Medyassino commited on
Commit
1a7bf48
·
verified ·
1 Parent(s): c1eb522

Add files using upload-large-folder tool

Browse files
Files changed (50) hide show
  1. security/datasets/cybersecurity-rules/.gitattributes +55 -0
  2. security/datasets/cybersecurity-rules/README.md +43 -0
  3. security/datasets/cybersecurity-rules/rules_dataset.jsonl +0 -0
  4. security/outputs/cysecbert-phishing-classifier/config.json +39 -0
  5. security/outputs/cysecbert-phishing-classifier/label_mapping.json +23 -0
  6. security/outputs/cysecbert-phishing-classifier/tokenizer.json +0 -0
  7. security/outputs/cysecbert-phishing-classifier/tokenizer_config.json +15 -0
  8. security/outputs/cysecbert-phishing-classifier/training_info.json +7 -0
  9. security/outputs/phishsense-10datasets-lora/README.md +207 -0
  10. security/outputs/phishsense-10datasets-lora/adapter_config.json +48 -0
  11. security/outputs/phishsense-10datasets-lora/chat_template.jinja +64 -0
  12. security/outputs/phishsense-10datasets-lora/tokenizer_config.json +15 -0
  13. security/outputs/phishsense-10datasets-lora/training_info.json +7 -0
  14. security/outputs/phishsense-phishing-lora/README.md +207 -0
  15. security/outputs/phishsense-phishing-lora/adapter_config.json +48 -0
  16. security/outputs/phishsense-phishing-lora/chat_template.jinja +64 -0
  17. security/outputs/phishsense-phishing-lora/checkpoint-2200/trainer_state.json +1574 -0
  18. security/outputs/phishsense-phishing-lora/tokenizer_config.json +15 -0
  19. security/outputs/phishsense-phishing-lora/training_info.json +7 -0
  20. security/outputs/secbert-phishing-classifier/config.json +38 -0
  21. security/outputs/secbert-phishing-classifier/label_mapping.json +23 -0
  22. security/outputs/secbert-phishing-classifier/tokenizer.json +0 -0
  23. security/outputs/secbert-phishing-classifier/tokenizer_config.json +15 -0
  24. security/outputs/secbert-phishing-classifier/training_info.json +7 -0
  25. security/outputs/securityllm-10datasets-lora/README.md +207 -0
  26. security/outputs/securityllm-10datasets-lora/adapter_config.json +48 -0
  27. security/outputs/securityllm-10datasets-lora/chat_template.jinja +15 -0
  28. security/outputs/securityllm-10datasets-lora/checkpoint-28600/README.md +207 -0
  29. security/outputs/securityllm-10datasets-lora/checkpoint-28600/adapter_config.json +48 -0
  30. security/outputs/securityllm-10datasets-lora/checkpoint-28600/chat_template.jinja +15 -0
  31. security/outputs/securityllm-10datasets-lora/checkpoint-28600/tokenizer.json +0 -0
  32. security/outputs/securityllm-10datasets-lora/checkpoint-28600/tokenizer_config.json +22 -0
  33. security/outputs/securityllm-10datasets-lora/checkpoint-28600/trainer_state.json +0 -0
  34. security/outputs/securityllm-10datasets-lora/checkpoint-28600/training_info.json +7 -0
  35. security/outputs/securityllm-10datasets-lora/checkpoint-28647/README.md +207 -0
  36. security/outputs/securityllm-10datasets-lora/checkpoint-28647/adapter_config.json +48 -0
  37. security/outputs/securityllm-10datasets-lora/checkpoint-28647/chat_template.jinja +15 -0
  38. security/outputs/securityllm-10datasets-lora/checkpoint-28647/tokenizer.json +0 -0
  39. security/outputs/securityllm-10datasets-lora/checkpoint-28647/tokenizer_config.json +22 -0
  40. security/outputs/securityllm-10datasets-lora/checkpoint-28647/trainer_state.json +0 -0
  41. security/outputs/securityllm-10datasets-lora/checkpoint-28647/training_info.json +7 -0
  42. security/outputs/securityllm-10datasets-lora/tokenizer.json +0 -0
  43. security/outputs/securityllm-10datasets-lora/tokenizer_config.json +22 -0
  44. security/outputs/securityllm-10datasets-lora/training_info.json +7 -0
  45. security/outputs/securityllm-rules-lora/README.md +207 -0
  46. security/outputs/securityllm-rules-lora/adapter_config.json +48 -0
  47. security/outputs/securityllm-rules-lora/chat_template.jinja +15 -0
  48. security/outputs/securityllm-rules-lora/tokenizer.json +0 -0
  49. security/outputs/securityllm-rules-lora/tokenizer_config.json +22 -0
  50. security/outputs/securityllm-rules-lora/training_info.json +7 -0
security/datasets/cybersecurity-rules/.gitattributes ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ *.7z filter=lfs diff=lfs merge=lfs -text
2
+ *.arrow filter=lfs diff=lfs merge=lfs -text
3
+ *.bin filter=lfs diff=lfs merge=lfs -text
4
+ *.bz2 filter=lfs diff=lfs merge=lfs -text
5
+ *.ckpt filter=lfs diff=lfs merge=lfs -text
6
+ *.ftz filter=lfs diff=lfs merge=lfs -text
7
+ *.gz filter=lfs diff=lfs merge=lfs -text
8
+ *.h5 filter=lfs diff=lfs merge=lfs -text
9
+ *.joblib filter=lfs diff=lfs merge=lfs -text
10
+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
11
+ *.lz4 filter=lfs diff=lfs merge=lfs -text
12
+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
13
+ *.model filter=lfs diff=lfs merge=lfs -text
14
+ *.msgpack filter=lfs diff=lfs merge=lfs -text
15
+ *.npy filter=lfs diff=lfs merge=lfs -text
16
+ *.npz filter=lfs diff=lfs merge=lfs -text
17
+ *.onnx filter=lfs diff=lfs merge=lfs -text
18
+ *.ot filter=lfs diff=lfs merge=lfs -text
19
+ *.parquet filter=lfs diff=lfs merge=lfs -text
20
+ *.pb filter=lfs diff=lfs merge=lfs -text
21
+ *.pickle filter=lfs diff=lfs merge=lfs -text
22
+ *.pkl filter=lfs diff=lfs merge=lfs -text
23
+ *.pt filter=lfs diff=lfs merge=lfs -text
24
+ *.pth filter=lfs diff=lfs merge=lfs -text
25
+ *.rar filter=lfs diff=lfs merge=lfs -text
26
+ *.safetensors filter=lfs diff=lfs merge=lfs -text
27
+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
28
+ *.tar.* filter=lfs diff=lfs merge=lfs -text
29
+ *.tar filter=lfs diff=lfs merge=lfs -text
30
+ *.tflite filter=lfs diff=lfs merge=lfs -text
31
+ *.tgz filter=lfs diff=lfs merge=lfs -text
32
+ *.wasm filter=lfs diff=lfs merge=lfs -text
33
+ *.xz filter=lfs diff=lfs merge=lfs -text
34
+ *.zip filter=lfs diff=lfs merge=lfs -text
35
+ *.zst filter=lfs diff=lfs merge=lfs -text
36
+ *tfevents* filter=lfs diff=lfs merge=lfs -text
37
+ # Audio files - uncompressed
38
+ *.pcm filter=lfs diff=lfs merge=lfs -text
39
+ *.sam filter=lfs diff=lfs merge=lfs -text
40
+ *.raw filter=lfs diff=lfs merge=lfs -text
41
+ # Audio files - compressed
42
+ *.aac filter=lfs diff=lfs merge=lfs -text
43
+ *.flac filter=lfs diff=lfs merge=lfs -text
44
+ *.mp3 filter=lfs diff=lfs merge=lfs -text
45
+ *.ogg filter=lfs diff=lfs merge=lfs -text
46
+ *.wav filter=lfs diff=lfs merge=lfs -text
47
+ # Image files - uncompressed
48
+ *.bmp filter=lfs diff=lfs merge=lfs -text
49
+ *.gif filter=lfs diff=lfs merge=lfs -text
50
+ *.png filter=lfs diff=lfs merge=lfs -text
51
+ *.tiff filter=lfs diff=lfs merge=lfs -text
52
+ # Image files - compressed
53
+ *.jpg filter=lfs diff=lfs merge=lfs -text
54
+ *.jpeg filter=lfs diff=lfs merge=lfs -text
55
+ *.webp filter=lfs diff=lfs merge=lfs -text
security/datasets/cybersecurity-rules/README.md ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ ---
4
+ # Cybersecurity Detection Rules Dataset
5
+
6
+ This dataset contains a collection of 950 detection rules from official SIGMA, YARA, and Suricata repositories. Knowledge distillation was applied to generate questions for each rule and enrich the responses, using 0dAI-7.5B.
7
+
8
+ ## Contents
9
+ - A set of detection rules for cybersecurity threat and intrusion detection in JSONL format (rules_dataset.jsonl). It contains the prompts and the associated responses.
10
+ - The rules have been obtained from SIGMA, YARA, and Suricata official repositories.
11
+
12
+
13
+ ## Usage
14
+ This dataset can be used to fine-tune LLM models or create cybersecurity applications focused on threat and intrusion detection. It is ideal for researchers and practitioners in cybersecurity looking to leverage a curated corpus of detection rules.
15
+
16
+ You can adjust this dataset with the prompt template of the model you want to fine-tune, in a simple way, with the following code, assuming that you have divided it into two sets for training and testing:
17
+
18
+ ```python
19
+
20
+ from datasets import load_dataset
21
+
22
+ # LLaMA3 prompt template
23
+
24
+ def format_prompt(prompt, response):
25
+ return f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\nYou are a helpful assistant.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n{response}<|eot_id|>\n"
26
+
27
+ # Dataset load
28
+ train_dataset = load_dataset("json", data_files="/path/to/train.json")
29
+ val_dataset = load_dataset("json", data_files="/path/to/val.json")
30
+
31
+ train_dataset["validation"] = val_dataset["train"]
32
+
33
+ # Map dataset with the prompt template
34
+
35
+ mapped_qa_dataset = train_dataset.map(lambda samples: tokenizer(format_prompt(samples['instruction'], samples['output'])))
36
+
37
+ # print some examples
38
+ print(mapped_qa_dataset['train'][0])
39
+ print(mapped_qa_dataset['train'][1])
40
+ ```
41
+
42
+ ## License
43
+ This dataset is shared under an apache-2.0 license. Please review the licensing terms in this repository to ensure compliance with any restrictions or requirements.
security/datasets/cybersecurity-rules/rules_dataset.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
security/outputs/cysecbert-phishing-classifier/config.json ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_cross_attention": false,
3
+ "architectures": [
4
+ "BertForSequenceClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": null,
8
+ "classifier_dropout": null,
9
+ "dtype": "float32",
10
+ "eos_token_id": null,
11
+ "gradient_checkpointing": false,
12
+ "hidden_act": "gelu",
13
+ "hidden_dropout_prob": 0.1,
14
+ "hidden_size": 768,
15
+ "id2label": {
16
+ "0": "Phishing Email",
17
+ "1": "Safe Email"
18
+ },
19
+ "initializer_range": 0.02,
20
+ "intermediate_size": 3072,
21
+ "is_decoder": false,
22
+ "label2id": {
23
+ "Phishing Email": 0,
24
+ "Safe Email": 1
25
+ },
26
+ "layer_norm_eps": 1e-12,
27
+ "max_position_embeddings": 512,
28
+ "model_type": "bert",
29
+ "num_attention_heads": 12,
30
+ "num_hidden_layers": 12,
31
+ "pad_token_id": 0,
32
+ "position_embedding_type": "absolute",
33
+ "problem_type": "single_label_classification",
34
+ "tie_word_embeddings": true,
35
+ "transformers_version": "5.6.2",
36
+ "type_vocab_size": 2,
37
+ "use_cache": false,
38
+ "vocab_size": 30522
39
+ }
security/outputs/cysecbert-phishing-classifier/label_mapping.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "label2id": {
3
+ "Phishing Email": 0,
4
+ "Safe Email": 1
5
+ },
6
+ "id2label": {
7
+ "0": "Phishing Email",
8
+ "1": "Safe Email"
9
+ },
10
+ "text_col": "Email Text",
11
+ "label_col": "Email Type",
12
+ "metrics": {
13
+ "eval_loss": 0.7898762822151184,
14
+ "eval_accuracy": 0.0,
15
+ "eval_precision": 0.0,
16
+ "eval_recall": 0.0,
17
+ "eval_f1": 0.0,
18
+ "eval_runtime": 0.0328,
19
+ "eval_samples_per_second": 91.43,
20
+ "eval_steps_per_second": 30.477,
21
+ "epoch": 1.0
22
+ }
23
+ }
security/outputs/cysecbert-phishing-classifier/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
security/outputs/cysecbert-phishing-classifier/tokenizer_config.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "backend": "tokenizers",
3
+ "cls_token": "[CLS]",
4
+ "do_lower_case": true,
5
+ "is_local": true,
6
+ "local_files_only": true,
7
+ "mask_token": "[MASK]",
8
+ "model_max_length": 512,
9
+ "pad_token": "[PAD]",
10
+ "sep_token": "[SEP]",
11
+ "strip_accents": null,
12
+ "tokenize_chinese_chars": true,
13
+ "tokenizer_class": "BertTokenizer",
14
+ "unk_token": "[UNK]"
15
+ }
security/outputs/cysecbert-phishing-classifier/training_info.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "latest_checkpoint": "security/outputs/cysecbert-phishing-classifier/training_args.bin",
3
+ "latest_mtime": 1777558873.6293423,
4
+ "latest_mtime_iso": "2026-04-30T14:21:13.629342+00:00",
5
+ "size_bytes": 5329,
6
+ "epoch": null
7
+ }
security/outputs/phishsense-10datasets-lora/README.md ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: /workspace/security/models/Llama-Phishsense-1B
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:/workspace/security/models/Llama-Phishsense-1B
7
+ - lora
8
+ - transformers
9
+ ---
10
+
11
+ # Model Card for Model ID
12
+
13
+ <!-- Provide a quick summary of what the model is/does. -->
14
+
15
+
16
+
17
+ ## Model Details
18
+
19
+ ### Model Description
20
+
21
+ <!-- Provide a longer summary of what this model is. -->
22
+
23
+
24
+
25
+ - **Developed by:** [More Information Needed]
26
+ - **Funded by [optional]:** [More Information Needed]
27
+ - **Shared by [optional]:** [More Information Needed]
28
+ - **Model type:** [More Information Needed]
29
+ - **Language(s) (NLP):** [More Information Needed]
30
+ - **License:** [More Information Needed]
31
+ - **Finetuned from model [optional]:** [More Information Needed]
32
+
33
+ ### Model Sources [optional]
34
+
35
+ <!-- Provide the basic links for the model. -->
36
+
37
+ - **Repository:** [More Information Needed]
38
+ - **Paper [optional]:** [More Information Needed]
39
+ - **Demo [optional]:** [More Information Needed]
40
+
41
+ ## Uses
42
+
43
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
44
+
45
+ ### Direct Use
46
+
47
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
48
+
49
+ [More Information Needed]
50
+
51
+ ### Downstream Use [optional]
52
+
53
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
54
+
55
+ [More Information Needed]
56
+
57
+ ### Out-of-Scope Use
58
+
59
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
60
+
61
+ [More Information Needed]
62
+
63
+ ## Bias, Risks, and Limitations
64
+
65
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
66
+
67
+ [More Information Needed]
68
+
69
+ ### Recommendations
70
+
71
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
72
+
73
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
74
+
75
+ ## How to Get Started with the Model
76
+
77
+ Use the code below to get started with the model.
78
+
79
+ [More Information Needed]
80
+
81
+ ## Training Details
82
+
83
+ ### Training Data
84
+
85
+ <!-- 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. -->
86
+
87
+ [More Information Needed]
88
+
89
+ ### Training Procedure
90
+
91
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
92
+
93
+ #### Preprocessing [optional]
94
+
95
+ [More Information Needed]
96
+
97
+
98
+ #### Training Hyperparameters
99
+
100
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
101
+
102
+ #### Speeds, Sizes, Times [optional]
103
+
104
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
105
+
106
+ [More Information Needed]
107
+
108
+ ## Evaluation
109
+
110
+ <!-- This section describes the evaluation protocols and provides the results. -->
111
+
112
+ ### Testing Data, Factors & Metrics
113
+
114
+ #### Testing Data
115
+
116
+ <!-- This should link to a Dataset Card if possible. -->
117
+
118
+ [More Information Needed]
119
+
120
+ #### Factors
121
+
122
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
123
+
124
+ [More Information Needed]
125
+
126
+ #### Metrics
127
+
128
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
129
+
130
+ [More Information Needed]
131
+
132
+ ### Results
133
+
134
+ [More Information Needed]
135
+
136
+ #### Summary
137
+
138
+
139
+
140
+ ## Model Examination [optional]
141
+
142
+ <!-- Relevant interpretability work for the model goes here -->
143
+
144
+ [More Information Needed]
145
+
146
+ ## Environmental Impact
147
+
148
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
149
+
150
+ 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).
151
+
152
+ - **Hardware Type:** [More Information Needed]
153
+ - **Hours used:** [More Information Needed]
154
+ - **Cloud Provider:** [More Information Needed]
155
+ - **Compute Region:** [More Information Needed]
156
+ - **Carbon Emitted:** [More Information Needed]
157
+
158
+ ## Technical Specifications [optional]
159
+
160
+ ### Model Architecture and Objective
161
+
162
+ [More Information Needed]
163
+
164
+ ### Compute Infrastructure
165
+
166
+ [More Information Needed]
167
+
168
+ #### Hardware
169
+
170
+ [More Information Needed]
171
+
172
+ #### Software
173
+
174
+ [More Information Needed]
175
+
176
+ ## Citation [optional]
177
+
178
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
179
+
180
+ **BibTeX:**
181
+
182
+ [More Information Needed]
183
+
184
+ **APA:**
185
+
186
+ [More Information Needed]
187
+
188
+ ## Glossary [optional]
189
+
190
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
191
+
192
+ [More Information Needed]
193
+
194
+ ## More Information [optional]
195
+
196
+ [More Information Needed]
197
+
198
+ ## Model Card Authors [optional]
199
+
200
+ [More Information Needed]
201
+
202
+ ## Model Card Contact
203
+
204
+ [More Information Needed]
205
+ ### Framework versions
206
+
207
+ - PEFT 0.19.1
security/outputs/phishsense-10datasets-lora/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": "/workspace/security/models/Llama-Phishsense-1B",
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": 0.05,
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": 16,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "gate_proj",
34
+ "down_proj",
35
+ "k_proj",
36
+ "o_proj",
37
+ "q_proj",
38
+ "up_proj",
39
+ "v_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
+ }
security/outputs/phishsense-10datasets-lora/chat_template.jinja ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if messages|length % 2 == 0 -%}
2
+ {%- set role = 'Agent' -%}
3
+ {%- else -%}
4
+ {%- set role = 'User' -%}
5
+ {%- endif -%}
6
+ {%- if categories is not defined -%}
7
+ {%-
8
+ set categories = ({
9
+ "S1": "Violent Crimes.",
10
+ "S2": "Non-Violent Crimes.",
11
+ "S3": "Sex Crimes.",
12
+ "S4": "Child Exploitation.",
13
+ "S5": "Defamation.",
14
+ "S6": "Specialized Advice.",
15
+ "S7": "Privacy.",
16
+ "S8": "Intellectual Property.",
17
+ "S9": "Indiscriminate Weapons.",
18
+ "S10": "Hate.",
19
+ "S11": "Self-Harm.",
20
+ "S12": "Sexual Content.",
21
+ "S13": "Elections.",
22
+ })
23
+ -%}
24
+ {%- endif -%}
25
+
26
+ {{- "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" -}}
27
+ {%- set ns = namespace(contains_image=false) -%}
28
+ {%- for message in messages -%}
29
+ {%- for content in message['content'] | selectattr('type', 'equalto', 'image') -%}
30
+ {%- set ns.contains_image = true -%}
31
+ {%- endfor -%}
32
+ {%- endfor -%}
33
+ {%- if ns.contains_image -%}
34
+ {{- "<|image|> "-}}
35
+ {%- endif -%}
36
+ {{- "Task: Check if there is unsafe content in '" + role + "' messages in conversations according our safety policy with the below categories.\n\n" -}}
37
+ {{- "<BEGIN UNSAFE CONTENT CATEGORIES>\n"-}}
38
+
39
+ {%- for key in categories -%}
40
+ {%- if key not in excluded_category_keys -%}
41
+ {{-key + ": " + categories[key] + "\n" -}}
42
+ {%- endif -%}
43
+ {%- endfor -%}
44
+
45
+ {{- "<END UNSAFE CONTENT CATEGORIES>\n\n"-}}
46
+ {{- "<BEGIN CONVERSATION>\n\n"-}}
47
+ {%- for message in messages -%}
48
+ {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
49
+ {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...')-}}
50
+ {%- endif -%}
51
+ {%- set content = message['content'] -%}
52
+ {%- if message['role'] == 'user' -%}
53
+ {%- set role = 'User' -%}
54
+ {%- elif message['role'] == 'assistant' -%}
55
+ {%- set role = 'Agent' -%}
56
+ {%- endif -%}
57
+ {%- for content in message['content'] | selectattr('type', 'equalto', 'text') -%}
58
+ {{- role + ': ' + content['text'] | trim + '\n\n'-}}
59
+ {%- endfor -%}
60
+ {%- endfor -%}
61
+ {{- "<END CONVERSATION>\n\n"-}}
62
+ {{- "Provide your safety assessment for ONLY THE LAST " + role + " message in the above conversation:\n"-}}
63
+ {{- " - First line must read 'safe' or 'unsafe'.\n"-}}
64
+ {{- " - If unsafe, a second line must include a comma-separated list of violated categories. <|eot_id|><|start_header_id|>assistant<|end_header_id|>"-}}
security/outputs/phishsense-10datasets-lora/tokenizer_config.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "backend": "tokenizers",
3
+ "bos_token": "<|begin_of_text|>",
4
+ "clean_up_tokenization_spaces": true,
5
+ "eos_token": "<|eot_id|>",
6
+ "is_local": true,
7
+ "local_files_only": true,
8
+ "model_input_names": [
9
+ "input_ids",
10
+ "attention_mask"
11
+ ],
12
+ "model_max_length": 131072,
13
+ "pad_token": "<|eot_id|>",
14
+ "tokenizer_class": "TokenizersBackend"
15
+ }
security/outputs/phishsense-10datasets-lora/training_info.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "latest_checkpoint": "security/outputs/phishsense-10datasets-lora/adapter_model.safetensors",
3
+ "latest_mtime": 1777556420.4314501,
4
+ "latest_mtime_iso": "2026-04-30T13:40:20.431450+00:00",
5
+ "size_bytes": 45118424,
6
+ "epoch": null
7
+ }
security/outputs/phishsense-phishing-lora/README.md ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: /workspace/security/models/Llama-Phishsense-1B
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:/workspace/security/models/Llama-Phishsense-1B
7
+ - lora
8
+ - transformers
9
+ ---
10
+
11
+ # Model Card for Model ID
12
+
13
+ <!-- Provide a quick summary of what the model is/does. -->
14
+
15
+
16
+
17
+ ## Model Details
18
+
19
+ ### Model Description
20
+
21
+ <!-- Provide a longer summary of what this model is. -->
22
+
23
+
24
+
25
+ - **Developed by:** [More Information Needed]
26
+ - **Funded by [optional]:** [More Information Needed]
27
+ - **Shared by [optional]:** [More Information Needed]
28
+ - **Model type:** [More Information Needed]
29
+ - **Language(s) (NLP):** [More Information Needed]
30
+ - **License:** [More Information Needed]
31
+ - **Finetuned from model [optional]:** [More Information Needed]
32
+
33
+ ### Model Sources [optional]
34
+
35
+ <!-- Provide the basic links for the model. -->
36
+
37
+ - **Repository:** [More Information Needed]
38
+ - **Paper [optional]:** [More Information Needed]
39
+ - **Demo [optional]:** [More Information Needed]
40
+
41
+ ## Uses
42
+
43
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
44
+
45
+ ### Direct Use
46
+
47
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
48
+
49
+ [More Information Needed]
50
+
51
+ ### Downstream Use [optional]
52
+
53
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
54
+
55
+ [More Information Needed]
56
+
57
+ ### Out-of-Scope Use
58
+
59
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
60
+
61
+ [More Information Needed]
62
+
63
+ ## Bias, Risks, and Limitations
64
+
65
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
66
+
67
+ [More Information Needed]
68
+
69
+ ### Recommendations
70
+
71
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
72
+
73
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
74
+
75
+ ## How to Get Started with the Model
76
+
77
+ Use the code below to get started with the model.
78
+
79
+ [More Information Needed]
80
+
81
+ ## Training Details
82
+
83
+ ### Training Data
84
+
85
+ <!-- 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. -->
86
+
87
+ [More Information Needed]
88
+
89
+ ### Training Procedure
90
+
91
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
92
+
93
+ #### Preprocessing [optional]
94
+
95
+ [More Information Needed]
96
+
97
+
98
+ #### Training Hyperparameters
99
+
100
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
101
+
102
+ #### Speeds, Sizes, Times [optional]
103
+
104
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
105
+
106
+ [More Information Needed]
107
+
108
+ ## Evaluation
109
+
110
+ <!-- This section describes the evaluation protocols and provides the results. -->
111
+
112
+ ### Testing Data, Factors & Metrics
113
+
114
+ #### Testing Data
115
+
116
+ <!-- This should link to a Dataset Card if possible. -->
117
+
118
+ [More Information Needed]
119
+
120
+ #### Factors
121
+
122
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
123
+
124
+ [More Information Needed]
125
+
126
+ #### Metrics
127
+
128
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
129
+
130
+ [More Information Needed]
131
+
132
+ ### Results
133
+
134
+ [More Information Needed]
135
+
136
+ #### Summary
137
+
138
+
139
+
140
+ ## Model Examination [optional]
141
+
142
+ <!-- Relevant interpretability work for the model goes here -->
143
+
144
+ [More Information Needed]
145
+
146
+ ## Environmental Impact
147
+
148
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
149
+
150
+ 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).
151
+
152
+ - **Hardware Type:** [More Information Needed]
153
+ - **Hours used:** [More Information Needed]
154
+ - **Cloud Provider:** [More Information Needed]
155
+ - **Compute Region:** [More Information Needed]
156
+ - **Carbon Emitted:** [More Information Needed]
157
+
158
+ ## Technical Specifications [optional]
159
+
160
+ ### Model Architecture and Objective
161
+
162
+ [More Information Needed]
163
+
164
+ ### Compute Infrastructure
165
+
166
+ [More Information Needed]
167
+
168
+ #### Hardware
169
+
170
+ [More Information Needed]
171
+
172
+ #### Software
173
+
174
+ [More Information Needed]
175
+
176
+ ## Citation [optional]
177
+
178
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
179
+
180
+ **BibTeX:**
181
+
182
+ [More Information Needed]
183
+
184
+ **APA:**
185
+
186
+ [More Information Needed]
187
+
188
+ ## Glossary [optional]
189
+
190
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
191
+
192
+ [More Information Needed]
193
+
194
+ ## More Information [optional]
195
+
196
+ [More Information Needed]
197
+
198
+ ## Model Card Authors [optional]
199
+
200
+ [More Information Needed]
201
+
202
+ ## Model Card Contact
203
+
204
+ [More Information Needed]
205
+ ### Framework versions
206
+
207
+ - PEFT 0.19.1
security/outputs/phishsense-phishing-lora/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": "/workspace/security/models/Llama-Phishsense-1B",
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": 0.05,
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": 16,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "v_proj",
34
+ "o_proj",
35
+ "gate_proj",
36
+ "k_proj",
37
+ "q_proj",
38
+ "down_proj",
39
+ "up_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
+ }
security/outputs/phishsense-phishing-lora/chat_template.jinja ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if messages|length % 2 == 0 -%}
2
+ {%- set role = 'Agent' -%}
3
+ {%- else -%}
4
+ {%- set role = 'User' -%}
5
+ {%- endif -%}
6
+ {%- if categories is not defined -%}
7
+ {%-
8
+ set categories = ({
9
+ "S1": "Violent Crimes.",
10
+ "S2": "Non-Violent Crimes.",
11
+ "S3": "Sex Crimes.",
12
+ "S4": "Child Exploitation.",
13
+ "S5": "Defamation.",
14
+ "S6": "Specialized Advice.",
15
+ "S7": "Privacy.",
16
+ "S8": "Intellectual Property.",
17
+ "S9": "Indiscriminate Weapons.",
18
+ "S10": "Hate.",
19
+ "S11": "Self-Harm.",
20
+ "S12": "Sexual Content.",
21
+ "S13": "Elections.",
22
+ })
23
+ -%}
24
+ {%- endif -%}
25
+
26
+ {{- "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" -}}
27
+ {%- set ns = namespace(contains_image=false) -%}
28
+ {%- for message in messages -%}
29
+ {%- for content in message['content'] | selectattr('type', 'equalto', 'image') -%}
30
+ {%- set ns.contains_image = true -%}
31
+ {%- endfor -%}
32
+ {%- endfor -%}
33
+ {%- if ns.contains_image -%}
34
+ {{- "<|image|> "-}}
35
+ {%- endif -%}
36
+ {{- "Task: Check if there is unsafe content in '" + role + "' messages in conversations according our safety policy with the below categories.\n\n" -}}
37
+ {{- "<BEGIN UNSAFE CONTENT CATEGORIES>\n"-}}
38
+
39
+ {%- for key in categories -%}
40
+ {%- if key not in excluded_category_keys -%}
41
+ {{-key + ": " + categories[key] + "\n" -}}
42
+ {%- endif -%}
43
+ {%- endfor -%}
44
+
45
+ {{- "<END UNSAFE CONTENT CATEGORIES>\n\n"-}}
46
+ {{- "<BEGIN CONVERSATION>\n\n"-}}
47
+ {%- for message in messages -%}
48
+ {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
49
+ {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...')-}}
50
+ {%- endif -%}
51
+ {%- set content = message['content'] -%}
52
+ {%- if message['role'] == 'user' -%}
53
+ {%- set role = 'User' -%}
54
+ {%- elif message['role'] == 'assistant' -%}
55
+ {%- set role = 'Agent' -%}
56
+ {%- endif -%}
57
+ {%- for content in message['content'] | selectattr('type', 'equalto', 'text') -%}
58
+ {{- role + ': ' + content['text'] | trim + '\n\n'-}}
59
+ {%- endfor -%}
60
+ {%- endfor -%}
61
+ {{- "<END CONVERSATION>\n\n"-}}
62
+ {{- "Provide your safety assessment for ONLY THE LAST " + role + " message in the above conversation:\n"-}}
63
+ {{- " - First line must read 'safe' or 'unsafe'.\n"-}}
64
+ {{- " - If unsafe, a second line must include a comma-separated list of violated categories. <|eot_id|><|start_header_id|>assistant<|end_header_id|>"-}}
security/outputs/phishsense-phishing-lora/checkpoint-2200/trainer_state.json ADDED
@@ -0,0 +1,1574 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 0.9436997319034852,
6
+ "eval_steps": 500,
7
+ "global_step": 2200,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "epoch": 0.004289544235924933,
14
+ "grad_norm": 2.461350917816162,
15
+ "learning_rate": 2.5714285714285714e-05,
16
+ "loss": 4.751891708374023,
17
+ "step": 10
18
+ },
19
+ {
20
+ "epoch": 0.008579088471849867,
21
+ "grad_norm": 1.740277647972107,
22
+ "learning_rate": 5.428571428571428e-05,
23
+ "loss": 4.051348876953125,
24
+ "step": 20
25
+ },
26
+ {
27
+ "epoch": 0.012868632707774798,
28
+ "grad_norm": 1.3302079439163208,
29
+ "learning_rate": 8.285714285714287e-05,
30
+ "loss": 3.239373779296875,
31
+ "step": 30
32
+ },
33
+ {
34
+ "epoch": 0.017158176943699734,
35
+ "grad_norm": 1.0828485488891602,
36
+ "learning_rate": 0.00011142857142857144,
37
+ "loss": 2.7260066986083986,
38
+ "step": 40
39
+ },
40
+ {
41
+ "epoch": 0.021447721179624665,
42
+ "grad_norm": 1.0427392721176147,
43
+ "learning_rate": 0.00014,
44
+ "loss": 2.565675163269043,
45
+ "step": 50
46
+ },
47
+ {
48
+ "epoch": 0.025737265415549597,
49
+ "grad_norm": 0.9723011255264282,
50
+ "learning_rate": 0.00016857142857142857,
51
+ "loss": 2.3718759536743166,
52
+ "step": 60
53
+ },
54
+ {
55
+ "epoch": 0.030026809651474532,
56
+ "grad_norm": 1.074137806892395,
57
+ "learning_rate": 0.00019714285714285716,
58
+ "loss": 2.394337272644043,
59
+ "step": 70
60
+ },
61
+ {
62
+ "epoch": 0.03431635388739947,
63
+ "grad_norm": 1.0547237396240234,
64
+ "learning_rate": 0.000199992187969813,
65
+ "loss": 2.3535043716430666,
66
+ "step": 80
67
+ },
68
+ {
69
+ "epoch": 0.0386058981233244,
70
+ "grad_norm": 0.9247291684150696,
71
+ "learning_rate": 0.00019996518498802463,
72
+ "loss": 2.4601144790649414,
73
+ "step": 90
74
+ },
75
+ {
76
+ "epoch": 0.04289544235924933,
77
+ "grad_norm": 2.0372612476348877,
78
+ "learning_rate": 0.00019991889981715698,
79
+ "loss": 2.3154342651367186,
80
+ "step": 100
81
+ },
82
+ {
83
+ "epoch": 0.04718498659517426,
84
+ "grad_norm": 1.2616114616394043,
85
+ "learning_rate": 0.00019985334138511237,
86
+ "loss": 2.449721908569336,
87
+ "step": 110
88
+ },
89
+ {
90
+ "epoch": 0.051474530831099194,
91
+ "grad_norm": 0.945946216583252,
92
+ "learning_rate": 0.00019976852233739427,
93
+ "loss": 2.364675521850586,
94
+ "step": 120
95
+ },
96
+ {
97
+ "epoch": 0.055764075067024126,
98
+ "grad_norm": 1.0374224185943604,
99
+ "learning_rate": 0.00019966445903466773,
100
+ "loss": 2.3558889389038087,
101
+ "step": 130
102
+ },
103
+ {
104
+ "epoch": 0.060053619302949064,
105
+ "grad_norm": 0.8756594657897949,
106
+ "learning_rate": 0.00019954117154960383,
107
+ "loss": 2.161049652099609,
108
+ "step": 140
109
+ },
110
+ {
111
+ "epoch": 0.064343163538874,
112
+ "grad_norm": 1.0294053554534912,
113
+ "learning_rate": 0.0001993986836630079,
114
+ "loss": 2.1054876327514647,
115
+ "step": 150
116
+ },
117
+ {
118
+ "epoch": 0.06863270777479893,
119
+ "grad_norm": 0.8740042448043823,
120
+ "learning_rate": 0.00019923702285923228,
121
+ "loss": 2.124430465698242,
122
+ "step": 160
123
+ },
124
+ {
125
+ "epoch": 0.07292225201072386,
126
+ "grad_norm": 0.77315753698349,
127
+ "learning_rate": 0.00019905622032087507,
128
+ "loss": 2.4021867752075194,
129
+ "step": 170
130
+ },
131
+ {
132
+ "epoch": 0.0772117962466488,
133
+ "grad_norm": 0.953255295753479,
134
+ "learning_rate": 0.00019885631092276526,
135
+ "loss": 2.0978525161743162,
136
+ "step": 180
137
+ },
138
+ {
139
+ "epoch": 0.08150134048257372,
140
+ "grad_norm": 0.8136326670646667,
141
+ "learning_rate": 0.00019863733322523578,
142
+ "loss": 2.32897834777832,
143
+ "step": 190
144
+ },
145
+ {
146
+ "epoch": 0.08579088471849866,
147
+ "grad_norm": 1.0012127161026,
148
+ "learning_rate": 0.00019839932946668552,
149
+ "loss": 2.2645999908447267,
150
+ "step": 200
151
+ },
152
+ {
153
+ "epoch": 0.09008042895442359,
154
+ "grad_norm": 0.9342105984687805,
155
+ "learning_rate": 0.0001981423455554323,
156
+ "loss": 2.230245590209961,
157
+ "step": 210
158
+ },
159
+ {
160
+ "epoch": 0.09436997319034852,
161
+ "grad_norm": 0.8320803046226501,
162
+ "learning_rate": 0.00019786643106085738,
163
+ "loss": 2.226487731933594,
164
+ "step": 220
165
+ },
166
+ {
167
+ "epoch": 0.09865951742627346,
168
+ "grad_norm": 0.8236033916473389,
169
+ "learning_rate": 0.0001975716392038441,
170
+ "loss": 2.278731346130371,
171
+ "step": 230
172
+ },
173
+ {
174
+ "epoch": 0.10294906166219839,
175
+ "grad_norm": 0.877591609954834,
176
+ "learning_rate": 0.00019725802684651233,
177
+ "loss": 2.009513282775879,
178
+ "step": 240
179
+ },
180
+ {
181
+ "epoch": 0.10723860589812333,
182
+ "grad_norm": 0.8955455422401428,
183
+ "learning_rate": 0.00019692565448125018,
184
+ "loss": 2.240833282470703,
185
+ "step": 250
186
+ },
187
+ {
188
+ "epoch": 0.11152815013404825,
189
+ "grad_norm": 0.8864295482635498,
190
+ "learning_rate": 0.00019657458621904583,
191
+ "loss": 2.2627416610717774,
192
+ "step": 260
193
+ },
194
+ {
195
+ "epoch": 0.11581769436997319,
196
+ "grad_norm": 1.0706095695495605,
197
+ "learning_rate": 0.00019620488977712116,
198
+ "loss": 2.196295738220215,
199
+ "step": 270
200
+ },
201
+ {
202
+ "epoch": 0.12010723860589813,
203
+ "grad_norm": 1.0405200719833374,
204
+ "learning_rate": 0.00019581663646586978,
205
+ "loss": 2.0924251556396483,
206
+ "step": 280
207
+ },
208
+ {
209
+ "epoch": 0.12439678284182305,
210
+ "grad_norm": 0.71753990650177,
211
+ "learning_rate": 0.00019540990117510227,
212
+ "loss": 2.020050811767578,
213
+ "step": 290
214
+ },
215
+ {
216
+ "epoch": 0.128686327077748,
217
+ "grad_norm": 0.80608731508255,
218
+ "learning_rate": 0.00019498476235960043,
219
+ "loss": 2.1187694549560545,
220
+ "step": 300
221
+ },
222
+ {
223
+ "epoch": 0.13297587131367292,
224
+ "grad_norm": 0.8830103278160095,
225
+ "learning_rate": 0.0001945413020239845,
226
+ "loss": 2.197436714172363,
227
+ "step": 310
228
+ },
229
+ {
230
+ "epoch": 0.13726541554959787,
231
+ "grad_norm": 0.778745710849762,
232
+ "learning_rate": 0.00019407960570689504,
233
+ "loss": 2.2908151626586912,
234
+ "step": 320
235
+ },
236
+ {
237
+ "epoch": 0.1415549597855228,
238
+ "grad_norm": 0.8987101912498474,
239
+ "learning_rate": 0.0001935997624644939,
240
+ "loss": 2.1507293701171877,
241
+ "step": 330
242
+ },
243
+ {
244
+ "epoch": 0.14584450402144772,
245
+ "grad_norm": 0.8126206994056702,
246
+ "learning_rate": 0.00019310186485328593,
247
+ "loss": 2.0694677352905275,
248
+ "step": 340
249
+ },
250
+ {
251
+ "epoch": 0.15013404825737264,
252
+ "grad_norm": 0.9921199679374695,
253
+ "learning_rate": 0.00019258600891226588,
254
+ "loss": 2.0286174774169923,
255
+ "step": 350
256
+ },
257
+ {
258
+ "epoch": 0.1544235924932976,
259
+ "grad_norm": 0.8410740494728088,
260
+ "learning_rate": 0.00019205229414439364,
261
+ "loss": 2.1866321563720703,
262
+ "step": 360
263
+ },
264
+ {
265
+ "epoch": 0.15871313672922252,
266
+ "grad_norm": 0.8364278674125671,
267
+ "learning_rate": 0.0001915008234974012,
268
+ "loss": 1.9998489379882813,
269
+ "step": 370
270
+ },
271
+ {
272
+ "epoch": 0.16300268096514745,
273
+ "grad_norm": 0.9343453645706177,
274
+ "learning_rate": 0.0001909317033439351,
275
+ "loss": 2.036051368713379,
276
+ "step": 380
277
+ },
278
+ {
279
+ "epoch": 0.1672922252010724,
280
+ "grad_norm": 0.9619212746620178,
281
+ "learning_rate": 0.00019034504346103823,
282
+ "loss": 2.1354415893554686,
283
+ "step": 390
284
+ },
285
+ {
286
+ "epoch": 0.17158176943699732,
287
+ "grad_norm": 0.8053330183029175,
288
+ "learning_rate": 0.0001897409570089753,
289
+ "loss": 2.005703353881836,
290
+ "step": 400
291
+ },
292
+ {
293
+ "epoch": 0.17587131367292225,
294
+ "grad_norm": 0.94014573097229,
295
+ "learning_rate": 0.00018911956050940516,
296
+ "loss": 2.105622100830078,
297
+ "step": 410
298
+ },
299
+ {
300
+ "epoch": 0.18016085790884717,
301
+ "grad_norm": 0.9183622598648071,
302
+ "learning_rate": 0.00018848097382290533,
303
+ "loss": 2.2289337158203124,
304
+ "step": 420
305
+ },
306
+ {
307
+ "epoch": 0.18445040214477212,
308
+ "grad_norm": 0.7824776768684387,
309
+ "learning_rate": 0.00018782532012585193,
310
+ "loss": 2.190073776245117,
311
+ "step": 430
312
+ },
313
+ {
314
+ "epoch": 0.18873994638069705,
315
+ "grad_norm": 0.8406926393508911,
316
+ "learning_rate": 0.00018715272588666062,
317
+ "loss": 2.039468193054199,
318
+ "step": 440
319
+ },
320
+ {
321
+ "epoch": 0.19302949061662197,
322
+ "grad_norm": 0.788490891456604,
323
+ "learning_rate": 0.00018646332084139189,
324
+ "loss": 2.172566032409668,
325
+ "step": 450
326
+ },
327
+ {
328
+ "epoch": 0.19731903485254693,
329
+ "grad_norm": 0.8548129200935364,
330
+ "learning_rate": 0.00018575723796872666,
331
+ "loss": 2.058650588989258,
332
+ "step": 460
333
+ },
334
+ {
335
+ "epoch": 0.20160857908847185,
336
+ "grad_norm": 0.9269851446151733,
337
+ "learning_rate": 0.00018503461346431604,
338
+ "loss": 2.03293399810791,
339
+ "step": 470
340
+ },
341
+ {
342
+ "epoch": 0.20589812332439678,
343
+ "grad_norm": 0.8605597019195557,
344
+ "learning_rate": 0.00018429558671451064,
345
+ "loss": 2.118448257446289,
346
+ "step": 480
347
+ },
348
+ {
349
+ "epoch": 0.21018766756032173,
350
+ "grad_norm": 0.897239625453949,
351
+ "learning_rate": 0.00018354030026947456,
352
+ "loss": 1.9954294204711913,
353
+ "step": 490
354
+ },
355
+ {
356
+ "epoch": 0.21447721179624665,
357
+ "grad_norm": 0.867357611656189,
358
+ "learning_rate": 0.00018276889981568906,
359
+ "loss": 2.3360008239746093,
360
+ "step": 500
361
+ },
362
+ {
363
+ "epoch": 0.21876675603217158,
364
+ "grad_norm": 0.7812759280204773,
365
+ "learning_rate": 0.000181981534147851,
366
+ "loss": 2.0705078125,
367
+ "step": 510
368
+ },
369
+ {
370
+ "epoch": 0.2230563002680965,
371
+ "grad_norm": 0.8635934591293335,
372
+ "learning_rate": 0.0001811783551401723,
373
+ "loss": 2.165178108215332,
374
+ "step": 520
375
+ },
376
+ {
377
+ "epoch": 0.22734584450402145,
378
+ "grad_norm": 1.018941879272461,
379
+ "learning_rate": 0.0001803595177170848,
380
+ "loss": 2.1993284225463867,
381
+ "step": 530
382
+ },
383
+ {
384
+ "epoch": 0.23163538873994638,
385
+ "grad_norm": 0.811452329158783,
386
+ "learning_rate": 0.00017952517982335712,
387
+ "loss": 2.1670595169067384,
388
+ "step": 540
389
+ },
390
+ {
391
+ "epoch": 0.2359249329758713,
392
+ "grad_norm": 0.8488320708274841,
393
+ "learning_rate": 0.00017867550239362894,
394
+ "loss": 2.068050575256348,
395
+ "step": 550
396
+ },
397
+ {
398
+ "epoch": 0.24021447721179626,
399
+ "grad_norm": 1.1068885326385498,
400
+ "learning_rate": 0.00017781064932136835,
401
+ "loss": 2.211918067932129,
402
+ "step": 560
403
+ },
404
+ {
405
+ "epoch": 0.24450402144772118,
406
+ "grad_norm": 0.8777617812156677,
407
+ "learning_rate": 0.00017693078742725865,
408
+ "loss": 1.995230484008789,
409
+ "step": 570
410
+ },
411
+ {
412
+ "epoch": 0.2487935656836461,
413
+ "grad_norm": 0.841608464717865,
414
+ "learning_rate": 0.00017603608642702045,
415
+ "loss": 2.093342399597168,
416
+ "step": 580
417
+ },
418
+ {
419
+ "epoch": 0.25308310991957106,
420
+ "grad_norm": 0.843050479888916,
421
+ "learning_rate": 0.00017512671889867535,
422
+ "loss": 2.0501258850097654,
423
+ "step": 590
424
+ },
425
+ {
426
+ "epoch": 0.257372654155496,
427
+ "grad_norm": 0.755862832069397,
428
+ "learning_rate": 0.00017420286024925758,
429
+ "loss": 2.1583385467529297,
430
+ "step": 600
431
+ },
432
+ {
433
+ "epoch": 0.2616621983914209,
434
+ "grad_norm": 0.7975772023200989,
435
+ "learning_rate": 0.00017326468868097985,
436
+ "loss": 2.170749282836914,
437
+ "step": 610
438
+ },
439
+ {
440
+ "epoch": 0.26595174262734583,
441
+ "grad_norm": 0.9260276556015015,
442
+ "learning_rate": 0.0001723123851568602,
443
+ "loss": 2.2822845458984373,
444
+ "step": 620
445
+ },
446
+ {
447
+ "epoch": 0.27024128686327076,
448
+ "grad_norm": 0.6979662179946899,
449
+ "learning_rate": 0.00017134613336581599,
450
+ "loss": 2.251565933227539,
451
+ "step": 630
452
+ },
453
+ {
454
+ "epoch": 0.27453083109919574,
455
+ "grad_norm": 0.7494871616363525,
456
+ "learning_rate": 0.00017036611968723265,
457
+ "loss": 2.2115200042724608,
458
+ "step": 640
459
+ },
460
+ {
461
+ "epoch": 0.27882037533512066,
462
+ "grad_norm": 0.8637067079544067,
463
+ "learning_rate": 0.00016937253315501278,
464
+ "loss": 2.0511417388916016,
465
+ "step": 650
466
+ },
467
+ {
468
+ "epoch": 0.2831099195710456,
469
+ "grad_norm": 0.9787566661834717,
470
+ "learning_rate": 0.0001683655654211139,
471
+ "loss": 2.109568214416504,
472
+ "step": 660
473
+ },
474
+ {
475
+ "epoch": 0.2873994638069705,
476
+ "grad_norm": 0.870245635509491,
477
+ "learning_rate": 0.00016734541071858065,
478
+ "loss": 2.118773651123047,
479
+ "step": 670
480
+ },
481
+ {
482
+ "epoch": 0.29168900804289544,
483
+ "grad_norm": 1.0233336687088013,
484
+ "learning_rate": 0.00016631226582407952,
485
+ "loss": 2.110629081726074,
486
+ "step": 680
487
+ },
488
+ {
489
+ "epoch": 0.29597855227882036,
490
+ "grad_norm": 0.9542198777198792,
491
+ "learning_rate": 0.0001652663300199427,
492
+ "loss": 2.0503299713134764,
493
+ "step": 690
494
+ },
495
+ {
496
+ "epoch": 0.3002680965147453,
497
+ "grad_norm": 0.9004696011543274,
498
+ "learning_rate": 0.00016420780505572871,
499
+ "loss": 2.0273651123046874,
500
+ "step": 700
501
+ },
502
+ {
503
+ "epoch": 0.30455764075067027,
504
+ "grad_norm": 0.7856536507606506,
505
+ "learning_rate": 0.00016313689510930713,
506
+ "loss": 1.978914451599121,
507
+ "step": 710
508
+ },
509
+ {
510
+ "epoch": 0.3088471849865952,
511
+ "grad_norm": 0.7488994598388672,
512
+ "learning_rate": 0.000162053806747475,
513
+ "loss": 2.0931522369384767,
514
+ "step": 720
515
+ },
516
+ {
517
+ "epoch": 0.3131367292225201,
518
+ "grad_norm": 0.8158746361732483,
519
+ "learning_rate": 0.0001609587488861122,
520
+ "loss": 2.0451580047607423,
521
+ "step": 730
522
+ },
523
+ {
524
+ "epoch": 0.31742627345844504,
525
+ "grad_norm": 0.8352906107902527,
526
+ "learning_rate": 0.00015985193274988397,
527
+ "loss": 1.9119682312011719,
528
+ "step": 740
529
+ },
530
+ {
531
+ "epoch": 0.32171581769436997,
532
+ "grad_norm": 0.9240970611572266,
533
+ "learning_rate": 0.00015873357183149805,
534
+ "loss": 1.9821479797363282,
535
+ "step": 750
536
+ },
537
+ {
538
+ "epoch": 0.3260053619302949,
539
+ "grad_norm": 0.9190706610679626,
540
+ "learning_rate": 0.00015760388185052398,
541
+ "loss": 2.0633893966674806,
542
+ "step": 760
543
+ },
544
+ {
545
+ "epoch": 0.3302949061662198,
546
+ "grad_norm": 1.095713496208191,
547
+ "learning_rate": 0.0001564630807117836,
548
+ "loss": 1.9157102584838868,
549
+ "step": 770
550
+ },
551
+ {
552
+ "epoch": 0.3345844504021448,
553
+ "grad_norm": 0.9237125515937805,
554
+ "learning_rate": 0.00015531138846331917,
555
+ "loss": 1.850246047973633,
556
+ "step": 780
557
+ },
558
+ {
559
+ "epoch": 0.3388739946380697,
560
+ "grad_norm": 0.9540524482727051,
561
+ "learning_rate": 0.00015414902725394884,
562
+ "loss": 2.1009103775024416,
563
+ "step": 790
564
+ },
565
+ {
566
+ "epoch": 0.34316353887399464,
567
+ "grad_norm": 1.1622545719146729,
568
+ "learning_rate": 0.0001529762212904164,
569
+ "loss": 1.938945198059082,
570
+ "step": 800
571
+ },
572
+ {
573
+ "epoch": 0.34745308310991957,
574
+ "grad_norm": 0.8726609945297241,
575
+ "learning_rate": 0.00015179319679414424,
576
+ "loss": 1.9050867080688476,
577
+ "step": 810
578
+ },
579
+ {
580
+ "epoch": 0.3517426273458445,
581
+ "grad_norm": 0.9203391075134277,
582
+ "learning_rate": 0.00015060018195759786,
583
+ "loss": 2.1391984939575197,
584
+ "step": 820
585
+ },
586
+ {
587
+ "epoch": 0.3560321715817694,
588
+ "grad_norm": 0.7829976677894592,
589
+ "learning_rate": 0.00014939740690026989,
590
+ "loss": 2.2050710678100587,
591
+ "step": 830
592
+ },
593
+ {
594
+ "epoch": 0.36032171581769434,
595
+ "grad_norm": 0.9230726361274719,
596
+ "learning_rate": 0.0001481851036242927,
597
+ "loss": 2.0734573364257813,
598
+ "step": 840
599
+ },
600
+ {
601
+ "epoch": 0.3646112600536193,
602
+ "grad_norm": 0.8753212094306946,
603
+ "learning_rate": 0.00014696350596968754,
604
+ "loss": 1.9630931854248046,
605
+ "step": 850
606
+ },
607
+ {
608
+ "epoch": 0.36890080428954425,
609
+ "grad_norm": 0.978103518486023,
610
+ "learning_rate": 0.00014573284956925962,
611
+ "loss": 2.0805789947509767,
612
+ "step": 860
613
+ },
614
+ {
615
+ "epoch": 0.3731903485254692,
616
+ "grad_norm": 0.9865938425064087,
617
+ "learning_rate": 0.00014449337180314682,
618
+ "loss": 2.2181297302246095,
619
+ "step": 870
620
+ },
621
+ {
622
+ "epoch": 0.3774798927613941,
623
+ "grad_norm": 1.0393383502960205,
624
+ "learning_rate": 0.00014324531175303201,
625
+ "loss": 2.2023292541503907,
626
+ "step": 880
627
+ },
628
+ {
629
+ "epoch": 0.381769436997319,
630
+ "grad_norm": 0.7477746605873108,
631
+ "learning_rate": 0.00014198891015602646,
632
+ "loss": 2.034115219116211,
633
+ "step": 890
634
+ },
635
+ {
636
+ "epoch": 0.38605898123324395,
637
+ "grad_norm": 0.9086453318595886,
638
+ "learning_rate": 0.0001407244093582346,
639
+ "loss": 1.9091089248657227,
640
+ "step": 900
641
+ },
642
+ {
643
+ "epoch": 0.3903485254691689,
644
+ "grad_norm": 0.9519720673561096,
645
+ "learning_rate": 0.0001394520532680078,
646
+ "loss": 1.9923809051513672,
647
+ "step": 910
648
+ },
649
+ {
650
+ "epoch": 0.39463806970509385,
651
+ "grad_norm": 0.7883255481719971,
652
+ "learning_rate": 0.00013817208730889746,
653
+ "loss": 2.046986389160156,
654
+ "step": 920
655
+ },
656
+ {
657
+ "epoch": 0.3989276139410188,
658
+ "grad_norm": 0.8608978390693665,
659
+ "learning_rate": 0.00013688475837231518,
660
+ "loss": 2.015116310119629,
661
+ "step": 930
662
+ },
663
+ {
664
+ "epoch": 0.4032171581769437,
665
+ "grad_norm": 0.9121685028076172,
666
+ "learning_rate": 0.00013559031476991035,
667
+ "loss": 1.9490964889526368,
668
+ "step": 940
669
+ },
670
+ {
671
+ "epoch": 0.4075067024128686,
672
+ "grad_norm": 0.8510801196098328,
673
+ "learning_rate": 0.00013428900618567342,
674
+ "loss": 1.9338602066040038,
675
+ "step": 950
676
+ },
677
+ {
678
+ "epoch": 0.41179624664879355,
679
+ "grad_norm": 0.8185077905654907,
680
+ "learning_rate": 0.0001329810836277746,
681
+ "loss": 2.2588035583496096,
682
+ "step": 960
683
+ },
684
+ {
685
+ "epoch": 0.4160857908847185,
686
+ "grad_norm": 0.8846076130867004,
687
+ "learning_rate": 0.00013166679938014726,
688
+ "loss": 1.9654979705810547,
689
+ "step": 970
690
+ },
691
+ {
692
+ "epoch": 0.42037533512064346,
693
+ "grad_norm": 0.8377469182014465,
694
+ "learning_rate": 0.0001303464069538249,
695
+ "loss": 2.033038330078125,
696
+ "step": 980
697
+ },
698
+ {
699
+ "epoch": 0.4246648793565684,
700
+ "grad_norm": 0.8446090817451477,
701
+ "learning_rate": 0.00012902016103804188,
702
+ "loss": 2.0526477813720705,
703
+ "step": 990
704
+ },
705
+ {
706
+ "epoch": 0.4289544235924933,
707
+ "grad_norm": 0.8034592270851135,
708
+ "learning_rate": 0.0001276883174511065,
709
+ "loss": 1.9629634857177733,
710
+ "step": 1000
711
+ },
712
+ {
713
+ "epoch": 0.43324396782841823,
714
+ "grad_norm": 0.7568860054016113,
715
+ "learning_rate": 0.0001263511330910565,
716
+ "loss": 2.1565860748291015,
717
+ "step": 1010
718
+ },
719
+ {
720
+ "epoch": 0.43753351206434316,
721
+ "grad_norm": 0.9239974617958069,
722
+ "learning_rate": 0.0001250088658861063,
723
+ "loss": 2.024600028991699,
724
+ "step": 1020
725
+ },
726
+ {
727
+ "epoch": 0.4418230563002681,
728
+ "grad_norm": 1.1254358291625977,
729
+ "learning_rate": 0.00012366177474489536,
730
+ "loss": 1.9708271026611328,
731
+ "step": 1030
732
+ },
733
+ {
734
+ "epoch": 0.446112600536193,
735
+ "grad_norm": 0.9336352944374084,
736
+ "learning_rate": 0.00012231011950654763,
737
+ "loss": 2.0802404403686525,
738
+ "step": 1040
739
+ },
740
+ {
741
+ "epoch": 0.450402144772118,
742
+ "grad_norm": 1.0495727062225342,
743
+ "learning_rate": 0.00012095416089055136,
744
+ "loss": 1.8638420104980469,
745
+ "step": 1050
746
+ },
747
+ {
748
+ "epoch": 0.4546916890080429,
749
+ "grad_norm": 0.8139879703521729,
750
+ "learning_rate": 0.00011959416044646926,
751
+ "loss": 1.9255853652954102,
752
+ "step": 1060
753
+ },
754
+ {
755
+ "epoch": 0.45898123324396783,
756
+ "grad_norm": 0.9188793897628784,
757
+ "learning_rate": 0.00011823038050348838,
758
+ "loss": 1.9905168533325195,
759
+ "step": 1070
760
+ },
761
+ {
762
+ "epoch": 0.46327077747989276,
763
+ "grad_norm": 1.086055040359497,
764
+ "learning_rate": 0.00011686308411981965,
765
+ "loss": 2.0909515380859376,
766
+ "step": 1080
767
+ },
768
+ {
769
+ "epoch": 0.4675603217158177,
770
+ "grad_norm": 1.1240793466567993,
771
+ "learning_rate": 0.00011549253503195705,
772
+ "loss": 1.823994255065918,
773
+ "step": 1090
774
+ },
775
+ {
776
+ "epoch": 0.4718498659517426,
777
+ "grad_norm": 0.9709937572479248,
778
+ "learning_rate": 0.00011411899760380532,
779
+ "loss": 2.07696475982666,
780
+ "step": 1100
781
+ },
782
+ {
783
+ "epoch": 0.47613941018766753,
784
+ "grad_norm": 0.7949770092964172,
785
+ "learning_rate": 0.00011274273677568754,
786
+ "loss": 1.9337072372436523,
787
+ "step": 1110
788
+ },
789
+ {
790
+ "epoch": 0.4804289544235925,
791
+ "grad_norm": 0.9525244235992432,
792
+ "learning_rate": 0.00011136401801324061,
793
+ "loss": 2.1522342681884767,
794
+ "step": 1120
795
+ },
796
+ {
797
+ "epoch": 0.48471849865951744,
798
+ "grad_norm": 0.8864313364028931,
799
+ "learning_rate": 0.00010998310725621024,
800
+ "loss": 2.048630142211914,
801
+ "step": 1130
802
+ },
803
+ {
804
+ "epoch": 0.48900804289544236,
805
+ "grad_norm": 0.9325012564659119,
806
+ "learning_rate": 0.00010860027086715365,
807
+ "loss": 2.029229164123535,
808
+ "step": 1140
809
+ },
810
+ {
811
+ "epoch": 0.4932975871313673,
812
+ "grad_norm": 1.0433061122894287,
813
+ "learning_rate": 0.00010721577558006164,
814
+ "loss": 1.873429298400879,
815
+ "step": 1150
816
+ },
817
+ {
818
+ "epoch": 0.4975871313672922,
819
+ "grad_norm": 0.9790089726448059,
820
+ "learning_rate": 0.000105829888448908,
821
+ "loss": 1.9442827224731445,
822
+ "step": 1160
823
+ },
824
+ {
825
+ "epoch": 0.5018766756032171,
826
+ "grad_norm": 0.8827151656150818,
827
+ "learning_rate": 0.00010444287679613824,
828
+ "loss": 2.090526008605957,
829
+ "step": 1170
830
+ },
831
+ {
832
+ "epoch": 0.5061662198391421,
833
+ "grad_norm": 0.8394389748573303,
834
+ "learning_rate": 0.00010305500816110558,
835
+ "loss": 1.854716682434082,
836
+ "step": 1180
837
+ },
838
+ {
839
+ "epoch": 0.510455764075067,
840
+ "grad_norm": 0.9192520380020142,
841
+ "learning_rate": 0.00010166655024846588,
842
+ "loss": 1.9326051712036132,
843
+ "step": 1190
844
+ },
845
+ {
846
+ "epoch": 0.514745308310992,
847
+ "grad_norm": 1.0769872665405273,
848
+ "learning_rate": 0.00010027777087654004,
849
+ "loss": 2.132567024230957,
850
+ "step": 1200
851
+ },
852
+ {
853
+ "epoch": 0.5190348525469168,
854
+ "grad_norm": 1.0510156154632568,
855
+ "learning_rate": 9.888893792565508e-05,
856
+ "loss": 2.066767120361328,
857
+ "step": 1210
858
+ },
859
+ {
860
+ "epoch": 0.5233243967828418,
861
+ "grad_norm": 0.9253979921340942,
862
+ "learning_rate": 9.750031928647269e-05,
863
+ "loss": 2.0386857986450195,
864
+ "step": 1220
865
+ },
866
+ {
867
+ "epoch": 0.5276139410187668,
868
+ "grad_norm": 0.8134991526603699,
869
+ "learning_rate": 9.611218280831633e-05,
870
+ "loss": 1.9016380310058594,
871
+ "step": 1230
872
+ },
873
+ {
874
+ "epoch": 0.5319034852546917,
875
+ "grad_norm": 0.9360663890838623,
876
+ "learning_rate": 9.472479624750574e-05,
877
+ "loss": 1.9612478256225585,
878
+ "step": 1240
879
+ },
880
+ {
881
+ "epoch": 0.5361930294906166,
882
+ "grad_norm": 0.867996871471405,
883
+ "learning_rate": 9.333842721570982e-05,
884
+ "loss": 1.7373517990112304,
885
+ "step": 1250
886
+ },
887
+ {
888
+ "epoch": 0.5404825737265415,
889
+ "grad_norm": 0.8762718439102173,
890
+ "learning_rate": 9.195334312832742e-05,
891
+ "loss": 2.180467414855957,
892
+ "step": 1260
893
+ },
894
+ {
895
+ "epoch": 0.5447721179624665,
896
+ "grad_norm": 1.0470011234283447,
897
+ "learning_rate": 9.056981115290553e-05,
898
+ "loss": 2.0069679260253905,
899
+ "step": 1270
900
+ },
901
+ {
902
+ "epoch": 0.5490616621983915,
903
+ "grad_norm": 0.9644089937210083,
904
+ "learning_rate": 8.918809815760585e-05,
905
+ "loss": 2.1384710311889648,
906
+ "step": 1280
907
+ },
908
+ {
909
+ "epoch": 0.5533512064343163,
910
+ "grad_norm": 1.1095678806304932,
911
+ "learning_rate": 8.780847065972862e-05,
912
+ "loss": 2.0052122116088866,
913
+ "step": 1290
914
+ },
915
+ {
916
+ "epoch": 0.5576407506702413,
917
+ "grad_norm": 0.8349754810333252,
918
+ "learning_rate": 8.643119477430464e-05,
919
+ "loss": 2.039333724975586,
920
+ "step": 1300
921
+ },
922
+ {
923
+ "epoch": 0.5619302949061662,
924
+ "grad_norm": 1.2297779321670532,
925
+ "learning_rate": 8.505653616276427e-05,
926
+ "loss": 1.9761917114257812,
927
+ "step": 1310
928
+ },
929
+ {
930
+ "epoch": 0.5662198391420912,
931
+ "grad_norm": 0.7800212502479553,
932
+ "learning_rate": 8.368475998169455e-05,
933
+ "loss": 2.067928504943848,
934
+ "step": 1320
935
+ },
936
+ {
937
+ "epoch": 0.570509383378016,
938
+ "grad_norm": 0.7608606219291687,
939
+ "learning_rate": 8.231613083169308e-05,
940
+ "loss": 2.085641288757324,
941
+ "step": 1330
942
+ },
943
+ {
944
+ "epoch": 0.574798927613941,
945
+ "grad_norm": 0.8361881375312805,
946
+ "learning_rate": 8.095091270632988e-05,
947
+ "loss": 2.0933313369750977,
948
+ "step": 1340
949
+ },
950
+ {
951
+ "epoch": 0.579088471849866,
952
+ "grad_norm": 0.965336799621582,
953
+ "learning_rate": 7.958936894122548e-05,
954
+ "loss": 1.911599349975586,
955
+ "step": 1350
956
+ },
957
+ {
958
+ "epoch": 0.5833780160857909,
959
+ "grad_norm": 0.8643521666526794,
960
+ "learning_rate": 7.823176216325662e-05,
961
+ "loss": 1.9457710266113282,
962
+ "step": 1360
963
+ },
964
+ {
965
+ "epoch": 0.5876675603217159,
966
+ "grad_norm": 0.9850629568099976,
967
+ "learning_rate": 7.687835423989846e-05,
968
+ "loss": 1.9454130172729491,
969
+ "step": 1370
970
+ },
971
+ {
972
+ "epoch": 0.5919571045576407,
973
+ "grad_norm": 0.9901652932167053,
974
+ "learning_rate": 7.552940622871297e-05,
975
+ "loss": 2.0675596237182616,
976
+ "step": 1380
977
+ },
978
+ {
979
+ "epoch": 0.5962466487935657,
980
+ "grad_norm": 0.9328491687774658,
981
+ "learning_rate": 7.418517832699397e-05,
982
+ "loss": 1.9514102935791016,
983
+ "step": 1390
984
+ },
985
+ {
986
+ "epoch": 0.6005361930294906,
987
+ "grad_norm": 0.9820194244384766,
988
+ "learning_rate": 7.284592982157783e-05,
989
+ "loss": 1.8550071716308594,
990
+ "step": 1400
991
+ },
992
+ {
993
+ "epoch": 0.6048257372654156,
994
+ "grad_norm": 1.116490364074707,
995
+ "learning_rate": 7.151191903883001e-05,
996
+ "loss": 1.8897455215454102,
997
+ "step": 1410
998
+ },
999
+ {
1000
+ "epoch": 0.6091152815013405,
1001
+ "grad_norm": 0.9118836522102356,
1002
+ "learning_rate": 7.018340329481663e-05,
1003
+ "loss": 2.0071475982666014,
1004
+ "step": 1420
1005
+ },
1006
+ {
1007
+ "epoch": 0.6134048257372654,
1008
+ "grad_norm": 1.204563856124878,
1009
+ "learning_rate": 6.886063884567088e-05,
1010
+ "loss": 1.8896291732788086,
1011
+ "step": 1430
1012
+ },
1013
+ {
1014
+ "epoch": 0.6176943699731904,
1015
+ "grad_norm": 1.017662763595581,
1016
+ "learning_rate": 6.754388083816444e-05,
1017
+ "loss": 2.0115915298461915,
1018
+ "step": 1440
1019
+ },
1020
+ {
1021
+ "epoch": 0.6219839142091153,
1022
+ "grad_norm": 0.938076376914978,
1023
+ "learning_rate": 6.623338326049197e-05,
1024
+ "loss": 1.8918901443481446,
1025
+ "step": 1450
1026
+ },
1027
+ {
1028
+ "epoch": 0.6262734584450402,
1029
+ "grad_norm": 0.9555648565292358,
1030
+ "learning_rate": 6.492939889327991e-05,
1031
+ "loss": 2.162403106689453,
1032
+ "step": 1460
1033
+ },
1034
+ {
1035
+ "epoch": 0.6305630026809651,
1036
+ "grad_norm": 0.9676245450973511,
1037
+ "learning_rate": 6.36321792608277e-05,
1038
+ "loss": 2.0239980697631834,
1039
+ "step": 1470
1040
+ },
1041
+ {
1042
+ "epoch": 0.6348525469168901,
1043
+ "grad_norm": 1.0883864164352417,
1044
+ "learning_rate": 6.234197458259164e-05,
1045
+ "loss": 2.042740058898926,
1046
+ "step": 1480
1047
+ },
1048
+ {
1049
+ "epoch": 0.6391420911528151,
1050
+ "grad_norm": 1.045640230178833,
1051
+ "learning_rate": 6.105903372492006e-05,
1052
+ "loss": 1.876373863220215,
1053
+ "step": 1490
1054
+ },
1055
+ {
1056
+ "epoch": 0.6434316353887399,
1057
+ "grad_norm": 0.9459392428398132,
1058
+ "learning_rate": 5.9783604153049996e-05,
1059
+ "loss": 1.9611021041870118,
1060
+ "step": 1500
1061
+ },
1062
+ {
1063
+ "epoch": 0.6477211796246649,
1064
+ "grad_norm": 0.8113487362861633,
1065
+ "learning_rate": 5.851593188337374e-05,
1066
+ "loss": 1.8251832962036132,
1067
+ "step": 1510
1068
+ },
1069
+ {
1070
+ "epoch": 0.6520107238605898,
1071
+ "grad_norm": 0.7706234455108643,
1072
+ "learning_rate": 5.725626143598492e-05,
1073
+ "loss": 1.9497014999389648,
1074
+ "step": 1520
1075
+ },
1076
+ {
1077
+ "epoch": 0.6563002680965148,
1078
+ "grad_norm": 0.8590229749679565,
1079
+ "learning_rate": 5.600483578751332e-05,
1080
+ "loss": 1.9892189025878906,
1081
+ "step": 1530
1082
+ },
1083
+ {
1084
+ "epoch": 0.6605898123324396,
1085
+ "grad_norm": 0.9329735040664673,
1086
+ "learning_rate": 5.476189632425732e-05,
1087
+ "loss": 2.0406734466552736,
1088
+ "step": 1540
1089
+ },
1090
+ {
1091
+ "epoch": 0.6648793565683646,
1092
+ "grad_norm": 0.8616867661476135,
1093
+ "learning_rate": 5.3527682795623146e-05,
1094
+ "loss": 2.088255500793457,
1095
+ "step": 1550
1096
+ },
1097
+ {
1098
+ "epoch": 0.6691689008042896,
1099
+ "grad_norm": 0.8837583661079407,
1100
+ "learning_rate": 5.230243326787987e-05,
1101
+ "loss": 2.218942642211914,
1102
+ "step": 1560
1103
+ },
1104
+ {
1105
+ "epoch": 0.6734584450402145,
1106
+ "grad_norm": 1.0596973896026611,
1107
+ "learning_rate": 5.108638407823891e-05,
1108
+ "loss": 1.9535799026489258,
1109
+ "step": 1570
1110
+ },
1111
+ {
1112
+ "epoch": 0.6777479892761394,
1113
+ "grad_norm": 0.8518194556236267,
1114
+ "learning_rate": 4.987976978926732e-05,
1115
+ "loss": 2.062208557128906,
1116
+ "step": 1580
1117
+ },
1118
+ {
1119
+ "epoch": 0.6820375335120643,
1120
+ "grad_norm": 0.8575644493103027,
1121
+ "learning_rate": 4.8682823143643144e-05,
1122
+ "loss": 1.901092529296875,
1123
+ "step": 1590
1124
+ },
1125
+ {
1126
+ "epoch": 0.6863270777479893,
1127
+ "grad_norm": 1.1114495992660522,
1128
+ "learning_rate": 4.7495775019261934e-05,
1129
+ "loss": 2.0766841888427736,
1130
+ "step": 1600
1131
+ },
1132
+ {
1133
+ "epoch": 0.6906166219839142,
1134
+ "grad_norm": 0.8162066340446472,
1135
+ "learning_rate": 4.631885438470294e-05,
1136
+ "loss": 2.0174964904785155,
1137
+ "step": 1610
1138
+ },
1139
+ {
1140
+ "epoch": 0.6949061662198391,
1141
+ "grad_norm": 1.0020586252212524,
1142
+ "learning_rate": 4.515228825506356e-05,
1143
+ "loss": 2.0796960830688476,
1144
+ "step": 1620
1145
+ },
1146
+ {
1147
+ "epoch": 0.6991957104557641,
1148
+ "grad_norm": 0.6889837384223938,
1149
+ "learning_rate": 4.3996301648170625e-05,
1150
+ "loss": 1.974833869934082,
1151
+ "step": 1630
1152
+ },
1153
+ {
1154
+ "epoch": 0.703485254691689,
1155
+ "grad_norm": 0.8110519647598267,
1156
+ "learning_rate": 4.2851117541176946e-05,
1157
+ "loss": 1.9366495132446289,
1158
+ "step": 1640
1159
+ },
1160
+ {
1161
+ "epoch": 0.707774798927614,
1162
+ "grad_norm": 0.93631511926651,
1163
+ "learning_rate": 4.1716956827551366e-05,
1164
+ "loss": 1.8632808685302735,
1165
+ "step": 1650
1166
+ },
1167
+ {
1168
+ "epoch": 0.7120643431635388,
1169
+ "grad_norm": 1.289156436920166,
1170
+ "learning_rate": 4.05940382744711e-05,
1171
+ "loss": 1.9934736251831056,
1172
+ "step": 1660
1173
+ },
1174
+ {
1175
+ "epoch": 0.7163538873994638,
1176
+ "grad_norm": 0.7346325516700745,
1177
+ "learning_rate": 3.948257848062351e-05,
1178
+ "loss": 2.129100227355957,
1179
+ "step": 1670
1180
+ },
1181
+ {
1182
+ "epoch": 0.7206434316353887,
1183
+ "grad_norm": 0.8185448050498962,
1184
+ "learning_rate": 3.8382791834426925e-05,
1185
+ "loss": 2.0799833297729493,
1186
+ "step": 1680
1187
+ },
1188
+ {
1189
+ "epoch": 0.7249329758713137,
1190
+ "grad_norm": 1.1663485765457153,
1191
+ "learning_rate": 3.729489047267746e-05,
1192
+ "loss": 2.0275983810424805,
1193
+ "step": 1690
1194
+ },
1195
+ {
1196
+ "epoch": 0.7292225201072386,
1197
+ "grad_norm": 0.9577205181121826,
1198
+ "learning_rate": 3.6219084239629995e-05,
1199
+ "loss": 1.939921760559082,
1200
+ "step": 1700
1201
+ },
1202
+ {
1203
+ "epoch": 0.7335120643431635,
1204
+ "grad_norm": 0.9435138702392578,
1205
+ "learning_rate": 3.5155580646521503e-05,
1206
+ "loss": 1.81829776763916,
1207
+ "step": 1710
1208
+ },
1209
+ {
1210
+ "epoch": 0.7378016085790885,
1211
+ "grad_norm": 1.3241472244262695,
1212
+ "learning_rate": 3.410458483154455e-05,
1213
+ "loss": 1.8586637496948242,
1214
+ "step": 1720
1215
+ },
1216
+ {
1217
+ "epoch": 0.7420911528150134,
1218
+ "grad_norm": 1.2306301593780518,
1219
+ "learning_rate": 3.306629952027841e-05,
1220
+ "loss": 2.099422836303711,
1221
+ "step": 1730
1222
+ },
1223
+ {
1224
+ "epoch": 0.7463806970509383,
1225
+ "grad_norm": 1.1324083805084229,
1226
+ "learning_rate": 3.204092498658534e-05,
1227
+ "loss": 1.9230274200439452,
1228
+ "step": 1740
1229
+ },
1230
+ {
1231
+ "epoch": 0.7506702412868632,
1232
+ "grad_norm": 1.1115933656692505,
1233
+ "learning_rate": 3.1028659013979965e-05,
1234
+ "loss": 2.031582069396973,
1235
+ "step": 1750
1236
+ },
1237
+ {
1238
+ "epoch": 0.7549597855227882,
1239
+ "grad_norm": 0.8495458364486694,
1240
+ "learning_rate": 3.0029696857479263e-05,
1241
+ "loss": 1.9778030395507813,
1242
+ "step": 1760
1243
+ },
1244
+ {
1245
+ "epoch": 0.7592493297587132,
1246
+ "grad_norm": 1.0844917297363281,
1247
+ "learning_rate": 2.9044231205939687e-05,
1248
+ "loss": 2.0065166473388674,
1249
+ "step": 1770
1250
+ },
1251
+ {
1252
+ "epoch": 0.763538873994638,
1253
+ "grad_norm": 1.0366203784942627,
1254
+ "learning_rate": 2.8072452144889795e-05,
1255
+ "loss": 1.82503662109375,
1256
+ "step": 1780
1257
+ },
1258
+ {
1259
+ "epoch": 0.767828418230563,
1260
+ "grad_norm": 0.8720079064369202,
1261
+ "learning_rate": 2.711454711986474e-05,
1262
+ "loss": 1.9994165420532226,
1263
+ "step": 1790
1264
+ },
1265
+ {
1266
+ "epoch": 0.7721179624664879,
1267
+ "grad_norm": 0.9573290944099426,
1268
+ "learning_rate": 2.6170700900250144e-05,
1269
+ "loss": 1.9931180953979493,
1270
+ "step": 1800
1271
+ },
1272
+ {
1273
+ "epoch": 0.7764075067024129,
1274
+ "grad_norm": 0.8816791772842407,
1275
+ "learning_rate": 2.5241095543642057e-05,
1276
+ "loss": 2.091500663757324,
1277
+ "step": 1810
1278
+ },
1279
+ {
1280
+ "epoch": 0.7806970509383379,
1281
+ "grad_norm": 1.289235234260559,
1282
+ "learning_rate": 2.4325910360730063e-05,
1283
+ "loss": 2.0379024505615235,
1284
+ "step": 1820
1285
+ },
1286
+ {
1287
+ "epoch": 0.7849865951742627,
1288
+ "grad_norm": 1.1555016040802002,
1289
+ "learning_rate": 2.3425321880710237e-05,
1290
+ "loss": 1.9599458694458007,
1291
+ "step": 1830
1292
+ },
1293
+ {
1294
+ "epoch": 0.7892761394101877,
1295
+ "grad_norm": 1.0887123346328735,
1296
+ "learning_rate": 2.2539503817234553e-05,
1297
+ "loss": 1.9658329010009765,
1298
+ "step": 1840
1299
+ },
1300
+ {
1301
+ "epoch": 0.7935656836461126,
1302
+ "grad_norm": 0.8617165684700012,
1303
+ "learning_rate": 2.166862703490342e-05,
1304
+ "loss": 1.8620195388793945,
1305
+ "step": 1850
1306
+ },
1307
+ {
1308
+ "epoch": 0.7978552278820376,
1309
+ "grad_norm": 0.9495421648025513,
1310
+ "learning_rate": 2.0812859516307815e-05,
1311
+ "loss": 1.9315826416015625,
1312
+ "step": 1860
1313
+ },
1314
+ {
1315
+ "epoch": 0.8021447721179624,
1316
+ "grad_norm": 1.0425649881362915,
1317
+ "learning_rate": 1.997236632962728e-05,
1318
+ "loss": 1.8580461502075196,
1319
+ "step": 1870
1320
+ },
1321
+ {
1322
+ "epoch": 0.8064343163538874,
1323
+ "grad_norm": 0.8383308053016663,
1324
+ "learning_rate": 1.914730959679002e-05,
1325
+ "loss": 2.0951725006103517,
1326
+ "step": 1880
1327
+ },
1328
+ {
1329
+ "epoch": 0.8107238605898124,
1330
+ "grad_norm": 1.1160863637924194,
1331
+ "learning_rate": 1.8337848462201424e-05,
1332
+ "loss": 2.0154560089111326,
1333
+ "step": 1890
1334
+ },
1335
+ {
1336
+ "epoch": 0.8150134048257373,
1337
+ "grad_norm": 0.9295897483825684,
1338
+ "learning_rate": 1.7544139062046827e-05,
1339
+ "loss": 2.205696678161621,
1340
+ "step": 1900
1341
+ },
1342
+ {
1343
+ "epoch": 0.8193029490616622,
1344
+ "grad_norm": 0.8836671113967896,
1345
+ "learning_rate": 1.6766334494174517e-05,
1346
+ "loss": 1.996354103088379,
1347
+ "step": 1910
1348
+ },
1349
+ {
1350
+ "epoch": 0.8235924932975871,
1351
+ "grad_norm": 0.9026775360107422,
1352
+ "learning_rate": 1.6004584788564915e-05,
1353
+ "loss": 1.8099235534667968,
1354
+ "step": 1920
1355
+ },
1356
+ {
1357
+ "epoch": 0.8278820375335121,
1358
+ "grad_norm": 0.9994946718215942,
1359
+ "learning_rate": 1.5259036878391343e-05,
1360
+ "loss": 1.9028573989868165,
1361
+ "step": 1930
1362
+ },
1363
+ {
1364
+ "epoch": 0.832171581769437,
1365
+ "grad_norm": 0.9214305281639099,
1366
+ "learning_rate": 1.4529834571678336e-05,
1367
+ "loss": 1.7730764389038085,
1368
+ "step": 1940
1369
+ },
1370
+ {
1371
+ "epoch": 0.8364611260053619,
1372
+ "grad_norm": 0.8134130835533142,
1373
+ "learning_rate": 1.3817118523562555e-05,
1374
+ "loss": 1.9758581161499023,
1375
+ "step": 1950
1376
+ },
1377
+ {
1378
+ "epoch": 0.8407506702412869,
1379
+ "grad_norm": 1.0482189655303955,
1380
+ "learning_rate": 1.3121026209161935e-05,
1381
+ "loss": 1.8773077011108399,
1382
+ "step": 1960
1383
+ },
1384
+ {
1385
+ "epoch": 0.8450402144772118,
1386
+ "grad_norm": 0.7803096771240234,
1387
+ "learning_rate": 1.2441691897058317e-05,
1388
+ "loss": 1.7848268508911134,
1389
+ "step": 1970
1390
+ },
1391
+ {
1392
+ "epoch": 0.8493297587131368,
1393
+ "grad_norm": 0.8199278712272644,
1394
+ "learning_rate": 1.1779246623398543e-05,
1395
+ "loss": 1.9367828369140625,
1396
+ "step": 1980
1397
+ },
1398
+ {
1399
+ "epoch": 0.8536193029490616,
1400
+ "grad_norm": 0.9916271567344666,
1401
+ "learning_rate": 1.1133818166618903e-05,
1402
+ "loss": 1.9832647323608399,
1403
+ "step": 1990
1404
+ },
1405
+ {
1406
+ "epoch": 0.8579088471849866,
1407
+ "grad_norm": 0.8474291563034058,
1408
+ "learning_rate": 1.0505531022798088e-05,
1409
+ "loss": 1.9872703552246094,
1410
+ "step": 2000
1411
+ },
1412
+ {
1413
+ "epoch": 0.8621983914209115,
1414
+ "grad_norm": 1.149411678314209,
1415
+ "learning_rate": 9.894506381643332e-06,
1416
+ "loss": 1.9120725631713866,
1417
+ "step": 2010
1418
+ },
1419
+ {
1420
+ "epoch": 0.8664879356568365,
1421
+ "grad_norm": 0.9936849474906921,
1422
+ "learning_rate": 9.300862103114283e-06,
1423
+ "loss": 1.892692756652832,
1424
+ "step": 2020
1425
+ },
1426
+ {
1427
+ "epoch": 0.8707774798927614,
1428
+ "grad_norm": 0.9433305263519287,
1429
+ "learning_rate": 8.724712694688963e-06,
1430
+ "loss": 1.9666515350341798,
1431
+ "step": 2030
1432
+ },
1433
+ {
1434
+ "epoch": 0.8750670241286863,
1435
+ "grad_norm": 0.8228591680526733,
1436
+ "learning_rate": 8.16616928927656e-06,
1437
+ "loss": 1.8853214263916016,
1438
+ "step": 2040
1439
+ },
1440
+ {
1441
+ "epoch": 0.8793565683646113,
1442
+ "grad_norm": 0.8398504853248596,
1443
+ "learning_rate": 7.62533962378128e-06,
1444
+ "loss": 1.8661935806274415,
1445
+ "step": 2050
1446
+ },
1447
+ {
1448
+ "epoch": 0.8836461126005362,
1449
+ "grad_norm": 0.9766001105308533,
1450
+ "learning_rate": 7.102328018320858e-06,
1451
+ "loss": 2.025430107116699,
1452
+ "step": 2060
1453
+ },
1454
+ {
1455
+ "epoch": 0.8879356568364611,
1456
+ "grad_norm": 1.0431119203567505,
1457
+ "learning_rate": 6.59723535610447e-06,
1458
+ "loss": 1.8737388610839845,
1459
+ "step": 2070
1460
+ },
1461
+ {
1462
+ "epoch": 0.892225201072386,
1463
+ "grad_norm": 0.9454013109207153,
1464
+ "learning_rate": 6.110159063973419e-06,
1465
+ "loss": 1.9102716445922852,
1466
+ "step": 2080
1467
+ },
1468
+ {
1469
+ "epoch": 0.896514745308311,
1470
+ "grad_norm": 1.2222461700439453,
1471
+ "learning_rate": 5.641193093608632e-06,
1472
+ "loss": 1.8740365982055665,
1473
+ "step": 2090
1474
+ },
1475
+ {
1476
+ "epoch": 0.900804289544236,
1477
+ "grad_norm": 0.842519998550415,
1478
+ "learning_rate": 5.190427903408313e-06,
1479
+ "loss": 1.8746870040893555,
1480
+ "step": 2100
1481
+ },
1482
+ {
1483
+ "epoch": 0.9050938337801608,
1484
+ "grad_norm": 0.7582686543464661,
1485
+ "learning_rate": 4.757950441039594e-06,
1486
+ "loss": 1.9830608367919922,
1487
+ "step": 2110
1488
+ },
1489
+ {
1490
+ "epoch": 0.9093833780160858,
1491
+ "grad_norm": 0.9554128646850586,
1492
+ "learning_rate": 4.343844126667229e-06,
1493
+ "loss": 1.873980712890625,
1494
+ "step": 2120
1495
+ },
1496
+ {
1497
+ "epoch": 0.9136729222520107,
1498
+ "grad_norm": 0.8336243033409119,
1499
+ "learning_rate": 3.948188836862776e-06,
1500
+ "loss": 2.0840463638305664,
1501
+ "step": 2130
1502
+ },
1503
+ {
1504
+ "epoch": 0.9179624664879357,
1505
+ "grad_norm": 0.8276010155677795,
1506
+ "learning_rate": 3.5710608891972976e-06,
1507
+ "loss": 1.8817314147949218,
1508
+ "step": 2140
1509
+ },
1510
+ {
1511
+ "epoch": 0.9222520107238605,
1512
+ "grad_norm": 0.9805457592010498,
1513
+ "learning_rate": 3.2125330275205147e-06,
1514
+ "loss": 1.8696676254272462,
1515
+ "step": 2150
1516
+ },
1517
+ {
1518
+ "epoch": 0.9265415549597855,
1519
+ "grad_norm": 0.9226967096328735,
1520
+ "learning_rate": 2.87267440792931e-06,
1521
+ "loss": 2.1158275604248047,
1522
+ "step": 2160
1523
+ },
1524
+ {
1525
+ "epoch": 0.9308310991957105,
1526
+ "grad_norm": 1.1522666215896606,
1527
+ "learning_rate": 2.5515505854283196e-06,
1528
+ "loss": 1.8417831420898438,
1529
+ "step": 2170
1530
+ },
1531
+ {
1532
+ "epoch": 0.9351206434316354,
1533
+ "grad_norm": 0.9451821446418762,
1534
+ "learning_rate": 2.249223501285003e-06,
1535
+ "loss": 1.9700094223022462,
1536
+ "step": 2180
1537
+ },
1538
+ {
1539
+ "epoch": 0.9394101876675603,
1540
+ "grad_norm": 0.924561083316803,
1541
+ "learning_rate": 1.965751471081978e-06,
1542
+ "loss": 1.8976779937744142,
1543
+ "step": 2190
1544
+ },
1545
+ {
1546
+ "epoch": 0.9436997319034852,
1547
+ "grad_norm": 1.2159491777420044,
1548
+ "learning_rate": 1.7011891734684538e-06,
1549
+ "loss": 2.063970184326172,
1550
+ "step": 2200
1551
+ }
1552
+ ],
1553
+ "logging_steps": 10,
1554
+ "max_steps": 2332,
1555
+ "num_input_tokens_seen": 0,
1556
+ "num_train_epochs": 1,
1557
+ "save_steps": 200,
1558
+ "stateful_callbacks": {
1559
+ "TrainerControl": {
1560
+ "args": {
1561
+ "should_epoch_stop": false,
1562
+ "should_evaluate": false,
1563
+ "should_log": false,
1564
+ "should_save": true,
1565
+ "should_training_stop": false
1566
+ },
1567
+ "attributes": {}
1568
+ }
1569
+ },
1570
+ "total_flos": 4.217221435041792e+16,
1571
+ "train_batch_size": 1,
1572
+ "trial_name": null,
1573
+ "trial_params": null
1574
+ }
security/outputs/phishsense-phishing-lora/tokenizer_config.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "backend": "tokenizers",
3
+ "bos_token": "<|begin_of_text|>",
4
+ "clean_up_tokenization_spaces": true,
5
+ "eos_token": "<|eot_id|>",
6
+ "is_local": true,
7
+ "local_files_only": true,
8
+ "model_input_names": [
9
+ "input_ids",
10
+ "attention_mask"
11
+ ],
12
+ "model_max_length": 131072,
13
+ "pad_token": "<|eot_id|>",
14
+ "tokenizer_class": "TokenizersBackend"
15
+ }
security/outputs/phishsense-phishing-lora/training_info.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "latest_checkpoint": "security/outputs/phishsense-phishing-lora/adapter_model.safetensors",
3
+ "latest_mtime": 1777324104.3519826,
4
+ "latest_mtime_iso": "2026-04-27T21:08:24.351983+00:00",
5
+ "size_bytes": 45118424,
6
+ "epoch": null
7
+ }
security/outputs/secbert-phishing-classifier/config.json ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_cross_attention": false,
3
+ "architectures": [
4
+ "BertForSequenceClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": null,
8
+ "classifier_dropout": null,
9
+ "dtype": "float32",
10
+ "eos_token_id": null,
11
+ "gradient_checkpointing": false,
12
+ "hidden_act": "gelu",
13
+ "hidden_dropout_prob": 0.1,
14
+ "hidden_size": 768,
15
+ "id2label": {
16
+ "0": "Phishing Email",
17
+ "1": "Safe Email"
18
+ },
19
+ "initializer_range": 0.02,
20
+ "intermediate_size": 3072,
21
+ "is_decoder": false,
22
+ "label2id": {
23
+ "Phishing Email": 0,
24
+ "Safe Email": 1
25
+ },
26
+ "layer_norm_eps": 1e-12,
27
+ "max_position_embeddings": 514,
28
+ "model_type": "bert",
29
+ "num_attention_heads": 12,
30
+ "num_hidden_layers": 6,
31
+ "pad_token_id": 0,
32
+ "problem_type": "single_label_classification",
33
+ "tie_word_embeddings": true,
34
+ "transformers_version": "5.6.2",
35
+ "type_vocab_size": 1,
36
+ "use_cache": false,
37
+ "vocab_size": 52000
38
+ }
security/outputs/secbert-phishing-classifier/label_mapping.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "label2id": {
3
+ "Phishing Email": 0,
4
+ "Safe Email": 1
5
+ },
6
+ "id2label": {
7
+ "0": "Phishing Email",
8
+ "1": "Safe Email"
9
+ },
10
+ "text_col": "Email Text",
11
+ "label_col": "Email Type",
12
+ "metrics": {
13
+ "eval_loss": 0.2722574770450592,
14
+ "eval_accuracy": 1.0,
15
+ "eval_precision": 1.0,
16
+ "eval_recall": 1.0,
17
+ "eval_f1": 1.0,
18
+ "eval_runtime": 0.0291,
19
+ "eval_samples_per_second": 102.997,
20
+ "eval_steps_per_second": 34.332,
21
+ "epoch": 1.0
22
+ }
23
+ }
security/outputs/secbert-phishing-classifier/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
security/outputs/secbert-phishing-classifier/tokenizer_config.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "backend": "tokenizers",
3
+ "cls_token": "[CLS]",
4
+ "do_lower_case": true,
5
+ "is_local": true,
6
+ "local_files_only": true,
7
+ "mask_token": "[MASK]",
8
+ "model_max_length": 1000000000000000019884624838656,
9
+ "pad_token": "[PAD]",
10
+ "sep_token": "[SEP]",
11
+ "strip_accents": null,
12
+ "tokenize_chinese_chars": true,
13
+ "tokenizer_class": "BertTokenizer",
14
+ "unk_token": "[UNK]"
15
+ }
security/outputs/secbert-phishing-classifier/training_info.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "latest_checkpoint": "security/outputs/secbert-phishing-classifier/training_args.bin",
3
+ "latest_mtime": 1777558880.1854646,
4
+ "latest_mtime_iso": "2026-04-30T14:21:20.185465+00:00",
5
+ "size_bytes": 5329,
6
+ "epoch": null
7
+ }
security/outputs/securityllm-10datasets-lora/README.md ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: /workspace/security/models/SecurityLLM
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:/workspace/security/models/SecurityLLM
7
+ - lora
8
+ - transformers
9
+ ---
10
+
11
+ # Model Card for Model ID
12
+
13
+ <!-- Provide a quick summary of what the model is/does. -->
14
+
15
+
16
+
17
+ ## Model Details
18
+
19
+ ### Model Description
20
+
21
+ <!-- Provide a longer summary of what this model is. -->
22
+
23
+
24
+
25
+ - **Developed by:** [More Information Needed]
26
+ - **Funded by [optional]:** [More Information Needed]
27
+ - **Shared by [optional]:** [More Information Needed]
28
+ - **Model type:** [More Information Needed]
29
+ - **Language(s) (NLP):** [More Information Needed]
30
+ - **License:** [More Information Needed]
31
+ - **Finetuned from model [optional]:** [More Information Needed]
32
+
33
+ ### Model Sources [optional]
34
+
35
+ <!-- Provide the basic links for the model. -->
36
+
37
+ - **Repository:** [More Information Needed]
38
+ - **Paper [optional]:** [More Information Needed]
39
+ - **Demo [optional]:** [More Information Needed]
40
+
41
+ ## Uses
42
+
43
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
44
+
45
+ ### Direct Use
46
+
47
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
48
+
49
+ [More Information Needed]
50
+
51
+ ### Downstream Use [optional]
52
+
53
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
54
+
55
+ [More Information Needed]
56
+
57
+ ### Out-of-Scope Use
58
+
59
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
60
+
61
+ [More Information Needed]
62
+
63
+ ## Bias, Risks, and Limitations
64
+
65
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
66
+
67
+ [More Information Needed]
68
+
69
+ ### Recommendations
70
+
71
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
72
+
73
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
74
+
75
+ ## How to Get Started with the Model
76
+
77
+ Use the code below to get started with the model.
78
+
79
+ [More Information Needed]
80
+
81
+ ## Training Details
82
+
83
+ ### Training Data
84
+
85
+ <!-- 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. -->
86
+
87
+ [More Information Needed]
88
+
89
+ ### Training Procedure
90
+
91
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
92
+
93
+ #### Preprocessing [optional]
94
+
95
+ [More Information Needed]
96
+
97
+
98
+ #### Training Hyperparameters
99
+
100
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
101
+
102
+ #### Speeds, Sizes, Times [optional]
103
+
104
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
105
+
106
+ [More Information Needed]
107
+
108
+ ## Evaluation
109
+
110
+ <!-- This section describes the evaluation protocols and provides the results. -->
111
+
112
+ ### Testing Data, Factors & Metrics
113
+
114
+ #### Testing Data
115
+
116
+ <!-- This should link to a Dataset Card if possible. -->
117
+
118
+ [More Information Needed]
119
+
120
+ #### Factors
121
+
122
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
123
+
124
+ [More Information Needed]
125
+
126
+ #### Metrics
127
+
128
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
129
+
130
+ [More Information Needed]
131
+
132
+ ### Results
133
+
134
+ [More Information Needed]
135
+
136
+ #### Summary
137
+
138
+
139
+
140
+ ## Model Examination [optional]
141
+
142
+ <!-- Relevant interpretability work for the model goes here -->
143
+
144
+ [More Information Needed]
145
+
146
+ ## Environmental Impact
147
+
148
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
149
+
150
+ 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).
151
+
152
+ - **Hardware Type:** [More Information Needed]
153
+ - **Hours used:** [More Information Needed]
154
+ - **Cloud Provider:** [More Information Needed]
155
+ - **Compute Region:** [More Information Needed]
156
+ - **Carbon Emitted:** [More Information Needed]
157
+
158
+ ## Technical Specifications [optional]
159
+
160
+ ### Model Architecture and Objective
161
+
162
+ [More Information Needed]
163
+
164
+ ### Compute Infrastructure
165
+
166
+ [More Information Needed]
167
+
168
+ #### Hardware
169
+
170
+ [More Information Needed]
171
+
172
+ #### Software
173
+
174
+ [More Information Needed]
175
+
176
+ ## Citation [optional]
177
+
178
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
179
+
180
+ **BibTeX:**
181
+
182
+ [More Information Needed]
183
+
184
+ **APA:**
185
+
186
+ [More Information Needed]
187
+
188
+ ## Glossary [optional]
189
+
190
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
191
+
192
+ [More Information Needed]
193
+
194
+ ## More Information [optional]
195
+
196
+ [More Information Needed]
197
+
198
+ ## Model Card Authors [optional]
199
+
200
+ [More Information Needed]
201
+
202
+ ## Model Card Contact
203
+
204
+ [More Information Needed]
205
+ ### Framework versions
206
+
207
+ - PEFT 0.19.1
security/outputs/securityllm-10datasets-lora/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": "/workspace/security/models/SecurityLLM",
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": 0.05,
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": 16,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "gate_proj",
34
+ "down_proj",
35
+ "k_proj",
36
+ "o_proj",
37
+ "q_proj",
38
+ "up_proj",
39
+ "v_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
+ }
security/outputs/securityllm-10datasets-lora/chat_template.jinja ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {% for message in messages %}
2
+ {% if message['role'] == 'user' %}
3
+ {{ '<|user|>
4
+ ' + message['content'] + eos_token }}
5
+ {% elif message['role'] == 'system' %}
6
+ {{ '<|system|>
7
+ ' + message['content'] + eos_token }}
8
+ {% elif message['role'] == 'assistant' %}
9
+ {{ '<|assistant|>
10
+ ' + message['content'] + eos_token }}
11
+ {% endif %}
12
+ {% if loop.last and add_generation_prompt %}
13
+ {{ '<|assistant|>' }}
14
+ {% endif %}
15
+ {% endfor %}
security/outputs/securityllm-10datasets-lora/checkpoint-28600/README.md ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: /workspace/security/models/SecurityLLM
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:/workspace/security/models/SecurityLLM
7
+ - lora
8
+ - transformers
9
+ ---
10
+
11
+ # Model Card for Model ID
12
+
13
+ <!-- Provide a quick summary of what the model is/does. -->
14
+
15
+
16
+
17
+ ## Model Details
18
+
19
+ ### Model Description
20
+
21
+ <!-- Provide a longer summary of what this model is. -->
22
+
23
+
24
+
25
+ - **Developed by:** [More Information Needed]
26
+ - **Funded by [optional]:** [More Information Needed]
27
+ - **Shared by [optional]:** [More Information Needed]
28
+ - **Model type:** [More Information Needed]
29
+ - **Language(s) (NLP):** [More Information Needed]
30
+ - **License:** [More Information Needed]
31
+ - **Finetuned from model [optional]:** [More Information Needed]
32
+
33
+ ### Model Sources [optional]
34
+
35
+ <!-- Provide the basic links for the model. -->
36
+
37
+ - **Repository:** [More Information Needed]
38
+ - **Paper [optional]:** [More Information Needed]
39
+ - **Demo [optional]:** [More Information Needed]
40
+
41
+ ## Uses
42
+
43
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
44
+
45
+ ### Direct Use
46
+
47
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
48
+
49
+ [More Information Needed]
50
+
51
+ ### Downstream Use [optional]
52
+
53
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
54
+
55
+ [More Information Needed]
56
+
57
+ ### Out-of-Scope Use
58
+
59
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
60
+
61
+ [More Information Needed]
62
+
63
+ ## Bias, Risks, and Limitations
64
+
65
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
66
+
67
+ [More Information Needed]
68
+
69
+ ### Recommendations
70
+
71
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
72
+
73
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
74
+
75
+ ## How to Get Started with the Model
76
+
77
+ Use the code below to get started with the model.
78
+
79
+ [More Information Needed]
80
+
81
+ ## Training Details
82
+
83
+ ### Training Data
84
+
85
+ <!-- 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. -->
86
+
87
+ [More Information Needed]
88
+
89
+ ### Training Procedure
90
+
91
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
92
+
93
+ #### Preprocessing [optional]
94
+
95
+ [More Information Needed]
96
+
97
+
98
+ #### Training Hyperparameters
99
+
100
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
101
+
102
+ #### Speeds, Sizes, Times [optional]
103
+
104
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
105
+
106
+ [More Information Needed]
107
+
108
+ ## Evaluation
109
+
110
+ <!-- This section describes the evaluation protocols and provides the results. -->
111
+
112
+ ### Testing Data, Factors & Metrics
113
+
114
+ #### Testing Data
115
+
116
+ <!-- This should link to a Dataset Card if possible. -->
117
+
118
+ [More Information Needed]
119
+
120
+ #### Factors
121
+
122
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
123
+
124
+ [More Information Needed]
125
+
126
+ #### Metrics
127
+
128
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
129
+
130
+ [More Information Needed]
131
+
132
+ ### Results
133
+
134
+ [More Information Needed]
135
+
136
+ #### Summary
137
+
138
+
139
+
140
+ ## Model Examination [optional]
141
+
142
+ <!-- Relevant interpretability work for the model goes here -->
143
+
144
+ [More Information Needed]
145
+
146
+ ## Environmental Impact
147
+
148
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
149
+
150
+ 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).
151
+
152
+ - **Hardware Type:** [More Information Needed]
153
+ - **Hours used:** [More Information Needed]
154
+ - **Cloud Provider:** [More Information Needed]
155
+ - **Compute Region:** [More Information Needed]
156
+ - **Carbon Emitted:** [More Information Needed]
157
+
158
+ ## Technical Specifications [optional]
159
+
160
+ ### Model Architecture and Objective
161
+
162
+ [More Information Needed]
163
+
164
+ ### Compute Infrastructure
165
+
166
+ [More Information Needed]
167
+
168
+ #### Hardware
169
+
170
+ [More Information Needed]
171
+
172
+ #### Software
173
+
174
+ [More Information Needed]
175
+
176
+ ## Citation [optional]
177
+
178
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
179
+
180
+ **BibTeX:**
181
+
182
+ [More Information Needed]
183
+
184
+ **APA:**
185
+
186
+ [More Information Needed]
187
+
188
+ ## Glossary [optional]
189
+
190
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
191
+
192
+ [More Information Needed]
193
+
194
+ ## More Information [optional]
195
+
196
+ [More Information Needed]
197
+
198
+ ## Model Card Authors [optional]
199
+
200
+ [More Information Needed]
201
+
202
+ ## Model Card Contact
203
+
204
+ [More Information Needed]
205
+ ### Framework versions
206
+
207
+ - PEFT 0.19.1
security/outputs/securityllm-10datasets-lora/checkpoint-28600/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": "/workspace/security/models/SecurityLLM",
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": 0.05,
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": 16,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "gate_proj",
34
+ "down_proj",
35
+ "k_proj",
36
+ "o_proj",
37
+ "q_proj",
38
+ "up_proj",
39
+ "v_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
+ }
security/outputs/securityllm-10datasets-lora/checkpoint-28600/chat_template.jinja ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {% for message in messages %}
2
+ {% if message['role'] == 'user' %}
3
+ {{ '<|user|>
4
+ ' + message['content'] + eos_token }}
5
+ {% elif message['role'] == 'system' %}
6
+ {{ '<|system|>
7
+ ' + message['content'] + eos_token }}
8
+ {% elif message['role'] == 'assistant' %}
9
+ {{ '<|assistant|>
10
+ ' + message['content'] + eos_token }}
11
+ {% endif %}
12
+ {% if loop.last and add_generation_prompt %}
13
+ {{ '<|assistant|>' }}
14
+ {% endif %}
15
+ {% endfor %}
security/outputs/securityllm-10datasets-lora/checkpoint-28600/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
security/outputs/securityllm-10datasets-lora/checkpoint-28600/tokenizer_config.json ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": null,
3
+ "backend": "tokenizers",
4
+ "bos_token": "<s>",
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "</s>",
7
+ "extra_special_tokens": [
8
+ "<unk>",
9
+ "<s>",
10
+ "</s>"
11
+ ],
12
+ "is_local": true,
13
+ "local_files_only": true,
14
+ "model_max_length": 1000000000000000019884624838656,
15
+ "pad_token": "</s>",
16
+ "sp_model_kwargs": {},
17
+ "spaces_between_special_tokens": false,
18
+ "tokenizer_class": "LlamaTokenizer",
19
+ "truncation_side": "left",
20
+ "unk_token": "<unk>",
21
+ "use_default_system_prompt": true
22
+ }
security/outputs/securityllm-10datasets-lora/checkpoint-28600/trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
security/outputs/securityllm-10datasets-lora/checkpoint-28600/training_info.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "latest_checkpoint": "security/outputs/securityllm-10datasets-lora/checkpoint-28600/optimizer.pt",
3
+ "latest_mtime": 1777492088.8392105,
4
+ "latest_mtime_iso": "2026-04-29T19:48:08.839211+00:00",
5
+ "size_bytes": 335922851,
6
+ "epoch": null
7
+ }
security/outputs/securityllm-10datasets-lora/checkpoint-28647/README.md ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: /workspace/security/models/SecurityLLM
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:/workspace/security/models/SecurityLLM
7
+ - lora
8
+ - transformers
9
+ ---
10
+
11
+ # Model Card for Model ID
12
+
13
+ <!-- Provide a quick summary of what the model is/does. -->
14
+
15
+
16
+
17
+ ## Model Details
18
+
19
+ ### Model Description
20
+
21
+ <!-- Provide a longer summary of what this model is. -->
22
+
23
+
24
+
25
+ - **Developed by:** [More Information Needed]
26
+ - **Funded by [optional]:** [More Information Needed]
27
+ - **Shared by [optional]:** [More Information Needed]
28
+ - **Model type:** [More Information Needed]
29
+ - **Language(s) (NLP):** [More Information Needed]
30
+ - **License:** [More Information Needed]
31
+ - **Finetuned from model [optional]:** [More Information Needed]
32
+
33
+ ### Model Sources [optional]
34
+
35
+ <!-- Provide the basic links for the model. -->
36
+
37
+ - **Repository:** [More Information Needed]
38
+ - **Paper [optional]:** [More Information Needed]
39
+ - **Demo [optional]:** [More Information Needed]
40
+
41
+ ## Uses
42
+
43
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
44
+
45
+ ### Direct Use
46
+
47
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
48
+
49
+ [More Information Needed]
50
+
51
+ ### Downstream Use [optional]
52
+
53
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
54
+
55
+ [More Information Needed]
56
+
57
+ ### Out-of-Scope Use
58
+
59
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
60
+
61
+ [More Information Needed]
62
+
63
+ ## Bias, Risks, and Limitations
64
+
65
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
66
+
67
+ [More Information Needed]
68
+
69
+ ### Recommendations
70
+
71
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
72
+
73
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
74
+
75
+ ## How to Get Started with the Model
76
+
77
+ Use the code below to get started with the model.
78
+
79
+ [More Information Needed]
80
+
81
+ ## Training Details
82
+
83
+ ### Training Data
84
+
85
+ <!-- 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. -->
86
+
87
+ [More Information Needed]
88
+
89
+ ### Training Procedure
90
+
91
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
92
+
93
+ #### Preprocessing [optional]
94
+
95
+ [More Information Needed]
96
+
97
+
98
+ #### Training Hyperparameters
99
+
100
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
101
+
102
+ #### Speeds, Sizes, Times [optional]
103
+
104
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
105
+
106
+ [More Information Needed]
107
+
108
+ ## Evaluation
109
+
110
+ <!-- This section describes the evaluation protocols and provides the results. -->
111
+
112
+ ### Testing Data, Factors & Metrics
113
+
114
+ #### Testing Data
115
+
116
+ <!-- This should link to a Dataset Card if possible. -->
117
+
118
+ [More Information Needed]
119
+
120
+ #### Factors
121
+
122
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
123
+
124
+ [More Information Needed]
125
+
126
+ #### Metrics
127
+
128
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
129
+
130
+ [More Information Needed]
131
+
132
+ ### Results
133
+
134
+ [More Information Needed]
135
+
136
+ #### Summary
137
+
138
+
139
+
140
+ ## Model Examination [optional]
141
+
142
+ <!-- Relevant interpretability work for the model goes here -->
143
+
144
+ [More Information Needed]
145
+
146
+ ## Environmental Impact
147
+
148
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
149
+
150
+ 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).
151
+
152
+ - **Hardware Type:** [More Information Needed]
153
+ - **Hours used:** [More Information Needed]
154
+ - **Cloud Provider:** [More Information Needed]
155
+ - **Compute Region:** [More Information Needed]
156
+ - **Carbon Emitted:** [More Information Needed]
157
+
158
+ ## Technical Specifications [optional]
159
+
160
+ ### Model Architecture and Objective
161
+
162
+ [More Information Needed]
163
+
164
+ ### Compute Infrastructure
165
+
166
+ [More Information Needed]
167
+
168
+ #### Hardware
169
+
170
+ [More Information Needed]
171
+
172
+ #### Software
173
+
174
+ [More Information Needed]
175
+
176
+ ## Citation [optional]
177
+
178
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
179
+
180
+ **BibTeX:**
181
+
182
+ [More Information Needed]
183
+
184
+ **APA:**
185
+
186
+ [More Information Needed]
187
+
188
+ ## Glossary [optional]
189
+
190
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
191
+
192
+ [More Information Needed]
193
+
194
+ ## More Information [optional]
195
+
196
+ [More Information Needed]
197
+
198
+ ## Model Card Authors [optional]
199
+
200
+ [More Information Needed]
201
+
202
+ ## Model Card Contact
203
+
204
+ [More Information Needed]
205
+ ### Framework versions
206
+
207
+ - PEFT 0.19.1
security/outputs/securityllm-10datasets-lora/checkpoint-28647/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": "/workspace/security/models/SecurityLLM",
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": 0.05,
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": 16,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "gate_proj",
34
+ "down_proj",
35
+ "k_proj",
36
+ "o_proj",
37
+ "q_proj",
38
+ "up_proj",
39
+ "v_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
+ }
security/outputs/securityllm-10datasets-lora/checkpoint-28647/chat_template.jinja ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {% for message in messages %}
2
+ {% if message['role'] == 'user' %}
3
+ {{ '<|user|>
4
+ ' + message['content'] + eos_token }}
5
+ {% elif message['role'] == 'system' %}
6
+ {{ '<|system|>
7
+ ' + message['content'] + eos_token }}
8
+ {% elif message['role'] == 'assistant' %}
9
+ {{ '<|assistant|>
10
+ ' + message['content'] + eos_token }}
11
+ {% endif %}
12
+ {% if loop.last and add_generation_prompt %}
13
+ {{ '<|assistant|>' }}
14
+ {% endif %}
15
+ {% endfor %}
security/outputs/securityllm-10datasets-lora/checkpoint-28647/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
security/outputs/securityllm-10datasets-lora/checkpoint-28647/tokenizer_config.json ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": null,
3
+ "backend": "tokenizers",
4
+ "bos_token": "<s>",
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "</s>",
7
+ "extra_special_tokens": [
8
+ "<unk>",
9
+ "<s>",
10
+ "</s>"
11
+ ],
12
+ "is_local": true,
13
+ "local_files_only": true,
14
+ "model_max_length": 1000000000000000019884624838656,
15
+ "pad_token": "</s>",
16
+ "sp_model_kwargs": {},
17
+ "spaces_between_special_tokens": false,
18
+ "tokenizer_class": "LlamaTokenizer",
19
+ "truncation_side": "left",
20
+ "unk_token": "<unk>",
21
+ "use_default_system_prompt": true
22
+ }
security/outputs/securityllm-10datasets-lora/checkpoint-28647/trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
security/outputs/securityllm-10datasets-lora/checkpoint-28647/training_info.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "latest_checkpoint": "security/outputs/securityllm-10datasets-lora/checkpoint-28647/rng_state.pth",
3
+ "latest_mtime": 1777492358.7721028,
4
+ "latest_mtime_iso": "2026-04-29T19:52:38.772103+00:00",
5
+ "size_bytes": 14645,
6
+ "epoch": null
7
+ }
security/outputs/securityllm-10datasets-lora/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
security/outputs/securityllm-10datasets-lora/tokenizer_config.json ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": null,
3
+ "backend": "tokenizers",
4
+ "bos_token": "<s>",
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "</s>",
7
+ "extra_special_tokens": [
8
+ "<unk>",
9
+ "<s>",
10
+ "</s>"
11
+ ],
12
+ "is_local": true,
13
+ "local_files_only": true,
14
+ "model_max_length": 1000000000000000019884624838656,
15
+ "pad_token": "</s>",
16
+ "sp_model_kwargs": {},
17
+ "spaces_between_special_tokens": false,
18
+ "tokenizer_class": "LlamaTokenizer",
19
+ "truncation_side": "left",
20
+ "unk_token": "<unk>",
21
+ "use_default_system_prompt": true
22
+ }
security/outputs/securityllm-10datasets-lora/training_info.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "latest_checkpoint": "security/outputs/securityllm-10datasets-lora/adapter_model.safetensors",
3
+ "latest_mtime": 1777492359.1841102,
4
+ "latest_mtime_iso": "2026-04-29T19:52:39.184110+00:00",
5
+ "size_bytes": 167832240,
6
+ "epoch": null
7
+ }
security/outputs/securityllm-rules-lora/README.md ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: /workspace/security/models/SecurityLLM
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:/workspace/security/models/SecurityLLM
7
+ - lora
8
+ - transformers
9
+ ---
10
+
11
+ # Model Card for Model ID
12
+
13
+ <!-- Provide a quick summary of what the model is/does. -->
14
+
15
+
16
+
17
+ ## Model Details
18
+
19
+ ### Model Description
20
+
21
+ <!-- Provide a longer summary of what this model is. -->
22
+
23
+
24
+
25
+ - **Developed by:** [More Information Needed]
26
+ - **Funded by [optional]:** [More Information Needed]
27
+ - **Shared by [optional]:** [More Information Needed]
28
+ - **Model type:** [More Information Needed]
29
+ - **Language(s) (NLP):** [More Information Needed]
30
+ - **License:** [More Information Needed]
31
+ - **Finetuned from model [optional]:** [More Information Needed]
32
+
33
+ ### Model Sources [optional]
34
+
35
+ <!-- Provide the basic links for the model. -->
36
+
37
+ - **Repository:** [More Information Needed]
38
+ - **Paper [optional]:** [More Information Needed]
39
+ - **Demo [optional]:** [More Information Needed]
40
+
41
+ ## Uses
42
+
43
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
44
+
45
+ ### Direct Use
46
+
47
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
48
+
49
+ [More Information Needed]
50
+
51
+ ### Downstream Use [optional]
52
+
53
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
54
+
55
+ [More Information Needed]
56
+
57
+ ### Out-of-Scope Use
58
+
59
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
60
+
61
+ [More Information Needed]
62
+
63
+ ## Bias, Risks, and Limitations
64
+
65
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
66
+
67
+ [More Information Needed]
68
+
69
+ ### Recommendations
70
+
71
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
72
+
73
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
74
+
75
+ ## How to Get Started with the Model
76
+
77
+ Use the code below to get started with the model.
78
+
79
+ [More Information Needed]
80
+
81
+ ## Training Details
82
+
83
+ ### Training Data
84
+
85
+ <!-- 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. -->
86
+
87
+ [More Information Needed]
88
+
89
+ ### Training Procedure
90
+
91
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
92
+
93
+ #### Preprocessing [optional]
94
+
95
+ [More Information Needed]
96
+
97
+
98
+ #### Training Hyperparameters
99
+
100
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
101
+
102
+ #### Speeds, Sizes, Times [optional]
103
+
104
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
105
+
106
+ [More Information Needed]
107
+
108
+ ## Evaluation
109
+
110
+ <!-- This section describes the evaluation protocols and provides the results. -->
111
+
112
+ ### Testing Data, Factors & Metrics
113
+
114
+ #### Testing Data
115
+
116
+ <!-- This should link to a Dataset Card if possible. -->
117
+
118
+ [More Information Needed]
119
+
120
+ #### Factors
121
+
122
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
123
+
124
+ [More Information Needed]
125
+
126
+ #### Metrics
127
+
128
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
129
+
130
+ [More Information Needed]
131
+
132
+ ### Results
133
+
134
+ [More Information Needed]
135
+
136
+ #### Summary
137
+
138
+
139
+
140
+ ## Model Examination [optional]
141
+
142
+ <!-- Relevant interpretability work for the model goes here -->
143
+
144
+ [More Information Needed]
145
+
146
+ ## Environmental Impact
147
+
148
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
149
+
150
+ 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).
151
+
152
+ - **Hardware Type:** [More Information Needed]
153
+ - **Hours used:** [More Information Needed]
154
+ - **Cloud Provider:** [More Information Needed]
155
+ - **Compute Region:** [More Information Needed]
156
+ - **Carbon Emitted:** [More Information Needed]
157
+
158
+ ## Technical Specifications [optional]
159
+
160
+ ### Model Architecture and Objective
161
+
162
+ [More Information Needed]
163
+
164
+ ### Compute Infrastructure
165
+
166
+ [More Information Needed]
167
+
168
+ #### Hardware
169
+
170
+ [More Information Needed]
171
+
172
+ #### Software
173
+
174
+ [More Information Needed]
175
+
176
+ ## Citation [optional]
177
+
178
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
179
+
180
+ **BibTeX:**
181
+
182
+ [More Information Needed]
183
+
184
+ **APA:**
185
+
186
+ [More Information Needed]
187
+
188
+ ## Glossary [optional]
189
+
190
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
191
+
192
+ [More Information Needed]
193
+
194
+ ## More Information [optional]
195
+
196
+ [More Information Needed]
197
+
198
+ ## Model Card Authors [optional]
199
+
200
+ [More Information Needed]
201
+
202
+ ## Model Card Contact
203
+
204
+ [More Information Needed]
205
+ ### Framework versions
206
+
207
+ - PEFT 0.19.1
security/outputs/securityllm-rules-lora/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": "/workspace/security/models/SecurityLLM",
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": 0.05,
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": 16,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "v_proj",
34
+ "o_proj",
35
+ "gate_proj",
36
+ "k_proj",
37
+ "q_proj",
38
+ "down_proj",
39
+ "up_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
+ }
security/outputs/securityllm-rules-lora/chat_template.jinja ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {% for message in messages %}
2
+ {% if message['role'] == 'user' %}
3
+ {{ '<|user|>
4
+ ' + message['content'] + eos_token }}
5
+ {% elif message['role'] == 'system' %}
6
+ {{ '<|system|>
7
+ ' + message['content'] + eos_token }}
8
+ {% elif message['role'] == 'assistant' %}
9
+ {{ '<|assistant|>
10
+ ' + message['content'] + eos_token }}
11
+ {% endif %}
12
+ {% if loop.last and add_generation_prompt %}
13
+ {{ '<|assistant|>' }}
14
+ {% endif %}
15
+ {% endfor %}
security/outputs/securityllm-rules-lora/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
security/outputs/securityllm-rules-lora/tokenizer_config.json ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": null,
3
+ "backend": "tokenizers",
4
+ "bos_token": "<s>",
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "</s>",
7
+ "extra_special_tokens": [
8
+ "<unk>",
9
+ "<s>",
10
+ "</s>"
11
+ ],
12
+ "is_local": true,
13
+ "local_files_only": true,
14
+ "model_max_length": 1000000000000000019884624838656,
15
+ "pad_token": "</s>",
16
+ "sp_model_kwargs": {},
17
+ "spaces_between_special_tokens": false,
18
+ "tokenizer_class": "LlamaTokenizer",
19
+ "truncation_side": "left",
20
+ "unk_token": "<unk>",
21
+ "use_default_system_prompt": true
22
+ }
security/outputs/securityllm-rules-lora/training_info.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "latest_checkpoint": "security/outputs/securityllm-rules-lora/adapter_model.safetensors",
3
+ "latest_mtime": 1777319946.9796846,
4
+ "latest_mtime_iso": "2026-04-27T19:59:06.979685+00:00",
5
+ "size_bytes": 167832240,
6
+ "epoch": null
7
+ }