erwannd commited on
Commit
22c3221
·
verified ·
1 Parent(s): da336c0

Upload 6 files

Browse files
README.md ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: liuhaotian/llava-v1.6-mistral-7b
3
+ library_name: peft
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- 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. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ 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).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+ ### Framework versions
201
+
202
+ - PEFT 0.12.0
adapter_config.json ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "liuhaotian/llava-v1.6-mistral-7b",
5
+ "bias": "none",
6
+ "fan_in_fan_out": false,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layer_replication": null,
10
+ "layers_pattern": null,
11
+ "layers_to_transform": null,
12
+ "loftq_config": {},
13
+ "lora_alpha": 256,
14
+ "lora_dropout": 0.05,
15
+ "megatron_config": null,
16
+ "megatron_core": "megatron.core",
17
+ "modules_to_save": null,
18
+ "peft_type": "LORA",
19
+ "r": 128,
20
+ "rank_pattern": {},
21
+ "revision": null,
22
+ "target_modules": [
23
+ "v_proj",
24
+ "k_proj",
25
+ "down_proj",
26
+ "o_proj",
27
+ "up_proj",
28
+ "q_proj",
29
+ "gate_proj"
30
+ ],
31
+ "task_type": "CAUSAL_LM",
32
+ "use_dora": false,
33
+ "use_rslora": false
34
+ }
adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6fab8e521194d72ab6f3050177e8495c2326c790445bc21737558164e830d923
3
+ size 708925520
config.json ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "liuhaotian/llava-v1.6-mistral-7b",
3
+ "architectures": [
4
+ "LlavaMistralForCausalLM"
5
+ ],
6
+ "attention_dropout": 0.0,
7
+ "bos_token_id": 1,
8
+ "eos_token_id": 2,
9
+ "freeze_mm_mlp_adapter": false,
10
+ "freeze_mm_vision_resampler": false,
11
+ "hidden_act": "silu",
12
+ "hidden_size": 4096,
13
+ "image_aspect_ratio": "pad",
14
+ "image_crop_resolution": 224,
15
+ "image_grid_pinpoints": [
16
+ [
17
+ 336,
18
+ 672
19
+ ],
20
+ [
21
+ 672,
22
+ 336
23
+ ],
24
+ [
25
+ 672,
26
+ 672
27
+ ],
28
+ [
29
+ 1008,
30
+ 336
31
+ ],
32
+ [
33
+ 336,
34
+ 1008
35
+ ]
36
+ ],
37
+ "image_split_resolution": 224,
38
+ "initializer_range": 0.02,
39
+ "intermediate_size": 14336,
40
+ "max_position_embeddings": 32768,
41
+ "mm_hidden_size": 1024,
42
+ "mm_patch_merge_type": "flat",
43
+ "mm_projector_lr": 2e-05,
44
+ "mm_projector_type": "mlp2x_gelu",
45
+ "mm_resampler_type": null,
46
+ "mm_use_im_patch_token": false,
47
+ "mm_use_im_start_end": false,
48
+ "mm_vision_select_feature": "patch",
49
+ "mm_vision_select_layer": -2,
50
+ "mm_vision_tower": "openai/clip-vit-large-patch14-336",
51
+ "mm_vision_tower_lr": 2e-06,
52
+ "model_type": "llava_mistral",
53
+ "num_attention_heads": 32,
54
+ "num_hidden_layers": 32,
55
+ "num_key_value_heads": 8,
56
+ "rms_norm_eps": 1e-05,
57
+ "rope_theta": 1000000.0,
58
+ "sliding_window": null,
59
+ "tie_word_embeddings": false,
60
+ "tokenizer_model_max_length": 2048,
61
+ "tokenizer_padding_side": "right",
62
+ "torch_dtype": "bfloat16",
63
+ "transformers_version": "4.37.2",
64
+ "tune_mm_mlp_adapter": false,
65
+ "tune_mm_vision_resampler": false,
66
+ "unfreeze_mm_vision_tower": true,
67
+ "use_cache": true,
68
+ "use_mm_proj": true,
69
+ "vocab_size": 32000
70
+ }
non_lora_trainables.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:149f712c05cd65f928b1f5141f92dafc2586b6771c887d4122c4bcb26d9d3773
3
+ size 41961648
trainer_state.json ADDED
@@ -0,0 +1,1332 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 1.0,
5
+ "eval_steps": 500,
6
+ "global_step": 217,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.0,
13
+ "learning_rate": 2.857142857142857e-05,
14
+ "loss": 1.536,
15
+ "step": 1
16
+ },
17
+ {
18
+ "epoch": 0.01,
19
+ "learning_rate": 5.714285714285714e-05,
20
+ "loss": 1.525,
21
+ "step": 2
22
+ },
23
+ {
24
+ "epoch": 0.01,
25
+ "learning_rate": 8.571428571428571e-05,
26
+ "loss": 1.2374,
27
+ "step": 3
28
+ },
29
+ {
30
+ "epoch": 0.02,
31
+ "learning_rate": 0.00011428571428571428,
32
+ "loss": 1.1414,
33
+ "step": 4
34
+ },
35
+ {
36
+ "epoch": 0.02,
37
+ "learning_rate": 0.00014285714285714287,
38
+ "loss": 1.0714,
39
+ "step": 5
40
+ },
41
+ {
42
+ "epoch": 0.03,
43
+ "learning_rate": 0.00017142857142857143,
44
+ "loss": 1.0713,
45
+ "step": 6
46
+ },
47
+ {
48
+ "epoch": 0.03,
49
+ "learning_rate": 0.0002,
50
+ "loss": 0.97,
51
+ "step": 7
52
+ },
53
+ {
54
+ "epoch": 0.04,
55
+ "learning_rate": 0.00019998881018102737,
56
+ "loss": 2.257,
57
+ "step": 8
58
+ },
59
+ {
60
+ "epoch": 0.04,
61
+ "learning_rate": 0.00019995524322835034,
62
+ "loss": 1.001,
63
+ "step": 9
64
+ },
65
+ {
66
+ "epoch": 0.05,
67
+ "learning_rate": 0.00019989930665413147,
68
+ "loss": 0.9339,
69
+ "step": 10
70
+ },
71
+ {
72
+ "epoch": 0.05,
73
+ "learning_rate": 0.0001998210129767735,
74
+ "loss": 0.9392,
75
+ "step": 11
76
+ },
77
+ {
78
+ "epoch": 0.06,
79
+ "learning_rate": 0.00019972037971811802,
80
+ "loss": 0.8991,
81
+ "step": 12
82
+ },
83
+ {
84
+ "epoch": 0.06,
85
+ "learning_rate": 0.00019959742939952392,
86
+ "loss": 0.9341,
87
+ "step": 13
88
+ },
89
+ {
90
+ "epoch": 0.06,
91
+ "learning_rate": 0.00019945218953682734,
92
+ "loss": 0.9036,
93
+ "step": 14
94
+ },
95
+ {
96
+ "epoch": 0.07,
97
+ "learning_rate": 0.00019928469263418374,
98
+ "loss": 0.9214,
99
+ "step": 15
100
+ },
101
+ {
102
+ "epoch": 0.07,
103
+ "learning_rate": 0.00019909497617679348,
104
+ "loss": 0.9046,
105
+ "step": 16
106
+ },
107
+ {
108
+ "epoch": 0.08,
109
+ "learning_rate": 0.00019888308262251285,
110
+ "loss": 0.91,
111
+ "step": 17
112
+ },
113
+ {
114
+ "epoch": 0.08,
115
+ "learning_rate": 0.00019864905939235214,
116
+ "loss": 0.971,
117
+ "step": 18
118
+ },
119
+ {
120
+ "epoch": 0.09,
121
+ "learning_rate": 0.00019839295885986296,
122
+ "loss": 0.9479,
123
+ "step": 19
124
+ },
125
+ {
126
+ "epoch": 0.09,
127
+ "learning_rate": 0.00019811483833941728,
128
+ "loss": 0.9127,
129
+ "step": 20
130
+ },
131
+ {
132
+ "epoch": 0.1,
133
+ "learning_rate": 0.00019781476007338058,
134
+ "loss": 0.9677,
135
+ "step": 21
136
+ },
137
+ {
138
+ "epoch": 0.1,
139
+ "learning_rate": 0.00019749279121818235,
140
+ "loss": 0.9415,
141
+ "step": 22
142
+ },
143
+ {
144
+ "epoch": 0.11,
145
+ "learning_rate": 0.00019714900382928675,
146
+ "loss": 1.0188,
147
+ "step": 23
148
+ },
149
+ {
150
+ "epoch": 0.11,
151
+ "learning_rate": 0.00019678347484506669,
152
+ "loss": 1.1678,
153
+ "step": 24
154
+ },
155
+ {
156
+ "epoch": 0.12,
157
+ "learning_rate": 0.00019639628606958533,
158
+ "loss": 1.1167,
159
+ "step": 25
160
+ },
161
+ {
162
+ "epoch": 0.12,
163
+ "learning_rate": 0.0001959875241542889,
164
+ "loss": 1.0513,
165
+ "step": 26
166
+ },
167
+ {
168
+ "epoch": 0.12,
169
+ "learning_rate": 0.0001955572805786141,
170
+ "loss": 0.9545,
171
+ "step": 27
172
+ },
173
+ {
174
+ "epoch": 0.13,
175
+ "learning_rate": 0.00019510565162951537,
176
+ "loss": 0.8698,
177
+ "step": 28
178
+ },
179
+ {
180
+ "epoch": 0.13,
181
+ "learning_rate": 0.00019463273837991643,
182
+ "loss": 0.8928,
183
+ "step": 29
184
+ },
185
+ {
186
+ "epoch": 0.14,
187
+ "learning_rate": 0.00019413864666609034,
188
+ "loss": 0.9672,
189
+ "step": 30
190
+ },
191
+ {
192
+ "epoch": 0.14,
193
+ "learning_rate": 0.00019362348706397373,
194
+ "loss": 0.847,
195
+ "step": 31
196
+ },
197
+ {
198
+ "epoch": 0.15,
199
+ "learning_rate": 0.00019308737486442045,
200
+ "loss": 0.8751,
201
+ "step": 32
202
+ },
203
+ {
204
+ "epoch": 0.15,
205
+ "learning_rate": 0.00019253043004739968,
206
+ "loss": 0.8916,
207
+ "step": 33
208
+ },
209
+ {
210
+ "epoch": 0.16,
211
+ "learning_rate": 0.0001919527772551451,
212
+ "loss": 0.8536,
213
+ "step": 34
214
+ },
215
+ {
216
+ "epoch": 0.16,
217
+ "learning_rate": 0.0001913545457642601,
218
+ "loss": 0.8425,
219
+ "step": 35
220
+ },
221
+ {
222
+ "epoch": 0.17,
223
+ "learning_rate": 0.0001907358694567865,
224
+ "loss": 0.8738,
225
+ "step": 36
226
+ },
227
+ {
228
+ "epoch": 0.17,
229
+ "learning_rate": 0.0001900968867902419,
230
+ "loss": 0.9022,
231
+ "step": 37
232
+ },
233
+ {
234
+ "epoch": 0.18,
235
+ "learning_rate": 0.0001894377407666337,
236
+ "loss": 0.9337,
237
+ "step": 38
238
+ },
239
+ {
240
+ "epoch": 0.18,
241
+ "learning_rate": 0.00018875857890045543,
242
+ "loss": 0.8617,
243
+ "step": 39
244
+ },
245
+ {
246
+ "epoch": 0.18,
247
+ "learning_rate": 0.0001880595531856738,
248
+ "loss": 0.8994,
249
+ "step": 40
250
+ },
251
+ {
252
+ "epoch": 0.19,
253
+ "learning_rate": 0.00018734082006171299,
254
+ "loss": 0.9195,
255
+ "step": 41
256
+ },
257
+ {
258
+ "epoch": 0.19,
259
+ "learning_rate": 0.00018660254037844388,
260
+ "loss": 0.9204,
261
+ "step": 42
262
+ },
263
+ {
264
+ "epoch": 0.2,
265
+ "learning_rate": 0.00018584487936018661,
266
+ "loss": 0.8778,
267
+ "step": 43
268
+ },
269
+ {
270
+ "epoch": 0.2,
271
+ "learning_rate": 0.00018506800656873398,
272
+ "loss": 0.8408,
273
+ "step": 44
274
+ },
275
+ {
276
+ "epoch": 0.21,
277
+ "learning_rate": 0.0001842720958654039,
278
+ "loss": 0.9217,
279
+ "step": 45
280
+ },
281
+ {
282
+ "epoch": 0.21,
283
+ "learning_rate": 0.00018345732537213027,
284
+ "loss": 0.871,
285
+ "step": 46
286
+ },
287
+ {
288
+ "epoch": 0.22,
289
+ "learning_rate": 0.0001826238774315995,
290
+ "loss": 0.9053,
291
+ "step": 47
292
+ },
293
+ {
294
+ "epoch": 0.22,
295
+ "learning_rate": 0.00018177193856644316,
296
+ "loss": 0.9292,
297
+ "step": 48
298
+ },
299
+ {
300
+ "epoch": 0.23,
301
+ "learning_rate": 0.00018090169943749476,
302
+ "loss": 0.8279,
303
+ "step": 49
304
+ },
305
+ {
306
+ "epoch": 0.23,
307
+ "learning_rate": 0.00018001335480112064,
308
+ "loss": 0.8972,
309
+ "step": 50
310
+ },
311
+ {
312
+ "epoch": 0.24,
313
+ "learning_rate": 0.00017910710346563416,
314
+ "loss": 0.8518,
315
+ "step": 51
316
+ },
317
+ {
318
+ "epoch": 0.24,
319
+ "learning_rate": 0.000178183148246803,
320
+ "loss": 0.8356,
321
+ "step": 52
322
+ },
323
+ {
324
+ "epoch": 0.24,
325
+ "learning_rate": 0.00017724169592245995,
326
+ "loss": 0.889,
327
+ "step": 53
328
+ },
329
+ {
330
+ "epoch": 0.25,
331
+ "learning_rate": 0.00017628295718622665,
332
+ "loss": 0.8499,
333
+ "step": 54
334
+ },
335
+ {
336
+ "epoch": 0.25,
337
+ "learning_rate": 0.00017530714660036112,
338
+ "loss": 0.8789,
339
+ "step": 55
340
+ },
341
+ {
342
+ "epoch": 0.26,
343
+ "learning_rate": 0.00017431448254773944,
344
+ "loss": 0.8757,
345
+ "step": 56
346
+ },
347
+ {
348
+ "epoch": 0.26,
349
+ "learning_rate": 0.00017330518718298264,
350
+ "loss": 0.9377,
351
+ "step": 57
352
+ },
353
+ {
354
+ "epoch": 0.27,
355
+ "learning_rate": 0.00017227948638273916,
356
+ "loss": 0.8636,
357
+ "step": 58
358
+ },
359
+ {
360
+ "epoch": 0.27,
361
+ "learning_rate": 0.0001712376096951345,
362
+ "loss": 0.8761,
363
+ "step": 59
364
+ },
365
+ {
366
+ "epoch": 0.28,
367
+ "learning_rate": 0.00017017979028839916,
368
+ "loss": 0.8737,
369
+ "step": 60
370
+ },
371
+ {
372
+ "epoch": 0.28,
373
+ "learning_rate": 0.00016910626489868649,
374
+ "loss": 0.8604,
375
+ "step": 61
376
+ },
377
+ {
378
+ "epoch": 0.29,
379
+ "learning_rate": 0.00016801727377709194,
380
+ "loss": 0.8085,
381
+ "step": 62
382
+ },
383
+ {
384
+ "epoch": 0.29,
385
+ "learning_rate": 0.00016691306063588583,
386
+ "loss": 0.8978,
387
+ "step": 63
388
+ },
389
+ {
390
+ "epoch": 0.29,
391
+ "learning_rate": 0.00016579387259397127,
392
+ "loss": 0.9272,
393
+ "step": 64
394
+ },
395
+ {
396
+ "epoch": 0.3,
397
+ "learning_rate": 0.00016465996012157995,
398
+ "loss": 0.976,
399
+ "step": 65
400
+ },
401
+ {
402
+ "epoch": 0.3,
403
+ "learning_rate": 0.0001635115769842179,
404
+ "loss": 0.8097,
405
+ "step": 66
406
+ },
407
+ {
408
+ "epoch": 0.31,
409
+ "learning_rate": 0.00016234898018587337,
410
+ "loss": 0.8192,
411
+ "step": 67
412
+ },
413
+ {
414
+ "epoch": 0.31,
415
+ "learning_rate": 0.00016117242991150064,
416
+ "loss": 0.8579,
417
+ "step": 68
418
+ },
419
+ {
420
+ "epoch": 0.32,
421
+ "learning_rate": 0.00015998218946879138,
422
+ "loss": 0.7813,
423
+ "step": 69
424
+ },
425
+ {
426
+ "epoch": 0.32,
427
+ "learning_rate": 0.00015877852522924732,
428
+ "loss": 0.8448,
429
+ "step": 70
430
+ },
431
+ {
432
+ "epoch": 0.33,
433
+ "learning_rate": 0.00015756170656856737,
434
+ "loss": 0.8173,
435
+ "step": 71
436
+ },
437
+ {
438
+ "epoch": 0.33,
439
+ "learning_rate": 0.0001563320058063622,
440
+ "loss": 0.8345,
441
+ "step": 72
442
+ },
443
+ {
444
+ "epoch": 0.34,
445
+ "learning_rate": 0.00015508969814521025,
446
+ "loss": 0.8009,
447
+ "step": 73
448
+ },
449
+ {
450
+ "epoch": 0.34,
451
+ "learning_rate": 0.00015383506160906825,
452
+ "loss": 0.8763,
453
+ "step": 74
454
+ },
455
+ {
456
+ "epoch": 0.35,
457
+ "learning_rate": 0.00015256837698105047,
458
+ "loss": 0.8516,
459
+ "step": 75
460
+ },
461
+ {
462
+ "epoch": 0.35,
463
+ "learning_rate": 0.00015128992774059063,
464
+ "loss": 0.8166,
465
+ "step": 76
466
+ },
467
+ {
468
+ "epoch": 0.35,
469
+ "learning_rate": 0.00015000000000000001,
470
+ "loss": 0.8473,
471
+ "step": 77
472
+ },
473
+ {
474
+ "epoch": 0.36,
475
+ "learning_rate": 0.00014869888244043673,
476
+ "loss": 0.8136,
477
+ "step": 78
478
+ },
479
+ {
480
+ "epoch": 0.36,
481
+ "learning_rate": 0.00014738686624729986,
482
+ "loss": 0.8285,
483
+ "step": 79
484
+ },
485
+ {
486
+ "epoch": 0.37,
487
+ "learning_rate": 0.00014606424504506324,
488
+ "loss": 0.8474,
489
+ "step": 80
490
+ },
491
+ {
492
+ "epoch": 0.37,
493
+ "learning_rate": 0.00014473131483156327,
494
+ "loss": 0.8421,
495
+ "step": 81
496
+ },
497
+ {
498
+ "epoch": 0.38,
499
+ "learning_rate": 0.00014338837391175582,
500
+ "loss": 0.7994,
501
+ "step": 82
502
+ },
503
+ {
504
+ "epoch": 0.38,
505
+ "learning_rate": 0.00014203572283095657,
506
+ "loss": 0.865,
507
+ "step": 83
508
+ },
509
+ {
510
+ "epoch": 0.39,
511
+ "learning_rate": 0.00014067366430758004,
512
+ "loss": 0.8603,
513
+ "step": 84
514
+ },
515
+ {
516
+ "epoch": 0.39,
517
+ "learning_rate": 0.00013930250316539238,
518
+ "loss": 0.8355,
519
+ "step": 85
520
+ },
521
+ {
522
+ "epoch": 0.4,
523
+ "learning_rate": 0.00013792254626529286,
524
+ "loss": 0.8517,
525
+ "step": 86
526
+ },
527
+ {
528
+ "epoch": 0.4,
529
+ "learning_rate": 0.00013653410243663952,
530
+ "loss": 0.8496,
531
+ "step": 87
532
+ },
533
+ {
534
+ "epoch": 0.41,
535
+ "learning_rate": 0.0001351374824081343,
536
+ "loss": 0.7882,
537
+ "step": 88
538
+ },
539
+ {
540
+ "epoch": 0.41,
541
+ "learning_rate": 0.00013373299873828303,
542
+ "loss": 0.7927,
543
+ "step": 89
544
+ },
545
+ {
546
+ "epoch": 0.41,
547
+ "learning_rate": 0.00013232096574544602,
548
+ "loss": 0.8434,
549
+ "step": 90
550
+ },
551
+ {
552
+ "epoch": 0.42,
553
+ "learning_rate": 0.00013090169943749476,
554
+ "loss": 0.9031,
555
+ "step": 91
556
+ },
557
+ {
558
+ "epoch": 0.42,
559
+ "learning_rate": 0.00012947551744109043,
560
+ "loss": 0.8491,
561
+ "step": 92
562
+ },
563
+ {
564
+ "epoch": 0.43,
565
+ "learning_rate": 0.00012804273893060028,
566
+ "loss": 0.8433,
567
+ "step": 93
568
+ },
569
+ {
570
+ "epoch": 0.43,
571
+ "learning_rate": 0.00012660368455666752,
572
+ "loss": 0.8862,
573
+ "step": 94
574
+ },
575
+ {
576
+ "epoch": 0.44,
577
+ "learning_rate": 0.00012515867637445086,
578
+ "loss": 0.8081,
579
+ "step": 95
580
+ },
581
+ {
582
+ "epoch": 0.44,
583
+ "learning_rate": 0.00012370803777154977,
584
+ "loss": 0.8594,
585
+ "step": 96
586
+ },
587
+ {
588
+ "epoch": 0.45,
589
+ "learning_rate": 0.00012225209339563145,
590
+ "loss": 0.8561,
591
+ "step": 97
592
+ },
593
+ {
594
+ "epoch": 0.45,
595
+ "learning_rate": 0.00012079116908177593,
596
+ "loss": 0.7771,
597
+ "step": 98
598
+ },
599
+ {
600
+ "epoch": 0.46,
601
+ "learning_rate": 0.00011932559177955533,
602
+ "loss": 0.8719,
603
+ "step": 99
604
+ },
605
+ {
606
+ "epoch": 0.46,
607
+ "learning_rate": 0.00011785568947986367,
608
+ "loss": 0.8254,
609
+ "step": 100
610
+ },
611
+ {
612
+ "epoch": 0.47,
613
+ "learning_rate": 0.00011638179114151377,
614
+ "loss": 0.8607,
615
+ "step": 101
616
+ },
617
+ {
618
+ "epoch": 0.47,
619
+ "learning_rate": 0.00011490422661761744,
620
+ "loss": 0.8523,
621
+ "step": 102
622
+ },
623
+ {
624
+ "epoch": 0.47,
625
+ "learning_rate": 0.00011342332658176555,
626
+ "loss": 0.7828,
627
+ "step": 103
628
+ },
629
+ {
630
+ "epoch": 0.48,
631
+ "learning_rate": 0.00011193942245402443,
632
+ "loss": 0.8125,
633
+ "step": 104
634
+ },
635
+ {
636
+ "epoch": 0.48,
637
+ "learning_rate": 0.00011045284632676536,
638
+ "loss": 0.8394,
639
+ "step": 105
640
+ },
641
+ {
642
+ "epoch": 0.49,
643
+ "learning_rate": 0.00010896393089034336,
644
+ "loss": 0.8442,
645
+ "step": 106
646
+ },
647
+ {
648
+ "epoch": 0.49,
649
+ "learning_rate": 0.00010747300935864243,
650
+ "loss": 0.7688,
651
+ "step": 107
652
+ },
653
+ {
654
+ "epoch": 0.5,
655
+ "learning_rate": 0.00010598041539450343,
656
+ "loss": 0.7935,
657
+ "step": 108
658
+ },
659
+ {
660
+ "epoch": 0.5,
661
+ "learning_rate": 0.00010448648303505151,
662
+ "loss": 0.8522,
663
+ "step": 109
664
+ },
665
+ {
666
+ "epoch": 0.51,
667
+ "learning_rate": 0.00010299154661693987,
668
+ "loss": 0.799,
669
+ "step": 110
670
+ },
671
+ {
672
+ "epoch": 0.51,
673
+ "learning_rate": 0.00010149594070152638,
674
+ "loss": 0.9002,
675
+ "step": 111
676
+ },
677
+ {
678
+ "epoch": 0.52,
679
+ "learning_rate": 0.0001,
680
+ "loss": 0.824,
681
+ "step": 112
682
+ },
683
+ {
684
+ "epoch": 0.52,
685
+ "learning_rate": 9.850405929847366e-05,
686
+ "loss": 0.8112,
687
+ "step": 113
688
+ },
689
+ {
690
+ "epoch": 0.53,
691
+ "learning_rate": 9.700845338306018e-05,
692
+ "loss": 0.7934,
693
+ "step": 114
694
+ },
695
+ {
696
+ "epoch": 0.53,
697
+ "learning_rate": 9.551351696494854e-05,
698
+ "loss": 0.757,
699
+ "step": 115
700
+ },
701
+ {
702
+ "epoch": 0.53,
703
+ "learning_rate": 9.401958460549658e-05,
704
+ "loss": 0.8035,
705
+ "step": 116
706
+ },
707
+ {
708
+ "epoch": 0.54,
709
+ "learning_rate": 9.252699064135758e-05,
710
+ "loss": 0.8351,
711
+ "step": 117
712
+ },
713
+ {
714
+ "epoch": 0.54,
715
+ "learning_rate": 9.103606910965666e-05,
716
+ "loss": 0.7876,
717
+ "step": 118
718
+ },
719
+ {
720
+ "epoch": 0.55,
721
+ "learning_rate": 8.954715367323468e-05,
722
+ "loss": 0.8538,
723
+ "step": 119
724
+ },
725
+ {
726
+ "epoch": 0.55,
727
+ "learning_rate": 8.806057754597558e-05,
728
+ "loss": 0.7701,
729
+ "step": 120
730
+ },
731
+ {
732
+ "epoch": 0.56,
733
+ "learning_rate": 8.657667341823448e-05,
734
+ "loss": 0.8407,
735
+ "step": 121
736
+ },
737
+ {
738
+ "epoch": 0.56,
739
+ "learning_rate": 8.509577338238255e-05,
740
+ "loss": 0.7777,
741
+ "step": 122
742
+ },
743
+ {
744
+ "epoch": 0.57,
745
+ "learning_rate": 8.361820885848624e-05,
746
+ "loss": 0.7863,
747
+ "step": 123
748
+ },
749
+ {
750
+ "epoch": 0.57,
751
+ "learning_rate": 8.214431052013634e-05,
752
+ "loss": 0.7934,
753
+ "step": 124
754
+ },
755
+ {
756
+ "epoch": 0.58,
757
+ "learning_rate": 8.067440822044469e-05,
758
+ "loss": 0.8408,
759
+ "step": 125
760
+ },
761
+ {
762
+ "epoch": 0.58,
763
+ "learning_rate": 7.920883091822408e-05,
764
+ "loss": 0.8094,
765
+ "step": 126
766
+ },
767
+ {
768
+ "epoch": 0.59,
769
+ "learning_rate": 7.774790660436858e-05,
770
+ "loss": 0.8085,
771
+ "step": 127
772
+ },
773
+ {
774
+ "epoch": 0.59,
775
+ "learning_rate": 7.629196222845026e-05,
776
+ "loss": 0.8305,
777
+ "step": 128
778
+ },
779
+ {
780
+ "epoch": 0.59,
781
+ "learning_rate": 7.484132362554915e-05,
782
+ "loss": 0.8376,
783
+ "step": 129
784
+ },
785
+ {
786
+ "epoch": 0.6,
787
+ "learning_rate": 7.339631544333249e-05,
788
+ "loss": 0.7636,
789
+ "step": 130
790
+ },
791
+ {
792
+ "epoch": 0.6,
793
+ "learning_rate": 7.195726106939974e-05,
794
+ "loss": 0.8408,
795
+ "step": 131
796
+ },
797
+ {
798
+ "epoch": 0.61,
799
+ "learning_rate": 7.052448255890957e-05,
800
+ "loss": 0.7903,
801
+ "step": 132
802
+ },
803
+ {
804
+ "epoch": 0.61,
805
+ "learning_rate": 6.909830056250527e-05,
806
+ "loss": 0.8249,
807
+ "step": 133
808
+ },
809
+ {
810
+ "epoch": 0.62,
811
+ "learning_rate": 6.767903425455401e-05,
812
+ "loss": 0.7758,
813
+ "step": 134
814
+ },
815
+ {
816
+ "epoch": 0.62,
817
+ "learning_rate": 6.626700126171702e-05,
818
+ "loss": 0.841,
819
+ "step": 135
820
+ },
821
+ {
822
+ "epoch": 0.63,
823
+ "learning_rate": 6.486251759186572e-05,
824
+ "loss": 0.7916,
825
+ "step": 136
826
+ },
827
+ {
828
+ "epoch": 0.63,
829
+ "learning_rate": 6.34658975633605e-05,
830
+ "loss": 0.7983,
831
+ "step": 137
832
+ },
833
+ {
834
+ "epoch": 0.64,
835
+ "learning_rate": 6.207745373470716e-05,
836
+ "loss": 0.8417,
837
+ "step": 138
838
+ },
839
+ {
840
+ "epoch": 0.64,
841
+ "learning_rate": 6.069749683460765e-05,
842
+ "loss": 0.7911,
843
+ "step": 139
844
+ },
845
+ {
846
+ "epoch": 0.65,
847
+ "learning_rate": 5.9326335692419995e-05,
848
+ "loss": 0.8527,
849
+ "step": 140
850
+ },
851
+ {
852
+ "epoch": 0.65,
853
+ "learning_rate": 5.796427716904347e-05,
854
+ "loss": 0.741,
855
+ "step": 141
856
+ },
857
+ {
858
+ "epoch": 0.65,
859
+ "learning_rate": 5.6611626088244194e-05,
860
+ "loss": 0.8258,
861
+ "step": 142
862
+ },
863
+ {
864
+ "epoch": 0.66,
865
+ "learning_rate": 5.526868516843673e-05,
866
+ "loss": 0.7294,
867
+ "step": 143
868
+ },
869
+ {
870
+ "epoch": 0.66,
871
+ "learning_rate": 5.393575495493679e-05,
872
+ "loss": 0.8324,
873
+ "step": 144
874
+ },
875
+ {
876
+ "epoch": 0.67,
877
+ "learning_rate": 5.261313375270014e-05,
878
+ "loss": 0.7886,
879
+ "step": 145
880
+ },
881
+ {
882
+ "epoch": 0.67,
883
+ "learning_rate": 5.130111755956327e-05,
884
+ "loss": 0.7995,
885
+ "step": 146
886
+ },
887
+ {
888
+ "epoch": 0.68,
889
+ "learning_rate": 5.000000000000002e-05,
890
+ "loss": 0.7352,
891
+ "step": 147
892
+ },
893
+ {
894
+ "epoch": 0.68,
895
+ "learning_rate": 4.87100722594094e-05,
896
+ "loss": 0.7491,
897
+ "step": 148
898
+ },
899
+ {
900
+ "epoch": 0.69,
901
+ "learning_rate": 4.743162301894952e-05,
902
+ "loss": 0.8072,
903
+ "step": 149
904
+ },
905
+ {
906
+ "epoch": 0.69,
907
+ "learning_rate": 4.616493839093179e-05,
908
+ "loss": 0.7869,
909
+ "step": 150
910
+ },
911
+ {
912
+ "epoch": 0.7,
913
+ "learning_rate": 4.491030185478976e-05,
914
+ "loss": 0.8552,
915
+ "step": 151
916
+ },
917
+ {
918
+ "epoch": 0.7,
919
+ "learning_rate": 4.3667994193637796e-05,
920
+ "loss": 0.796,
921
+ "step": 152
922
+ },
923
+ {
924
+ "epoch": 0.71,
925
+ "learning_rate": 4.2438293431432665e-05,
926
+ "loss": 0.8093,
927
+ "step": 153
928
+ },
929
+ {
930
+ "epoch": 0.71,
931
+ "learning_rate": 4.12214747707527e-05,
932
+ "loss": 0.7211,
933
+ "step": 154
934
+ },
935
+ {
936
+ "epoch": 0.71,
937
+ "learning_rate": 4.001781053120863e-05,
938
+ "loss": 0.7327,
939
+ "step": 155
940
+ },
941
+ {
942
+ "epoch": 0.72,
943
+ "learning_rate": 3.8827570088499356e-05,
944
+ "loss": 0.7804,
945
+ "step": 156
946
+ },
947
+ {
948
+ "epoch": 0.72,
949
+ "learning_rate": 3.7651019814126654e-05,
950
+ "loss": 0.7857,
951
+ "step": 157
952
+ },
953
+ {
954
+ "epoch": 0.73,
955
+ "learning_rate": 3.6488423015782125e-05,
956
+ "loss": 0.8032,
957
+ "step": 158
958
+ },
959
+ {
960
+ "epoch": 0.73,
961
+ "learning_rate": 3.534003987842005e-05,
962
+ "loss": 0.8019,
963
+ "step": 159
964
+ },
965
+ {
966
+ "epoch": 0.74,
967
+ "learning_rate": 3.4206127406028745e-05,
968
+ "loss": 0.7965,
969
+ "step": 160
970
+ },
971
+ {
972
+ "epoch": 0.74,
973
+ "learning_rate": 3.308693936411421e-05,
974
+ "loss": 0.822,
975
+ "step": 161
976
+ },
977
+ {
978
+ "epoch": 0.75,
979
+ "learning_rate": 3.198272622290804e-05,
980
+ "loss": 0.7934,
981
+ "step": 162
982
+ },
983
+ {
984
+ "epoch": 0.75,
985
+ "learning_rate": 3.089373510131354e-05,
986
+ "loss": 0.7945,
987
+ "step": 163
988
+ },
989
+ {
990
+ "epoch": 0.76,
991
+ "learning_rate": 2.9820209711600854e-05,
992
+ "loss": 0.7876,
993
+ "step": 164
994
+ },
995
+ {
996
+ "epoch": 0.76,
997
+ "learning_rate": 2.876239030486554e-05,
998
+ "loss": 0.8116,
999
+ "step": 165
1000
+ },
1001
+ {
1002
+ "epoch": 0.76,
1003
+ "learning_rate": 2.7720513617260856e-05,
1004
+ "loss": 0.718,
1005
+ "step": 166
1006
+ },
1007
+ {
1008
+ "epoch": 0.77,
1009
+ "learning_rate": 2.669481281701739e-05,
1010
+ "loss": 0.7309,
1011
+ "step": 167
1012
+ },
1013
+ {
1014
+ "epoch": 0.77,
1015
+ "learning_rate": 2.5685517452260567e-05,
1016
+ "loss": 0.7675,
1017
+ "step": 168
1018
+ },
1019
+ {
1020
+ "epoch": 0.78,
1021
+ "learning_rate": 2.4692853399638917e-05,
1022
+ "loss": 0.7928,
1023
+ "step": 169
1024
+ },
1025
+ {
1026
+ "epoch": 0.78,
1027
+ "learning_rate": 2.371704281377335e-05,
1028
+ "loss": 0.7445,
1029
+ "step": 170
1030
+ },
1031
+ {
1032
+ "epoch": 0.79,
1033
+ "learning_rate": 2.275830407754006e-05,
1034
+ "loss": 0.781,
1035
+ "step": 171
1036
+ },
1037
+ {
1038
+ "epoch": 0.79,
1039
+ "learning_rate": 2.181685175319702e-05,
1040
+ "loss": 0.7934,
1041
+ "step": 172
1042
+ },
1043
+ {
1044
+ "epoch": 0.8,
1045
+ "learning_rate": 2.0892896534365904e-05,
1046
+ "loss": 0.7815,
1047
+ "step": 173
1048
+ },
1049
+ {
1050
+ "epoch": 0.8,
1051
+ "learning_rate": 1.9986645198879385e-05,
1052
+ "loss": 0.7563,
1053
+ "step": 174
1054
+ },
1055
+ {
1056
+ "epoch": 0.81,
1057
+ "learning_rate": 1.9098300562505266e-05,
1058
+ "loss": 0.8097,
1059
+ "step": 175
1060
+ },
1061
+ {
1062
+ "epoch": 0.81,
1063
+ "learning_rate": 1.8228061433556866e-05,
1064
+ "loss": 0.8249,
1065
+ "step": 176
1066
+ },
1067
+ {
1068
+ "epoch": 0.82,
1069
+ "learning_rate": 1.7376122568400532e-05,
1070
+ "loss": 0.8009,
1071
+ "step": 177
1072
+ },
1073
+ {
1074
+ "epoch": 0.82,
1075
+ "learning_rate": 1.6542674627869737e-05,
1076
+ "loss": 0.7568,
1077
+ "step": 178
1078
+ },
1079
+ {
1080
+ "epoch": 0.82,
1081
+ "learning_rate": 1.5727904134596083e-05,
1082
+ "loss": 0.7726,
1083
+ "step": 179
1084
+ },
1085
+ {
1086
+ "epoch": 0.83,
1087
+ "learning_rate": 1.4931993431266056e-05,
1088
+ "loss": 0.7236,
1089
+ "step": 180
1090
+ },
1091
+ {
1092
+ "epoch": 0.83,
1093
+ "learning_rate": 1.415512063981339e-05,
1094
+ "loss": 0.7927,
1095
+ "step": 181
1096
+ },
1097
+ {
1098
+ "epoch": 0.84,
1099
+ "learning_rate": 1.339745962155613e-05,
1100
+ "loss": 0.7514,
1101
+ "step": 182
1102
+ },
1103
+ {
1104
+ "epoch": 0.84,
1105
+ "learning_rate": 1.2659179938287035e-05,
1106
+ "loss": 0.7458,
1107
+ "step": 183
1108
+ },
1109
+ {
1110
+ "epoch": 0.85,
1111
+ "learning_rate": 1.19404468143262e-05,
1112
+ "loss": 0.7872,
1113
+ "step": 184
1114
+ },
1115
+ {
1116
+ "epoch": 0.85,
1117
+ "learning_rate": 1.124142109954459e-05,
1118
+ "loss": 0.7613,
1119
+ "step": 185
1120
+ },
1121
+ {
1122
+ "epoch": 0.86,
1123
+ "learning_rate": 1.0562259233366334e-05,
1124
+ "loss": 0.7444,
1125
+ "step": 186
1126
+ },
1127
+ {
1128
+ "epoch": 0.86,
1129
+ "learning_rate": 9.903113209758096e-06,
1130
+ "loss": 0.7934,
1131
+ "step": 187
1132
+ },
1133
+ {
1134
+ "epoch": 0.87,
1135
+ "learning_rate": 9.264130543213512e-06,
1136
+ "loss": 0.7432,
1137
+ "step": 188
1138
+ },
1139
+ {
1140
+ "epoch": 0.87,
1141
+ "learning_rate": 8.645454235739903e-06,
1142
+ "loss": 0.7907,
1143
+ "step": 189
1144
+ },
1145
+ {
1146
+ "epoch": 0.88,
1147
+ "learning_rate": 8.047222744854943e-06,
1148
+ "loss": 0.7454,
1149
+ "step": 190
1150
+ },
1151
+ {
1152
+ "epoch": 0.88,
1153
+ "learning_rate": 7.46956995260033e-06,
1154
+ "loss": 0.7942,
1155
+ "step": 191
1156
+ },
1157
+ {
1158
+ "epoch": 0.88,
1159
+ "learning_rate": 6.9126251355795864e-06,
1160
+ "loss": 0.8348,
1161
+ "step": 192
1162
+ },
1163
+ {
1164
+ "epoch": 0.89,
1165
+ "learning_rate": 6.37651293602628e-06,
1166
+ "loss": 0.7413,
1167
+ "step": 193
1168
+ },
1169
+ {
1170
+ "epoch": 0.89,
1171
+ "learning_rate": 5.861353333909692e-06,
1172
+ "loss": 0.8235,
1173
+ "step": 194
1174
+ },
1175
+ {
1176
+ "epoch": 0.9,
1177
+ "learning_rate": 5.367261620083575e-06,
1178
+ "loss": 0.7685,
1179
+ "step": 195
1180
+ },
1181
+ {
1182
+ "epoch": 0.9,
1183
+ "learning_rate": 4.8943483704846475e-06,
1184
+ "loss": 0.7457,
1185
+ "step": 196
1186
+ },
1187
+ {
1188
+ "epoch": 0.91,
1189
+ "learning_rate": 4.442719421385922e-06,
1190
+ "loss": 0.8274,
1191
+ "step": 197
1192
+ },
1193
+ {
1194
+ "epoch": 0.91,
1195
+ "learning_rate": 4.012475845711106e-06,
1196
+ "loss": 0.8045,
1197
+ "step": 198
1198
+ },
1199
+ {
1200
+ "epoch": 0.92,
1201
+ "learning_rate": 3.6037139304146762e-06,
1202
+ "loss": 0.7463,
1203
+ "step": 199
1204
+ },
1205
+ {
1206
+ "epoch": 0.92,
1207
+ "learning_rate": 3.2165251549333587e-06,
1208
+ "loss": 0.7905,
1209
+ "step": 200
1210
+ },
1211
+ {
1212
+ "epoch": 0.93,
1213
+ "learning_rate": 2.8509961707132494e-06,
1214
+ "loss": 0.8179,
1215
+ "step": 201
1216
+ },
1217
+ {
1218
+ "epoch": 0.93,
1219
+ "learning_rate": 2.5072087818176382e-06,
1220
+ "loss": 0.7379,
1221
+ "step": 202
1222
+ },
1223
+ {
1224
+ "epoch": 0.94,
1225
+ "learning_rate": 2.1852399266194314e-06,
1226
+ "loss": 0.7861,
1227
+ "step": 203
1228
+ },
1229
+ {
1230
+ "epoch": 0.94,
1231
+ "learning_rate": 1.885161660582746e-06,
1232
+ "loss": 0.7344,
1233
+ "step": 204
1234
+ },
1235
+ {
1236
+ "epoch": 0.94,
1237
+ "learning_rate": 1.6070411401370334e-06,
1238
+ "loss": 0.7692,
1239
+ "step": 205
1240
+ },
1241
+ {
1242
+ "epoch": 0.95,
1243
+ "learning_rate": 1.350940607647866e-06,
1244
+ "loss": 0.7955,
1245
+ "step": 206
1246
+ },
1247
+ {
1248
+ "epoch": 0.95,
1249
+ "learning_rate": 1.1169173774871478e-06,
1250
+ "loss": 0.8031,
1251
+ "step": 207
1252
+ },
1253
+ {
1254
+ "epoch": 0.96,
1255
+ "learning_rate": 9.0502382320653e-07,
1256
+ "loss": 0.7685,
1257
+ "step": 208
1258
+ },
1259
+ {
1260
+ "epoch": 0.96,
1261
+ "learning_rate": 7.153073658162646e-07,
1262
+ "loss": 0.8191,
1263
+ "step": 209
1264
+ },
1265
+ {
1266
+ "epoch": 0.97,
1267
+ "learning_rate": 5.478104631726711e-07,
1268
+ "loss": 0.7824,
1269
+ "step": 210
1270
+ },
1271
+ {
1272
+ "epoch": 0.97,
1273
+ "learning_rate": 4.025706004760932e-07,
1274
+ "loss": 0.8028,
1275
+ "step": 211
1276
+ },
1277
+ {
1278
+ "epoch": 0.98,
1279
+ "learning_rate": 2.7962028188198706e-07,
1280
+ "loss": 0.7194,
1281
+ "step": 212
1282
+ },
1283
+ {
1284
+ "epoch": 0.98,
1285
+ "learning_rate": 1.7898702322648453e-07,
1286
+ "loss": 0.8275,
1287
+ "step": 213
1288
+ },
1289
+ {
1290
+ "epoch": 0.99,
1291
+ "learning_rate": 1.0069334586854107e-07,
1292
+ "loss": 0.7808,
1293
+ "step": 214
1294
+ },
1295
+ {
1296
+ "epoch": 0.99,
1297
+ "learning_rate": 4.475677164966774e-08,
1298
+ "loss": 0.7297,
1299
+ "step": 215
1300
+ },
1301
+ {
1302
+ "epoch": 1.0,
1303
+ "learning_rate": 1.1189818972656696e-08,
1304
+ "loss": 0.7465,
1305
+ "step": 216
1306
+ },
1307
+ {
1308
+ "epoch": 1.0,
1309
+ "learning_rate": 0.0,
1310
+ "loss": 0.7947,
1311
+ "step": 217
1312
+ },
1313
+ {
1314
+ "epoch": 1.0,
1315
+ "step": 217,
1316
+ "total_flos": 3685402214400.0,
1317
+ "train_loss": 0.8509236571426215,
1318
+ "train_runtime": 481.0178,
1319
+ "train_samples_per_second": 7.201,
1320
+ "train_steps_per_second": 0.451
1321
+ }
1322
+ ],
1323
+ "logging_steps": 1.0,
1324
+ "max_steps": 217,
1325
+ "num_input_tokens_seen": 0,
1326
+ "num_train_epochs": 1,
1327
+ "save_steps": 50000,
1328
+ "total_flos": 3685402214400.0,
1329
+ "train_batch_size": 16,
1330
+ "trial_name": null,
1331
+ "trial_params": null
1332
+ }