": 0.6792078614234924,
147
+ "learning_rate": 7.523155873870194e-05,
148
+ "loss": 0.561871337890625,
149
+ "mean_token_accuracy": 0.8307154527306557,
150
+ "num_tokens": 1551971.0,
151
+ "step": 650
152
+ },
153
+ {
154
+ "entropy": 0.6222476975619793,
155
+ "epoch": 1.5323110624315444,
156
+ "grad_norm": 0.6532447934150696,
157
+ "learning_rate": 7.499327051286336e-05,
158
+ "loss": 0.5612493515014648,
159
+ "mean_token_accuracy": 0.8296262130141259,
160
+ "num_tokens": 1673401.0,
161
+ "step": 700
162
+ },
163
+ {
164
+ "entropy": 0.6251883202791214,
165
+ "epoch": 1.6418400876232202,
166
+ "grad_norm": 0.7107782959938049,
167
+ "learning_rate": 7.47007678675144e-05,
168
+ "loss": 0.565279769897461,
169
+ "mean_token_accuracy": 0.8299687370657921,
170
+ "num_tokens": 1788813.0,
171
+ "step": 750
172
+ },
173
+ {
174
+ "entropy": 0.6177104935050011,
175
+ "epoch": 1.751369112814896,
176
+ "grad_norm": 0.7850057482719421,
177
+ "learning_rate": 7.435447738153122e-05,
178
+ "loss": 0.5564990234375,
179
+ "mean_token_accuracy": 0.8291967037320137,
180
+ "num_tokens": 1911556.0,
181
+ "step": 800
182
+ },
183
+ {
184
+ "entropy": 0.6029443763196468,
185
+ "epoch": 1.8608981380065717,
186
+ "grad_norm": 0.7116957306861877,
187
+ "learning_rate": 7.395490407669285e-05,
188
+ "loss": 0.5501844406127929,
189
+ "mean_token_accuracy": 0.8335196697711944,
190
+ "num_tokens": 2029129.0,
191
+ "step": 850
192
+ },
193
+ {
194
+ "entropy": 0.6042105440795421,
195
+ "epoch": 1.9704271631982475,
196
+ "grad_norm": 0.6712159514427185,
197
+ "learning_rate": 7.350263068116955e-05,
198
+ "loss": 0.5517086029052735,
199
+ "mean_token_accuracy": 0.8321291375160217,
200
+ "num_tokens": 2155392.0,
201
+ "step": 900
202
+ },
203
+ {
204
+ "epoch": 2.0,
205
+ "eval_entropy": 0.5401396741942753,
206
+ "eval_loss": 0.5797445774078369,
207
+ "eval_mean_token_accuracy": 0.8243241475688087,
208
+ "eval_num_tokens": 2188948.0,
209
+ "eval_runtime": 95.5407,
210
+ "eval_samples_per_second": 10.488,
211
+ "eval_steps_per_second": 1.319,
212
+ "step": 914
213
+ },
214
+ {
215
+ "entropy": 0.5675096463675451,
216
+ "epoch": 2.078860898138007,
217
+ "grad_norm": 0.5713562369346619,
218
+ "learning_rate": 7.299831677968588e-05,
219
+ "loss": 0.5120392227172852,
220
+ "mean_token_accuracy": 0.8414448993374603,
221
+ "num_tokens": 2277503.0,
222
+ "step": 950
223
+ },
224
+ {
225
+ "entropy": 0.5500032117962838,
226
+ "epoch": 2.1883899233296824,
227
+ "grad_norm": 0.5951120257377625,
228
+ "learning_rate": 7.244269785159817e-05,
229
+ "loss": 0.49384498596191406,
230
+ "mean_token_accuracy": 0.8470860269665718,
231
+ "num_tokens": 2392968.0,
232
+ "step": 1000
233
+ },
234
+ {
235
+ "entropy": 0.5683873899281024,
236
+ "epoch": 2.297918948521358,
237
+ "grad_norm": 0.6816521286964417,
238
+ "learning_rate": 7.183658419828891e-05,
239
+ "loss": 0.5088459014892578,
240
+ "mean_token_accuracy": 0.8413213565945625,
241
+ "num_tokens": 2507108.0,
242
+ "step": 1050
243
+ },
244
+ {
245
+ "entropy": 0.5481136417388917,
246
+ "epoch": 2.407447973713034,
247
+ "grad_norm": 0.6417970657348633,
248
+ "learning_rate": 7.118085976144257e-05,
249
+ "loss": 0.49456378936767575,
250
+ "mean_token_accuracy": 0.8468478980660439,
251
+ "num_tokens": 2633824.0,
252
+ "step": 1100
253
+ },
254
+ {
255
+ "entropy": 0.5413966289162636,
256
+ "epoch": 2.5169769989047097,
257
+ "grad_norm": 0.631996214389801,
258
+ "learning_rate": 7.047648083392619e-05,
259
+ "loss": 0.49154373168945314,
260
+ "mean_token_accuracy": 0.8461908429861069,
261
+ "num_tokens": 2753131.0,
262
+ "step": 1150
263
+ },
264
+ {
265
+ "entropy": 0.5583206915855408,
266
+ "epoch": 2.6265060240963853,
267
+ "grad_norm": 0.7409902215003967,
268
+ "learning_rate": 6.972447466515462e-05,
269
+ "loss": 0.4927285385131836,
270
+ "mean_token_accuracy": 0.8451325806975365,
271
+ "num_tokens": 2865112.0,
272
+ "step": 1200
273
+ },
274
+ {
275
+ "entropy": 0.556266717761755,
276
+ "epoch": 2.7360350492880614,
277
+ "grad_norm": 0.5275787115097046,
278
+ "learning_rate": 6.892593796297452e-05,
279
+ "loss": 0.499769401550293,
280
+ "mean_token_accuracy": 0.8451313543319702,
281
+ "num_tokens": 2980972.0,
282
+ "step": 1250
283
+ },
284
+ {
285
+ "entropy": 0.5362468618154526,
286
+ "epoch": 2.845564074479737,
287
+ "grad_norm": 0.5450661182403564,
288
+ "learning_rate": 6.808203529425189e-05,
289
+ "loss": 0.4860528945922852,
290
+ "mean_token_accuracy": 0.8479894894361496,
291
+ "num_tokens": 3108708.0,
292
+ "step": 1300
293
+ },
294
+ {
295
+ "entropy": 0.5334719524532556,
296
+ "epoch": 2.955093099671413,
297
+ "grad_norm": 0.4825150966644287,
298
+ "learning_rate": 6.719399738649542e-05,
299
+ "loss": 0.48385780334472656,
300
+ "mean_token_accuracy": 0.8489498183131218,
301
+ "num_tokens": 3232991.0,
302
+ "step": 1350
303
+ },
304
+ {
305
+ "epoch": 3.0,
306
+ "eval_entropy": 0.5111288797287714,
307
+ "eval_loss": 0.5738435387611389,
308
+ "eval_mean_token_accuracy": 0.8259959235077813,
309
+ "eval_num_tokens": 3283422.0,
310
+ "eval_runtime": 95.6002,
311
+ "eval_samples_per_second": 10.481,
312
+ "eval_steps_per_second": 1.318,
313
+ "step": 1371
314
+ },
315
+ {
316
+ "entropy": 0.5024978819519582,
317
+ "epoch": 3.063526834611172,
318
+ "grad_norm": 0.6228470802307129,
319
+ "learning_rate": 6.626311933299292e-05,
320
+ "loss": 0.4451956939697266,
321
+ "mean_token_accuracy": 0.8579568441468056,
322
+ "num_tokens": 3352672.0,
323
+ "step": 1400
324
+ },
325
+ {
326
+ "entropy": 0.49351966604590414,
327
+ "epoch": 3.1730558598028478,
328
+ "grad_norm": 0.648960530757904,
329
+ "learning_rate": 6.529075870407823e-05,
330
+ "loss": 0.4324279022216797,
331
+ "mean_token_accuracy": 0.8607994091510772,
332
+ "num_tokens": 3472463.0,
333
+ "step": 1450
334
+ },
335
+ {
336
+ "entropy": 0.48342301592230796,
337
+ "epoch": 3.2825848849945234,
338
+ "grad_norm": 0.8443573713302612,
339
+ "learning_rate": 6.427833356728302e-05,
340
+ "loss": 0.4237791442871094,
341
+ "mean_token_accuracy": 0.8643713328242302,
342
+ "num_tokens": 3593837.0,
343
+ "step": 1500
344
+ },
345
+ {
346
+ "entropy": 0.4779162485897541,
347
+ "epoch": 3.3921139101861995,
348
+ "grad_norm": 0.7882111072540283,
349
+ "learning_rate": 6.32273204192609e-05,
350
+ "loss": 0.42386363983154296,
351
+ "mean_token_accuracy": 0.8635856115818024,
352
+ "num_tokens": 3716058.0,
353
+ "step": 1550
354
+ },
355
+ {
356
+ "entropy": 0.48870255261659623,
357
+ "epoch": 3.501642935377875,
358
+ "grad_norm": 0.7637454867362976,
359
+ "learning_rate": 6.213925203250001e-05,
360
+ "loss": 0.4301974105834961,
361
+ "mean_token_accuracy": 0.861739870607853,
362
+ "num_tokens": 3838629.0,
363
+ "step": 1600
364
+ },
365
+ {
366
+ "entropy": 0.49970791533589365,
367
+ "epoch": 3.6111719605695507,
368
+ "grad_norm": 0.7269095182418823,
369
+ "learning_rate": 6.101571521996419e-05,
370
+ "loss": 0.4372034454345703,
371
+ "mean_token_accuracy": 0.8592326313257217,
372
+ "num_tokens": 3955462.0,
373
+ "step": 1650
374
+ },
375
+ {
376
+ "entropy": 0.5049038740992546,
377
+ "epoch": 3.7207009857612268,
378
+ "grad_norm": 0.6023766398429871,
379
+ "learning_rate": 5.98583485209228e-05,
380
+ "loss": 0.445910758972168,
381
+ "mean_token_accuracy": 0.8579568776488304,
382
+ "num_tokens": 4075933.0,
383
+ "step": 1700
384
+ },
385
+ {
386
+ "entropy": 0.4927579787373543,
387
+ "epoch": 3.8302300109529024,
388
+ "grad_norm": 0.7742004990577698,
389
+ "learning_rate": 5.866883981134422e-05,
390
+ "loss": 0.4280668640136719,
391
+ "mean_token_accuracy": 0.86241753667593,
392
+ "num_tokens": 4192915.0,
393
+ "step": 1750
394
+ },
395
+ {
396
+ "entropy": 0.5011553263664246,
397
+ "epoch": 3.9397590361445785,
398
+ "grad_norm": 0.5748111605644226,
399
+ "learning_rate": 5.7448923842337736e-05,
400
+ "loss": 0.43842597961425783,
401
+ "mean_token_accuracy": 0.8605782136321067,
402
+ "num_tokens": 4310431.0,
403
+ "step": 1800
404
+ },
405
+ {
406
+ "epoch": 4.0,
407
+ "eval_entropy": 0.4624898036321004,
408
+ "eval_loss": 0.590175986289978,
409
+ "eval_mean_token_accuracy": 0.8287760076068696,
410
+ "eval_num_tokens": 4377896.0,
411
+ "eval_runtime": 95.3934,
412
+ "eval_samples_per_second": 10.504,
413
+ "eval_steps_per_second": 1.321,
414
+ "step": 1828
415
+ },
416
+ {
417
+ "entropy": 0.4502198097079691,
418
+ "epoch": 4.048192771084337,
419
+ "grad_norm": 0.7170541882514954,
420
+ "learning_rate": 5.620037971023403e-05,
421
+ "loss": 0.38744712829589845,
422
+ "mean_token_accuracy": 0.8735785065877317,
423
+ "num_tokens": 4430089.0,
424
+ "step": 1850
425
+ },
426
+ {
427
+ "entropy": 0.41583337262272835,
428
+ "epoch": 4.157721796276014,
429
+ "grad_norm": 0.9511433243751526,
430
+ "learning_rate": 5.4925028261993515e-05,
431
+ "loss": 0.3562023162841797,
432
+ "mean_token_accuracy": 0.881559683084488,
433
+ "num_tokens": 4554484.0,
434
+ "step": 1900
435
+ },
436
+ {
437
+ "entropy": 0.4208242034912109,
438
+ "epoch": 4.267250821467689,
439
+ "grad_norm": 0.8741114139556885,
440
+ "learning_rate": 5.3624729439726544e-05,
441
+ "loss": 0.3612668991088867,
442
+ "mean_token_accuracy": 0.8802913293242455,
443
+ "num_tokens": 4675472.0,
444
+ "step": 1950
445
+ },
446
+ {
447
+ "entropy": 0.4188448017835617,
448
+ "epoch": 4.376779846659365,
449
+ "grad_norm": 1.1690106391906738,
450
+ "learning_rate": 5.23013795681983e-05,
451
+ "loss": 0.3627183151245117,
452
+ "mean_token_accuracy": 0.8805234292149544,
453
+ "num_tokens": 4793479.0,
454
+ "step": 2000
455
+ },
456
+ {
457
+ "entropy": 0.42332509815692904,
458
+ "epoch": 4.48630887185104,
459
+ "grad_norm": 0.8150995969772339,
460
+ "learning_rate": 5.095690858927403e-05,
461
+ "loss": 0.3626524353027344,
462
+ "mean_token_accuracy": 0.879372145831585,
463
+ "num_tokens": 4911343.0,
464
+ "step": 2050
465
+ },
466
+ {
467
+ "entropy": 0.42394075110554696,
468
+ "epoch": 4.595837897042716,
469
+ "grad_norm": 0.8282762169837952,
470
+ "learning_rate": 4.959327724733778e-05,
471
+ "loss": 0.3573355865478516,
472
+ "mean_token_accuracy": 0.8799301481246948,
473
+ "num_tokens": 5028364.0,
474
+ "step": 2100
475
+ },
476
+ {
477
+ "entropy": 0.4259473057091236,
478
+ "epoch": 4.705366922234392,
479
+ "grad_norm": 0.7610743045806885,
480
+ "learning_rate": 4.8212474229789754e-05,
481
+ "loss": 0.3665072631835937,
482
+ "mean_token_accuracy": 0.879306109547615,
483
+ "num_tokens": 5146995.0,
484
+ "step": 2150
485
+ },
486
+ {
487
+ "entropy": 0.42407046899199485,
488
+ "epoch": 4.814895947426068,
489
+ "grad_norm": 0.6995375156402588,
490
+ "learning_rate": 4.681651326679193e-05,
491
+ "loss": 0.3689637756347656,
492
+ "mean_token_accuracy": 0.878270491361618,
493
+ "num_tokens": 5263483.0,
494
+ "step": 2200
495
+ },
496
+ {
497
+ "entropy": 0.41719858527183534,
498
+ "epoch": 4.924424972617744,
499
+ "grad_norm": 0.8921851515769958,
500
+ "learning_rate": 4.5407430194492145e-05,
501
+ "loss": 0.36366527557373046,
502
+ "mean_token_accuracy": 0.8810832899808884,
503
+ "num_tokens": 5387955.0,
504
+ "step": 2250
505
+ },
506
+ {
507
+ "epoch": 5.0,
508
+ "eval_entropy": 0.40321539318750776,
509
+ "eval_loss": 0.6344199776649475,
510
+ "eval_mean_token_accuracy": 0.8290003571245406,
511
+ "eval_num_tokens": 5472370.0,
512
+ "eval_runtime": 95.4906,
513
+ "eval_samples_per_second": 10.493,
514
+ "eval_steps_per_second": 1.32,
515
+ "step": 2285
516
+ }
517
+ ],
518
+ "logging_steps": 50,
519
+ "max_steps": 4570,
520
+ "num_input_tokens_seen": 0,
521
+ "num_train_epochs": 10,
522
+ "save_steps": 500,
523
+ "stateful_callbacks": {
524
+ "TrainerControl": {
525
+ "args": {
526
+ "should_epoch_stop": false,
527
+ "should_evaluate": false,
528
+ "should_log": false,
529
+ "should_save": true,
530
+ "should_training_stop": false
531
+ },
532
+ "attributes": {}
533
+ }
534
+ },
535
+ "total_flos": 9.05719219281623e+17,
536
+ "train_batch_size": 4,
537
+ "trial_name": null,
538
+ "trial_params": null
539
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 64,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.021223473447411947,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "down_proj",
34
+ "gate_proj",
35
+ "v_proj",
36
+ "up_proj",
37
+ "k_proj",
38
+ "o_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# 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>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\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" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
+ {%- if loop.last or (not loop.last and reasoning_content) %}
41
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
42
+ {%- else %}
43
+ {{- '<|im_start|>' + message.role + '\n' + content }}
44
+ {%- endif %}
45
+ {%- else %}
46
+ {{- '<|im_start|>' + message.role + '\n' + content }}
47
+ {%- endif %}
48
+ {%- if message.tool_calls %}
49
+ {%- for tool_call in message.tool_calls %}
50
+ {%- if (loop.first and content) or (not loop.first) %}
51
+ {{- '\n' }}
52
+ {%- endif %}
53
+ {%- if tool_call.function %}
54
+ {%- set tool_call = tool_call.function %}
55
+ {%- endif %}
56
+ {{- '<tool_call>\n{"name": "' }}
57
+ {{- tool_call.name }}
58
+ {{- '", "arguments": ' }}
59
+ {%- if tool_call.arguments is string %}
60
+ {{- tool_call.arguments }}
61
+ {%- else %}
62
+ {{- tool_call.arguments | tojson }}
63
+ {%- endif %}
64
+ {{- '}\n</tool_call>' }}
65
+ {%- endfor %}
66
+ {%- endif %}
67
+ {{- '<|im_end|>\n' }}
68
+ {%- elif message.role == "tool" %}
69
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
70
+ {{- '<|im_start|>user' }}
71
+ {%- endif %}
72
+ {{- '\n<tool_response>\n' }}
73
+ {{- message.content }}
74
+ {{- '\n</tool_response>' }}
75
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
76
+ {{- '<|im_end|>\n' }}
77
+ {%- endif %}
78
+ {%- endif %}
79
+ {%- endfor %}
80
+ {%- if add_generation_prompt %}
81
+ {{- '<|im_start|>assistant\n' }}
82
+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
84
+ {%- endif %}
85
+ {%- endif %}
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-2742/trainer_state.json ADDED
@@ -0,0 +1,640 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 6.0,
6
+ "eval_steps": 500,
7
+ "global_step": 2742,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 2.002540482878685,
14
+ "epoch": 0.10952902519167579,
15
+ "grad_norm": 0.5552461743354797,
16
+ "learning_rate": 8.109930924389919e-06,
17
+ "loss": 1.9067156982421876,
18
+ "mean_token_accuracy": 0.6026969534158707,
19
+ "num_tokens": 119060.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 1.3068562260270118,
24
+ "epoch": 0.21905805038335158,
25
+ "grad_norm": 1.0465189218521118,
26
+ "learning_rate": 1.6385370643155144e-05,
27
+ "loss": 1.1655167388916015,
28
+ "mean_token_accuracy": 0.7124340075254441,
29
+ "num_tokens": 242713.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 0.8696084088087082,
34
+ "epoch": 0.32858707557502737,
35
+ "grad_norm": 0.6686795949935913,
36
+ "learning_rate": 2.4660810361920367e-05,
37
+ "loss": 0.8031976318359375,
38
+ "mean_token_accuracy": 0.7754033240675926,
39
+ "num_tokens": 360160.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 0.7853927117586136,
44
+ "epoch": 0.43811610076670315,
45
+ "grad_norm": 0.9354436993598938,
46
+ "learning_rate": 3.293625008068559e-05,
47
+ "loss": 0.7226885986328125,
48
+ "mean_token_accuracy": 0.791197614967823,
49
+ "num_tokens": 485403.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 0.7382483741641045,
54
+ "epoch": 0.547645125958379,
55
+ "grad_norm": 0.7535398006439209,
56
+ "learning_rate": 4.121168979945081e-05,
57
+ "loss": 0.6755471801757813,
58
+ "mean_token_accuracy": 0.8054334259033203,
59
+ "num_tokens": 601578.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "entropy": 0.7218390592932701,
64
+ "epoch": 0.6571741511500547,
65
+ "grad_norm": 0.8160243034362793,
66
+ "learning_rate": 4.948712951821604e-05,
67
+ "loss": 0.6614183044433594,
68
+ "mean_token_accuracy": 0.8060630604624748,
69
+ "num_tokens": 713917.0,
70
+ "step": 300
71
+ },
72
+ {
73
+ "entropy": 0.7012957927584648,
74
+ "epoch": 0.7667031763417306,
75
+ "grad_norm": 0.7985761165618896,
76
+ "learning_rate": 5.776256923698126e-05,
77
+ "loss": 0.6415711975097657,
78
+ "mean_token_accuracy": 0.8100408402085304,
79
+ "num_tokens": 832995.0,
80
+ "step": 350
81
+ },
82
+ {
83
+ "entropy": 0.6763210469484329,
84
+ "epoch": 0.8762322015334063,
85
+ "grad_norm": 0.6829700469970703,
86
+ "learning_rate": 6.603800895574648e-05,
87
+ "loss": 0.6193617248535156,
88
+ "mean_token_accuracy": 0.8140933158993721,
89
+ "num_tokens": 959304.0,
90
+ "step": 400
91
+ },
92
+ {
93
+ "entropy": 0.6874829810857773,
94
+ "epoch": 0.9857612267250822,
95
+ "grad_norm": 0.6983632445335388,
96
+ "learning_rate": 7.43134486745117e-05,
97
+ "loss": 0.6262834167480469,
98
+ "mean_token_accuracy": 0.8163631609082223,
99
+ "num_tokens": 1078880.0,
100
+ "step": 450
101
+ },
102
+ {
103
+ "epoch": 1.0,
104
+ "eval_entropy": 0.6085402492492918,
105
+ "eval_loss": 0.6388216018676758,
106
+ "eval_mean_token_accuracy": 0.8123693073552752,
107
+ "eval_num_tokens": 1094474.0,
108
+ "eval_runtime": 95.9846,
109
+ "eval_samples_per_second": 10.439,
110
+ "eval_steps_per_second": 1.313,
111
+ "step": 457
112
+ },
113
+ {
114
+ "entropy": 0.6471366012337232,
115
+ "epoch": 1.0941949616648412,
116
+ "grad_norm": 0.7878272533416748,
117
+ "learning_rate": 7.561806001041411e-05,
118
+ "loss": 0.589820671081543,
119
+ "mean_token_accuracy": 0.8218218798589225,
120
+ "num_tokens": 1190065.0,
121
+ "step": 500
122
+ },
123
+ {
124
+ "entropy": 0.6404865515232087,
125
+ "epoch": 1.203723986856517,
126
+ "grad_norm": 0.7976964712142944,
127
+ "learning_rate": 7.554418144822605e-05,
128
+ "loss": 0.5836894607543945,
129
+ "mean_token_accuracy": 0.8222010856866837,
130
+ "num_tokens": 1313079.0,
131
+ "step": 550
132
+ },
133
+ {
134
+ "entropy": 0.6355892798304558,
135
+ "epoch": 1.3132530120481927,
136
+ "grad_norm": 0.7428044080734253,
137
+ "learning_rate": 7.541528503116934e-05,
138
+ "loss": 0.577253189086914,
139
+ "mean_token_accuracy": 0.8260676205158234,
140
+ "num_tokens": 1428305.0,
141
+ "step": 600
142
+ },
143
+ {
144
+ "entropy": 0.6170084626972675,
145
+ "epoch": 1.4227820372398685,
146
+ "grad_norm": 0.6792078614234924,
147
+ "learning_rate": 7.523155873870194e-05,
148
+ "loss": 0.561871337890625,
149
+ "mean_token_accuracy": 0.8307154527306557,
150
+ "num_tokens": 1551971.0,
151
+ "step": 650
152
+ },
153
+ {
154
+ "entropy": 0.6222476975619793,
155
+ "epoch": 1.5323110624315444,
156
+ "grad_norm": 0.6532447934150696,
157
+ "learning_rate": 7.499327051286336e-05,
158
+ "loss": 0.5612493515014648,
159
+ "mean_token_accuracy": 0.8296262130141259,
160
+ "num_tokens": 1673401.0,
161
+ "step": 700
162
+ },
163
+ {
164
+ "entropy": 0.6251883202791214,
165
+ "epoch": 1.6418400876232202,
166
+ "grad_norm": 0.7107782959938049,
167
+ "learning_rate": 7.47007678675144e-05,
168
+ "loss": 0.565279769897461,
169
+ "mean_token_accuracy": 0.8299687370657921,
170
+ "num_tokens": 1788813.0,
171
+ "step": 750
172
+ },
173
+ {
174
+ "entropy": 0.6177104935050011,
175
+ "epoch": 1.751369112814896,
176
+ "grad_norm": 0.7850057482719421,
177
+ "learning_rate": 7.435447738153122e-05,
178
+ "loss": 0.5564990234375,
179
+ "mean_token_accuracy": 0.8291967037320137,
180
+ "num_tokens": 1911556.0,
181
+ "step": 800
182
+ },
183
+ {
184
+ "entropy": 0.6029443763196468,
185
+ "epoch": 1.8608981380065717,
186
+ "grad_norm": 0.7116957306861877,
187
+ "learning_rate": 7.395490407669285e-05,
188
+ "loss": 0.5501844406127929,
189
+ "mean_token_accuracy": 0.8335196697711944,
190
+ "num_tokens": 2029129.0,
191
+ "step": 850
192
+ },
193
+ {
194
+ "entropy": 0.6042105440795421,
195
+ "epoch": 1.9704271631982475,
196
+ "grad_norm": 0.6712159514427185,
197
+ "learning_rate": 7.350263068116955e-05,
198
+ "loss": 0.5517086029052735,
199
+ "mean_token_accuracy": 0.8321291375160217,
200
+ "num_tokens": 2155392.0,
201
+ "step": 900
202
+ },
203
+ {
204
+ "epoch": 2.0,
205
+ "eval_entropy": 0.5401396741942753,
206
+ "eval_loss": 0.5797445774078369,
207
+ "eval_mean_token_accuracy": 0.8243241475688087,
208
+ "eval_num_tokens": 2188948.0,
209
+ "eval_runtime": 95.5407,
210
+ "eval_samples_per_second": 10.488,
211
+ "eval_steps_per_second": 1.319,
212
+ "step": 914
213
+ },
214
+ {
215
+ "entropy": 0.5675096463675451,
216
+ "epoch": 2.078860898138007,
217
+ "grad_norm": 0.5713562369346619,
218
+ "learning_rate": 7.299831677968588e-05,
219
+ "loss": 0.5120392227172852,
220
+ "mean_token_accuracy": 0.8414448993374603,
221
+ "num_tokens": 2277503.0,
222
+ "step": 950
223
+ },
224
+ {
225
+ "entropy": 0.5500032117962838,
226
+ "epoch": 2.1883899233296824,
227
+ "grad_norm": 0.5951120257377625,
228
+ "learning_rate": 7.244269785159817e-05,
229
+ "loss": 0.49384498596191406,
230
+ "mean_token_accuracy": 0.8470860269665718,
231
+ "num_tokens": 2392968.0,
232
+ "step": 1000
233
+ },
234
+ {
235
+ "entropy": 0.5683873899281024,
236
+ "epoch": 2.297918948521358,
237
+ "grad_norm": 0.6816521286964417,
238
+ "learning_rate": 7.183658419828891e-05,
239
+ "loss": 0.5088459014892578,
240
+ "mean_token_accuracy": 0.8413213565945625,
241
+ "num_tokens": 2507108.0,
242
+ "step": 1050
243
+ },
244
+ {
245
+ "entropy": 0.5481136417388917,
246
+ "epoch": 2.407447973713034,
247
+ "grad_norm": 0.6417970657348633,
248
+ "learning_rate": 7.118085976144257e-05,
249
+ "loss": 0.49456378936767575,
250
+ "mean_token_accuracy": 0.8468478980660439,
251
+ "num_tokens": 2633824.0,
252
+ "step": 1100
253
+ },
254
+ {
255
+ "entropy": 0.5413966289162636,
256
+ "epoch": 2.5169769989047097,
257
+ "grad_norm": 0.631996214389801,
258
+ "learning_rate": 7.047648083392619e-05,
259
+ "loss": 0.49154373168945314,
260
+ "mean_token_accuracy": 0.8461908429861069,
261
+ "num_tokens": 2753131.0,
262
+ "step": 1150
263
+ },
264
+ {
265
+ "entropy": 0.5583206915855408,
266
+ "epoch": 2.6265060240963853,
267
+ "grad_norm": 0.7409902215003967,
268
+ "learning_rate": 6.972447466515462e-05,
269
+ "loss": 0.4927285385131836,
270
+ "mean_token_accuracy": 0.8451325806975365,
271
+ "num_tokens": 2865112.0,
272
+ "step": 1200
273
+ },
274
+ {
275
+ "entropy": 0.556266717761755,
276
+ "epoch": 2.7360350492880614,
277
+ "grad_norm": 0.5275787115097046,
278
+ "learning_rate": 6.892593796297452e-05,
279
+ "loss": 0.499769401550293,
280
+ "mean_token_accuracy": 0.8451313543319702,
281
+ "num_tokens": 2980972.0,
282
+ "step": 1250
283
+ },
284
+ {
285
+ "entropy": 0.5362468618154526,
286
+ "epoch": 2.845564074479737,
287
+ "grad_norm": 0.5450661182403564,
288
+ "learning_rate": 6.808203529425189e-05,
289
+ "loss": 0.4860528945922852,
290
+ "mean_token_accuracy": 0.8479894894361496,
291
+ "num_tokens": 3108708.0,
292
+ "step": 1300
293
+ },
294
+ {
295
+ "entropy": 0.5334719524532556,
296
+ "epoch": 2.955093099671413,
297
+ "grad_norm": 0.4825150966644287,
298
+ "learning_rate": 6.719399738649542e-05,
299
+ "loss": 0.48385780334472656,
300
+ "mean_token_accuracy": 0.8489498183131218,
301
+ "num_tokens": 3232991.0,
302
+ "step": 1350
303
+ },
304
+ {
305
+ "epoch": 3.0,
306
+ "eval_entropy": 0.5111288797287714,
307
+ "eval_loss": 0.5738435387611389,
308
+ "eval_mean_token_accuracy": 0.8259959235077813,
309
+ "eval_num_tokens": 3283422.0,
310
+ "eval_runtime": 95.6002,
311
+ "eval_samples_per_second": 10.481,
312
+ "eval_steps_per_second": 1.318,
313
+ "step": 1371
314
+ },
315
+ {
316
+ "entropy": 0.5024978819519582,
317
+ "epoch": 3.063526834611172,
318
+ "grad_norm": 0.6228470802307129,
319
+ "learning_rate": 6.626311933299292e-05,
320
+ "loss": 0.4451956939697266,
321
+ "mean_token_accuracy": 0.8579568441468056,
322
+ "num_tokens": 3352672.0,
323
+ "step": 1400
324
+ },
325
+ {
326
+ "entropy": 0.49351966604590414,
327
+ "epoch": 3.1730558598028478,
328
+ "grad_norm": 0.648960530757904,
329
+ "learning_rate": 6.529075870407823e-05,
330
+ "loss": 0.4324279022216797,
331
+ "mean_token_accuracy": 0.8607994091510772,
332
+ "num_tokens": 3472463.0,
333
+ "step": 1450
334
+ },
335
+ {
336
+ "entropy": 0.48342301592230796,
337
+ "epoch": 3.2825848849945234,
338
+ "grad_norm": 0.8443573713302612,
339
+ "learning_rate": 6.427833356728302e-05,
340
+ "loss": 0.4237791442871094,
341
+ "mean_token_accuracy": 0.8643713328242302,
342
+ "num_tokens": 3593837.0,
343
+ "step": 1500
344
+ },
345
+ {
346
+ "entropy": 0.4779162485897541,
347
+ "epoch": 3.3921139101861995,
348
+ "grad_norm": 0.7882111072540283,
349
+ "learning_rate": 6.32273204192609e-05,
350
+ "loss": 0.42386363983154296,
351
+ "mean_token_accuracy": 0.8635856115818024,
352
+ "num_tokens": 3716058.0,
353
+ "step": 1550
354
+ },
355
+ {
356
+ "entropy": 0.48870255261659623,
357
+ "epoch": 3.501642935377875,
358
+ "grad_norm": 0.7637454867362976,
359
+ "learning_rate": 6.213925203250001e-05,
360
+ "loss": 0.4301974105834961,
361
+ "mean_token_accuracy": 0.861739870607853,
362
+ "num_tokens": 3838629.0,
363
+ "step": 1600
364
+ },
365
+ {
366
+ "entropy": 0.49970791533589365,
367
+ "epoch": 3.6111719605695507,
368
+ "grad_norm": 0.7269095182418823,
369
+ "learning_rate": 6.101571521996419e-05,
370
+ "loss": 0.4372034454345703,
371
+ "mean_token_accuracy": 0.8592326313257217,
372
+ "num_tokens": 3955462.0,
373
+ "step": 1650
374
+ },
375
+ {
376
+ "entropy": 0.5049038740992546,
377
+ "epoch": 3.7207009857612268,
378
+ "grad_norm": 0.6023766398429871,
379
+ "learning_rate": 5.98583485209228e-05,
380
+ "loss": 0.445910758972168,
381
+ "mean_token_accuracy": 0.8579568776488304,
382
+ "num_tokens": 4075933.0,
383
+ "step": 1700
384
+ },
385
+ {
386
+ "entropy": 0.4927579787373543,
387
+ "epoch": 3.8302300109529024,
388
+ "grad_norm": 0.7742004990577698,
389
+ "learning_rate": 5.866883981134422e-05,
390
+ "loss": 0.4280668640136719,
391
+ "mean_token_accuracy": 0.86241753667593,
392
+ "num_tokens": 4192915.0,
393
+ "step": 1750
394
+ },
395
+ {
396
+ "entropy": 0.5011553263664246,
397
+ "epoch": 3.9397590361445785,
398
+ "grad_norm": 0.5748111605644226,
399
+ "learning_rate": 5.7448923842337736e-05,
400
+ "loss": 0.43842597961425783,
401
+ "mean_token_accuracy": 0.8605782136321067,
402
+ "num_tokens": 4310431.0,
403
+ "step": 1800
404
+ },
405
+ {
406
+ "epoch": 4.0,
407
+ "eval_entropy": 0.4624898036321004,
408
+ "eval_loss": 0.590175986289978,
409
+ "eval_mean_token_accuracy": 0.8287760076068696,
410
+ "eval_num_tokens": 4377896.0,
411
+ "eval_runtime": 95.3934,
412
+ "eval_samples_per_second": 10.504,
413
+ "eval_steps_per_second": 1.321,
414
+ "step": 1828
415
+ },
416
+ {
417
+ "entropy": 0.4502198097079691,
418
+ "epoch": 4.048192771084337,
419
+ "grad_norm": 0.7170541882514954,
420
+ "learning_rate": 5.620037971023403e-05,
421
+ "loss": 0.38744712829589845,
422
+ "mean_token_accuracy": 0.8735785065877317,
423
+ "num_tokens": 4430089.0,
424
+ "step": 1850
425
+ },
426
+ {
427
+ "entropy": 0.41583337262272835,
428
+ "epoch": 4.157721796276014,
429
+ "grad_norm": 0.9511433243751526,
430
+ "learning_rate": 5.4925028261993515e-05,
431
+ "loss": 0.3562023162841797,
432
+ "mean_token_accuracy": 0.881559683084488,
433
+ "num_tokens": 4554484.0,
434
+ "step": 1900
435
+ },
436
+ {
437
+ "entropy": 0.4208242034912109,
438
+ "epoch": 4.267250821467689,
439
+ "grad_norm": 0.8741114139556885,
440
+ "learning_rate": 5.3624729439726544e-05,
441
+ "loss": 0.3612668991088867,
442
+ "mean_token_accuracy": 0.8802913293242455,
443
+ "num_tokens": 4675472.0,
444
+ "step": 1950
445
+ },
446
+ {
447
+ "entropy": 0.4188448017835617,
448
+ "epoch": 4.376779846659365,
449
+ "grad_norm": 1.1690106391906738,
450
+ "learning_rate": 5.23013795681983e-05,
451
+ "loss": 0.3627183151245117,
452
+ "mean_token_accuracy": 0.8805234292149544,
453
+ "num_tokens": 4793479.0,
454
+ "step": 2000
455
+ },
456
+ {
457
+ "entropy": 0.42332509815692904,
458
+ "epoch": 4.48630887185104,
459
+ "grad_norm": 0.8150995969772339,
460
+ "learning_rate": 5.095690858927403e-05,
461
+ "loss": 0.3626524353027344,
462
+ "mean_token_accuracy": 0.879372145831585,
463
+ "num_tokens": 4911343.0,
464
+ "step": 2050
465
+ },
466
+ {
467
+ "entropy": 0.42394075110554696,
468
+ "epoch": 4.595837897042716,
469
+ "grad_norm": 0.8282762169837952,
470
+ "learning_rate": 4.959327724733778e-05,
471
+ "loss": 0.3573355865478516,
472
+ "mean_token_accuracy": 0.8799301481246948,
473
+ "num_tokens": 5028364.0,
474
+ "step": 2100
475
+ },
476
+ {
477
+ "entropy": 0.4259473057091236,
478
+ "epoch": 4.705366922234392,
479
+ "grad_norm": 0.7610743045806885,
480
+ "learning_rate": 4.8212474229789754e-05,
481
+ "loss": 0.3665072631835937,
482
+ "mean_token_accuracy": 0.879306109547615,
483
+ "num_tokens": 5146995.0,
484
+ "step": 2150
485
+ },
486
+ {
487
+ "entropy": 0.42407046899199485,
488
+ "epoch": 4.814895947426068,
489
+ "grad_norm": 0.6995375156402588,
490
+ "learning_rate": 4.681651326679193e-05,
491
+ "loss": 0.3689637756347656,
492
+ "mean_token_accuracy": 0.878270491361618,
493
+ "num_tokens": 5263483.0,
494
+ "step": 2200
495
+ },
496
+ {
497
+ "entropy": 0.41719858527183534,
498
+ "epoch": 4.924424972617744,
499
+ "grad_norm": 0.8921851515769958,
500
+ "learning_rate": 4.5407430194492145e-05,
501
+ "loss": 0.36366527557373046,
502
+ "mean_token_accuracy": 0.8810832899808884,
503
+ "num_tokens": 5387955.0,
504
+ "step": 2250
505
+ },
506
+ {
507
+ "epoch": 5.0,
508
+ "eval_entropy": 0.40321539318750776,
509
+ "eval_loss": 0.6344199776649475,
510
+ "eval_mean_token_accuracy": 0.8290003571245406,
511
+ "eval_num_tokens": 5472370.0,
512
+ "eval_runtime": 95.4906,
513
+ "eval_samples_per_second": 10.493,
514
+ "eval_steps_per_second": 1.32,
515
+ "step": 2285
516
+ },
517
+ {
518
+ "entropy": 0.39761772337887025,
519
+ "epoch": 5.032858707557502,
520
+ "grad_norm": 0.8363515734672546,
521
+ "learning_rate": 4.3987279986009235e-05,
522
+ "loss": 0.3363536834716797,
523
+ "mean_token_accuracy": 0.8891537577816935,
524
+ "num_tokens": 5508058.0,
525
+ "step": 2300
526
+ },
527
+ {
528
+ "entropy": 0.3406807939708233,
529
+ "epoch": 5.142387732749179,
530
+ "grad_norm": 0.9140804409980774,
531
+ "learning_rate": 4.2558133754509274e-05,
532
+ "loss": 0.2747584533691406,
533
+ "mean_token_accuracy": 0.9082898917794228,
534
+ "num_tokens": 5625635.0,
535
+ "step": 2350
536
+ },
537
+ {
538
+ "entropy": 0.34594309888780117,
539
+ "epoch": 5.2519167579408546,
540
+ "grad_norm": 0.9676663875579834,
541
+ "learning_rate": 4.112207573274355e-05,
542
+ "loss": 0.2821139907836914,
543
+ "mean_token_accuracy": 0.9047485241293907,
544
+ "num_tokens": 5741941.0,
545
+ "step": 2400
546
+ },
547
+ {
548
+ "entropy": 0.3354563079029322,
549
+ "epoch": 5.36144578313253,
550
+ "grad_norm": 1.1732304096221924,
551
+ "learning_rate": 3.968120023345335e-05,
552
+ "loss": 0.2757284355163574,
553
+ "mean_token_accuracy": 0.905784958600998,
554
+ "num_tokens": 5864741.0,
555
+ "step": 2450
556
+ },
557
+ {
558
+ "entropy": 0.3486690762639046,
559
+ "epoch": 5.470974808324206,
560
+ "grad_norm": 0.9858622550964355,
561
+ "learning_rate": 3.823760859507414e-05,
562
+ "loss": 0.28488592147827146,
563
+ "mean_token_accuracy": 0.9029894617199897,
564
+ "num_tokens": 5984809.0,
565
+ "step": 2500
566
+ },
567
+ {
568
+ "entropy": 0.35116296328604224,
569
+ "epoch": 5.580503833515881,
570
+ "grad_norm": 0.8295992016792297,
571
+ "learning_rate": 3.679340611719382e-05,
572
+ "loss": 0.28818355560302733,
573
+ "mean_token_accuracy": 0.9026859793066978,
574
+ "num_tokens": 6101316.0,
575
+ "step": 2550
576
+ },
577
+ {
578
+ "entropy": 0.34762901581823824,
579
+ "epoch": 5.690032858707557,
580
+ "grad_norm": 1.2317472696304321,
581
+ "learning_rate": 3.5350698990234046e-05,
582
+ "loss": 0.2834972381591797,
583
+ "mean_token_accuracy": 0.9035492998361587,
584
+ "num_tokens": 6222300.0,
585
+ "step": 2600
586
+ },
587
+ {
588
+ "entropy": 0.3509623434394598,
589
+ "epoch": 5.7995618838992335,
590
+ "grad_norm": 1.1071515083312988,
591
+ "learning_rate": 3.391159122383239e-05,
592
+ "loss": 0.28417932510375976,
593
+ "mean_token_accuracy": 0.9017772257328034,
594
+ "num_tokens": 6339706.0,
595
+ "step": 2650
596
+ },
597
+ {
598
+ "entropy": 0.3327887299656868,
599
+ "epoch": 5.909090909090909,
600
+ "grad_norm": 0.9410860538482666,
601
+ "learning_rate": 3.247818157840487e-05,
602
+ "loss": 0.27511814117431643,
603
+ "mean_token_accuracy": 0.9069757598638535,
604
+ "num_tokens": 6466571.0,
605
+ "step": 2700
606
+ },
607
+ {
608
+ "epoch": 6.0,
609
+ "eval_entropy": 0.37359742701999726,
610
+ "eval_loss": 0.6801024079322815,
611
+ "eval_mean_token_accuracy": 0.8251904424220796,
612
+ "eval_num_tokens": 6566844.0,
613
+ "eval_runtime": 95.5582,
614
+ "eval_samples_per_second": 10.486,
615
+ "eval_steps_per_second": 1.319,
616
+ "step": 2742
617
+ }
618
+ ],
619
+ "logging_steps": 50,
620
+ "max_steps": 4570,
621
+ "num_input_tokens_seen": 0,
622
+ "num_train_epochs": 10,
623
+ "save_steps": 500,
624
+ "stateful_callbacks": {
625
+ "TrainerControl": {
626
+ "args": {
627
+ "should_epoch_stop": false,
628
+ "should_evaluate": false,
629
+ "should_log": false,
630
+ "should_save": true,
631
+ "should_training_stop": false
632
+ },
633
+ "attributes": {}
634
+ }
635
+ },
636
+ "total_flos": 1.087346418987817e+18,
637
+ "train_batch_size": 4,
638
+ "trial_name": null,
639
+ "trial_params": null
640
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 64,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.021223473447411947,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "down_proj",
34
+ "gate_proj",
35
+ "v_proj",
36
+ "up_proj",
37
+ "k_proj",
38
+ "o_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# 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>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\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" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
+ {%- if loop.last or (not loop.last and reasoning_content) %}
41
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
42
+ {%- else %}
43
+ {{- '<|im_start|>' + message.role + '\n' + content }}
44
+ {%- endif %}
45
+ {%- else %}
46
+ {{- '<|im_start|>' + message.role + '\n' + content }}
47
+ {%- endif %}
48
+ {%- if message.tool_calls %}
49
+ {%- for tool_call in message.tool_calls %}
50
+ {%- if (loop.first and content) or (not loop.first) %}
51
+ {{- '\n' }}
52
+ {%- endif %}
53
+ {%- if tool_call.function %}
54
+ {%- set tool_call = tool_call.function %}
55
+ {%- endif %}
56
+ {{- '<tool_call>\n{"name": "' }}
57
+ {{- tool_call.name }}
58
+ {{- '", "arguments": ' }}
59
+ {%- if tool_call.arguments is string %}
60
+ {{- tool_call.arguments }}
61
+ {%- else %}
62
+ {{- tool_call.arguments | tojson }}
63
+ {%- endif %}
64
+ {{- '}\n</tool_call>' }}
65
+ {%- endfor %}
66
+ {%- endif %}
67
+ {{- '<|im_end|>\n' }}
68
+ {%- elif message.role == "tool" %}
69
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
70
+ {{- '<|im_start|>user' }}
71
+ {%- endif %}
72
+ {{- '\n<tool_response>\n' }}
73
+ {{- message.content }}
74
+ {{- '\n</tool_response>' }}
75
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
76
+ {{- '<|im_end|>\n' }}
77
+ {%- endif %}
78
+ {%- endif %}
79
+ {%- endfor %}
80
+ {%- if add_generation_prompt %}
81
+ {{- '<|im_start|>assistant\n' }}
82
+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
84
+ {%- endif %}
85
+ {%- endif %}
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|endoftext|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 131072,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3199/trainer_state.json ADDED
@@ -0,0 +1,741 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 7.0,
6
+ "eval_steps": 500,
7
+ "global_step": 3199,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 2.002540482878685,
14
+ "epoch": 0.10952902519167579,
15
+ "grad_norm": 0.5552461743354797,
16
+ "learning_rate": 8.109930924389919e-06,
17
+ "loss": 1.9067156982421876,
18
+ "mean_token_accuracy": 0.6026969534158707,
19
+ "num_tokens": 119060.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 1.3068562260270118,
24
+ "epoch": 0.21905805038335158,
25
+ "grad_norm": 1.0465189218521118,
26
+ "learning_rate": 1.6385370643155144e-05,
27
+ "loss": 1.1655167388916015,
28
+ "mean_token_accuracy": 0.7124340075254441,
29
+ "num_tokens": 242713.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 0.8696084088087082,
34
+ "epoch": 0.32858707557502737,
35
+ "grad_norm": 0.6686795949935913,
36
+ "learning_rate": 2.4660810361920367e-05,
37
+ "loss": 0.8031976318359375,
38
+ "mean_token_accuracy": 0.7754033240675926,
39
+ "num_tokens": 360160.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 0.7853927117586136,
44
+ "epoch": 0.43811610076670315,
45
+ "grad_norm": 0.9354436993598938,
46
+ "learning_rate": 3.293625008068559e-05,
47
+ "loss": 0.7226885986328125,
48
+ "mean_token_accuracy": 0.791197614967823,
49
+ "num_tokens": 485403.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 0.7382483741641045,
54
+ "epoch": 0.547645125958379,
55
+ "grad_norm": 0.7535398006439209,
56
+ "learning_rate": 4.121168979945081e-05,
57
+ "loss": 0.6755471801757813,
58
+ "mean_token_accuracy": 0.8054334259033203,
59
+ "num_tokens": 601578.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "entropy": 0.7218390592932701,
64
+ "epoch": 0.6571741511500547,
65
+ "grad_norm": 0.8160243034362793,
66
+ "learning_rate": 4.948712951821604e-05,
67
+ "loss": 0.6614183044433594,
68
+ "mean_token_accuracy": 0.8060630604624748,
69
+ "num_tokens": 713917.0,
70
+ "step": 300
71
+ },
72
+ {
73
+ "entropy": 0.7012957927584648,
74
+ "epoch": 0.7667031763417306,
75
+ "grad_norm": 0.7985761165618896,
76
+ "learning_rate": 5.776256923698126e-05,
77
+ "loss": 0.6415711975097657,
78
+ "mean_token_accuracy": 0.8100408402085304,
79
+ "num_tokens": 832995.0,
80
+ "step": 350
81
+ },
82
+ {
83
+ "entropy": 0.6763210469484329,
84
+ "epoch": 0.8762322015334063,
85
+ "grad_norm": 0.6829700469970703,
86
+ "learning_rate": 6.603800895574648e-05,
87
+ "loss": 0.6193617248535156,
88
+ "mean_token_accuracy": 0.8140933158993721,
89
+ "num_tokens": 959304.0,
90
+ "step": 400
91
+ },
92
+ {
93
+ "entropy": 0.6874829810857773,
94
+ "epoch": 0.9857612267250822,
95
+ "grad_norm": 0.6983632445335388,
96
+ "learning_rate": 7.43134486745117e-05,
97
+ "loss": 0.6262834167480469,
98
+ "mean_token_accuracy": 0.8163631609082223,
99
+ "num_tokens": 1078880.0,
100
+ "step": 450
101
+ },
102
+ {
103
+ "epoch": 1.0,
104
+ "eval_entropy": 0.6085402492492918,
105
+ "eval_loss": 0.6388216018676758,
106
+ "eval_mean_token_accuracy": 0.8123693073552752,
107
+ "eval_num_tokens": 1094474.0,
108
+ "eval_runtime": 95.9846,
109
+ "eval_samples_per_second": 10.439,
110
+ "eval_steps_per_second": 1.313,
111
+ "step": 457
112
+ },
113
+ {
114
+ "entropy": 0.6471366012337232,
115
+ "epoch": 1.0941949616648412,
116
+ "grad_norm": 0.7878272533416748,
117
+ "learning_rate": 7.561806001041411e-05,
118
+ "loss": 0.589820671081543,
119
+ "mean_token_accuracy": 0.8218218798589225,
120
+ "num_tokens": 1190065.0,
121
+ "step": 500
122
+ },
123
+ {
124
+ "entropy": 0.6404865515232087,
125
+ "epoch": 1.203723986856517,
126
+ "grad_norm": 0.7976964712142944,
127
+ "learning_rate": 7.554418144822605e-05,
128
+ "loss": 0.5836894607543945,
129
+ "mean_token_accuracy": 0.8222010856866837,
130
+ "num_tokens": 1313079.0,
131
+ "step": 550
132
+ },
133
+ {
134
+ "entropy": 0.6355892798304558,
135
+ "epoch": 1.3132530120481927,
136
+ "grad_norm": 0.7428044080734253,
137
+ "learning_rate": 7.541528503116934e-05,
138
+ "loss": 0.577253189086914,
139
+ "mean_token_accuracy": 0.8260676205158234,
140
+ "num_tokens": 1428305.0,
141
+ "step": 600
142
+ },
143
+ {
144
+ "entropy": 0.6170084626972675,
145
+ "epoch": 1.4227820372398685,
146
+ "grad_norm": 0.6792078614234924,
147
+ "learning_rate": 7.523155873870194e-05,
148
+ "loss": 0.561871337890625,
149
+ "mean_token_accuracy": 0.8307154527306557,
150
+ "num_tokens": 1551971.0,
151
+ "step": 650
152
+ },
153
+ {
154
+ "entropy": 0.6222476975619793,
155
+ "epoch": 1.5323110624315444,
156
+ "grad_norm": 0.6532447934150696,
157
+ "learning_rate": 7.499327051286336e-05,
158
+ "loss": 0.5612493515014648,
159
+ "mean_token_accuracy": 0.8296262130141259,
160
+ "num_tokens": 1673401.0,
161
+ "step": 700
162
+ },
163
+ {
164
+ "entropy": 0.6251883202791214,
165
+ "epoch": 1.6418400876232202,
166
+ "grad_norm": 0.7107782959938049,
167
+ "learning_rate": 7.47007678675144e-05,
168
+ "loss": 0.565279769897461,
169
+ "mean_token_accuracy": 0.8299687370657921,
170
+ "num_tokens": 1788813.0,
171
+ "step": 750
172
+ },
173
+ {
174
+ "entropy": 0.6177104935050011,
175
+ "epoch": 1.751369112814896,
176
+ "grad_norm": 0.7850057482719421,
177
+ "learning_rate": 7.435447738153122e-05,
178
+ "loss": 0.5564990234375,
179
+ "mean_token_accuracy": 0.8291967037320137,
180
+ "num_tokens": 1911556.0,
181
+ "step": 800
182
+ },
183
+ {
184
+ "entropy": 0.6029443763196468,
185
+ "epoch": 1.8608981380065717,
186
+ "grad_norm": 0.7116957306861877,
187
+ "learning_rate": 7.395490407669285e-05,
188
+ "loss": 0.5501844406127929,
189
+ "mean_token_accuracy": 0.8335196697711944,
190
+ "num_tokens": 2029129.0,
191
+ "step": 850
192
+ },
193
+ {
194
+ "entropy": 0.6042105440795421,
195
+ "epoch": 1.9704271631982475,
196
+ "grad_norm": 0.6712159514427185,
197
+ "learning_rate": 7.350263068116955e-05,
198
+ "loss": 0.5517086029052735,
199
+ "mean_token_accuracy": 0.8321291375160217,
200
+ "num_tokens": 2155392.0,
201
+ "step": 900
202
+ },
203
+ {
204
+ "epoch": 2.0,
205
+ "eval_entropy": 0.5401396741942753,
206
+ "eval_loss": 0.5797445774078369,
207
+ "eval_mean_token_accuracy": 0.8243241475688087,
208
+ "eval_num_tokens": 2188948.0,
209
+ "eval_runtime": 95.5407,
210
+ "eval_samples_per_second": 10.488,
211
+ "eval_steps_per_second": 1.319,
212
+ "step": 914
213
+ },
214
+ {
215
+ "entropy": 0.5675096463675451,
216
+ "epoch": 2.078860898138007,
217
+ "grad_norm": 0.5713562369346619,
218
+ "learning_rate": 7.299831677968588e-05,
219
+ "loss": 0.5120392227172852,
220
+ "mean_token_accuracy": 0.8414448993374603,
221
+ "num_tokens": 2277503.0,
222
+ "step": 950
223
+ },
224
+ {
225
+ "entropy": 0.5500032117962838,
226
+ "epoch": 2.1883899233296824,
227
+ "grad_norm": 0.5951120257377625,
228
+ "learning_rate": 7.244269785159817e-05,
229
+ "loss": 0.49384498596191406,
230
+ "mean_token_accuracy": 0.8470860269665718,
231
+ "num_tokens": 2392968.0,
232
+ "step": 1000
233
+ },
234
+ {
235
+ "entropy": 0.5683873899281024,
236
+ "epoch": 2.297918948521358,
237
+ "grad_norm": 0.6816521286964417,
238
+ "learning_rate": 7.183658419828891e-05,
239
+ "loss": 0.5088459014892578,
240
+ "mean_token_accuracy": 0.8413213565945625,
241
+ "num_tokens": 2507108.0,
242
+ "step": 1050
243
+ },
244
+ {
245
+ "entropy": 0.5481136417388917,
246
+ "epoch": 2.407447973713034,
247
+ "grad_norm": 0.6417970657348633,
248
+ "learning_rate": 7.118085976144257e-05,
249
+ "loss": 0.49456378936767575,
250
+ "mean_token_accuracy": 0.8468478980660439,
251
+ "num_tokens": 2633824.0,
252
+ "step": 1100
253
+ },
254
+ {
255
+ "entropy": 0.5413966289162636,
256
+ "epoch": 2.5169769989047097,
257
+ "grad_norm": 0.631996214389801,
258
+ "learning_rate": 7.047648083392619e-05,
259
+ "loss": 0.49154373168945314,
260
+ "mean_token_accuracy": 0.8461908429861069,
261
+ "num_tokens": 2753131.0,
262
+ "step": 1150
263
+ },
264
+ {
265
+ "entropy": 0.5583206915855408,
266
+ "epoch": 2.6265060240963853,
267
+ "grad_norm": 0.7409902215003967,
268
+ "learning_rate": 6.972447466515462e-05,
269
+ "loss": 0.4927285385131836,
270
+ "mean_token_accuracy": 0.8451325806975365,
271
+ "num_tokens": 2865112.0,
272
+ "step": 1200
273
+ },
274
+ {
275
+ "entropy": 0.556266717761755,
276
+ "epoch": 2.7360350492880614,
277
+ "grad_norm": 0.5275787115097046,
278
+ "learning_rate": 6.892593796297452e-05,
279
+ "loss": 0.499769401550293,
280
+ "mean_token_accuracy": 0.8451313543319702,
281
+ "num_tokens": 2980972.0,
282
+ "step": 1250
283
+ },
284
+ {
285
+ "entropy": 0.5362468618154526,
286
+ "epoch": 2.845564074479737,
287
+ "grad_norm": 0.5450661182403564,
288
+ "learning_rate": 6.808203529425189e-05,
289
+ "loss": 0.4860528945922852,
290
+ "mean_token_accuracy": 0.8479894894361496,
291
+ "num_tokens": 3108708.0,
292
+ "step": 1300
293
+ },
294
+ {
295
+ "entropy": 0.5334719524532556,
296
+ "epoch": 2.955093099671413,
297
+ "grad_norm": 0.4825150966644287,
298
+ "learning_rate": 6.719399738649542e-05,
299
+ "loss": 0.48385780334472656,
300
+ "mean_token_accuracy": 0.8489498183131218,
301
+ "num_tokens": 3232991.0,
302
+ "step": 1350
303
+ },
304
+ {
305
+ "epoch": 3.0,
306
+ "eval_entropy": 0.5111288797287714,
307
+ "eval_loss": 0.5738435387611389,
308
+ "eval_mean_token_accuracy": 0.8259959235077813,
309
+ "eval_num_tokens": 3283422.0,
310
+ "eval_runtime": 95.6002,
311
+ "eval_samples_per_second": 10.481,
312
+ "eval_steps_per_second": 1.318,
313
+ "step": 1371
314
+ },
315
+ {
316
+ "entropy": 0.5024978819519582,
317
+ "epoch": 3.063526834611172,
318
+ "grad_norm": 0.6228470802307129,
319
+ "learning_rate": 6.626311933299292e-05,
320
+ "loss": 0.4451956939697266,
321
+ "mean_token_accuracy": 0.8579568441468056,
322
+ "num_tokens": 3352672.0,
323
+ "step": 1400
324
+ },
325
+ {
326
+ "entropy": 0.49351966604590414,
327
+ "epoch": 3.1730558598028478,
328
+ "grad_norm": 0.648960530757904,
329
+ "learning_rate": 6.529075870407823e-05,
330
+ "loss": 0.4324279022216797,
331
+ "mean_token_accuracy": 0.8607994091510772,
332
+ "num_tokens": 3472463.0,
333
+ "step": 1450
334
+ },
335
+ {
336
+ "entropy": 0.48342301592230796,
337
+ "epoch": 3.2825848849945234,
338
+ "grad_norm": 0.8443573713302612,
339
+ "learning_rate": 6.427833356728302e-05,
340
+ "loss": 0.4237791442871094,
341
+ "mean_token_accuracy": 0.8643713328242302,
342
+ "num_tokens": 3593837.0,
343
+ "step": 1500
344
+ },
345
+ {
346
+ "entropy": 0.4779162485897541,
347
+ "epoch": 3.3921139101861995,
348
+ "grad_norm": 0.7882111072540283,
349
+ "learning_rate": 6.32273204192609e-05,
350
+ "loss": 0.42386363983154296,
351
+ "mean_token_accuracy": 0.8635856115818024,
352
+ "num_tokens": 3716058.0,
353
+ "step": 1550
354
+ },
355
+ {
356
+ "entropy": 0.48870255261659623,
357
+ "epoch": 3.501642935377875,
358
+ "grad_norm": 0.7637454867362976,
359
+ "learning_rate": 6.213925203250001e-05,
360
+ "loss": 0.4301974105834961,
361
+ "mean_token_accuracy": 0.861739870607853,
362
+ "num_tokens": 3838629.0,
363
+ "step": 1600
364
+ },
365
+ {
366
+ "entropy": 0.49970791533589365,
367
+ "epoch": 3.6111719605695507,
368
+ "grad_norm": 0.7269095182418823,
369
+ "learning_rate": 6.101571521996419e-05,
370
+ "loss": 0.4372034454345703,
371
+ "mean_token_accuracy": 0.8592326313257217,
372
+ "num_tokens": 3955462.0,
373
+ "step": 1650
374
+ },
375
+ {
376
+ "entropy": 0.5049038740992546,
377
+ "epoch": 3.7207009857612268,
378
+ "grad_norm": 0.6023766398429871,
379
+ "learning_rate": 5.98583485209228e-05,
380
+ "loss": 0.445910758972168,
381
+ "mean_token_accuracy": 0.8579568776488304,
382
+ "num_tokens": 4075933.0,
383
+ "step": 1700
384
+ },
385
+ {
386
+ "entropy": 0.4927579787373543,
387
+ "epoch": 3.8302300109529024,
388
+ "grad_norm": 0.7742004990577698,
389
+ "learning_rate": 5.866883981134422e-05,
390
+ "loss": 0.4280668640136719,
391
+ "mean_token_accuracy": 0.86241753667593,
392
+ "num_tokens": 4192915.0,
393
+ "step": 1750
394
+ },
395
+ {
396
+ "entropy": 0.5011553263664246,
397
+ "epoch": 3.9397590361445785,
398
+ "grad_norm": 0.5748111605644226,
399
+ "learning_rate": 5.7448923842337736e-05,
400
+ "loss": 0.43842597961425783,
401
+ "mean_token_accuracy": 0.8605782136321067,
402
+ "num_tokens": 4310431.0,
403
+ "step": 1800
404
+ },
405
+ {
406
+ "epoch": 4.0,
407
+ "eval_entropy": 0.4624898036321004,
408
+ "eval_loss": 0.590175986289978,
409
+ "eval_mean_token_accuracy": 0.8287760076068696,
410
+ "eval_num_tokens": 4377896.0,
411
+ "eval_runtime": 95.3934,
412
+ "eval_samples_per_second": 10.504,
413
+ "eval_steps_per_second": 1.321,
414
+ "step": 1828
415
+ },
416
+ {
417
+ "entropy": 0.4502198097079691,
418
+ "epoch": 4.048192771084337,
419
+ "grad_norm": 0.7170541882514954,
420
+ "learning_rate": 5.620037971023403e-05,
421
+ "loss": 0.38744712829589845,
422
+ "mean_token_accuracy": 0.8735785065877317,
423
+ "num_tokens": 4430089.0,
424
+ "step": 1850
425
+ },
426
+ {
427
+ "entropy": 0.41583337262272835,
428
+ "epoch": 4.157721796276014,
429
+ "grad_norm": 0.9511433243751526,
430
+ "learning_rate": 5.4925028261993515e-05,
431
+ "loss": 0.3562023162841797,
432
+ "mean_token_accuracy": 0.881559683084488,
433
+ "num_tokens": 4554484.0,
434
+ "step": 1900
435
+ },
436
+ {
437
+ "entropy": 0.4208242034912109,
438
+ "epoch": 4.267250821467689,
439
+ "grad_norm": 0.8741114139556885,
440
+ "learning_rate": 5.3624729439726544e-05,
441
+ "loss": 0.3612668991088867,
442
+ "mean_token_accuracy": 0.8802913293242455,
443
+ "num_tokens": 4675472.0,
444
+ "step": 1950
445
+ },
446
+ {
447
+ "entropy": 0.4188448017835617,
448
+ "epoch": 4.376779846659365,
449
+ "grad_norm": 1.1690106391906738,
450
+ "learning_rate": 5.23013795681983e-05,
451
+ "loss": 0.3627183151245117,
452
+ "mean_token_accuracy": 0.8805234292149544,
453
+ "num_tokens": 4793479.0,
454
+ "step": 2000
455
+ },
456
+ {
457
+ "entropy": 0.42332509815692904,
458
+ "epoch": 4.48630887185104,
459
+ "grad_norm": 0.8150995969772339,
460
+ "learning_rate": 5.095690858927403e-05,
461
+ "loss": 0.3626524353027344,
462
+ "mean_token_accuracy": 0.879372145831585,
463
+ "num_tokens": 4911343.0,
464
+ "step": 2050
465
+ },
466
+ {
467
+ "entropy": 0.42394075110554696,
468
+ "epoch": 4.595837897042716,
469
+ "grad_norm": 0.8282762169837952,
470
+ "learning_rate": 4.959327724733778e-05,
471
+ "loss": 0.3573355865478516,
472
+ "mean_token_accuracy": 0.8799301481246948,
473
+ "num_tokens": 5028364.0,
474
+ "step": 2100
475
+ },
476
+ {
477
+ "entropy": 0.4259473057091236,
478
+ "epoch": 4.705366922234392,
479
+ "grad_norm": 0.7610743045806885,
480
+ "learning_rate": 4.8212474229789754e-05,
481
+ "loss": 0.3665072631835937,
482
+ "mean_token_accuracy": 0.879306109547615,
483
+ "num_tokens": 5146995.0,
484
+ "step": 2150
485
+ },
486
+ {
487
+ "entropy": 0.42407046899199485,
488
+ "epoch": 4.814895947426068,
489
+ "grad_norm": 0.6995375156402588,
490
+ "learning_rate": 4.681651326679193e-05,
491
+ "loss": 0.3689637756347656,
492
+ "mean_token_accuracy": 0.878270491361618,
493
+ "num_tokens": 5263483.0,
494
+ "step": 2200
495
+ },
496
+ {
497
+ "entropy": 0.41719858527183534,
498
+ "epoch": 4.924424972617744,
499
+ "grad_norm": 0.8921851515769958,
500
+ "learning_rate": 4.5407430194492145e-05,
501
+ "loss": 0.36366527557373046,
502
+ "mean_token_accuracy": 0.8810832899808884,
503
+ "num_tokens": 5387955.0,
504
+ "step": 2250
505
+ },
506
+ {
507
+ "epoch": 5.0,
508
+ "eval_entropy": 0.40321539318750776,
509
+ "eval_loss": 0.6344199776649475,
510
+ "eval_mean_token_accuracy": 0.8290003571245406,
511
+ "eval_num_tokens": 5472370.0,
512
+ "eval_runtime": 95.4906,
513
+ "eval_samples_per_second": 10.493,
514
+ "eval_steps_per_second": 1.32,
515
+ "step": 2285
516
+ },
517
+ {
518
+ "entropy": 0.39761772337887025,
519
+ "epoch": 5.032858707557502,
520
+ "grad_norm": 0.8363515734672546,
521
+ "learning_rate": 4.3987279986009235e-05,
522
+ "loss": 0.3363536834716797,
523
+ "mean_token_accuracy": 0.8891537577816935,
524
+ "num_tokens": 5508058.0,
525
+ "step": 2300
526
+ },
527
+ {
528
+ "entropy": 0.3406807939708233,
529
+ "epoch": 5.142387732749179,
530
+ "grad_norm": 0.9140804409980774,
531
+ "learning_rate": 4.2558133754509274e-05,
532
+ "loss": 0.2747584533691406,
533
+ "mean_token_accuracy": 0.9082898917794228,
534
+ "num_tokens": 5625635.0,
535
+ "step": 2350
536
+ },
537
+ {
538
+ "entropy": 0.34594309888780117,
539
+ "epoch": 5.2519167579408546,
540
+ "grad_norm": 0.9676663875579834,
541
+ "learning_rate": 4.112207573274355e-05,
542
+ "loss": 0.2821139907836914,
543
+ "mean_token_accuracy": 0.9047485241293907,
544
+ "num_tokens": 5741941.0,
545
+ "step": 2400
546
+ },
547
+ {
548
+ "entropy": 0.3354563079029322,
549
+ "epoch": 5.36144578313253,
550
+ "grad_norm": 1.1732304096221924,
551
+ "learning_rate": 3.968120023345335e-05,
552
+ "loss": 0.2757284355163574,
553
+ "mean_token_accuracy": 0.905784958600998,
554
+ "num_tokens": 5864741.0,
555
+ "step": 2450
556
+ },
557
+ {
558
+ "entropy": 0.3486690762639046,
559
+ "epoch": 5.470974808324206,
560
+ "grad_norm": 0.9858622550964355,
561
+ "learning_rate": 3.823760859507414e-05,
562
+ "loss": 0.28488592147827146,
563
+ "mean_token_accuracy": 0.9029894617199897,
564
+ "num_tokens": 5984809.0,
565
+ "step": 2500
566
+ },
567
+ {
568
+ "entropy": 0.35116296328604224,
569
+ "epoch": 5.580503833515881,
570
+ "grad_norm": 0.8295992016792297,
571
+ "learning_rate": 3.679340611719382e-05,
572
+ "loss": 0.28818355560302733,
573
+ "mean_token_accuracy": 0.9026859793066978,
574
+ "num_tokens": 6101316.0,
575
+ "step": 2550
576
+ },
577
+ {
578
+ "entropy": 0.34762901581823824,
579
+ "epoch": 5.690032858707557,
580
+ "grad_norm": 1.2317472696304321,
581
+ "learning_rate": 3.5350698990234046e-05,
582
+ "loss": 0.2834972381591797,
583
+ "mean_token_accuracy": 0.9035492998361587,
584
+ "num_tokens": 6222300.0,
585
+ "step": 2600
586
+ },
587
+ {
588
+ "entropy": 0.3509623434394598,
589
+ "epoch": 5.7995618838992335,
590
+ "grad_norm": 1.1071515083312988,
591
+ "learning_rate": 3.391159122383239e-05,
592
+ "loss": 0.28417932510375976,
593
+ "mean_token_accuracy": 0.9017772257328034,
594
+ "num_tokens": 6339706.0,
595
+ "step": 2650
596
+ },
597
+ {
598
+ "entropy": 0.3327887299656868,
599
+ "epoch": 5.909090909090909,
600
+ "grad_norm": 0.9410860538482666,
601
+ "learning_rate": 3.247818157840487e-05,
602
+ "loss": 0.27511814117431643,
603
+ "mean_token_accuracy": 0.9069757598638535,
604
+ "num_tokens": 6466571.0,
605
+ "step": 2700
606
+ },
607
+ {
608
+ "epoch": 6.0,
609
+ "eval_entropy": 0.37359742701999726,
610
+ "eval_loss": 0.6801024079322815,
611
+ "eval_mean_token_accuracy": 0.8251904424220796,
612
+ "eval_num_tokens": 6566844.0,
613
+ "eval_runtime": 95.5582,
614
+ "eval_samples_per_second": 10.486,
615
+ "eval_steps_per_second": 1.319,
616
+ "step": 2742
617
+ },
618
+ {
619
+ "entropy": 0.33517336732510367,
620
+ "epoch": 6.017524644030668,
621
+ "grad_norm": 1.1120449304580688,
622
+ "learning_rate": 3.105256050436392e-05,
623
+ "loss": 0.2726051139831543,
624
+ "mean_token_accuracy": 0.9074887001153195,
625
+ "num_tokens": 6588372.0,
626
+ "step": 2750
627
+ },
628
+ {
629
+ "entropy": 0.2580332762002945,
630
+ "epoch": 6.127053669222344,
631
+ "grad_norm": 1.1544626951217651,
632
+ "learning_rate": 2.9636807093455337e-05,
633
+ "loss": 0.1894158172607422,
634
+ "mean_token_accuracy": 0.934169539809227,
635
+ "num_tokens": 6712562.0,
636
+ "step": 2800
637
+ },
638
+ {
639
+ "entropy": 0.2648825005441904,
640
+ "epoch": 6.23658269441402,
641
+ "grad_norm": 1.2022897005081177,
642
+ "learning_rate": 2.823298604666056e-05,
643
+ "loss": 0.19440870285034179,
644
+ "mean_token_accuracy": 0.9334753274917602,
645
+ "num_tokens": 6832843.0,
646
+ "step": 2850
647
+ },
648
+ {
649
+ "entropy": 0.27026796594262126,
650
+ "epoch": 6.3461117196056955,
651
+ "grad_norm": 1.0150978565216064,
652
+ "learning_rate": 2.6843144663086045e-05,
653
+ "loss": 0.19671850204467772,
654
+ "mean_token_accuracy": 0.9316870296001434,
655
+ "num_tokens": 6952871.0,
656
+ "step": 2900
657
+ },
658
+ {
659
+ "entropy": 0.2634010723978281,
660
+ "epoch": 6.455640744797371,
661
+ "grad_norm": 1.430558681488037,
662
+ "learning_rate": 2.546930985423105e-05,
663
+ "loss": 0.19457483291625977,
664
+ "mean_token_accuracy": 0.9327938884496689,
665
+ "num_tokens": 7072067.0,
666
+ "step": 2950
667
+ },
668
+ {
669
+ "entropy": 0.2702385004609823,
670
+ "epoch": 6.565169769989047,
671
+ "grad_norm": 1.0556726455688477,
672
+ "learning_rate": 2.4113485187988342e-05,
673
+ "loss": 0.19928800582885742,
674
+ "mean_token_accuracy": 0.9298818710446358,
675
+ "num_tokens": 7187280.0,
676
+ "step": 3000
677
+ },
678
+ {
679
+ "entropy": 0.2702864905446768,
680
+ "epoch": 6.674698795180722,
681
+ "grad_norm": 1.1694364547729492,
682
+ "learning_rate": 2.2777647966688595e-05,
683
+ "loss": 0.20284730911254883,
684
+ "mean_token_accuracy": 0.9301017987728118,
685
+ "num_tokens": 7305186.0,
686
+ "step": 3050
687
+ },
688
+ {
689
+ "entropy": 0.2645207424461842,
690
+ "epoch": 6.784227820372399,
691
+ "grad_norm": 1.2039296627044678,
692
+ "learning_rate": 2.146374634344989e-05,
693
+ "loss": 0.1961233139038086,
694
+ "mean_token_accuracy": 0.9320311924815178,
695
+ "num_tokens": 7427803.0,
696
+ "step": 3100
697
+ },
698
+ {
699
+ "entropy": 0.2703990802913904,
700
+ "epoch": 6.8937568455640745,
701
+ "grad_norm": 1.122693657875061,
702
+ "learning_rate": 2.01736964810376e-05,
703
+ "loss": 0.19937246322631835,
704
+ "mean_token_accuracy": 0.9296649679541588,
705
+ "num_tokens": 7543735.0,
706
+ "step": 3150
707
+ },
708
+ {
709
+ "epoch": 7.0,
710
+ "eval_entropy": 0.31011983941471766,
711
+ "eval_loss": 0.7977674007415771,
712
+ "eval_mean_token_accuracy": 0.8200774944963909,
713
+ "eval_num_tokens": 7661318.0,
714
+ "eval_runtime": 95.5905,
715
+ "eval_samples_per_second": 10.482,
716
+ "eval_steps_per_second": 1.318,
717
+ "step": 3199
718
+ }
719
+ ],
720
+ "logging_steps": 50,
721
+ "max_steps": 4570,
722
+ "num_input_tokens_seen": 0,
723
+ "num_train_epochs": 10,
724
+ "save_steps": 500,
725
+ "stateful_callbacks": {
726
+ "TrainerControl": {
727
+ "args": {
728
+ "should_epoch_stop": false,
729
+ "should_evaluate": false,
730
+ "should_log": false,
731
+ "should_save": true,
732
+ "should_training_stop": false
733
+ },
734
+ "attributes": {}
735
+ }
736
+ },
737
+ "total_flos": 1.268827488643707e+18,
738
+ "train_batch_size": 4,
739
+ "trial_name": null,
740
+ "trial_params": null
741
+ }
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3656/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-14B-Base
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-14B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.19.1
systematicity_original_Estonian/Qwen3-14B-Base_systematicity_splits_original_features_train_systematicity_splits_original_features_test1/checkpoint-3656/adapter_config.json ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen3-14B-Base",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 64,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.021223473447411947,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 32,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "down_proj",
34
+ "gate_proj",
35
+ "v_proj",
36
+ "up_proj",
37
+ "k_proj",
38
+ "o_proj",
39
+ "q_proj"
40
+ ],
41
+ "target_parameters": null,
42
+ "task_type": "CAUSAL_LM",
43
+ "trainable_token_indices": null,
44
+ "use_bdlora": null,
45
+ "use_dora": false,
46
+ "use_qalora": false,
47
+ "use_rslora": false
48
+ }