Hugging Face's logo

mohmzm
/
llama_3_1_8b_instruct_document_relevance_test

PEFT
Safetensors
Model card Files Community
mohmzm commited on
Commit
c92c333
·
verified ·
1 Parent(s): 0393f31

Upload folder using huggingface_hub

Browse files
Files changed (24) hide show
  1. .gitattributes +1 -0
  2. README.md +202 -0
  3. adapter_config.json +32 -0
  4. adapter_model.safetensors +3 -0
  5. global_step253/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt +3 -0
  6. global_step253/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt +3 -0
  7. global_step253/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt +3 -0
  8. global_step253/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt +3 -0
  9. global_step253/zero_pp_rank_0_mp_rank_00_model_states.pt +3 -0
  10. global_step253/zero_pp_rank_1_mp_rank_00_model_states.pt +3 -0
  11. global_step253/zero_pp_rank_2_mp_rank_00_model_states.pt +3 -0
  12. global_step253/zero_pp_rank_3_mp_rank_00_model_states.pt +3 -0
  13. latest +1 -0
  14. rng_state_0.pth +3 -0
  15. rng_state_1.pth +3 -0
  16. rng_state_2.pth +3 -0
  17. rng_state_3.pth +3 -0
  18. scheduler.pt +3 -0
  19. special_tokens_map.json +17 -0
  20. tokenizer.json +3 -0
  21. tokenizer_config.json +2064 -0
  22. trainer_state.json +2441 -0
  23. training_args.bin +3 -0
  24. zero_to_fp32.py +760 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: meta-llama/Llama-3.1-8B-Instruct
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 0.14.0
adapter_config.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
5
+ "bias": "none",
6
+ "eva_config": null,
7
+ "exclude_modules": null,
8
+ "fan_in_fan_out": false,
9
+ "inference_mode": true,
10
+ "init_lora_weights": true,
11
+ "layer_replication": null,
12
+ "layers_pattern": null,
13
+ "layers_to_transform": null,
14
+ "loftq_config": {},
15
+ "lora_alpha": 16,
16
+ "lora_bias": false,
17
+ "lora_dropout": 0.05,
18
+ "megatron_config": null,
19
+ "megatron_core": "megatron.core",
20
+ "modules_to_save": null,
21
+ "peft_type": "LORA",
22
+ "r": 64,
23
+ "rank_pattern": {},
24
+ "revision": null,
25
+ "target_modules": [
26
+ "v_proj",
27
+ "q_proj"
28
+ ],
29
+ "task_type": "CAUSAL_LM",
30
+ "use_dora": false,
31
+ "use_rslora": false
32
+ }
adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:166001445f586cb8dc5e433aabc1c36df6a14aaf0e3eead015cd146a9d390892
3
+ size 54543312
global_step253/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7ad83aa39cc5a1cf252dcdb8b1b033b5ef62e2f97981de3e9b7959b6de2e28f7
3
+ size 81792496
global_step253/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:22b913587f5d2775116f09a19ed1a26b05fd5f16d72477479c07d77f42cbda76
3
+ size 81792496
global_step253/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9c51341aef3c05ffc3fe9e7bc2d7043fffbab87e3aeebded5a4362c4f9f29f48
3
+ size 81792496
global_step253/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6713da10395363afaffb58d25d4496919d8eb2bd7330c69a293921eef52f548c
3
+ size 81792496
global_step253/zero_pp_rank_0_mp_rank_00_model_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7a07683501062da23f745f746c72d8a56a3e4b01d70632556b89c81193c64573
3
+ size 152238
global_step253/zero_pp_rank_1_mp_rank_00_model_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:74d60756600fcbe4ad9996496ae60b5d734af99c74b392531f249a1f5d456b36
3
+ size 152238
global_step253/zero_pp_rank_2_mp_rank_00_model_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:093c5f78a0b5b1229784b047cab99e8f611b3d5ea8eb04e73f6e53291159b7c3
3
+ size 152238
global_step253/zero_pp_rank_3_mp_rank_00_model_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e36ed836cd182dff9370d40960cc763091e902e3657d50ece59abe1c12ef89c3
3
+ size 152238
latest ADDED
@@ -0,0 +1 @@
 
 
1
+ global_step253
rng_state_0.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6372fd037b7262e280fb7c3adae6996e2755d43e66cbf91cc6bc3d40a96685f4
3
+ size 15024
rng_state_1.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ee9cc6fafe319c312427638670297c4ab429b3d5dea80d614b5734848ed4178c
3
+ size 15024
rng_state_2.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f286b889455ee27f2311d3704ea24ab8332dfb4ad28a541a42eb6367b5fce96d
3
+ size 15024
rng_state_3.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:51e42a99807a66dd69ae1ddb45eaa42a652b215331d0ab1e3f8498648e3a1d48
3
+ size 15024
scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:501b9f0c6996779e98672b4b52bc54f01764665832105e323deaa9f0fab39f5b
3
+ size 1064
special_tokens_map.json ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|begin_of_text|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|eot_id|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": "<|eot_id|>"
17
+ }
tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
3
+ size 17209920
tokenizer_config.json ADDED
@@ -0,0 +1,2064 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "128000": {
4
+ "content": "<|begin_of_text|>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "128001": {
12
+ "content": "<|end_of_text|>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "128002": {
20
+ "content": "<|reserved_special_token_0|>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "128003": {
28
+ "content": "<|reserved_special_token_1|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "128004": {
36
+ "content": "<|finetune_right_pad_id|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "128005": {
44
+ "content": "<|reserved_special_token_2|>",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "128006": {
52
+ "content": "<|start_header_id|>",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "128007": {
60
+ "content": "<|end_header_id|>",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "128008": {
68
+ "content": "<|eom_id|>",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "128009": {
76
+ "content": "<|eot_id|>",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "128010": {
84
+ "content": "<|python_tag|>",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "128011": {
92
+ "content": "<|reserved_special_token_3|>",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
+ "128012": {
100
+ "content": "<|reserved_special_token_4|>",
101
+ "lstrip": false,
102
+ "normalized": false,
103
+ "rstrip": false,
104
+ "single_word": false,
105
+ "special": true
106
+ },
107
+ "128013": {
108
+ "content": "<|reserved_special_token_5|>",
109
+ "lstrip": false,
110
+ "normalized": false,
111
+ "rstrip": false,
112
+ "single_word": false,
113
+ "special": true
114
+ },
115
+ "128014": {
116
+ "content": "<|reserved_special_token_6|>",
117
+ "lstrip": false,
118
+ "normalized": false,
119
+ "rstrip": false,
120
+ "single_word": false,
121
+ "special": true
122
+ },
123
+ "128015": {
124
+ "content": "<|reserved_special_token_7|>",
125
+ "lstrip": false,
126
+ "normalized": false,
127
+ "rstrip": false,
128
+ "single_word": false,
129
+ "special": true
130
+ },
131
+ "128016": {
132
+ "content": "<|reserved_special_token_8|>",
133
+ "lstrip": false,
134
+ "normalized": false,
135
+ "rstrip": false,
136
+ "single_word": false,
137
+ "special": true
138
+ },
139
+ "128017": {
140
+ "content": "<|reserved_special_token_9|>",
141
+ "lstrip": false,
142
+ "normalized": false,
143
+ "rstrip": false,
144
+ "single_word": false,
145
+ "special": true
146
+ },
147
+ "128018": {
148
+ "content": "<|reserved_special_token_10|>",
149
+ "lstrip": false,
150
+ "normalized": false,
151
+ "rstrip": false,
152
+ "single_word": false,
153
+ "special": true
154
+ },
155
+ "128019": {
156
+ "content": "<|reserved_special_token_11|>",
157
+ "lstrip": false,
158
+ "normalized": false,
159
+ "rstrip": false,
160
+ "single_word": false,
161
+ "special": true
162
+ },
163
+ "128020": {
164
+ "content": "<|reserved_special_token_12|>",
165
+ "lstrip": false,
166
+ "normalized": false,
167
+ "rstrip": false,
168
+ "single_word": false,
169
+ "special": true
170
+ },
171
+ "128021": {
172
+ "content": "<|reserved_special_token_13|>",
173
+ "lstrip": false,
174
+ "normalized": false,
175
+ "rstrip": false,
176
+ "single_word": false,
177
+ "special": true
178
+ },
179
+ "128022": {
180
+ "content": "<|reserved_special_token_14|>",
181
+ "lstrip": false,
182
+ "normalized": false,
183
+ "rstrip": false,
184
+ "single_word": false,
185
+ "special": true
186
+ },
187
+ "128023": {
188
+ "content": "<|reserved_special_token_15|>",
189
+ "lstrip": false,
190
+ "normalized": false,
191
+ "rstrip": false,
192
+ "single_word": false,
193
+ "special": true
194
+ },
195
+ "128024": {
196
+ "content": "<|reserved_special_token_16|>",
197
+ "lstrip": false,
198
+ "normalized": false,
199
+ "rstrip": false,
200
+ "single_word": false,
201
+ "special": true
202
+ },
203
+ "128025": {
204
+ "content": "<|reserved_special_token_17|>",
205
+ "lstrip": false,
206
+ "normalized": false,
207
+ "rstrip": false,
208
+ "single_word": false,
209
+ "special": true
210
+ },
211
+ "128026": {
212
+ "content": "<|reserved_special_token_18|>",
213
+ "lstrip": false,
214
+ "normalized": false,
215
+ "rstrip": false,
216
+ "single_word": false,
217
+ "special": true
218
+ },
219
+ "128027": {
220
+ "content": "<|reserved_special_token_19|>",
221
+ "lstrip": false,
222
+ "normalized": false,
223
+ "rstrip": false,
224
+ "single_word": false,
225
+ "special": true
226
+ },
227
+ "128028": {
228
+ "content": "<|reserved_special_token_20|>",
229
+ "lstrip": false,
230
+ "normalized": false,
231
+ "rstrip": false,
232
+ "single_word": false,
233
+ "special": true
234
+ },
235
+ "128029": {
236
+ "content": "<|reserved_special_token_21|>",
237
+ "lstrip": false,
238
+ "normalized": false,
239
+ "rstrip": false,
240
+ "single_word": false,
241
+ "special": true
242
+ },
243
+ "128030": {
244
+ "content": "<|reserved_special_token_22|>",
245
+ "lstrip": false,
246
+ "normalized": false,
247
+ "rstrip": false,
248
+ "single_word": false,
249
+ "special": true
250
+ },
251
+ "128031": {
252
+ "content": "<|reserved_special_token_23|>",
253
+ "lstrip": false,
254
+ "normalized": false,
255
+ "rstrip": false,
256
+ "single_word": false,
257
+ "special": true
258
+ },
259
+ "128032": {
260
+ "content": "<|reserved_special_token_24|>",
261
+ "lstrip": false,
262
+ "normalized": false,
263
+ "rstrip": false,
264
+ "single_word": false,
265
+ "special": true
266
+ },
267
+ "128033": {
268
+ "content": "<|reserved_special_token_25|>",
269
+ "lstrip": false,
270
+ "normalized": false,
271
+ "rstrip": false,
272
+ "single_word": false,
273
+ "special": true
274
+ },
275
+ "128034": {
276
+ "content": "<|reserved_special_token_26|>",
277
+ "lstrip": false,
278
+ "normalized": false,
279
+ "rstrip": false,
280
+ "single_word": false,
281
+ "special": true
282
+ },
283
+ "128035": {
284
+ "content": "<|reserved_special_token_27|>",
285
+ "lstrip": false,
286
+ "normalized": false,
287
+ "rstrip": false,
288
+ "single_word": false,
289
+ "special": true
290
+ },
291
+ "128036": {
292
+ "content": "<|reserved_special_token_28|>",
293
+ "lstrip": false,
294
+ "normalized": false,
295
+ "rstrip": false,
296
+ "single_word": false,
297
+ "special": true
298
+ },
299
+ "128037": {
300
+ "content": "<|reserved_special_token_29|>",
301
+ "lstrip": false,
302
+ "normalized": false,
303
+ "rstrip": false,
304
+ "single_word": false,
305
+ "special": true
306
+ },
307
+ "128038": {
308
+ "content": "<|reserved_special_token_30|>",
309
+ "lstrip": false,
310
+ "normalized": false,
311
+ "rstrip": false,
312
+ "single_word": false,
313
+ "special": true
314
+ },
315
+ "128039": {
316
+ "content": "<|reserved_special_token_31|>",
317
+ "lstrip": false,
318
+ "normalized": false,
319
+ "rstrip": false,
320
+ "single_word": false,
321
+ "special": true
322
+ },
323
+ "128040": {
324
+ "content": "<|reserved_special_token_32|>",
325
+ "lstrip": false,
326
+ "normalized": false,
327
+ "rstrip": false,
328
+ "single_word": false,
329
+ "special": true
330
+ },
331
+ "128041": {
332
+ "content": "<|reserved_special_token_33|>",
333
+ "lstrip": false,
334
+ "normalized": false,
335
+ "rstrip": false,
336
+ "single_word": false,
337
+ "special": true
338
+ },
339
+ "128042": {
340
+ "content": "<|reserved_special_token_34|>",
341
+ "lstrip": false,
342
+ "normalized": false,
343
+ "rstrip": false,
344
+ "single_word": false,
345
+ "special": true
346
+ },
347
+ "128043": {
348
+ "content": "<|reserved_special_token_35|>",
349
+ "lstrip": false,
350
+ "normalized": false,
351
+ "rstrip": false,
352
+ "single_word": false,
353
+ "special": true
354
+ },
355
+ "128044": {
356
+ "content": "<|reserved_special_token_36|>",
357
+ "lstrip": false,
358
+ "normalized": false,
359
+ "rstrip": false,
360
+ "single_word": false,
361
+ "special": true
362
+ },
363
+ "128045": {
364
+ "content": "<|reserved_special_token_37|>",
365
+ "lstrip": false,
366
+ "normalized": false,
367
+ "rstrip": false,
368
+ "single_word": false,
369
+ "special": true
370
+ },
371
+ "128046": {
372
+ "content": "<|reserved_special_token_38|>",
373
+ "lstrip": false,
374
+ "normalized": false,
375
+ "rstrip": false,
376
+ "single_word": false,
377
+ "special": true
378
+ },
379
+ "128047": {
380
+ "content": "<|reserved_special_token_39|>",
381
+ "lstrip": false,
382
+ "normalized": false,
383
+ "rstrip": false,
384
+ "single_word": false,
385
+ "special": true
386
+ },
387
+ "128048": {
388
+ "content": "<|reserved_special_token_40|>",
389
+ "lstrip": false,
390
+ "normalized": false,
391
+ "rstrip": false,
392
+ "single_word": false,
393
+ "special": true
394
+ },
395
+ "128049": {
396
+ "content": "<|reserved_special_token_41|>",
397
+ "lstrip": false,
398
+ "normalized": false,
399
+ "rstrip": false,
400
+ "single_word": false,
401
+ "special": true
402
+ },
403
+ "128050": {
404
+ "content": "<|reserved_special_token_42|>",
405
+ "lstrip": false,
406
+ "normalized": false,
407
+ "rstrip": false,
408
+ "single_word": false,
409
+ "special": true
410
+ },
411
+ "128051": {
412
+ "content": "<|reserved_special_token_43|>",
413
+ "lstrip": false,
414
+ "normalized": false,
415
+ "rstrip": false,
416
+ "single_word": false,
417
+ "special": true
418
+ },
419
+ "128052": {
420
+ "content": "<|reserved_special_token_44|>",
421
+ "lstrip": false,
422
+ "normalized": false,
423
+ "rstrip": false,
424
+ "single_word": false,
425
+ "special": true
426
+ },
427
+ "128053": {
428
+ "content": "<|reserved_special_token_45|>",
429
+ "lstrip": false,
430
+ "normalized": false,
431
+ "rstrip": false,
432
+ "single_word": false,
433
+ "special": true
434
+ },
435
+ "128054": {
436
+ "content": "<|reserved_special_token_46|>",
437
+ "lstrip": false,
438
+ "normalized": false,
439
+ "rstrip": false,
440
+ "single_word": false,
441
+ "special": true
442
+ },
443
+ "128055": {
444
+ "content": "<|reserved_special_token_47|>",
445
+ "lstrip": false,
446
+ "normalized": false,
447
+ "rstrip": false,
448
+ "single_word": false,
449
+ "special": true
450
+ },
451
+ "128056": {
452
+ "content": "<|reserved_special_token_48|>",
453
+ "lstrip": false,
454
+ "normalized": false,
455
+ "rstrip": false,
456
+ "single_word": false,
457
+ "special": true
458
+ },
459
+ "128057": {
460
+ "content": "<|reserved_special_token_49|>",
461
+ "lstrip": false,
462
+ "normalized": false,
463
+ "rstrip": false,
464
+ "single_word": false,
465
+ "special": true
466
+ },
467
+ "128058": {
468
+ "content": "<|reserved_special_token_50|>",
469
+ "lstrip": false,
470
+ "normalized": false,
471
+ "rstrip": false,
472
+ "single_word": false,
473
+ "special": true
474
+ },
475
+ "128059": {
476
+ "content": "<|reserved_special_token_51|>",
477
+ "lstrip": false,
478
+ "normalized": false,
479
+ "rstrip": false,
480
+ "single_word": false,
481
+ "special": true
482
+ },
483
+ "128060": {
484
+ "content": "<|reserved_special_token_52|>",
485
+ "lstrip": false,
486
+ "normalized": false,
487
+ "rstrip": false,
488
+ "single_word": false,
489
+ "special": true
490
+ },
491
+ "128061": {
492
+ "content": "<|reserved_special_token_53|>",
493
+ "lstrip": false,
494
+ "normalized": false,
495
+ "rstrip": false,
496
+ "single_word": false,
497
+ "special": true
498
+ },
499
+ "128062": {
500
+ "content": "<|reserved_special_token_54|>",
501
+ "lstrip": false,
502
+ "normalized": false,
503
+ "rstrip": false,
504
+ "single_word": false,
505
+ "special": true
506
+ },
507
+ "128063": {
508
+ "content": "<|reserved_special_token_55|>",
509
+ "lstrip": false,
510
+ "normalized": false,
511
+ "rstrip": false,
512
+ "single_word": false,
513
+ "special": true
514
+ },
515
+ "128064": {
516
+ "content": "<|reserved_special_token_56|>",
517
+ "lstrip": false,
518
+ "normalized": false,
519
+ "rstrip": false,
520
+ "single_word": false,
521
+ "special": true
522
+ },
523
+ "128065": {
524
+ "content": "<|reserved_special_token_57|>",
525
+ "lstrip": false,
526
+ "normalized": false,
527
+ "rstrip": false,
528
+ "single_word": false,
529
+ "special": true
530
+ },
531
+ "128066": {
532
+ "content": "<|reserved_special_token_58|>",
533
+ "lstrip": false,
534
+ "normalized": false,
535
+ "rstrip": false,
536
+ "single_word": false,
537
+ "special": true
538
+ },
539
+ "128067": {
540
+ "content": "<|reserved_special_token_59|>",
541
+ "lstrip": false,
542
+ "normalized": false,
543
+ "rstrip": false,
544
+ "single_word": false,
545
+ "special": true
546
+ },
547
+ "128068": {
548
+ "content": "<|reserved_special_token_60|>",
549
+ "lstrip": false,
550
+ "normalized": false,
551
+ "rstrip": false,
552
+ "single_word": false,
553
+ "special": true
554
+ },
555
+ "128069": {
556
+ "content": "<|reserved_special_token_61|>",
557
+ "lstrip": false,
558
+ "normalized": false,
559
+ "rstrip": false,
560
+ "single_word": false,
561
+ "special": true
562
+ },
563
+ "128070": {
564
+ "content": "<|reserved_special_token_62|>",
565
+ "lstrip": false,
566
+ "normalized": false,
567
+ "rstrip": false,
568
+ "single_word": false,
569
+ "special": true
570
+ },
571
+ "128071": {
572
+ "content": "<|reserved_special_token_63|>",
573
+ "lstrip": false,
574
+ "normalized": false,
575
+ "rstrip": false,
576
+ "single_word": false,
577
+ "special": true
578
+ },
579
+ "128072": {
580
+ "content": "<|reserved_special_token_64|>",
581
+ "lstrip": false,
582
+ "normalized": false,
583
+ "rstrip": false,
584
+ "single_word": false,
585
+ "special": true
586
+ },
587
+ "128073": {
588
+ "content": "<|reserved_special_token_65|>",
589
+ "lstrip": false,
590
+ "normalized": false,
591
+ "rstrip": false,
592
+ "single_word": false,
593
+ "special": true
594
+ },
595
+ "128074": {
596
+ "content": "<|reserved_special_token_66|>",
597
+ "lstrip": false,
598
+ "normalized": false,
599
+ "rstrip": false,
600
+ "single_word": false,
601
+ "special": true
602
+ },
603
+ "128075": {
604
+ "content": "<|reserved_special_token_67|>",
605
+ "lstrip": false,
606
+ "normalized": false,
607
+ "rstrip": false,
608
+ "single_word": false,
609
+ "special": true
610
+ },
611
+ "128076": {
612
+ "content": "<|reserved_special_token_68|>",
613
+ "lstrip": false,
614
+ "normalized": false,
615
+ "rstrip": false,
616
+ "single_word": false,
617
+ "special": true
618
+ },
619
+ "128077": {
620
+ "content": "<|reserved_special_token_69|>",
621
+ "lstrip": false,
622
+ "normalized": false,
623
+ "rstrip": false,
624
+ "single_word": false,
625
+ "special": true
626
+ },
627
+ "128078": {
628
+ "content": "<|reserved_special_token_70|>",
629
+ "lstrip": false,
630
+ "normalized": false,
631
+ "rstrip": false,
632
+ "single_word": false,
633
+ "special": true
634
+ },
635
+ "128079": {
636
+ "content": "<|reserved_special_token_71|>",
637
+ "lstrip": false,
638
+ "normalized": false,
639
+ "rstrip": false,
640
+ "single_word": false,
641
+ "special": true
642
+ },
643
+ "128080": {
644
+ "content": "<|reserved_special_token_72|>",
645
+ "lstrip": false,
646
+ "normalized": false,
647
+ "rstrip": false,
648
+ "single_word": false,
649
+ "special": true
650
+ },
651
+ "128081": {
652
+ "content": "<|reserved_special_token_73|>",
653
+ "lstrip": false,
654
+ "normalized": false,
655
+ "rstrip": false,
656
+ "single_word": false,
657
+ "special": true
658
+ },
659
+ "128082": {
660
+ "content": "<|reserved_special_token_74|>",
661
+ "lstrip": false,
662
+ "normalized": false,
663
+ "rstrip": false,
664
+ "single_word": false,
665
+ "special": true
666
+ },
667
+ "128083": {
668
+ "content": "<|reserved_special_token_75|>",
669
+ "lstrip": false,
670
+ "normalized": false,
671
+ "rstrip": false,
672
+ "single_word": false,
673
+ "special": true
674
+ },
675
+ "128084": {
676
+ "content": "<|reserved_special_token_76|>",
677
+ "lstrip": false,
678
+ "normalized": false,
679
+ "rstrip": false,
680
+ "single_word": false,
681
+ "special": true
682
+ },
683
+ "128085": {
684
+ "content": "<|reserved_special_token_77|>",
685
+ "lstrip": false,
686
+ "normalized": false,
687
+ "rstrip": false,
688
+ "single_word": false,
689
+ "special": true
690
+ },
691
+ "128086": {
692
+ "content": "<|reserved_special_token_78|>",
693
+ "lstrip": false,
694
+ "normalized": false,
695
+ "rstrip": false,
696
+ "single_word": false,
697
+ "special": true
698
+ },
699
+ "128087": {
700
+ "content": "<|reserved_special_token_79|>",
701
+ "lstrip": false,
702
+ "normalized": false,
703
+ "rstrip": false,
704
+ "single_word": false,
705
+ "special": true
706
+ },
707
+ "128088": {
708
+ "content": "<|reserved_special_token_80|>",
709
+ "lstrip": false,
710
+ "normalized": false,
711
+ "rstrip": false,
712
+ "single_word": false,
713
+ "special": true
714
+ },
715
+ "128089": {
716
+ "content": "<|reserved_special_token_81|>",
717
+ "lstrip": false,
718
+ "normalized": false,
719
+ "rstrip": false,
720
+ "single_word": false,
721
+ "special": true
722
+ },
723
+ "128090": {
724
+ "content": "<|reserved_special_token_82|>",
725
+ "lstrip": false,
726
+ "normalized": false,
727
+ "rstrip": false,
728
+ "single_word": false,
729
+ "special": true
730
+ },
731
+ "128091": {
732
+ "content": "<|reserved_special_token_83|>",
733
+ "lstrip": false,
734
+ "normalized": false,
735
+ "rstrip": false,
736
+ "single_word": false,
737
+ "special": true
738
+ },
739
+ "128092": {
740
+ "content": "<|reserved_special_token_84|>",
741
+ "lstrip": false,
742
+ "normalized": false,
743
+ "rstrip": false,
744
+ "single_word": false,
745
+ "special": true
746
+ },
747
+ "128093": {
748
+ "content": "<|reserved_special_token_85|>",
749
+ "lstrip": false,
750
+ "normalized": false,
751
+ "rstrip": false,
752
+ "single_word": false,
753
+ "special": true
754
+ },
755
+ "128094": {
756
+ "content": "<|reserved_special_token_86|>",
757
+ "lstrip": false,
758
+ "normalized": false,
759
+ "rstrip": false,
760
+ "single_word": false,
761
+ "special": true
762
+ },
763
+ "128095": {
764
+ "content": "<|reserved_special_token_87|>",
765
+ "lstrip": false,
766
+ "normalized": false,
767
+ "rstrip": false,
768
+ "single_word": false,
769
+ "special": true
770
+ },
771
+ "128096": {
772
+ "content": "<|reserved_special_token_88|>",
773
+ "lstrip": false,
774
+ "normalized": false,
775
+ "rstrip": false,
776
+ "single_word": false,
777
+ "special": true
778
+ },
779
+ "128097": {
780
+ "content": "<|reserved_special_token_89|>",
781
+ "lstrip": false,
782
+ "normalized": false,
783
+ "rstrip": false,
784
+ "single_word": false,
785
+ "special": true
786
+ },
787
+ "128098": {
788
+ "content": "<|reserved_special_token_90|>",
789
+ "lstrip": false,
790
+ "normalized": false,
791
+ "rstrip": false,
792
+ "single_word": false,
793
+ "special": true
794
+ },
795
+ "128099": {
796
+ "content": "<|reserved_special_token_91|>",
797
+ "lstrip": false,
798
+ "normalized": false,
799
+ "rstrip": false,
800
+ "single_word": false,
801
+ "special": true
802
+ },
803
+ "128100": {
804
+ "content": "<|reserved_special_token_92|>",
805
+ "lstrip": false,
806
+ "normalized": false,
807
+ "rstrip": false,
808
+ "single_word": false,
809
+ "special": true
810
+ },
811
+ "128101": {
812
+ "content": "<|reserved_special_token_93|>",
813
+ "lstrip": false,
814
+ "normalized": false,
815
+ "rstrip": false,
816
+ "single_word": false,
817
+ "special": true
818
+ },
819
+ "128102": {
820
+ "content": "<|reserved_special_token_94|>",
821
+ "lstrip": false,
822
+ "normalized": false,
823
+ "rstrip": false,
824
+ "single_word": false,
825
+ "special": true
826
+ },
827
+ "128103": {
828
+ "content": "<|reserved_special_token_95|>",
829
+ "lstrip": false,
830
+ "normalized": false,
831
+ "rstrip": false,
832
+ "single_word": false,
833
+ "special": true
834
+ },
835
+ "128104": {
836
+ "content": "<|reserved_special_token_96|>",
837
+ "lstrip": false,
838
+ "normalized": false,
839
+ "rstrip": false,
840
+ "single_word": false,
841
+ "special": true
842
+ },
843
+ "128105": {
844
+ "content": "<|reserved_special_token_97|>",
845
+ "lstrip": false,
846
+ "normalized": false,
847
+ "rstrip": false,
848
+ "single_word": false,
849
+ "special": true
850
+ },
851
+ "128106": {
852
+ "content": "<|reserved_special_token_98|>",
853
+ "lstrip": false,
854
+ "normalized": false,
855
+ "rstrip": false,
856
+ "single_word": false,
857
+ "special": true
858
+ },
859
+ "128107": {
860
+ "content": "<|reserved_special_token_99|>",
861
+ "lstrip": false,
862
+ "normalized": false,
863
+ "rstrip": false,
864
+ "single_word": false,
865
+ "special": true
866
+ },
867
+ "128108": {
868
+ "content": "<|reserved_special_token_100|>",
869
+ "lstrip": false,
870
+ "normalized": false,
871
+ "rstrip": false,
872
+ "single_word": false,
873
+ "special": true
874
+ },
875
+ "128109": {
876
+ "content": "<|reserved_special_token_101|>",
877
+ "lstrip": false,
878
+ "normalized": false,
879
+ "rstrip": false,
880
+ "single_word": false,
881
+ "special": true
882
+ },
883
+ "128110": {
884
+ "content": "<|reserved_special_token_102|>",
885
+ "lstrip": false,
886
+ "normalized": false,
887
+ "rstrip": false,
888
+ "single_word": false,
889
+ "special": true
890
+ },
891
+ "128111": {
892
+ "content": "<|reserved_special_token_103|>",
893
+ "lstrip": false,
894
+ "normalized": false,
895
+ "rstrip": false,
896
+ "single_word": false,
897
+ "special": true
898
+ },
899
+ "128112": {
900
+ "content": "<|reserved_special_token_104|>",
901
+ "lstrip": false,
902
+ "normalized": false,
903
+ "rstrip": false,
904
+ "single_word": false,
905
+ "special": true
906
+ },
907
+ "128113": {
908
+ "content": "<|reserved_special_token_105|>",
909
+ "lstrip": false,
910
+ "normalized": false,
911
+ "rstrip": false,
912
+ "single_word": false,
913
+ "special": true
914
+ },
915
+ "128114": {
916
+ "content": "<|reserved_special_token_106|>",
917
+ "lstrip": false,
918
+ "normalized": false,
919
+ "rstrip": false,
920
+ "single_word": false,
921
+ "special": true
922
+ },
923
+ "128115": {
924
+ "content": "<|reserved_special_token_107|>",
925
+ "lstrip": false,
926
+ "normalized": false,
927
+ "rstrip": false,
928
+ "single_word": false,
929
+ "special": true
930
+ },
931
+ "128116": {
932
+ "content": "<|reserved_special_token_108|>",
933
+ "lstrip": false,
934
+ "normalized": false,
935
+ "rstrip": false,
936
+ "single_word": false,
937
+ "special": true
938
+ },
939
+ "128117": {
940
+ "content": "<|reserved_special_token_109|>",
941
+ "lstrip": false,
942
+ "normalized": false,
943
+ "rstrip": false,
944
+ "single_word": false,
945
+ "special": true
946
+ },
947
+ "128118": {
948
+ "content": "<|reserved_special_token_110|>",
949
+ "lstrip": false,
950
+ "normalized": false,
951
+ "rstrip": false,
952
+ "single_word": false,
953
+ "special": true
954
+ },
955
+ "128119": {
956
+ "content": "<|reserved_special_token_111|>",
957
+ "lstrip": false,
958
+ "normalized": false,
959
+ "rstrip": false,
960
+ "single_word": false,
961
+ "special": true
962
+ },
963
+ "128120": {
964
+ "content": "<|reserved_special_token_112|>",
965
+ "lstrip": false,
966
+ "normalized": false,
967
+ "rstrip": false,
968
+ "single_word": false,
969
+ "special": true
970
+ },
971
+ "128121": {
972
+ "content": "<|reserved_special_token_113|>",
973
+ "lstrip": false,
974
+ "normalized": false,
975
+ "rstrip": false,
976
+ "single_word": false,
977
+ "special": true
978
+ },
979
+ "128122": {
980
+ "content": "<|reserved_special_token_114|>",
981
+ "lstrip": false,
982
+ "normalized": false,
983
+ "rstrip": false,
984
+ "single_word": false,
985
+ "special": true
986
+ },
987
+ "128123": {
988
+ "content": "<|reserved_special_token_115|>",
989
+ "lstrip": false,
990
+ "normalized": false,
991
+ "rstrip": false,
992
+ "single_word": false,
993
+ "special": true
994
+ },
995
+ "128124": {
996
+ "content": "<|reserved_special_token_116|>",
997
+ "lstrip": false,
998
+ "normalized": false,
999
+ "rstrip": false,
1000
+ "single_word": false,
1001
+ "special": true
1002
+ },
1003
+ "128125": {
1004
+ "content": "<|reserved_special_token_117|>",
1005
+ "lstrip": false,
1006
+ "normalized": false,
1007
+ "rstrip": false,
1008
+ "single_word": false,
1009
+ "special": true
1010
+ },
1011
+ "128126": {
1012
+ "content": "<|reserved_special_token_118|>",
1013
+ "lstrip": false,
1014
+ "normalized": false,
1015
+ "rstrip": false,
1016
+ "single_word": false,
1017
+ "special": true
1018
+ },
1019
+ "128127": {
1020
+ "content": "<|reserved_special_token_119|>",
1021
+ "lstrip": false,
1022
+ "normalized": false,
1023
+ "rstrip": false,
1024
+ "single_word": false,
1025
+ "special": true
1026
+ },
1027
+ "128128": {
1028
+ "content": "<|reserved_special_token_120|>",
1029
+ "lstrip": false,
1030
+ "normalized": false,
1031
+ "rstrip": false,
1032
+ "single_word": false,
1033
+ "special": true
1034
+ },
1035
+ "128129": {
1036
+ "content": "<|reserved_special_token_121|>",
1037
+ "lstrip": false,
1038
+ "normalized": false,
1039
+ "rstrip": false,
1040
+ "single_word": false,
1041
+ "special": true
1042
+ },
1043
+ "128130": {
1044
+ "content": "<|reserved_special_token_122|>",
1045
+ "lstrip": false,
1046
+ "normalized": false,
1047
+ "rstrip": false,
1048
+ "single_word": false,
1049
+ "special": true
1050
+ },
1051
+ "128131": {
1052
+ "content": "<|reserved_special_token_123|>",
1053
+ "lstrip": false,
1054
+ "normalized": false,
1055
+ "rstrip": false,
1056
+ "single_word": false,
1057
+ "special": true
1058
+ },
1059
+ "128132": {
1060
+ "content": "<|reserved_special_token_124|>",
1061
+ "lstrip": false,
1062
+ "normalized": false,
1063
+ "rstrip": false,
1064
+ "single_word": false,
1065
+ "special": true
1066
+ },
1067
+ "128133": {
1068
+ "content": "<|reserved_special_token_125|>",
1069
+ "lstrip": false,
1070
+ "normalized": false,
1071
+ "rstrip": false,
1072
+ "single_word": false,
1073
+ "special": true
1074
+ },
1075
+ "128134": {
1076
+ "content": "<|reserved_special_token_126|>",
1077
+ "lstrip": false,
1078
+ "normalized": false,
1079
+ "rstrip": false,
1080
+ "single_word": false,
1081
+ "special": true
1082
+ },
1083
+ "128135": {
1084
+ "content": "<|reserved_special_token_127|>",
1085
+ "lstrip": false,
1086
+ "normalized": false,
1087
+ "rstrip": false,
1088
+ "single_word": false,
1089
+ "special": true
1090
+ },
1091
+ "128136": {
1092
+ "content": "<|reserved_special_token_128|>",
1093
+ "lstrip": false,
1094
+ "normalized": false,
1095
+ "rstrip": false,
1096
+ "single_word": false,
1097
+ "special": true
1098
+ },
1099
+ "128137": {
1100
+ "content": "<|reserved_special_token_129|>",
1101
+ "lstrip": false,
1102
+ "normalized": false,
1103
+ "rstrip": false,
1104
+ "single_word": false,
1105
+ "special": true
1106
+ },
1107
+ "128138": {
1108
+ "content": "<|reserved_special_token_130|>",
1109
+ "lstrip": false,
1110
+ "normalized": false,
1111
+ "rstrip": false,
1112
+ "single_word": false,
1113
+ "special": true
1114
+ },
1115
+ "128139": {
1116
+ "content": "<|reserved_special_token_131|>",
1117
+ "lstrip": false,
1118
+ "normalized": false,
1119
+ "rstrip": false,
1120
+ "single_word": false,
1121
+ "special": true
1122
+ },
1123
+ "128140": {
1124
+ "content": "<|reserved_special_token_132|>",
1125
+ "lstrip": false,
1126
+ "normalized": false,
1127
+ "rstrip": false,
1128
+ "single_word": false,
1129
+ "special": true
1130
+ },
1131
+ "128141": {
1132
+ "content": "<|reserved_special_token_133|>",
1133
+ "lstrip": false,
1134
+ "normalized": false,
1135
+ "rstrip": false,
1136
+ "single_word": false,
1137
+ "special": true
1138
+ },
1139
+ "128142": {
1140
+ "content": "<|reserved_special_token_134|>",
1141
+ "lstrip": false,
1142
+ "normalized": false,
1143
+ "rstrip": false,
1144
+ "single_word": false,
1145
+ "special": true
1146
+ },
1147
+ "128143": {
1148
+ "content": "<|reserved_special_token_135|>",
1149
+ "lstrip": false,
1150
+ "normalized": false,
1151
+ "rstrip": false,
1152
+ "single_word": false,
1153
+ "special": true
1154
+ },
1155
+ "128144": {
1156
+ "content": "<|reserved_special_token_136|>",
1157
+ "lstrip": false,
1158
+ "normalized": false,
1159
+ "rstrip": false,
1160
+ "single_word": false,
1161
+ "special": true
1162
+ },
1163
+ "128145": {
1164
+ "content": "<|reserved_special_token_137|>",
1165
+ "lstrip": false,
1166
+ "normalized": false,
1167
+ "rstrip": false,
1168
+ "single_word": false,
1169
+ "special": true
1170
+ },
1171
+ "128146": {
1172
+ "content": "<|reserved_special_token_138|>",
1173
+ "lstrip": false,
1174
+ "normalized": false,
1175
+ "rstrip": false,
1176
+ "single_word": false,
1177
+ "special": true
1178
+ },
1179
+ "128147": {
1180
+ "content": "<|reserved_special_token_139|>",
1181
+ "lstrip": false,
1182
+ "normalized": false,
1183
+ "rstrip": false,
1184
+ "single_word": false,
1185
+ "special": true
1186
+ },
1187
+ "128148": {
1188
+ "content": "<|reserved_special_token_140|>",
1189
+ "lstrip": false,
1190
+ "normalized": false,
1191
+ "rstrip": false,
1192
+ "single_word": false,
1193
+ "special": true
1194
+ },
1195
+ "128149": {
1196
+ "content": "<|reserved_special_token_141|>",
1197
+ "lstrip": false,
1198
+ "normalized": false,
1199
+ "rstrip": false,
1200
+ "single_word": false,
1201
+ "special": true
1202
+ },
1203
+ "128150": {
1204
+ "content": "<|reserved_special_token_142|>",
1205
+ "lstrip": false,
1206
+ "normalized": false,
1207
+ "rstrip": false,
1208
+ "single_word": false,
1209
+ "special": true
1210
+ },
1211
+ "128151": {
1212
+ "content": "<|reserved_special_token_143|>",
1213
+ "lstrip": false,
1214
+ "normalized": false,
1215
+ "rstrip": false,
1216
+ "single_word": false,
1217
+ "special": true
1218
+ },
1219
+ "128152": {
1220
+ "content": "<|reserved_special_token_144|>",
1221
+ "lstrip": false,
1222
+ "normalized": false,
1223
+ "rstrip": false,
1224
+ "single_word": false,
1225
+ "special": true
1226
+ },
1227
+ "128153": {
1228
+ "content": "<|reserved_special_token_145|>",
1229
+ "lstrip": false,
1230
+ "normalized": false,
1231
+ "rstrip": false,
1232
+ "single_word": false,
1233
+ "special": true
1234
+ },
1235
+ "128154": {
1236
+ "content": "<|reserved_special_token_146|>",
1237
+ "lstrip": false,
1238
+ "normalized": false,
1239
+ "rstrip": false,
1240
+ "single_word": false,
1241
+ "special": true
1242
+ },
1243
+ "128155": {
1244
+ "content": "<|reserved_special_token_147|>",
1245
+ "lstrip": false,
1246
+ "normalized": false,
1247
+ "rstrip": false,
1248
+ "single_word": false,
1249
+ "special": true
1250
+ },
1251
+ "128156": {
1252
+ "content": "<|reserved_special_token_148|>",
1253
+ "lstrip": false,
1254
+ "normalized": false,
1255
+ "rstrip": false,
1256
+ "single_word": false,
1257
+ "special": true
1258
+ },
1259
+ "128157": {
1260
+ "content": "<|reserved_special_token_149|>",
1261
+ "lstrip": false,
1262
+ "normalized": false,
1263
+ "rstrip": false,
1264
+ "single_word": false,
1265
+ "special": true
1266
+ },
1267
+ "128158": {
1268
+ "content": "<|reserved_special_token_150|>",
1269
+ "lstrip": false,
1270
+ "normalized": false,
1271
+ "rstrip": false,
1272
+ "single_word": false,
1273
+ "special": true
1274
+ },
1275
+ "128159": {
1276
+ "content": "<|reserved_special_token_151|>",
1277
+ "lstrip": false,
1278
+ "normalized": false,
1279
+ "rstrip": false,
1280
+ "single_word": false,
1281
+ "special": true
1282
+ },
1283
+ "128160": {
1284
+ "content": "<|reserved_special_token_152|>",
1285
+ "lstrip": false,
1286
+ "normalized": false,
1287
+ "rstrip": false,
1288
+ "single_word": false,
1289
+ "special": true
1290
+ },
1291
+ "128161": {
1292
+ "content": "<|reserved_special_token_153|>",
1293
+ "lstrip": false,
1294
+ "normalized": false,
1295
+ "rstrip": false,
1296
+ "single_word": false,
1297
+ "special": true
1298
+ },
1299
+ "128162": {
1300
+ "content": "<|reserved_special_token_154|>",
1301
+ "lstrip": false,
1302
+ "normalized": false,
1303
+ "rstrip": false,
1304
+ "single_word": false,
1305
+ "special": true
1306
+ },
1307
+ "128163": {
1308
+ "content": "<|reserved_special_token_155|>",
1309
+ "lstrip": false,
1310
+ "normalized": false,
1311
+ "rstrip": false,
1312
+ "single_word": false,
1313
+ "special": true
1314
+ },
1315
+ "128164": {
1316
+ "content": "<|reserved_special_token_156|>",
1317
+ "lstrip": false,
1318
+ "normalized": false,
1319
+ "rstrip": false,
1320
+ "single_word": false,
1321
+ "special": true
1322
+ },
1323
+ "128165": {
1324
+ "content": "<|reserved_special_token_157|>",
1325
+ "lstrip": false,
1326
+ "normalized": false,
1327
+ "rstrip": false,
1328
+ "single_word": false,
1329
+ "special": true
1330
+ },
1331
+ "128166": {
1332
+ "content": "<|reserved_special_token_158|>",
1333
+ "lstrip": false,
1334
+ "normalized": false,
1335
+ "rstrip": false,
1336
+ "single_word": false,
1337
+ "special": true
1338
+ },
1339
+ "128167": {
1340
+ "content": "<|reserved_special_token_159|>",
1341
+ "lstrip": false,
1342
+ "normalized": false,
1343
+ "rstrip": false,
1344
+ "single_word": false,
1345
+ "special": true
1346
+ },
1347
+ "128168": {
1348
+ "content": "<|reserved_special_token_160|>",
1349
+ "lstrip": false,
1350
+ "normalized": false,
1351
+ "rstrip": false,
1352
+ "single_word": false,
1353
+ "special": true
1354
+ },
1355
+ "128169": {
1356
+ "content": "<|reserved_special_token_161|>",
1357
+ "lstrip": false,
1358
+ "normalized": false,
1359
+ "rstrip": false,
1360
+ "single_word": false,
1361
+ "special": true
1362
+ },
1363
+ "128170": {
1364
+ "content": "<|reserved_special_token_162|>",
1365
+ "lstrip": false,
1366
+ "normalized": false,
1367
+ "rstrip": false,
1368
+ "single_word": false,
1369
+ "special": true
1370
+ },
1371
+ "128171": {
1372
+ "content": "<|reserved_special_token_163|>",
1373
+ "lstrip": false,
1374
+ "normalized": false,
1375
+ "rstrip": false,
1376
+ "single_word": false,
1377
+ "special": true
1378
+ },
1379
+ "128172": {
1380
+ "content": "<|reserved_special_token_164|>",
1381
+ "lstrip": false,
1382
+ "normalized": false,
1383
+ "rstrip": false,
1384
+ "single_word": false,
1385
+ "special": true
1386
+ },
1387
+ "128173": {
1388
+ "content": "<|reserved_special_token_165|>",
1389
+ "lstrip": false,
1390
+ "normalized": false,
1391
+ "rstrip": false,
1392
+ "single_word": false,
1393
+ "special": true
1394
+ },
1395
+ "128174": {
1396
+ "content": "<|reserved_special_token_166|>",
1397
+ "lstrip": false,
1398
+ "normalized": false,
1399
+ "rstrip": false,
1400
+ "single_word": false,
1401
+ "special": true
1402
+ },
1403
+ "128175": {
1404
+ "content": "<|reserved_special_token_167|>",
1405
+ "lstrip": false,
1406
+ "normalized": false,
1407
+ "rstrip": false,
1408
+ "single_word": false,
1409
+ "special": true
1410
+ },
1411
+ "128176": {
1412
+ "content": "<|reserved_special_token_168|>",
1413
+ "lstrip": false,
1414
+ "normalized": false,
1415
+ "rstrip": false,
1416
+ "single_word": false,
1417
+ "special": true
1418
+ },
1419
+ "128177": {
1420
+ "content": "<|reserved_special_token_169|>",
1421
+ "lstrip": false,
1422
+ "normalized": false,
1423
+ "rstrip": false,
1424
+ "single_word": false,
1425
+ "special": true
1426
+ },
1427
+ "128178": {
1428
+ "content": "<|reserved_special_token_170|>",
1429
+ "lstrip": false,
1430
+ "normalized": false,
1431
+ "rstrip": false,
1432
+ "single_word": false,
1433
+ "special": true
1434
+ },
1435
+ "128179": {
1436
+ "content": "<|reserved_special_token_171|>",
1437
+ "lstrip": false,
1438
+ "normalized": false,
1439
+ "rstrip": false,
1440
+ "single_word": false,
1441
+ "special": true
1442
+ },
1443
+ "128180": {
1444
+ "content": "<|reserved_special_token_172|>",
1445
+ "lstrip": false,
1446
+ "normalized": false,
1447
+ "rstrip": false,
1448
+ "single_word": false,
1449
+ "special": true
1450
+ },
1451
+ "128181": {
1452
+ "content": "<|reserved_special_token_173|>",
1453
+ "lstrip": false,
1454
+ "normalized": false,
1455
+ "rstrip": false,
1456
+ "single_word": false,
1457
+ "special": true
1458
+ },
1459
+ "128182": {
1460
+ "content": "<|reserved_special_token_174|>",
1461
+ "lstrip": false,
1462
+ "normalized": false,
1463
+ "rstrip": false,
1464
+ "single_word": false,
1465
+ "special": true
1466
+ },
1467
+ "128183": {
1468
+ "content": "<|reserved_special_token_175|>",
1469
+ "lstrip": false,
1470
+ "normalized": false,
1471
+ "rstrip": false,
1472
+ "single_word": false,
1473
+ "special": true
1474
+ },
1475
+ "128184": {
1476
+ "content": "<|reserved_special_token_176|>",
1477
+ "lstrip": false,
1478
+ "normalized": false,
1479
+ "rstrip": false,
1480
+ "single_word": false,
1481
+ "special": true
1482
+ },
1483
+ "128185": {
1484
+ "content": "<|reserved_special_token_177|>",
1485
+ "lstrip": false,
1486
+ "normalized": false,
1487
+ "rstrip": false,
1488
+ "single_word": false,
1489
+ "special": true
1490
+ },
1491
+ "128186": {
1492
+ "content": "<|reserved_special_token_178|>",
1493
+ "lstrip": false,
1494
+ "normalized": false,
1495
+ "rstrip": false,
1496
+ "single_word": false,
1497
+ "special": true
1498
+ },
1499
+ "128187": {
1500
+ "content": "<|reserved_special_token_179|>",
1501
+ "lstrip": false,
1502
+ "normalized": false,
1503
+ "rstrip": false,
1504
+ "single_word": false,
1505
+ "special": true
1506
+ },
1507
+ "128188": {
1508
+ "content": "<|reserved_special_token_180|>",
1509
+ "lstrip": false,
1510
+ "normalized": false,
1511
+ "rstrip": false,
1512
+ "single_word": false,
1513
+ "special": true
1514
+ },
1515
+ "128189": {
1516
+ "content": "<|reserved_special_token_181|>",
1517
+ "lstrip": false,
1518
+ "normalized": false,
1519
+ "rstrip": false,
1520
+ "single_word": false,
1521
+ "special": true
1522
+ },
1523
+ "128190": {
1524
+ "content": "<|reserved_special_token_182|>",
1525
+ "lstrip": false,
1526
+ "normalized": false,
1527
+ "rstrip": false,
1528
+ "single_word": false,
1529
+ "special": true
1530
+ },
1531
+ "128191": {
1532
+ "content": "<|reserved_special_token_183|>",
1533
+ "lstrip": false,
1534
+ "normalized": false,
1535
+ "rstrip": false,
1536
+ "single_word": false,
1537
+ "special": true
1538
+ },
1539
+ "128192": {
1540
+ "content": "<|reserved_special_token_184|>",
1541
+ "lstrip": false,
1542
+ "normalized": false,
1543
+ "rstrip": false,
1544
+ "single_word": false,
1545
+ "special": true
1546
+ },
1547
+ "128193": {
1548
+ "content": "<|reserved_special_token_185|>",
1549
+ "lstrip": false,
1550
+ "normalized": false,
1551
+ "rstrip": false,
1552
+ "single_word": false,
1553
+ "special": true
1554
+ },
1555
+ "128194": {
1556
+ "content": "<|reserved_special_token_186|>",
1557
+ "lstrip": false,
1558
+ "normalized": false,
1559
+ "rstrip": false,
1560
+ "single_word": false,
1561
+ "special": true
1562
+ },
1563
+ "128195": {
1564
+ "content": "<|reserved_special_token_187|>",
1565
+ "lstrip": false,
1566
+ "normalized": false,
1567
+ "rstrip": false,
1568
+ "single_word": false,
1569
+ "special": true
1570
+ },
1571
+ "128196": {
1572
+ "content": "<|reserved_special_token_188|>",
1573
+ "lstrip": false,
1574
+ "normalized": false,
1575
+ "rstrip": false,
1576
+ "single_word": false,
1577
+ "special": true
1578
+ },
1579
+ "128197": {
1580
+ "content": "<|reserved_special_token_189|>",
1581
+ "lstrip": false,
1582
+ "normalized": false,
1583
+ "rstrip": false,
1584
+ "single_word": false,
1585
+ "special": true
1586
+ },
1587
+ "128198": {
1588
+ "content": "<|reserved_special_token_190|>",
1589
+ "lstrip": false,
1590
+ "normalized": false,
1591
+ "rstrip": false,
1592
+ "single_word": false,
1593
+ "special": true
1594
+ },
1595
+ "128199": {
1596
+ "content": "<|reserved_special_token_191|>",
1597
+ "lstrip": false,
1598
+ "normalized": false,
1599
+ "rstrip": false,
1600
+ "single_word": false,
1601
+ "special": true
1602
+ },
1603
+ "128200": {
1604
+ "content": "<|reserved_special_token_192|>",
1605
+ "lstrip": false,
1606
+ "normalized": false,
1607
+ "rstrip": false,
1608
+ "single_word": false,
1609
+ "special": true
1610
+ },
1611
+ "128201": {
1612
+ "content": "<|reserved_special_token_193|>",
1613
+ "lstrip": false,
1614
+ "normalized": false,
1615
+ "rstrip": false,
1616
+ "single_word": false,
1617
+ "special": true
1618
+ },
1619
+ "128202": {
1620
+ "content": "<|reserved_special_token_194|>",
1621
+ "lstrip": false,
1622
+ "normalized": false,
1623
+ "rstrip": false,
1624
+ "single_word": false,
1625
+ "special": true
1626
+ },
1627
+ "128203": {
1628
+ "content": "<|reserved_special_token_195|>",
1629
+ "lstrip": false,
1630
+ "normalized": false,
1631
+ "rstrip": false,
1632
+ "single_word": false,
1633
+ "special": true
1634
+ },
1635
+ "128204": {
1636
+ "content": "<|reserved_special_token_196|>",
1637
+ "lstrip": false,
1638
+ "normalized": false,
1639
+ "rstrip": false,
1640
+ "single_word": false,
1641
+ "special": true
1642
+ },
1643
+ "128205": {
1644
+ "content": "<|reserved_special_token_197|>",
1645
+ "lstrip": false,
1646
+ "normalized": false,
1647
+ "rstrip": false,
1648
+ "single_word": false,
1649
+ "special": true
1650
+ },
1651
+ "128206": {
1652
+ "content": "<|reserved_special_token_198|>",
1653
+ "lstrip": false,
1654
+ "normalized": false,
1655
+ "rstrip": false,
1656
+ "single_word": false,
1657
+ "special": true
1658
+ },
1659
+ "128207": {
1660
+ "content": "<|reserved_special_token_199|>",
1661
+ "lstrip": false,
1662
+ "normalized": false,
1663
+ "rstrip": false,
1664
+ "single_word": false,
1665
+ "special": true
1666
+ },
1667
+ "128208": {
1668
+ "content": "<|reserved_special_token_200|>",
1669
+ "lstrip": false,
1670
+ "normalized": false,
1671
+ "rstrip": false,
1672
+ "single_word": false,
1673
+ "special": true
1674
+ },
1675
+ "128209": {
1676
+ "content": "<|reserved_special_token_201|>",
1677
+ "lstrip": false,
1678
+ "normalized": false,
1679
+ "rstrip": false,
1680
+ "single_word": false,
1681
+ "special": true
1682
+ },
1683
+ "128210": {
1684
+ "content": "<|reserved_special_token_202|>",
1685
+ "lstrip": false,
1686
+ "normalized": false,
1687
+ "rstrip": false,
1688
+ "single_word": false,
1689
+ "special": true
1690
+ },
1691
+ "128211": {
1692
+ "content": "<|reserved_special_token_203|>",
1693
+ "lstrip": false,
1694
+ "normalized": false,
1695
+ "rstrip": false,
1696
+ "single_word": false,
1697
+ "special": true
1698
+ },
1699
+ "128212": {
1700
+ "content": "<|reserved_special_token_204|>",
1701
+ "lstrip": false,
1702
+ "normalized": false,
1703
+ "rstrip": false,
1704
+ "single_word": false,
1705
+ "special": true
1706
+ },
1707
+ "128213": {
1708
+ "content": "<|reserved_special_token_205|>",
1709
+ "lstrip": false,
1710
+ "normalized": false,
1711
+ "rstrip": false,
1712
+ "single_word": false,
1713
+ "special": true
1714
+ },
1715
+ "128214": {
1716
+ "content": "<|reserved_special_token_206|>",
1717
+ "lstrip": false,
1718
+ "normalized": false,
1719
+ "rstrip": false,
1720
+ "single_word": false,
1721
+ "special": true
1722
+ },
1723
+ "128215": {
1724
+ "content": "<|reserved_special_token_207|>",
1725
+ "lstrip": false,
1726
+ "normalized": false,
1727
+ "rstrip": false,
1728
+ "single_word": false,
1729
+ "special": true
1730
+ },
1731
+ "128216": {
1732
+ "content": "<|reserved_special_token_208|>",
1733
+ "lstrip": false,
1734
+ "normalized": false,
1735
+ "rstrip": false,
1736
+ "single_word": false,
1737
+ "special": true
1738
+ },
1739
+ "128217": {
1740
+ "content": "<|reserved_special_token_209|>",
1741
+ "lstrip": false,
1742
+ "normalized": false,
1743
+ "rstrip": false,
1744
+ "single_word": false,
1745
+ "special": true
1746
+ },
1747
+ "128218": {
1748
+ "content": "<|reserved_special_token_210|>",
1749
+ "lstrip": false,
1750
+ "normalized": false,
1751
+ "rstrip": false,
1752
+ "single_word": false,
1753
+ "special": true
1754
+ },
1755
+ "128219": {
1756
+ "content": "<|reserved_special_token_211|>",
1757
+ "lstrip": false,
1758
+ "normalized": false,
1759
+ "rstrip": false,
1760
+ "single_word": false,
1761
+ "special": true
1762
+ },
1763
+ "128220": {
1764
+ "content": "<|reserved_special_token_212|>",
1765
+ "lstrip": false,
1766
+ "normalized": false,
1767
+ "rstrip": false,
1768
+ "single_word": false,
1769
+ "special": true
1770
+ },
1771
+ "128221": {
1772
+ "content": "<|reserved_special_token_213|>",
1773
+ "lstrip": false,
1774
+ "normalized": false,
1775
+ "rstrip": false,
1776
+ "single_word": false,
1777
+ "special": true
1778
+ },
1779
+ "128222": {
1780
+ "content": "<|reserved_special_token_214|>",
1781
+ "lstrip": false,
1782
+ "normalized": false,
1783
+ "rstrip": false,
1784
+ "single_word": false,
1785
+ "special": true
1786
+ },
1787
+ "128223": {
1788
+ "content": "<|reserved_special_token_215|>",
1789
+ "lstrip": false,
1790
+ "normalized": false,
1791
+ "rstrip": false,
1792
+ "single_word": false,
1793
+ "special": true
1794
+ },
1795
+ "128224": {
1796
+ "content": "<|reserved_special_token_216|>",
1797
+ "lstrip": false,
1798
+ "normalized": false,
1799
+ "rstrip": false,
1800
+ "single_word": false,
1801
+ "special": true
1802
+ },
1803
+ "128225": {
1804
+ "content": "<|reserved_special_token_217|>",
1805
+ "lstrip": false,
1806
+ "normalized": false,
1807
+ "rstrip": false,
1808
+ "single_word": false,
1809
+ "special": true
1810
+ },
1811
+ "128226": {
1812
+ "content": "<|reserved_special_token_218|>",
1813
+ "lstrip": false,
1814
+ "normalized": false,
1815
+ "rstrip": false,
1816
+ "single_word": false,
1817
+ "special": true
1818
+ },
1819
+ "128227": {
1820
+ "content": "<|reserved_special_token_219|>",
1821
+ "lstrip": false,
1822
+ "normalized": false,
1823
+ "rstrip": false,
1824
+ "single_word": false,
1825
+ "special": true
1826
+ },
1827
+ "128228": {
1828
+ "content": "<|reserved_special_token_220|>",
1829
+ "lstrip": false,
1830
+ "normalized": false,
1831
+ "rstrip": false,
1832
+ "single_word": false,
1833
+ "special": true
1834
+ },
1835
+ "128229": {
1836
+ "content": "<|reserved_special_token_221|>",
1837
+ "lstrip": false,
1838
+ "normalized": false,
1839
+ "rstrip": false,
1840
+ "single_word": false,
1841
+ "special": true
1842
+ },
1843
+ "128230": {
1844
+ "content": "<|reserved_special_token_222|>",
1845
+ "lstrip": false,
1846
+ "normalized": false,
1847
+ "rstrip": false,
1848
+ "single_word": false,
1849
+ "special": true
1850
+ },
1851
+ "128231": {
1852
+ "content": "<|reserved_special_token_223|>",
1853
+ "lstrip": false,
1854
+ "normalized": false,
1855
+ "rstrip": false,
1856
+ "single_word": false,
1857
+ "special": true
1858
+ },
1859
+ "128232": {
1860
+ "content": "<|reserved_special_token_224|>",
1861
+ "lstrip": false,
1862
+ "normalized": false,
1863
+ "rstrip": false,
1864
+ "single_word": false,
1865
+ "special": true
1866
+ },
1867
+ "128233": {
1868
+ "content": "<|reserved_special_token_225|>",
1869
+ "lstrip": false,
1870
+ "normalized": false,
1871
+ "rstrip": false,
1872
+ "single_word": false,
1873
+ "special": true
1874
+ },
1875
+ "128234": {
1876
+ "content": "<|reserved_special_token_226|>",
1877
+ "lstrip": false,
1878
+ "normalized": false,
1879
+ "rstrip": false,
1880
+ "single_word": false,
1881
+ "special": true
1882
+ },
1883
+ "128235": {
1884
+ "content": "<|reserved_special_token_227|>",
1885
+ "lstrip": false,
1886
+ "normalized": false,
1887
+ "rstrip": false,
1888
+ "single_word": false,
1889
+ "special": true
1890
+ },
1891
+ "128236": {
1892
+ "content": "<|reserved_special_token_228|>",
1893
+ "lstrip": false,
1894
+ "normalized": false,
1895
+ "rstrip": false,
1896
+ "single_word": false,
1897
+ "special": true
1898
+ },
1899
+ "128237": {
1900
+ "content": "<|reserved_special_token_229|>",
1901
+ "lstrip": false,
1902
+ "normalized": false,
1903
+ "rstrip": false,
1904
+ "single_word": false,
1905
+ "special": true
1906
+ },
1907
+ "128238": {
1908
+ "content": "<|reserved_special_token_230|>",
1909
+ "lstrip": false,
1910
+ "normalized": false,
1911
+ "rstrip": false,
1912
+ "single_word": false,
1913
+ "special": true
1914
+ },
1915
+ "128239": {
1916
+ "content": "<|reserved_special_token_231|>",
1917
+ "lstrip": false,
1918
+ "normalized": false,
1919
+ "rstrip": false,
1920
+ "single_word": false,
1921
+ "special": true
1922
+ },
1923
+ "128240": {
1924
+ "content": "<|reserved_special_token_232|>",
1925
+ "lstrip": false,
1926
+ "normalized": false,
1927
+ "rstrip": false,
1928
+ "single_word": false,
1929
+ "special": true
1930
+ },
1931
+ "128241": {
1932
+ "content": "<|reserved_special_token_233|>",
1933
+ "lstrip": false,
1934
+ "normalized": false,
1935
+ "rstrip": false,
1936
+ "single_word": false,
1937
+ "special": true
1938
+ },
1939
+ "128242": {
1940
+ "content": "<|reserved_special_token_234|>",
1941
+ "lstrip": false,
1942
+ "normalized": false,
1943
+ "rstrip": false,
1944
+ "single_word": false,
1945
+ "special": true
1946
+ },
1947
+ "128243": {
1948
+ "content": "<|reserved_special_token_235|>",
1949
+ "lstrip": false,
1950
+ "normalized": false,
1951
+ "rstrip": false,
1952
+ "single_word": false,
1953
+ "special": true
1954
+ },
1955
+ "128244": {
1956
+ "content": "<|reserved_special_token_236|>",
1957
+ "lstrip": false,
1958
+ "normalized": false,
1959
+ "rstrip": false,
1960
+ "single_word": false,
1961
+ "special": true
1962
+ },
1963
+ "128245": {
1964
+ "content": "<|reserved_special_token_237|>",
1965
+ "lstrip": false,
1966
+ "normalized": false,
1967
+ "rstrip": false,
1968
+ "single_word": false,
1969
+ "special": true
1970
+ },
1971
+ "128246": {
1972
+ "content": "<|reserved_special_token_238|>",
1973
+ "lstrip": false,
1974
+ "normalized": false,
1975
+ "rstrip": false,
1976
+ "single_word": false,
1977
+ "special": true
1978
+ },
1979
+ "128247": {
1980
+ "content": "<|reserved_special_token_239|>",
1981
+ "lstrip": false,
1982
+ "normalized": false,
1983
+ "rstrip": false,
1984
+ "single_word": false,
1985
+ "special": true
1986
+ },
1987
+ "128248": {
1988
+ "content": "<|reserved_special_token_240|>",
1989
+ "lstrip": false,
1990
+ "normalized": false,
1991
+ "rstrip": false,
1992
+ "single_word": false,
1993
+ "special": true
1994
+ },
1995
+ "128249": {
1996
+ "content": "<|reserved_special_token_241|>",
1997
+ "lstrip": false,
1998
+ "normalized": false,
1999
+ "rstrip": false,
2000
+ "single_word": false,
2001
+ "special": true
2002
+ },
2003
+ "128250": {
2004
+ "content": "<|reserved_special_token_242|>",
2005
+ "lstrip": false,
2006
+ "normalized": false,
2007
+ "rstrip": false,
2008
+ "single_word": false,
2009
+ "special": true
2010
+ },
2011
+ "128251": {
2012
+ "content": "<|reserved_special_token_243|>",
2013
+ "lstrip": false,
2014
+ "normalized": false,
2015
+ "rstrip": false,
2016
+ "single_word": false,
2017
+ "special": true
2018
+ },
2019
+ "128252": {
2020
+ "content": "<|reserved_special_token_244|>",
2021
+ "lstrip": false,
2022
+ "normalized": false,
2023
+ "rstrip": false,
2024
+ "single_word": false,
2025
+ "special": true
2026
+ },
2027
+ "128253": {
2028
+ "content": "<|reserved_special_token_245|>",
2029
+ "lstrip": false,
2030
+ "normalized": false,
2031
+ "rstrip": false,
2032
+ "single_word": false,
2033
+ "special": true
2034
+ },
2035
+ "128254": {
2036
+ "content": "<|reserved_special_token_246|>",
2037
+ "lstrip": false,
2038
+ "normalized": false,
2039
+ "rstrip": false,
2040
+ "single_word": false,
2041
+ "special": true
2042
+ },
2043
+ "128255": {
2044
+ "content": "<|reserved_special_token_247|>",
2045
+ "lstrip": false,
2046
+ "normalized": false,
2047
+ "rstrip": false,
2048
+ "single_word": false,
2049
+ "special": true
2050
+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "extra_special_tokens": {},
2057
+ "model_input_names": [
2058
+ "input_ids",
2059
+ "attention_mask"
2060
+ ],
2061
+ "model_max_length": 131072,
2062
+ "pad_token": "<|eot_id|>",
2063
+ "tokenizer_class": "PreTrainedTokenizerFast"
2064
+ }
trainer_state.json ADDED
@@ -0,0 +1,2441 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 35.0,
5
+ "eval_steps": 5,
6
+ "global_step": 280,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.13793103448275862,
13
+ "grad_norm": 0.05582835289468266,
14
+ "learning_rate": 0.0005,
15
+ "loss": 2.128,
16
+ "step": 1
17
+ },
18
+ {
19
+ "epoch": 0.27586206896551724,
20
+ "grad_norm": 0.05333964739666034,
21
+ "learning_rate": 0.001,
22
+ "loss": 2.1302,
23
+ "step": 2
24
+ },
25
+ {
26
+ "epoch": 0.41379310344827586,
27
+ "grad_norm": 0.058016931195404416,
28
+ "learning_rate": 0.0009964028776978417,
29
+ "loss": 2.0564,
30
+ "step": 3
31
+ },
32
+ {
33
+ "epoch": 0.5517241379310345,
34
+ "grad_norm": 0.04326663872610641,
35
+ "learning_rate": 0.0009928057553956835,
36
+ "loss": 1.9059,
37
+ "step": 4
38
+ },
39
+ {
40
+ "epoch": 0.6896551724137931,
41
+ "grad_norm": 0.054612579586085404,
42
+ "learning_rate": 0.0009892086330935252,
43
+ "loss": 1.8347,
44
+ "step": 5
45
+ },
46
+ {
47
+ "epoch": 0.6896551724137931,
48
+ "eval_loss": 1.7626737356185913,
49
+ "eval_runtime": 8.6889,
50
+ "eval_samples_per_second": 39.706,
51
+ "eval_steps_per_second": 0.921,
52
+ "step": 5
53
+ },
54
+ {
55
+ "epoch": 0.8275862068965517,
56
+ "grad_norm": 0.050666883911120446,
57
+ "learning_rate": 0.0009856115107913668,
58
+ "loss": 1.7702,
59
+ "step": 6
60
+ },
61
+ {
62
+ "epoch": 0.9655172413793104,
63
+ "grad_norm": 0.04281312629708349,
64
+ "learning_rate": 0.0009820143884892087,
65
+ "loss": 1.6843,
66
+ "step": 7
67
+ },
68
+ {
69
+ "epoch": 1.0,
70
+ "grad_norm": 0.04281312629708349,
71
+ "learning_rate": 0.0009784172661870504,
72
+ "loss": 1.5992,
73
+ "step": 8
74
+ },
75
+ {
76
+ "epoch": 1.1379310344827587,
77
+ "grad_norm": 0.0631213593072518,
78
+ "learning_rate": 0.0009748201438848921,
79
+ "loss": 1.5775,
80
+ "step": 9
81
+ },
82
+ {
83
+ "epoch": 1.2758620689655173,
84
+ "grad_norm": 0.040411682652170385,
85
+ "learning_rate": 0.0009712230215827338,
86
+ "loss": 1.4854,
87
+ "step": 10
88
+ },
89
+ {
90
+ "epoch": 1.2758620689655173,
91
+ "eval_loss": 1.4402364492416382,
92
+ "eval_runtime": 8.7029,
93
+ "eval_samples_per_second": 39.642,
94
+ "eval_steps_per_second": 0.919,
95
+ "step": 10
96
+ },
97
+ {
98
+ "epoch": 1.4137931034482758,
99
+ "grad_norm": 0.03883956348319155,
100
+ "learning_rate": 0.0009676258992805755,
101
+ "loss": 1.437,
102
+ "step": 11
103
+ },
104
+ {
105
+ "epoch": 1.5517241379310345,
106
+ "grad_norm": 0.03519878500508298,
107
+ "learning_rate": 0.0009640287769784173,
108
+ "loss": 1.3767,
109
+ "step": 12
110
+ },
111
+ {
112
+ "epoch": 1.6896551724137931,
113
+ "grad_norm": 0.024476692501359094,
114
+ "learning_rate": 0.000960431654676259,
115
+ "loss": 1.3413,
116
+ "step": 13
117
+ },
118
+ {
119
+ "epoch": 1.8275862068965516,
120
+ "grad_norm": 0.024592187290776446,
121
+ "learning_rate": 0.0009568345323741008,
122
+ "loss": 1.3432,
123
+ "step": 14
124
+ },
125
+ {
126
+ "epoch": 1.9655172413793105,
127
+ "grad_norm": 0.05715431884474389,
128
+ "learning_rate": 0.0009532374100719424,
129
+ "loss": 1.3107,
130
+ "step": 15
131
+ },
132
+ {
133
+ "epoch": 1.9655172413793105,
134
+ "eval_loss": 1.2948569059371948,
135
+ "eval_runtime": 8.7259,
136
+ "eval_samples_per_second": 39.538,
137
+ "eval_steps_per_second": 0.917,
138
+ "step": 15
139
+ },
140
+ {
141
+ "epoch": 2.0,
142
+ "grad_norm": 0.05715431884474389,
143
+ "learning_rate": 0.0009496402877697842,
144
+ "loss": 1.2613,
145
+ "step": 16
146
+ },
147
+ {
148
+ "epoch": 2.1379310344827585,
149
+ "grad_norm": 0.03512336843673436,
150
+ "learning_rate": 0.000946043165467626,
151
+ "loss": 1.2852,
152
+ "step": 17
153
+ },
154
+ {
155
+ "epoch": 2.2758620689655173,
156
+ "grad_norm": 0.018548899425460064,
157
+ "learning_rate": 0.0009424460431654677,
158
+ "loss": 1.2644,
159
+ "step": 18
160
+ },
161
+ {
162
+ "epoch": 2.413793103448276,
163
+ "grad_norm": 0.09098552487877788,
164
+ "learning_rate": 0.0009388489208633094,
165
+ "loss": 1.2403,
166
+ "step": 19
167
+ },
168
+ {
169
+ "epoch": 2.5517241379310347,
170
+ "grad_norm": 0.019520411340674417,
171
+ "learning_rate": 0.000935251798561151,
172
+ "loss": 1.222,
173
+ "step": 20
174
+ },
175
+ {
176
+ "epoch": 2.5517241379310347,
177
+ "eval_loss": 1.2261666059494019,
178
+ "eval_runtime": 8.7861,
179
+ "eval_samples_per_second": 39.266,
180
+ "eval_steps_per_second": 0.911,
181
+ "step": 20
182
+ },
183
+ {
184
+ "epoch": 2.689655172413793,
185
+ "grad_norm": 0.022289099431300925,
186
+ "learning_rate": 0.0009316546762589928,
187
+ "loss": 1.2195,
188
+ "step": 21
189
+ },
190
+ {
191
+ "epoch": 2.8275862068965516,
192
+ "grad_norm": 0.02381063004327571,
193
+ "learning_rate": 0.0009280575539568345,
194
+ "loss": 1.2012,
195
+ "step": 22
196
+ },
197
+ {
198
+ "epoch": 2.9655172413793105,
199
+ "grad_norm": 0.025450542884096917,
200
+ "learning_rate": 0.0009244604316546763,
201
+ "loss": 1.1793,
202
+ "step": 23
203
+ },
204
+ {
205
+ "epoch": 3.0,
206
+ "grad_norm": 0.025450542884096917,
207
+ "learning_rate": 0.0009208633093525181,
208
+ "loss": 1.1313,
209
+ "step": 24
210
+ },
211
+ {
212
+ "epoch": 3.1379310344827585,
213
+ "grad_norm": 0.044921725406371966,
214
+ "learning_rate": 0.0009172661870503597,
215
+ "loss": 1.143,
216
+ "step": 25
217
+ },
218
+ {
219
+ "epoch": 3.1379310344827585,
220
+ "eval_loss": 1.1603233814239502,
221
+ "eval_runtime": 8.7738,
222
+ "eval_samples_per_second": 39.321,
223
+ "eval_steps_per_second": 0.912,
224
+ "step": 25
225
+ },
226
+ {
227
+ "epoch": 3.2758620689655173,
228
+ "grad_norm": 0.033768741492488374,
229
+ "learning_rate": 0.0009136690647482015,
230
+ "loss": 1.1499,
231
+ "step": 26
232
+ },
233
+ {
234
+ "epoch": 3.413793103448276,
235
+ "grad_norm": 0.018050418431123687,
236
+ "learning_rate": 0.0009100719424460432,
237
+ "loss": 1.1255,
238
+ "step": 27
239
+ },
240
+ {
241
+ "epoch": 3.5517241379310347,
242
+ "grad_norm": 0.01940716693226271,
243
+ "learning_rate": 0.000906474820143885,
244
+ "loss": 1.1145,
245
+ "step": 28
246
+ },
247
+ {
248
+ "epoch": 3.689655172413793,
249
+ "grad_norm": 0.018694788902298046,
250
+ "learning_rate": 0.0009028776978417266,
251
+ "loss": 1.1122,
252
+ "step": 29
253
+ },
254
+ {
255
+ "epoch": 3.8275862068965516,
256
+ "grad_norm": 0.018519707307311912,
257
+ "learning_rate": 0.0008992805755395683,
258
+ "loss": 1.1218,
259
+ "step": 30
260
+ },
261
+ {
262
+ "epoch": 3.8275862068965516,
263
+ "eval_loss": 1.106053113937378,
264
+ "eval_runtime": 8.7938,
265
+ "eval_samples_per_second": 39.232,
266
+ "eval_steps_per_second": 0.91,
267
+ "step": 30
268
+ },
269
+ {
270
+ "epoch": 3.9655172413793105,
271
+ "grad_norm": 0.020117770541105317,
272
+ "learning_rate": 0.00089568345323741,
273
+ "loss": 1.0801,
274
+ "step": 31
275
+ },
276
+ {
277
+ "epoch": 4.0,
278
+ "grad_norm": 0.030151182759044853,
279
+ "learning_rate": 0.0008920863309352518,
280
+ "loss": 1.0499,
281
+ "step": 32
282
+ },
283
+ {
284
+ "epoch": 4.137931034482759,
285
+ "grad_norm": 0.016403602467013206,
286
+ "learning_rate": 0.0008884892086330936,
287
+ "loss": 1.0646,
288
+ "step": 33
289
+ },
290
+ {
291
+ "epoch": 4.275862068965517,
292
+ "grad_norm": 0.020152302519675643,
293
+ "learning_rate": 0.0008848920863309353,
294
+ "loss": 1.0788,
295
+ "step": 34
296
+ },
297
+ {
298
+ "epoch": 4.413793103448276,
299
+ "grad_norm": 0.01685621089470334,
300
+ "learning_rate": 0.000881294964028777,
301
+ "loss": 1.0712,
302
+ "step": 35
303
+ },
304
+ {
305
+ "epoch": 4.413793103448276,
306
+ "eval_loss": 1.063632607460022,
307
+ "eval_runtime": 8.7826,
308
+ "eval_samples_per_second": 39.282,
309
+ "eval_steps_per_second": 0.911,
310
+ "step": 35
311
+ },
312
+ {
313
+ "epoch": 4.551724137931035,
314
+ "grad_norm": 0.020260493044968315,
315
+ "learning_rate": 0.0008776978417266187,
316
+ "loss": 1.0575,
317
+ "step": 36
318
+ },
319
+ {
320
+ "epoch": 4.689655172413794,
321
+ "grad_norm": 0.021005158921908743,
322
+ "learning_rate": 0.0008741007194244605,
323
+ "loss": 1.0365,
324
+ "step": 37
325
+ },
326
+ {
327
+ "epoch": 4.827586206896552,
328
+ "grad_norm": 0.023784804335841993,
329
+ "learning_rate": 0.0008705035971223022,
330
+ "loss": 1.0385,
331
+ "step": 38
332
+ },
333
+ {
334
+ "epoch": 4.9655172413793105,
335
+ "grad_norm": 0.032167116031741036,
336
+ "learning_rate": 0.000866906474820144,
337
+ "loss": 1.0251,
338
+ "step": 39
339
+ },
340
+ {
341
+ "epoch": 5.0,
342
+ "grad_norm": 0.032167116031741036,
343
+ "learning_rate": 0.0008633093525179855,
344
+ "loss": 1.019,
345
+ "step": 40
346
+ },
347
+ {
348
+ "epoch": 5.0,
349
+ "eval_loss": 1.0340641736984253,
350
+ "eval_runtime": 8.8007,
351
+ "eval_samples_per_second": 39.201,
352
+ "eval_steps_per_second": 0.909,
353
+ "step": 40
354
+ },
355
+ {
356
+ "epoch": 5.137931034482759,
357
+ "grad_norm": 0.07798049000204828,
358
+ "learning_rate": 0.0008597122302158273,
359
+ "loss": 1.01,
360
+ "step": 41
361
+ },
362
+ {
363
+ "epoch": 5.275862068965517,
364
+ "grad_norm": 0.09886590876551558,
365
+ "learning_rate": 0.0008561151079136691,
366
+ "loss": 1.0233,
367
+ "step": 42
368
+ },
369
+ {
370
+ "epoch": 5.413793103448276,
371
+ "grad_norm": 0.023884493715335922,
372
+ "learning_rate": 0.0008525179856115108,
373
+ "loss": 1.0097,
374
+ "step": 43
375
+ },
376
+ {
377
+ "epoch": 5.551724137931035,
378
+ "grad_norm": 0.06979205001715891,
379
+ "learning_rate": 0.0008489208633093526,
380
+ "loss": 0.9985,
381
+ "step": 44
382
+ },
383
+ {
384
+ "epoch": 5.689655172413794,
385
+ "grad_norm": 0.021564959285893665,
386
+ "learning_rate": 0.0008453237410071942,
387
+ "loss": 0.9957,
388
+ "step": 45
389
+ },
390
+ {
391
+ "epoch": 5.689655172413794,
392
+ "eval_loss": 1.0002745389938354,
393
+ "eval_runtime": 8.9445,
394
+ "eval_samples_per_second": 38.571,
395
+ "eval_steps_per_second": 0.894,
396
+ "step": 45
397
+ },
398
+ {
399
+ "epoch": 5.827586206896552,
400
+ "grad_norm": 0.0376324980443176,
401
+ "learning_rate": 0.000841726618705036,
402
+ "loss": 0.9786,
403
+ "step": 46
404
+ },
405
+ {
406
+ "epoch": 5.9655172413793105,
407
+ "grad_norm": 0.020857211459805413,
408
+ "learning_rate": 0.0008381294964028777,
409
+ "loss": 0.9601,
410
+ "step": 47
411
+ },
412
+ {
413
+ "epoch": 6.0,
414
+ "grad_norm": 0.020857211459805413,
415
+ "learning_rate": 0.0008345323741007195,
416
+ "loss": 0.9555,
417
+ "step": 48
418
+ },
419
+ {
420
+ "epoch": 6.137931034482759,
421
+ "grad_norm": 0.052502983051829426,
422
+ "learning_rate": 0.0008309352517985613,
423
+ "loss": 0.9629,
424
+ "step": 49
425
+ },
426
+ {
427
+ "epoch": 6.275862068965517,
428
+ "grad_norm": 0.024811733910064117,
429
+ "learning_rate": 0.0008273381294964029,
430
+ "loss": 0.9292,
431
+ "step": 50
432
+ },
433
+ {
434
+ "epoch": 6.275862068965517,
435
+ "eval_loss": 0.9714928865432739,
436
+ "eval_runtime": 8.8044,
437
+ "eval_samples_per_second": 39.185,
438
+ "eval_steps_per_second": 0.909,
439
+ "step": 50
440
+ },
441
+ {
442
+ "epoch": 6.413793103448276,
443
+ "grad_norm": 0.03582733633923353,
444
+ "learning_rate": 0.0008237410071942446,
445
+ "loss": 0.9518,
446
+ "step": 51
447
+ },
448
+ {
449
+ "epoch": 6.551724137931035,
450
+ "grad_norm": 0.02468880739545474,
451
+ "learning_rate": 0.0008201438848920863,
452
+ "loss": 0.9488,
453
+ "step": 52
454
+ },
455
+ {
456
+ "epoch": 6.689655172413794,
457
+ "grad_norm": 0.03558549438558043,
458
+ "learning_rate": 0.0008165467625899281,
459
+ "loss": 0.916,
460
+ "step": 53
461
+ },
462
+ {
463
+ "epoch": 6.827586206896552,
464
+ "grad_norm": 0.02452310420777109,
465
+ "learning_rate": 0.0008129496402877698,
466
+ "loss": 0.92,
467
+ "step": 54
468
+ },
469
+ {
470
+ "epoch": 6.9655172413793105,
471
+ "grad_norm": 0.038316990644975756,
472
+ "learning_rate": 0.0008093525179856115,
473
+ "loss": 0.9101,
474
+ "step": 55
475
+ },
476
+ {
477
+ "epoch": 6.9655172413793105,
478
+ "eval_loss": 0.9289326071739197,
479
+ "eval_runtime": 8.7145,
480
+ "eval_samples_per_second": 39.589,
481
+ "eval_steps_per_second": 0.918,
482
+ "step": 55
483
+ },
484
+ {
485
+ "epoch": 7.0,
486
+ "grad_norm": 0.038316990644975756,
487
+ "learning_rate": 0.0008057553956834532,
488
+ "loss": 0.9212,
489
+ "step": 56
490
+ },
491
+ {
492
+ "epoch": 7.137931034482759,
493
+ "grad_norm": 0.040080779501324854,
494
+ "learning_rate": 0.000802158273381295,
495
+ "loss": 0.8971,
496
+ "step": 57
497
+ },
498
+ {
499
+ "epoch": 7.275862068965517,
500
+ "grad_norm": 0.03601608661035335,
501
+ "learning_rate": 0.0007985611510791368,
502
+ "loss": 0.8705,
503
+ "step": 58
504
+ },
505
+ {
506
+ "epoch": 7.413793103448276,
507
+ "grad_norm": 0.026799201398559586,
508
+ "learning_rate": 0.0007949640287769785,
509
+ "loss": 0.8878,
510
+ "step": 59
511
+ },
512
+ {
513
+ "epoch": 7.551724137931035,
514
+ "grad_norm": 0.04133511342290006,
515
+ "learning_rate": 0.0007913669064748202,
516
+ "loss": 0.8789,
517
+ "step": 60
518
+ },
519
+ {
520
+ "epoch": 7.551724137931035,
521
+ "eval_loss": 0.8996085524559021,
522
+ "eval_runtime": 8.8063,
523
+ "eval_samples_per_second": 39.176,
524
+ "eval_steps_per_second": 0.908,
525
+ "step": 60
526
+ },
527
+ {
528
+ "epoch": 7.689655172413794,
529
+ "grad_norm": 0.02192309181262501,
530
+ "learning_rate": 0.0007877697841726618,
531
+ "loss": 0.8406,
532
+ "step": 61
533
+ },
534
+ {
535
+ "epoch": 7.827586206896552,
536
+ "grad_norm": 0.05098293083781006,
537
+ "learning_rate": 0.0007841726618705036,
538
+ "loss": 0.8722,
539
+ "step": 62
540
+ },
541
+ {
542
+ "epoch": 7.9655172413793105,
543
+ "grad_norm": 0.06015423238471803,
544
+ "learning_rate": 0.0007805755395683453,
545
+ "loss": 0.8417,
546
+ "step": 63
547
+ },
548
+ {
549
+ "epoch": 8.0,
550
+ "grad_norm": 0.06701582066856104,
551
+ "learning_rate": 0.0007769784172661871,
552
+ "loss": 0.8912,
553
+ "step": 64
554
+ },
555
+ {
556
+ "epoch": 8.137931034482758,
557
+ "grad_norm": 0.04301906429758133,
558
+ "learning_rate": 0.0007733812949640287,
559
+ "loss": 0.8058,
560
+ "step": 65
561
+ },
562
+ {
563
+ "epoch": 8.137931034482758,
564
+ "eval_loss": 0.8623361587524414,
565
+ "eval_runtime": 8.7802,
566
+ "eval_samples_per_second": 39.293,
567
+ "eval_steps_per_second": 0.911,
568
+ "step": 65
569
+ },
570
+ {
571
+ "epoch": 8.275862068965518,
572
+ "grad_norm": 0.029341471978980453,
573
+ "learning_rate": 0.0007697841726618705,
574
+ "loss": 0.7979,
575
+ "step": 66
576
+ },
577
+ {
578
+ "epoch": 8.413793103448276,
579
+ "grad_norm": 0.03780244186794758,
580
+ "learning_rate": 0.0007661870503597123,
581
+ "loss": 0.8187,
582
+ "step": 67
583
+ },
584
+ {
585
+ "epoch": 8.551724137931034,
586
+ "grad_norm": 0.03481163736217214,
587
+ "learning_rate": 0.000762589928057554,
588
+ "loss": 0.8073,
589
+ "step": 68
590
+ },
591
+ {
592
+ "epoch": 8.689655172413794,
593
+ "grad_norm": 0.06785007140950954,
594
+ "learning_rate": 0.0007589928057553958,
595
+ "loss": 0.8179,
596
+ "step": 69
597
+ },
598
+ {
599
+ "epoch": 8.827586206896552,
600
+ "grad_norm": 0.058863786122936974,
601
+ "learning_rate": 0.0007553956834532374,
602
+ "loss": 0.7895,
603
+ "step": 70
604
+ },
605
+ {
606
+ "epoch": 8.827586206896552,
607
+ "eval_loss": 0.8234646320343018,
608
+ "eval_runtime": 8.7585,
609
+ "eval_samples_per_second": 39.39,
610
+ "eval_steps_per_second": 0.913,
611
+ "step": 70
612
+ },
613
+ {
614
+ "epoch": 8.96551724137931,
615
+ "grad_norm": 0.03230624596425253,
616
+ "learning_rate": 0.0007517985611510792,
617
+ "loss": 0.7912,
618
+ "step": 71
619
+ },
620
+ {
621
+ "epoch": 9.0,
622
+ "grad_norm": 0.03230624596425253,
623
+ "learning_rate": 0.0007482014388489208,
624
+ "loss": 0.8106,
625
+ "step": 72
626
+ },
627
+ {
628
+ "epoch": 9.137931034482758,
629
+ "grad_norm": 0.1554303102807074,
630
+ "learning_rate": 0.0007446043165467626,
631
+ "loss": 0.7882,
632
+ "step": 73
633
+ },
634
+ {
635
+ "epoch": 9.275862068965518,
636
+ "grad_norm": 0.09569660143827638,
637
+ "learning_rate": 0.0007410071942446042,
638
+ "loss": 0.8088,
639
+ "step": 74
640
+ },
641
+ {
642
+ "epoch": 9.413793103448276,
643
+ "grad_norm": 0.06806996265094252,
644
+ "learning_rate": 0.000737410071942446,
645
+ "loss": 0.7893,
646
+ "step": 75
647
+ },
648
+ {
649
+ "epoch": 9.413793103448276,
650
+ "eval_loss": 0.8113399744033813,
651
+ "eval_runtime": 8.6957,
652
+ "eval_samples_per_second": 39.675,
653
+ "eval_steps_per_second": 0.92,
654
+ "step": 75
655
+ },
656
+ {
657
+ "epoch": 9.551724137931034,
658
+ "grad_norm": 0.044713722929739026,
659
+ "learning_rate": 0.0007338129496402878,
660
+ "loss": 0.7779,
661
+ "step": 76
662
+ },
663
+ {
664
+ "epoch": 9.689655172413794,
665
+ "grad_norm": 0.056782348247107005,
666
+ "learning_rate": 0.0007302158273381295,
667
+ "loss": 0.7457,
668
+ "step": 77
669
+ },
670
+ {
671
+ "epoch": 9.827586206896552,
672
+ "grad_norm": 0.044800375007794616,
673
+ "learning_rate": 0.0007266187050359713,
674
+ "loss": 0.7279,
675
+ "step": 78
676
+ },
677
+ {
678
+ "epoch": 9.96551724137931,
679
+ "grad_norm": 0.03786200648699165,
680
+ "learning_rate": 0.0007230215827338129,
681
+ "loss": 0.7033,
682
+ "step": 79
683
+ },
684
+ {
685
+ "epoch": 10.0,
686
+ "grad_norm": 0.03786200648699165,
687
+ "learning_rate": 0.0007194244604316547,
688
+ "loss": 0.7284,
689
+ "step": 80
690
+ },
691
+ {
692
+ "epoch": 10.0,
693
+ "eval_loss": 0.780044674873352,
694
+ "eval_runtime": 8.7266,
695
+ "eval_samples_per_second": 39.534,
696
+ "eval_steps_per_second": 0.917,
697
+ "step": 80
698
+ },
699
+ {
700
+ "epoch": 10.137931034482758,
701
+ "grad_norm": 0.07073689526642765,
702
+ "learning_rate": 0.0007158273381294965,
703
+ "loss": 0.7194,
704
+ "step": 81
705
+ },
706
+ {
707
+ "epoch": 10.275862068965518,
708
+ "grad_norm": 0.03398473440554854,
709
+ "learning_rate": 0.0007122302158273382,
710
+ "loss": 0.7017,
711
+ "step": 82
712
+ },
713
+ {
714
+ "epoch": 10.413793103448276,
715
+ "grad_norm": 0.04856422095760259,
716
+ "learning_rate": 0.0007086330935251799,
717
+ "loss": 0.701,
718
+ "step": 83
719
+ },
720
+ {
721
+ "epoch": 10.551724137931034,
722
+ "grad_norm": 0.036514989665613964,
723
+ "learning_rate": 0.0007050359712230215,
724
+ "loss": 0.6944,
725
+ "step": 84
726
+ },
727
+ {
728
+ "epoch": 10.689655172413794,
729
+ "grad_norm": 0.035374283328207815,
730
+ "learning_rate": 0.0007014388489208633,
731
+ "loss": 0.6915,
732
+ "step": 85
733
+ },
734
+ {
735
+ "epoch": 10.689655172413794,
736
+ "eval_loss": 0.7417200207710266,
737
+ "eval_runtime": 8.6895,
738
+ "eval_samples_per_second": 39.703,
739
+ "eval_steps_per_second": 0.921,
740
+ "step": 85
741
+ },
742
+ {
743
+ "epoch": 10.827586206896552,
744
+ "grad_norm": 0.03318794955539885,
745
+ "learning_rate": 0.000697841726618705,
746
+ "loss": 0.6814,
747
+ "step": 86
748
+ },
749
+ {
750
+ "epoch": 10.96551724137931,
751
+ "grad_norm": 0.041523642549677074,
752
+ "learning_rate": 0.0006942446043165468,
753
+ "loss": 0.6727,
754
+ "step": 87
755
+ },
756
+ {
757
+ "epoch": 11.0,
758
+ "grad_norm": 0.041523642549677074,
759
+ "learning_rate": 0.0006906474820143885,
760
+ "loss": 0.6353,
761
+ "step": 88
762
+ },
763
+ {
764
+ "epoch": 11.137931034482758,
765
+ "grad_norm": 0.06435409175849668,
766
+ "learning_rate": 0.0006870503597122302,
767
+ "loss": 0.6512,
768
+ "step": 89
769
+ },
770
+ {
771
+ "epoch": 11.275862068965518,
772
+ "grad_norm": 0.0579037672022612,
773
+ "learning_rate": 0.000683453237410072,
774
+ "loss": 0.6511,
775
+ "step": 90
776
+ },
777
+ {
778
+ "epoch": 11.275862068965518,
779
+ "eval_loss": 0.7169574499130249,
780
+ "eval_runtime": 8.7917,
781
+ "eval_samples_per_second": 39.242,
782
+ "eval_steps_per_second": 0.91,
783
+ "step": 90
784
+ },
785
+ {
786
+ "epoch": 11.413793103448276,
787
+ "grad_norm": 0.04520629109098542,
788
+ "learning_rate": 0.0006798561151079137,
789
+ "loss": 0.6484,
790
+ "step": 91
791
+ },
792
+ {
793
+ "epoch": 11.551724137931034,
794
+ "grad_norm": 0.04227711846522402,
795
+ "learning_rate": 0.0006762589928057555,
796
+ "loss": 0.6335,
797
+ "step": 92
798
+ },
799
+ {
800
+ "epoch": 11.689655172413794,
801
+ "grad_norm": 0.039826762648682984,
802
+ "learning_rate": 0.0006726618705035971,
803
+ "loss": 0.5983,
804
+ "step": 93
805
+ },
806
+ {
807
+ "epoch": 11.827586206896552,
808
+ "grad_norm": 0.03439114332083754,
809
+ "learning_rate": 0.0006690647482014388,
810
+ "loss": 0.6394,
811
+ "step": 94
812
+ },
813
+ {
814
+ "epoch": 11.96551724137931,
815
+ "grad_norm": 0.038036102867766125,
816
+ "learning_rate": 0.0006654676258992805,
817
+ "loss": 0.595,
818
+ "step": 95
819
+ },
820
+ {
821
+ "epoch": 11.96551724137931,
822
+ "eval_loss": 0.6772060394287109,
823
+ "eval_runtime": 8.7801,
824
+ "eval_samples_per_second": 39.293,
825
+ "eval_steps_per_second": 0.911,
826
+ "step": 95
827
+ },
828
+ {
829
+ "epoch": 12.0,
830
+ "grad_norm": 0.05604044246725643,
831
+ "learning_rate": 0.0006618705035971223,
832
+ "loss": 0.6135,
833
+ "step": 96
834
+ },
835
+ {
836
+ "epoch": 12.137931034482758,
837
+ "grad_norm": 0.04994151352050706,
838
+ "learning_rate": 0.000658273381294964,
839
+ "loss": 0.575,
840
+ "step": 97
841
+ },
842
+ {
843
+ "epoch": 12.275862068965518,
844
+ "grad_norm": 0.04184399046532961,
845
+ "learning_rate": 0.0006546762589928058,
846
+ "loss": 0.6,
847
+ "step": 98
848
+ },
849
+ {
850
+ "epoch": 12.413793103448276,
851
+ "grad_norm": 0.03803711123284359,
852
+ "learning_rate": 0.0006510791366906475,
853
+ "loss": 0.5731,
854
+ "step": 99
855
+ },
856
+ {
857
+ "epoch": 12.551724137931034,
858
+ "grad_norm": 0.03801862718301205,
859
+ "learning_rate": 0.0006474820143884892,
860
+ "loss": 0.5683,
861
+ "step": 100
862
+ },
863
+ {
864
+ "epoch": 12.551724137931034,
865
+ "eval_loss": 0.6432012915611267,
866
+ "eval_runtime": 8.7336,
867
+ "eval_samples_per_second": 39.503,
868
+ "eval_steps_per_second": 0.916,
869
+ "step": 100
870
+ },
871
+ {
872
+ "epoch": 12.689655172413794,
873
+ "grad_norm": 0.03586500697965485,
874
+ "learning_rate": 0.000643884892086331,
875
+ "loss": 0.5817,
876
+ "step": 101
877
+ },
878
+ {
879
+ "epoch": 12.827586206896552,
880
+ "grad_norm": 0.03495031944650168,
881
+ "learning_rate": 0.0006402877697841727,
882
+ "loss": 0.5365,
883
+ "step": 102
884
+ },
885
+ {
886
+ "epoch": 12.96551724137931,
887
+ "grad_norm": 0.05928855832341936,
888
+ "learning_rate": 0.0006366906474820145,
889
+ "loss": 0.5656,
890
+ "step": 103
891
+ },
892
+ {
893
+ "epoch": 13.0,
894
+ "grad_norm": 0.05928855832341936,
895
+ "learning_rate": 0.000633093525179856,
896
+ "loss": 0.5351,
897
+ "step": 104
898
+ },
899
+ {
900
+ "epoch": 13.137931034482758,
901
+ "grad_norm": 0.11612564961031191,
902
+ "learning_rate": 0.0006294964028776978,
903
+ "loss": 0.5208,
904
+ "step": 105
905
+ },
906
+ {
907
+ "epoch": 13.137931034482758,
908
+ "eval_loss": 0.6467034220695496,
909
+ "eval_runtime": 8.7001,
910
+ "eval_samples_per_second": 39.655,
911
+ "eval_steps_per_second": 0.92,
912
+ "step": 105
913
+ },
914
+ {
915
+ "epoch": 13.275862068965518,
916
+ "grad_norm": 0.22137091513173082,
917
+ "learning_rate": 0.0006258992805755395,
918
+ "loss": 0.5483,
919
+ "step": 106
920
+ },
921
+ {
922
+ "epoch": 13.413793103448276,
923
+ "grad_norm": 0.1199040574963519,
924
+ "learning_rate": 0.0006223021582733813,
925
+ "loss": 0.5436,
926
+ "step": 107
927
+ },
928
+ {
929
+ "epoch": 13.551724137931034,
930
+ "grad_norm": 0.09018438521280102,
931
+ "learning_rate": 0.0006187050359712231,
932
+ "loss": 0.5144,
933
+ "step": 108
934
+ },
935
+ {
936
+ "epoch": 13.689655172413794,
937
+ "grad_norm": 0.06555940691015907,
938
+ "learning_rate": 0.0006151079136690647,
939
+ "loss": 0.5284,
940
+ "step": 109
941
+ },
942
+ {
943
+ "epoch": 13.827586206896552,
944
+ "grad_norm": 0.11451636039679007,
945
+ "learning_rate": 0.0006115107913669065,
946
+ "loss": 0.5315,
947
+ "step": 110
948
+ },
949
+ {
950
+ "epoch": 13.827586206896552,
951
+ "eval_loss": 0.6052789092063904,
952
+ "eval_runtime": 8.6991,
953
+ "eval_samples_per_second": 39.659,
954
+ "eval_steps_per_second": 0.92,
955
+ "step": 110
956
+ },
957
+ {
958
+ "epoch": 13.96551724137931,
959
+ "grad_norm": 0.07906239963728381,
960
+ "learning_rate": 0.0006079136690647482,
961
+ "loss": 0.5475,
962
+ "step": 111
963
+ },
964
+ {
965
+ "epoch": 14.0,
966
+ "grad_norm": 0.07906239963728381,
967
+ "learning_rate": 0.00060431654676259,
968
+ "loss": 0.5278,
969
+ "step": 112
970
+ },
971
+ {
972
+ "epoch": 14.137931034482758,
973
+ "grad_norm": 0.11206457612747601,
974
+ "learning_rate": 0.0006007194244604317,
975
+ "loss": 0.4842,
976
+ "step": 113
977
+ },
978
+ {
979
+ "epoch": 14.275862068965518,
980
+ "grad_norm": 0.09068382690004628,
981
+ "learning_rate": 0.0005971223021582734,
982
+ "loss": 0.5257,
983
+ "step": 114
984
+ },
985
+ {
986
+ "epoch": 14.413793103448276,
987
+ "grad_norm": 0.09905034332530972,
988
+ "learning_rate": 0.000593525179856115,
989
+ "loss": 0.4842,
990
+ "step": 115
991
+ },
992
+ {
993
+ "epoch": 14.413793103448276,
994
+ "eval_loss": 0.5792981386184692,
995
+ "eval_runtime": 8.7684,
996
+ "eval_samples_per_second": 39.346,
997
+ "eval_steps_per_second": 0.912,
998
+ "step": 115
999
+ },
1000
+ {
1001
+ "epoch": 14.551724137931034,
1002
+ "grad_norm": 0.05257671493896148,
1003
+ "learning_rate": 0.0005899280575539568,
1004
+ "loss": 0.496,
1005
+ "step": 116
1006
+ },
1007
+ {
1008
+ "epoch": 14.689655172413794,
1009
+ "grad_norm": 0.07740518006114924,
1010
+ "learning_rate": 0.0005863309352517986,
1011
+ "loss": 0.479,
1012
+ "step": 117
1013
+ },
1014
+ {
1015
+ "epoch": 14.827586206896552,
1016
+ "grad_norm": 0.047893001607463556,
1017
+ "learning_rate": 0.0005827338129496403,
1018
+ "loss": 0.4554,
1019
+ "step": 118
1020
+ },
1021
+ {
1022
+ "epoch": 14.96551724137931,
1023
+ "grad_norm": 0.07309479828787178,
1024
+ "learning_rate": 0.000579136690647482,
1025
+ "loss": 0.4788,
1026
+ "step": 119
1027
+ },
1028
+ {
1029
+ "epoch": 15.0,
1030
+ "grad_norm": 0.07309479828787178,
1031
+ "learning_rate": 0.0005755395683453237,
1032
+ "loss": 0.433,
1033
+ "step": 120
1034
+ },
1035
+ {
1036
+ "epoch": 15.0,
1037
+ "eval_loss": 0.5587322115898132,
1038
+ "eval_runtime": 8.7696,
1039
+ "eval_samples_per_second": 39.34,
1040
+ "eval_steps_per_second": 0.912,
1041
+ "step": 120
1042
+ },
1043
+ {
1044
+ "epoch": 15.137931034482758,
1045
+ "grad_norm": 0.08904773564364181,
1046
+ "learning_rate": 0.0005719424460431655,
1047
+ "loss": 0.4487,
1048
+ "step": 121
1049
+ },
1050
+ {
1051
+ "epoch": 15.275862068965518,
1052
+ "grad_norm": 0.04894681286589121,
1053
+ "learning_rate": 0.0005683453237410072,
1054
+ "loss": 0.4359,
1055
+ "step": 122
1056
+ },
1057
+ {
1058
+ "epoch": 15.413793103448276,
1059
+ "grad_norm": 0.06703788904754221,
1060
+ "learning_rate": 0.000564748201438849,
1061
+ "loss": 0.4538,
1062
+ "step": 123
1063
+ },
1064
+ {
1065
+ "epoch": 15.551724137931034,
1066
+ "grad_norm": 0.047102787540723966,
1067
+ "learning_rate": 0.0005611510791366907,
1068
+ "loss": 0.4396,
1069
+ "step": 124
1070
+ },
1071
+ {
1072
+ "epoch": 15.689655172413794,
1073
+ "grad_norm": 0.05558056329945917,
1074
+ "learning_rate": 0.0005575539568345323,
1075
+ "loss": 0.4496,
1076
+ "step": 125
1077
+ },
1078
+ {
1079
+ "epoch": 15.689655172413794,
1080
+ "eval_loss": 0.5378495454788208,
1081
+ "eval_runtime": 8.7147,
1082
+ "eval_samples_per_second": 39.588,
1083
+ "eval_steps_per_second": 0.918,
1084
+ "step": 125
1085
+ },
1086
+ {
1087
+ "epoch": 15.827586206896552,
1088
+ "grad_norm": 0.05758259424706525,
1089
+ "learning_rate": 0.0005539568345323741,
1090
+ "loss": 0.424,
1091
+ "step": 126
1092
+ },
1093
+ {
1094
+ "epoch": 15.96551724137931,
1095
+ "grad_norm": 0.043929928558949984,
1096
+ "learning_rate": 0.0005503597122302158,
1097
+ "loss": 0.43,
1098
+ "step": 127
1099
+ },
1100
+ {
1101
+ "epoch": 16.0,
1102
+ "grad_norm": 0.10300395929702866,
1103
+ "learning_rate": 0.0005467625899280576,
1104
+ "loss": 0.4455,
1105
+ "step": 128
1106
+ },
1107
+ {
1108
+ "epoch": 16.137931034482758,
1109
+ "grad_norm": 0.07432283583853015,
1110
+ "learning_rate": 0.0005431654676258992,
1111
+ "loss": 0.4064,
1112
+ "step": 129
1113
+ },
1114
+ {
1115
+ "epoch": 16.275862068965516,
1116
+ "grad_norm": 0.05977021258371512,
1117
+ "learning_rate": 0.000539568345323741,
1118
+ "loss": 0.3885,
1119
+ "step": 130
1120
+ },
1121
+ {
1122
+ "epoch": 16.275862068965516,
1123
+ "eval_loss": 0.5139353275299072,
1124
+ "eval_runtime": 8.7344,
1125
+ "eval_samples_per_second": 39.499,
1126
+ "eval_steps_per_second": 0.916,
1127
+ "step": 130
1128
+ },
1129
+ {
1130
+ "epoch": 16.413793103448278,
1131
+ "grad_norm": 0.06869220801115782,
1132
+ "learning_rate": 0.0005359712230215828,
1133
+ "loss": 0.415,
1134
+ "step": 131
1135
+ },
1136
+ {
1137
+ "epoch": 16.551724137931036,
1138
+ "grad_norm": 0.061674024982612694,
1139
+ "learning_rate": 0.0005323741007194245,
1140
+ "loss": 0.4049,
1141
+ "step": 132
1142
+ },
1143
+ {
1144
+ "epoch": 16.689655172413794,
1145
+ "grad_norm": 0.051882717089137026,
1146
+ "learning_rate": 0.0005287769784172663,
1147
+ "loss": 0.4017,
1148
+ "step": 133
1149
+ },
1150
+ {
1151
+ "epoch": 16.82758620689655,
1152
+ "grad_norm": 0.05369901449470005,
1153
+ "learning_rate": 0.0005251798561151079,
1154
+ "loss": 0.3951,
1155
+ "step": 134
1156
+ },
1157
+ {
1158
+ "epoch": 16.96551724137931,
1159
+ "grad_norm": 0.048162328192559466,
1160
+ "learning_rate": 0.0005215827338129497,
1161
+ "loss": 0.402,
1162
+ "step": 135
1163
+ },
1164
+ {
1165
+ "epoch": 16.96551724137931,
1166
+ "eval_loss": 0.49061039090156555,
1167
+ "eval_runtime": 8.6979,
1168
+ "eval_samples_per_second": 39.665,
1169
+ "eval_steps_per_second": 0.92,
1170
+ "step": 135
1171
+ },
1172
+ {
1173
+ "epoch": 17.0,
1174
+ "grad_norm": 0.048162328192559466,
1175
+ "learning_rate": 0.0005179856115107913,
1176
+ "loss": 0.397,
1177
+ "step": 136
1178
+ },
1179
+ {
1180
+ "epoch": 17.137931034482758,
1181
+ "grad_norm": 0.09302817250149495,
1182
+ "learning_rate": 0.0005143884892086331,
1183
+ "loss": 0.3792,
1184
+ "step": 137
1185
+ },
1186
+ {
1187
+ "epoch": 17.275862068965516,
1188
+ "grad_norm": 0.05435540163439829,
1189
+ "learning_rate": 0.0005107913669064748,
1190
+ "loss": 0.3709,
1191
+ "step": 138
1192
+ },
1193
+ {
1194
+ "epoch": 17.413793103448278,
1195
+ "grad_norm": 0.05840578597211101,
1196
+ "learning_rate": 0.0005071942446043165,
1197
+ "loss": 0.3764,
1198
+ "step": 139
1199
+ },
1200
+ {
1201
+ "epoch": 17.551724137931036,
1202
+ "grad_norm": 0.06195300966607209,
1203
+ "learning_rate": 0.0005035971223021583,
1204
+ "loss": 0.364,
1205
+ "step": 140
1206
+ },
1207
+ {
1208
+ "epoch": 17.551724137931036,
1209
+ "eval_loss": 0.47675666213035583,
1210
+ "eval_runtime": 8.6989,
1211
+ "eval_samples_per_second": 39.66,
1212
+ "eval_steps_per_second": 0.92,
1213
+ "step": 140
1214
+ },
1215
+ {
1216
+ "epoch": 17.689655172413794,
1217
+ "grad_norm": 0.04857113738664563,
1218
+ "learning_rate": 0.0005,
1219
+ "loss": 0.3685,
1220
+ "step": 141
1221
+ },
1222
+ {
1223
+ "epoch": 17.82758620689655,
1224
+ "grad_norm": 0.0627055530935345,
1225
+ "learning_rate": 0.0004964028776978418,
1226
+ "loss": 0.3559,
1227
+ "step": 142
1228
+ },
1229
+ {
1230
+ "epoch": 17.96551724137931,
1231
+ "grad_norm": 0.04650519405530641,
1232
+ "learning_rate": 0.0004928057553956834,
1233
+ "loss": 0.3634,
1234
+ "step": 143
1235
+ },
1236
+ {
1237
+ "epoch": 18.0,
1238
+ "grad_norm": 0.04650519405530641,
1239
+ "learning_rate": 0.0004892086330935252,
1240
+ "loss": 0.3448,
1241
+ "step": 144
1242
+ },
1243
+ {
1244
+ "epoch": 18.137931034482758,
1245
+ "grad_norm": 0.08991065499789733,
1246
+ "learning_rate": 0.0004856115107913669,
1247
+ "loss": 0.3354,
1248
+ "step": 145
1249
+ },
1250
+ {
1251
+ "epoch": 18.137931034482758,
1252
+ "eval_loss": 0.4616415202617645,
1253
+ "eval_runtime": 8.7144,
1254
+ "eval_samples_per_second": 39.59,
1255
+ "eval_steps_per_second": 0.918,
1256
+ "step": 145
1257
+ },
1258
+ {
1259
+ "epoch": 18.275862068965516,
1260
+ "grad_norm": 0.03790952903581309,
1261
+ "learning_rate": 0.00048201438848920864,
1262
+ "loss": 0.3357,
1263
+ "step": 146
1264
+ },
1265
+ {
1266
+ "epoch": 18.413793103448278,
1267
+ "grad_norm": 0.052239621371967,
1268
+ "learning_rate": 0.0004784172661870504,
1269
+ "loss": 0.3519,
1270
+ "step": 147
1271
+ },
1272
+ {
1273
+ "epoch": 18.551724137931036,
1274
+ "grad_norm": 0.04321045990500223,
1275
+ "learning_rate": 0.0004748201438848921,
1276
+ "loss": 0.3383,
1277
+ "step": 148
1278
+ },
1279
+ {
1280
+ "epoch": 18.689655172413794,
1281
+ "grad_norm": 0.03829096991939794,
1282
+ "learning_rate": 0.00047122302158273386,
1283
+ "loss": 0.3246,
1284
+ "step": 149
1285
+ },
1286
+ {
1287
+ "epoch": 18.82758620689655,
1288
+ "grad_norm": 0.04168126851743768,
1289
+ "learning_rate": 0.0004676258992805755,
1290
+ "loss": 0.3469,
1291
+ "step": 150
1292
+ },
1293
+ {
1294
+ "epoch": 18.82758620689655,
1295
+ "eval_loss": 0.4487021565437317,
1296
+ "eval_runtime": 8.7601,
1297
+ "eval_samples_per_second": 39.383,
1298
+ "eval_steps_per_second": 0.913,
1299
+ "step": 150
1300
+ },
1301
+ {
1302
+ "epoch": 18.96551724137931,
1303
+ "grad_norm": 0.037249139298965485,
1304
+ "learning_rate": 0.00046402877697841727,
1305
+ "loss": 0.3334,
1306
+ "step": 151
1307
+ },
1308
+ {
1309
+ "epoch": 19.0,
1310
+ "grad_norm": 0.037249139298965485,
1311
+ "learning_rate": 0.00046043165467625903,
1312
+ "loss": 0.3545,
1313
+ "step": 152
1314
+ },
1315
+ {
1316
+ "epoch": 19.137931034482758,
1317
+ "grad_norm": 0.07450770548392305,
1318
+ "learning_rate": 0.00045683453237410073,
1319
+ "loss": 0.3066,
1320
+ "step": 153
1321
+ },
1322
+ {
1323
+ "epoch": 19.275862068965516,
1324
+ "grad_norm": 0.05135925856181853,
1325
+ "learning_rate": 0.0004532374100719425,
1326
+ "loss": 0.3099,
1327
+ "step": 154
1328
+ },
1329
+ {
1330
+ "epoch": 19.413793103448278,
1331
+ "grad_norm": 0.04689786473811723,
1332
+ "learning_rate": 0.00044964028776978414,
1333
+ "loss": 0.3373,
1334
+ "step": 155
1335
+ },
1336
+ {
1337
+ "epoch": 19.413793103448278,
1338
+ "eval_loss": 0.4410878121852875,
1339
+ "eval_runtime": 8.7261,
1340
+ "eval_samples_per_second": 39.537,
1341
+ "eval_steps_per_second": 0.917,
1342
+ "step": 155
1343
+ },
1344
+ {
1345
+ "epoch": 19.551724137931036,
1346
+ "grad_norm": 0.05708703739775451,
1347
+ "learning_rate": 0.0004460431654676259,
1348
+ "loss": 0.3123,
1349
+ "step": 156
1350
+ },
1351
+ {
1352
+ "epoch": 19.689655172413794,
1353
+ "grad_norm": 0.042115444569519274,
1354
+ "learning_rate": 0.00044244604316546766,
1355
+ "loss": 0.3002,
1356
+ "step": 157
1357
+ },
1358
+ {
1359
+ "epoch": 19.82758620689655,
1360
+ "grad_norm": 0.040217848237229105,
1361
+ "learning_rate": 0.00043884892086330936,
1362
+ "loss": 0.3228,
1363
+ "step": 158
1364
+ },
1365
+ {
1366
+ "epoch": 19.96551724137931,
1367
+ "grad_norm": 0.04391216766980618,
1368
+ "learning_rate": 0.0004352517985611511,
1369
+ "loss": 0.3115,
1370
+ "step": 159
1371
+ },
1372
+ {
1373
+ "epoch": 20.0,
1374
+ "grad_norm": 0.06542875294767495,
1375
+ "learning_rate": 0.00043165467625899277,
1376
+ "loss": 0.2962,
1377
+ "step": 160
1378
+ },
1379
+ {
1380
+ "epoch": 20.0,
1381
+ "eval_loss": 0.4252021312713623,
1382
+ "eval_runtime": 8.7286,
1383
+ "eval_samples_per_second": 39.525,
1384
+ "eval_steps_per_second": 0.917,
1385
+ "step": 160
1386
+ },
1387
+ {
1388
+ "epoch": 20.137931034482758,
1389
+ "grad_norm": 0.03736150483708675,
1390
+ "learning_rate": 0.00042805755395683453,
1391
+ "loss": 0.2859,
1392
+ "step": 161
1393
+ },
1394
+ {
1395
+ "epoch": 20.275862068965516,
1396
+ "grad_norm": 0.042703785723630386,
1397
+ "learning_rate": 0.0004244604316546763,
1398
+ "loss": 0.3114,
1399
+ "step": 162
1400
+ },
1401
+ {
1402
+ "epoch": 20.413793103448278,
1403
+ "grad_norm": 0.037142960728971,
1404
+ "learning_rate": 0.000420863309352518,
1405
+ "loss": 0.2877,
1406
+ "step": 163
1407
+ },
1408
+ {
1409
+ "epoch": 20.551724137931036,
1410
+ "grad_norm": 0.038600240088635114,
1411
+ "learning_rate": 0.00041726618705035975,
1412
+ "loss": 0.2999,
1413
+ "step": 164
1414
+ },
1415
+ {
1416
+ "epoch": 20.689655172413794,
1417
+ "grad_norm": 0.04060052059908159,
1418
+ "learning_rate": 0.00041366906474820146,
1419
+ "loss": 0.2941,
1420
+ "step": 165
1421
+ },
1422
+ {
1423
+ "epoch": 20.689655172413794,
1424
+ "eval_loss": 0.4126673936843872,
1425
+ "eval_runtime": 8.7863,
1426
+ "eval_samples_per_second": 39.266,
1427
+ "eval_steps_per_second": 0.911,
1428
+ "step": 165
1429
+ },
1430
+ {
1431
+ "epoch": 20.82758620689655,
1432
+ "grad_norm": 0.03344897491878835,
1433
+ "learning_rate": 0.00041007194244604316,
1434
+ "loss": 0.2689,
1435
+ "step": 166
1436
+ },
1437
+ {
1438
+ "epoch": 20.96551724137931,
1439
+ "grad_norm": 0.03539176663761955,
1440
+ "learning_rate": 0.0004064748201438849,
1441
+ "loss": 0.3139,
1442
+ "step": 167
1443
+ },
1444
+ {
1445
+ "epoch": 21.0,
1446
+ "grad_norm": 0.03539176663761955,
1447
+ "learning_rate": 0.0004028776978417266,
1448
+ "loss": 0.2613,
1449
+ "step": 168
1450
+ },
1451
+ {
1452
+ "epoch": 21.137931034482758,
1453
+ "grad_norm": 0.07957396726394156,
1454
+ "learning_rate": 0.0003992805755395684,
1455
+ "loss": 0.2856,
1456
+ "step": 169
1457
+ },
1458
+ {
1459
+ "epoch": 21.275862068965516,
1460
+ "grad_norm": 0.040248146057723054,
1461
+ "learning_rate": 0.0003956834532374101,
1462
+ "loss": 0.28,
1463
+ "step": 170
1464
+ },
1465
+ {
1466
+ "epoch": 21.275862068965516,
1467
+ "eval_loss": 0.4109073281288147,
1468
+ "eval_runtime": 8.7332,
1469
+ "eval_samples_per_second": 39.505,
1470
+ "eval_steps_per_second": 0.916,
1471
+ "step": 170
1472
+ },
1473
+ {
1474
+ "epoch": 21.413793103448278,
1475
+ "grad_norm": 0.06332762701916422,
1476
+ "learning_rate": 0.0003920863309352518,
1477
+ "loss": 0.289,
1478
+ "step": 171
1479
+ },
1480
+ {
1481
+ "epoch": 21.551724137931036,
1482
+ "grad_norm": 0.035129838427540666,
1483
+ "learning_rate": 0.00038848920863309355,
1484
+ "loss": 0.2722,
1485
+ "step": 172
1486
+ },
1487
+ {
1488
+ "epoch": 21.689655172413794,
1489
+ "grad_norm": 0.04750807040734642,
1490
+ "learning_rate": 0.00038489208633093525,
1491
+ "loss": 0.2663,
1492
+ "step": 173
1493
+ },
1494
+ {
1495
+ "epoch": 21.82758620689655,
1496
+ "grad_norm": 0.04471638668012776,
1497
+ "learning_rate": 0.000381294964028777,
1498
+ "loss": 0.2673,
1499
+ "step": 174
1500
+ },
1501
+ {
1502
+ "epoch": 21.96551724137931,
1503
+ "grad_norm": 0.03406278017825759,
1504
+ "learning_rate": 0.0003776978417266187,
1505
+ "loss": 0.2657,
1506
+ "step": 175
1507
+ },
1508
+ {
1509
+ "epoch": 21.96551724137931,
1510
+ "eval_loss": 0.398811399936676,
1511
+ "eval_runtime": 8.6856,
1512
+ "eval_samples_per_second": 39.721,
1513
+ "eval_steps_per_second": 0.921,
1514
+ "step": 175
1515
+ },
1516
+ {
1517
+ "epoch": 22.0,
1518
+ "grad_norm": 0.03406278017825759,
1519
+ "learning_rate": 0.0003741007194244604,
1520
+ "loss": 0.2744,
1521
+ "step": 176
1522
+ },
1523
+ {
1524
+ "epoch": 22.137931034482758,
1525
+ "grad_norm": 0.09349094487498505,
1526
+ "learning_rate": 0.0003705035971223021,
1527
+ "loss": 0.2682,
1528
+ "step": 177
1529
+ },
1530
+ {
1531
+ "epoch": 22.275862068965516,
1532
+ "grad_norm": 0.04597064195976921,
1533
+ "learning_rate": 0.0003669064748201439,
1534
+ "loss": 0.2616,
1535
+ "step": 178
1536
+ },
1537
+ {
1538
+ "epoch": 22.413793103448278,
1539
+ "grad_norm": 0.05408403971696931,
1540
+ "learning_rate": 0.00036330935251798564,
1541
+ "loss": 0.2563,
1542
+ "step": 179
1543
+ },
1544
+ {
1545
+ "epoch": 22.551724137931036,
1546
+ "grad_norm": 0.04216428707136434,
1547
+ "learning_rate": 0.00035971223021582735,
1548
+ "loss": 0.2569,
1549
+ "step": 180
1550
+ },
1551
+ {
1552
+ "epoch": 22.551724137931036,
1553
+ "eval_loss": 0.3913760781288147,
1554
+ "eval_runtime": 8.7333,
1555
+ "eval_samples_per_second": 39.504,
1556
+ "eval_steps_per_second": 0.916,
1557
+ "step": 180
1558
+ },
1559
+ {
1560
+ "epoch": 22.689655172413794,
1561
+ "grad_norm": 0.04041451765028027,
1562
+ "learning_rate": 0.0003561151079136691,
1563
+ "loss": 0.2514,
1564
+ "step": 181
1565
+ },
1566
+ {
1567
+ "epoch": 22.82758620689655,
1568
+ "grad_norm": 0.03850929190523714,
1569
+ "learning_rate": 0.00035251798561151075,
1570
+ "loss": 0.2685,
1571
+ "step": 182
1572
+ },
1573
+ {
1574
+ "epoch": 22.96551724137931,
1575
+ "grad_norm": 0.033756769458399456,
1576
+ "learning_rate": 0.0003489208633093525,
1577
+ "loss": 0.2598,
1578
+ "step": 183
1579
+ },
1580
+ {
1581
+ "epoch": 23.0,
1582
+ "grad_norm": 0.033756769458399456,
1583
+ "learning_rate": 0.00034532374100719427,
1584
+ "loss": 0.2344,
1585
+ "step": 184
1586
+ },
1587
+ {
1588
+ "epoch": 23.137931034482758,
1589
+ "grad_norm": 0.06315900117346844,
1590
+ "learning_rate": 0.000341726618705036,
1591
+ "loss": 0.24,
1592
+ "step": 185
1593
+ },
1594
+ {
1595
+ "epoch": 23.137931034482758,
1596
+ "eval_loss": 0.3822570741176605,
1597
+ "eval_runtime": 8.7277,
1598
+ "eval_samples_per_second": 39.529,
1599
+ "eval_steps_per_second": 0.917,
1600
+ "step": 185
1601
+ },
1602
+ {
1603
+ "epoch": 23.275862068965516,
1604
+ "grad_norm": 0.031196700608439997,
1605
+ "learning_rate": 0.00033812949640287773,
1606
+ "loss": 0.2496,
1607
+ "step": 186
1608
+ },
1609
+ {
1610
+ "epoch": 23.413793103448278,
1611
+ "grad_norm": 0.04892198101751926,
1612
+ "learning_rate": 0.0003345323741007194,
1613
+ "loss": 0.2447,
1614
+ "step": 187
1615
+ },
1616
+ {
1617
+ "epoch": 23.551724137931036,
1618
+ "grad_norm": 0.032906558396489385,
1619
+ "learning_rate": 0.00033093525179856114,
1620
+ "loss": 0.2462,
1621
+ "step": 188
1622
+ },
1623
+ {
1624
+ "epoch": 23.689655172413794,
1625
+ "grad_norm": 0.03666873309236629,
1626
+ "learning_rate": 0.0003273381294964029,
1627
+ "loss": 0.2517,
1628
+ "step": 189
1629
+ },
1630
+ {
1631
+ "epoch": 23.82758620689655,
1632
+ "grad_norm": 0.03356010590459969,
1633
+ "learning_rate": 0.0003237410071942446,
1634
+ "loss": 0.2404,
1635
+ "step": 190
1636
+ },
1637
+ {
1638
+ "epoch": 23.82758620689655,
1639
+ "eval_loss": 0.375196635723114,
1640
+ "eval_runtime": 8.7062,
1641
+ "eval_samples_per_second": 39.627,
1642
+ "eval_steps_per_second": 0.919,
1643
+ "step": 190
1644
+ },
1645
+ {
1646
+ "epoch": 23.96551724137931,
1647
+ "grad_norm": 0.030000023541020618,
1648
+ "learning_rate": 0.00032014388489208636,
1649
+ "loss": 0.2406,
1650
+ "step": 191
1651
+ },
1652
+ {
1653
+ "epoch": 24.0,
1654
+ "grad_norm": 0.07015509892130062,
1655
+ "learning_rate": 0.000316546762589928,
1656
+ "loss": 0.246,
1657
+ "step": 192
1658
+ },
1659
+ {
1660
+ "epoch": 24.137931034482758,
1661
+ "grad_norm": 0.030790760613129277,
1662
+ "learning_rate": 0.0003129496402877698,
1663
+ "loss": 0.2354,
1664
+ "step": 193
1665
+ },
1666
+ {
1667
+ "epoch": 24.275862068965516,
1668
+ "grad_norm": 0.04000111760962232,
1669
+ "learning_rate": 0.00030935251798561153,
1670
+ "loss": 0.2269,
1671
+ "step": 194
1672
+ },
1673
+ {
1674
+ "epoch": 24.413793103448278,
1675
+ "grad_norm": 0.032229019674074516,
1676
+ "learning_rate": 0.00030575539568345324,
1677
+ "loss": 0.2331,
1678
+ "step": 195
1679
+ },
1680
+ {
1681
+ "epoch": 24.413793103448278,
1682
+ "eval_loss": 0.36978286504745483,
1683
+ "eval_runtime": 8.6527,
1684
+ "eval_samples_per_second": 39.872,
1685
+ "eval_steps_per_second": 0.925,
1686
+ "step": 195
1687
+ },
1688
+ {
1689
+ "epoch": 24.551724137931036,
1690
+ "grad_norm": 0.0331291665919528,
1691
+ "learning_rate": 0.000302158273381295,
1692
+ "loss": 0.24,
1693
+ "step": 196
1694
+ },
1695
+ {
1696
+ "epoch": 24.689655172413794,
1697
+ "grad_norm": 0.035337891437746904,
1698
+ "learning_rate": 0.0002985611510791367,
1699
+ "loss": 0.2296,
1700
+ "step": 197
1701
+ },
1702
+ {
1703
+ "epoch": 24.82758620689655,
1704
+ "grad_norm": 0.03246763460811474,
1705
+ "learning_rate": 0.0002949640287769784,
1706
+ "loss": 0.2359,
1707
+ "step": 198
1708
+ },
1709
+ {
1710
+ "epoch": 24.96551724137931,
1711
+ "grad_norm": 0.032585723345190463,
1712
+ "learning_rate": 0.00029136690647482016,
1713
+ "loss": 0.2258,
1714
+ "step": 199
1715
+ },
1716
+ {
1717
+ "epoch": 25.0,
1718
+ "grad_norm": 0.032585723345190463,
1719
+ "learning_rate": 0.00028776978417266187,
1720
+ "loss": 0.2347,
1721
+ "step": 200
1722
+ },
1723
+ {
1724
+ "epoch": 25.0,
1725
+ "eval_loss": 0.36209040880203247,
1726
+ "eval_runtime": 8.7477,
1727
+ "eval_samples_per_second": 39.439,
1728
+ "eval_steps_per_second": 0.915,
1729
+ "step": 200
1730
+ },
1731
+ {
1732
+ "epoch": 25.137931034482758,
1733
+ "grad_norm": 0.060945417678054344,
1734
+ "learning_rate": 0.0002841726618705036,
1735
+ "loss": 0.223,
1736
+ "step": 201
1737
+ },
1738
+ {
1739
+ "epoch": 25.275862068965516,
1740
+ "grad_norm": 0.026603419432751657,
1741
+ "learning_rate": 0.00028057553956834533,
1742
+ "loss": 0.2285,
1743
+ "step": 202
1744
+ },
1745
+ {
1746
+ "epoch": 25.413793103448278,
1747
+ "grad_norm": 0.039536624419691684,
1748
+ "learning_rate": 0.00027697841726618703,
1749
+ "loss": 0.224,
1750
+ "step": 203
1751
+ },
1752
+ {
1753
+ "epoch": 25.551724137931036,
1754
+ "grad_norm": 0.03482479660523006,
1755
+ "learning_rate": 0.0002733812949640288,
1756
+ "loss": 0.2303,
1757
+ "step": 204
1758
+ },
1759
+ {
1760
+ "epoch": 25.689655172413794,
1761
+ "grad_norm": 0.026943071456632342,
1762
+ "learning_rate": 0.0002697841726618705,
1763
+ "loss": 0.2082,
1764
+ "step": 205
1765
+ },
1766
+ {
1767
+ "epoch": 25.689655172413794,
1768
+ "eval_loss": 0.35640284419059753,
1769
+ "eval_runtime": 8.7773,
1770
+ "eval_samples_per_second": 39.306,
1771
+ "eval_steps_per_second": 0.911,
1772
+ "step": 205
1773
+ },
1774
+ {
1775
+ "epoch": 25.82758620689655,
1776
+ "grad_norm": 0.03644232546830274,
1777
+ "learning_rate": 0.00026618705035971225,
1778
+ "loss": 0.2158,
1779
+ "step": 206
1780
+ },
1781
+ {
1782
+ "epoch": 25.96551724137931,
1783
+ "grad_norm": 0.027185164839843755,
1784
+ "learning_rate": 0.00026258992805755396,
1785
+ "loss": 0.2227,
1786
+ "step": 207
1787
+ },
1788
+ {
1789
+ "epoch": 26.0,
1790
+ "grad_norm": 0.027185164839843755,
1791
+ "learning_rate": 0.00025899280575539566,
1792
+ "loss": 0.2198,
1793
+ "step": 208
1794
+ },
1795
+ {
1796
+ "epoch": 26.137931034482758,
1797
+ "grad_norm": 0.057513393490420256,
1798
+ "learning_rate": 0.0002553956834532374,
1799
+ "loss": 0.2035,
1800
+ "step": 209
1801
+ },
1802
+ {
1803
+ "epoch": 26.275862068965516,
1804
+ "grad_norm": 0.024938487312607692,
1805
+ "learning_rate": 0.0002517985611510791,
1806
+ "loss": 0.2049,
1807
+ "step": 210
1808
+ },
1809
+ {
1810
+ "epoch": 26.275862068965516,
1811
+ "eval_loss": 0.35771024227142334,
1812
+ "eval_runtime": 8.7957,
1813
+ "eval_samples_per_second": 39.224,
1814
+ "eval_steps_per_second": 0.91,
1815
+ "step": 210
1816
+ },
1817
+ {
1818
+ "epoch": 26.413793103448278,
1819
+ "grad_norm": 0.03299670327554692,
1820
+ "learning_rate": 0.0002482014388489209,
1821
+ "loss": 0.2298,
1822
+ "step": 211
1823
+ },
1824
+ {
1825
+ "epoch": 26.551724137931036,
1826
+ "grad_norm": 0.028039539266024115,
1827
+ "learning_rate": 0.0002446043165467626,
1828
+ "loss": 0.2052,
1829
+ "step": 212
1830
+ },
1831
+ {
1832
+ "epoch": 26.689655172413794,
1833
+ "grad_norm": 0.027719263972104783,
1834
+ "learning_rate": 0.00024100719424460432,
1835
+ "loss": 0.2161,
1836
+ "step": 213
1837
+ },
1838
+ {
1839
+ "epoch": 26.82758620689655,
1840
+ "grad_norm": 0.03108727605645677,
1841
+ "learning_rate": 0.00023741007194244605,
1842
+ "loss": 0.2172,
1843
+ "step": 214
1844
+ },
1845
+ {
1846
+ "epoch": 26.96551724137931,
1847
+ "grad_norm": 0.02577274985726259,
1848
+ "learning_rate": 0.00023381294964028776,
1849
+ "loss": 0.2122,
1850
+ "step": 215
1851
+ },
1852
+ {
1853
+ "epoch": 26.96551724137931,
1854
+ "eval_loss": 0.3507482409477234,
1855
+ "eval_runtime": 8.9803,
1856
+ "eval_samples_per_second": 38.418,
1857
+ "eval_steps_per_second": 0.891,
1858
+ "step": 215
1859
+ },
1860
+ {
1861
+ "epoch": 27.0,
1862
+ "grad_norm": 0.02577274985726259,
1863
+ "learning_rate": 0.00023021582733812951,
1864
+ "loss": 0.2046,
1865
+ "step": 216
1866
+ },
1867
+ {
1868
+ "epoch": 27.137931034482758,
1869
+ "grad_norm": 0.04989878938146838,
1870
+ "learning_rate": 0.00022661870503597125,
1871
+ "loss": 0.2098,
1872
+ "step": 217
1873
+ },
1874
+ {
1875
+ "epoch": 27.275862068965516,
1876
+ "grad_norm": 0.026344047850079245,
1877
+ "learning_rate": 0.00022302158273381295,
1878
+ "loss": 0.2146,
1879
+ "step": 218
1880
+ },
1881
+ {
1882
+ "epoch": 27.413793103448278,
1883
+ "grad_norm": 0.027447977141796382,
1884
+ "learning_rate": 0.00021942446043165468,
1885
+ "loss": 0.199,
1886
+ "step": 219
1887
+ },
1888
+ {
1889
+ "epoch": 27.551724137931036,
1890
+ "grad_norm": 0.02983731838629343,
1891
+ "learning_rate": 0.00021582733812949639,
1892
+ "loss": 0.195,
1893
+ "step": 220
1894
+ },
1895
+ {
1896
+ "epoch": 27.551724137931036,
1897
+ "eval_loss": 0.34887072443962097,
1898
+ "eval_runtime": 8.8366,
1899
+ "eval_samples_per_second": 39.042,
1900
+ "eval_steps_per_second": 0.905,
1901
+ "step": 220
1902
+ },
1903
+ {
1904
+ "epoch": 27.689655172413794,
1905
+ "grad_norm": 0.027330930307340396,
1906
+ "learning_rate": 0.00021223021582733814,
1907
+ "loss": 0.2036,
1908
+ "step": 221
1909
+ },
1910
+ {
1911
+ "epoch": 27.82758620689655,
1912
+ "grad_norm": 0.02427664160150073,
1913
+ "learning_rate": 0.00020863309352517988,
1914
+ "loss": 0.2101,
1915
+ "step": 222
1916
+ },
1917
+ {
1918
+ "epoch": 27.96551724137931,
1919
+ "grad_norm": 0.027273654615751472,
1920
+ "learning_rate": 0.00020503597122302158,
1921
+ "loss": 0.2012,
1922
+ "step": 223
1923
+ },
1924
+ {
1925
+ "epoch": 28.0,
1926
+ "grad_norm": 0.07560769661401563,
1927
+ "learning_rate": 0.0002014388489208633,
1928
+ "loss": 0.202,
1929
+ "step": 224
1930
+ },
1931
+ {
1932
+ "epoch": 28.137931034482758,
1933
+ "grad_norm": 0.022692209210168963,
1934
+ "learning_rate": 0.00019784172661870504,
1935
+ "loss": 0.1849,
1936
+ "step": 225
1937
+ },
1938
+ {
1939
+ "epoch": 28.137931034482758,
1940
+ "eval_loss": 0.34698358178138733,
1941
+ "eval_runtime": 8.7351,
1942
+ "eval_samples_per_second": 39.496,
1943
+ "eval_steps_per_second": 0.916,
1944
+ "step": 225
1945
+ },
1946
+ {
1947
+ "epoch": 28.275862068965516,
1948
+ "grad_norm": 0.02787720449348246,
1949
+ "learning_rate": 0.00019424460431654677,
1950
+ "loss": 0.2017,
1951
+ "step": 226
1952
+ },
1953
+ {
1954
+ "epoch": 28.413793103448278,
1955
+ "grad_norm": 0.03220364317895431,
1956
+ "learning_rate": 0.0001906474820143885,
1957
+ "loss": 0.197,
1958
+ "step": 227
1959
+ },
1960
+ {
1961
+ "epoch": 28.551724137931036,
1962
+ "grad_norm": 0.027621374654249947,
1963
+ "learning_rate": 0.0001870503597122302,
1964
+ "loss": 0.2056,
1965
+ "step": 228
1966
+ },
1967
+ {
1968
+ "epoch": 28.689655172413794,
1969
+ "grad_norm": 0.023010338769110017,
1970
+ "learning_rate": 0.00018345323741007194,
1971
+ "loss": 0.1918,
1972
+ "step": 229
1973
+ },
1974
+ {
1975
+ "epoch": 28.82758620689655,
1976
+ "grad_norm": 0.024485870812953354,
1977
+ "learning_rate": 0.00017985611510791367,
1978
+ "loss": 0.1933,
1979
+ "step": 230
1980
+ },
1981
+ {
1982
+ "epoch": 28.82758620689655,
1983
+ "eval_loss": 0.3406597673892975,
1984
+ "eval_runtime": 8.7608,
1985
+ "eval_samples_per_second": 39.38,
1986
+ "eval_steps_per_second": 0.913,
1987
+ "step": 230
1988
+ },
1989
+ {
1990
+ "epoch": 28.96551724137931,
1991
+ "grad_norm": 0.02754333091454538,
1992
+ "learning_rate": 0.00017625899280575538,
1993
+ "loss": 0.21,
1994
+ "step": 231
1995
+ },
1996
+ {
1997
+ "epoch": 29.0,
1998
+ "grad_norm": 0.02754333091454538,
1999
+ "learning_rate": 0.00017266187050359714,
2000
+ "loss": 0.1859,
2001
+ "step": 232
2002
+ },
2003
+ {
2004
+ "epoch": 29.137931034482758,
2005
+ "grad_norm": 0.04985161178709588,
2006
+ "learning_rate": 0.00016906474820143887,
2007
+ "loss": 0.1988,
2008
+ "step": 233
2009
+ },
2010
+ {
2011
+ "epoch": 29.275862068965516,
2012
+ "grad_norm": 0.021260118090854402,
2013
+ "learning_rate": 0.00016546762589928057,
2014
+ "loss": 0.1903,
2015
+ "step": 234
2016
+ },
2017
+ {
2018
+ "epoch": 29.413793103448278,
2019
+ "grad_norm": 0.027528018770327255,
2020
+ "learning_rate": 0.0001618705035971223,
2021
+ "loss": 0.2038,
2022
+ "step": 235
2023
+ },
2024
+ {
2025
+ "epoch": 29.413793103448278,
2026
+ "eval_loss": 0.3423880636692047,
2027
+ "eval_runtime": 8.7468,
2028
+ "eval_samples_per_second": 39.443,
2029
+ "eval_steps_per_second": 0.915,
2030
+ "step": 235
2031
+ },
2032
+ {
2033
+ "epoch": 29.551724137931036,
2034
+ "grad_norm": 0.026249255657925016,
2035
+ "learning_rate": 0.000158273381294964,
2036
+ "loss": 0.1881,
2037
+ "step": 236
2038
+ },
2039
+ {
2040
+ "epoch": 29.689655172413794,
2041
+ "grad_norm": 0.025540354360044117,
2042
+ "learning_rate": 0.00015467625899280577,
2043
+ "loss": 0.1859,
2044
+ "step": 237
2045
+ },
2046
+ {
2047
+ "epoch": 29.82758620689655,
2048
+ "grad_norm": 0.0237042723418601,
2049
+ "learning_rate": 0.0001510791366906475,
2050
+ "loss": 0.1897,
2051
+ "step": 238
2052
+ },
2053
+ {
2054
+ "epoch": 29.96551724137931,
2055
+ "grad_norm": 0.024197633660553358,
2056
+ "learning_rate": 0.0001474820143884892,
2057
+ "loss": 0.1885,
2058
+ "step": 239
2059
+ },
2060
+ {
2061
+ "epoch": 30.0,
2062
+ "grad_norm": 0.024197633660553358,
2063
+ "learning_rate": 0.00014388489208633093,
2064
+ "loss": 0.1784,
2065
+ "step": 240
2066
+ },
2067
+ {
2068
+ "epoch": 30.0,
2069
+ "eval_loss": 0.3388298749923706,
2070
+ "eval_runtime": 8.7758,
2071
+ "eval_samples_per_second": 39.312,
2072
+ "eval_steps_per_second": 0.912,
2073
+ "step": 240
2074
+ },
2075
+ {
2076
+ "epoch": 30.137931034482758,
2077
+ "grad_norm": 0.046306822309064986,
2078
+ "learning_rate": 0.00014028776978417266,
2079
+ "loss": 0.1941,
2080
+ "step": 241
2081
+ },
2082
+ {
2083
+ "epoch": 30.275862068965516,
2084
+ "grad_norm": 0.022017020545703918,
2085
+ "learning_rate": 0.0001366906474820144,
2086
+ "loss": 0.1957,
2087
+ "step": 242
2088
+ },
2089
+ {
2090
+ "epoch": 30.413793103448278,
2091
+ "grad_norm": 0.023823141668119413,
2092
+ "learning_rate": 0.00013309352517985613,
2093
+ "loss": 0.1898,
2094
+ "step": 243
2095
+ },
2096
+ {
2097
+ "epoch": 30.551724137931036,
2098
+ "grad_norm": 0.024728389236420366,
2099
+ "learning_rate": 0.00012949640287769783,
2100
+ "loss": 0.1851,
2101
+ "step": 244
2102
+ },
2103
+ {
2104
+ "epoch": 30.689655172413794,
2105
+ "grad_norm": 0.023816082819974667,
2106
+ "learning_rate": 0.00012589928057553956,
2107
+ "loss": 0.1811,
2108
+ "step": 245
2109
+ },
2110
+ {
2111
+ "epoch": 30.689655172413794,
2112
+ "eval_loss": 0.33752110600471497,
2113
+ "eval_runtime": 8.7421,
2114
+ "eval_samples_per_second": 39.464,
2115
+ "eval_steps_per_second": 0.915,
2116
+ "step": 245
2117
+ },
2118
+ {
2119
+ "epoch": 30.82758620689655,
2120
+ "grad_norm": 0.02387725422411895,
2121
+ "learning_rate": 0.0001223021582733813,
2122
+ "loss": 0.1714,
2123
+ "step": 246
2124
+ },
2125
+ {
2126
+ "epoch": 30.96551724137931,
2127
+ "grad_norm": 0.02217079184880683,
2128
+ "learning_rate": 0.00011870503597122303,
2129
+ "loss": 0.1912,
2130
+ "step": 247
2131
+ },
2132
+ {
2133
+ "epoch": 31.0,
2134
+ "grad_norm": 0.02217079184880683,
2135
+ "learning_rate": 0.00011510791366906476,
2136
+ "loss": 0.1968,
2137
+ "step": 248
2138
+ },
2139
+ {
2140
+ "epoch": 31.137931034482758,
2141
+ "grad_norm": 0.05271210046176835,
2142
+ "learning_rate": 0.00011151079136690648,
2143
+ "loss": 0.1851,
2144
+ "step": 249
2145
+ },
2146
+ {
2147
+ "epoch": 31.275862068965516,
2148
+ "grad_norm": 0.019881912312787,
2149
+ "learning_rate": 0.00010791366906474819,
2150
+ "loss": 0.1883,
2151
+ "step": 250
2152
+ },
2153
+ {
2154
+ "epoch": 31.275862068965516,
2155
+ "eval_loss": 0.3365817666053772,
2156
+ "eval_runtime": 8.7549,
2157
+ "eval_samples_per_second": 39.406,
2158
+ "eval_steps_per_second": 0.914,
2159
+ "step": 250
2160
+ },
2161
+ {
2162
+ "epoch": 31.413793103448278,
2163
+ "grad_norm": 0.02406978375959548,
2164
+ "learning_rate": 0.00010431654676258994,
2165
+ "loss": 0.1793,
2166
+ "step": 251
2167
+ },
2168
+ {
2169
+ "epoch": 31.551724137931036,
2170
+ "grad_norm": 0.02241126008657753,
2171
+ "learning_rate": 0.00010071942446043166,
2172
+ "loss": 0.1738,
2173
+ "step": 252
2174
+ },
2175
+ {
2176
+ "epoch": 31.689655172413794,
2177
+ "grad_norm": 0.02164882916894415,
2178
+ "learning_rate": 9.712230215827339e-05,
2179
+ "loss": 0.1908,
2180
+ "step": 253
2181
+ },
2182
+ {
2183
+ "epoch": 31.82758620689655,
2184
+ "grad_norm": 0.02299736733387471,
2185
+ "learning_rate": 9.35251798561151e-05,
2186
+ "loss": 0.1754,
2187
+ "step": 254
2188
+ },
2189
+ {
2190
+ "epoch": 31.96551724137931,
2191
+ "grad_norm": 0.02118602975232773,
2192
+ "learning_rate": 8.992805755395684e-05,
2193
+ "loss": 0.1819,
2194
+ "step": 255
2195
+ },
2196
+ {
2197
+ "epoch": 31.96551724137931,
2198
+ "eval_loss": 0.33423885703086853,
2199
+ "eval_runtime": 8.7374,
2200
+ "eval_samples_per_second": 39.485,
2201
+ "eval_steps_per_second": 0.916,
2202
+ "step": 255
2203
+ },
2204
+ {
2205
+ "epoch": 32.0,
2206
+ "grad_norm": 0.046026174738473216,
2207
+ "learning_rate": 8.633093525179857e-05,
2208
+ "loss": 0.1949,
2209
+ "step": 256
2210
+ },
2211
+ {
2212
+ "epoch": 32.13793103448276,
2213
+ "grad_norm": 0.020406095680489074,
2214
+ "learning_rate": 8.273381294964029e-05,
2215
+ "loss": 0.1819,
2216
+ "step": 257
2217
+ },
2218
+ {
2219
+ "epoch": 32.275862068965516,
2220
+ "grad_norm": 0.020759549602663758,
2221
+ "learning_rate": 7.9136690647482e-05,
2222
+ "loss": 0.181,
2223
+ "step": 258
2224
+ },
2225
+ {
2226
+ "epoch": 32.41379310344828,
2227
+ "grad_norm": 0.02011993734738676,
2228
+ "learning_rate": 7.553956834532375e-05,
2229
+ "loss": 0.1775,
2230
+ "step": 259
2231
+ },
2232
+ {
2233
+ "epoch": 32.55172413793103,
2234
+ "grad_norm": 0.021140993984083818,
2235
+ "learning_rate": 7.194244604316547e-05,
2236
+ "loss": 0.1856,
2237
+ "step": 260
2238
+ },
2239
+ {
2240
+ "epoch": 32.55172413793103,
2241
+ "eval_loss": 0.333390474319458,
2242
+ "eval_runtime": 8.7702,
2243
+ "eval_samples_per_second": 39.338,
2244
+ "eval_steps_per_second": 0.912,
2245
+ "step": 260
2246
+ },
2247
+ {
2248
+ "epoch": 32.689655172413794,
2249
+ "grad_norm": 0.020308588795970313,
2250
+ "learning_rate": 6.83453237410072e-05,
2251
+ "loss": 0.1748,
2252
+ "step": 261
2253
+ },
2254
+ {
2255
+ "epoch": 32.827586206896555,
2256
+ "grad_norm": 0.020264899770792336,
2257
+ "learning_rate": 6.474820143884892e-05,
2258
+ "loss": 0.1851,
2259
+ "step": 262
2260
+ },
2261
+ {
2262
+ "epoch": 32.96551724137931,
2263
+ "grad_norm": 0.02143862893742038,
2264
+ "learning_rate": 6.115107913669065e-05,
2265
+ "loss": 0.1741,
2266
+ "step": 263
2267
+ },
2268
+ {
2269
+ "epoch": 33.0,
2270
+ "grad_norm": 0.02143862893742038,
2271
+ "learning_rate": 5.755395683453238e-05,
2272
+ "loss": 0.1761,
2273
+ "step": 264
2274
+ },
2275
+ {
2276
+ "epoch": 33.13793103448276,
2277
+ "grad_norm": 0.04329337579145695,
2278
+ "learning_rate": 5.3956834532374096e-05,
2279
+ "loss": 0.1803,
2280
+ "step": 265
2281
+ },
2282
+ {
2283
+ "epoch": 33.13793103448276,
2284
+ "eval_loss": 0.33235102891921997,
2285
+ "eval_runtime": 8.7755,
2286
+ "eval_samples_per_second": 39.314,
2287
+ "eval_steps_per_second": 0.912,
2288
+ "step": 265
2289
+ },
2290
+ {
2291
+ "epoch": 33.275862068965516,
2292
+ "grad_norm": 0.019954487446773046,
2293
+ "learning_rate": 5.035971223021583e-05,
2294
+ "loss": 0.1873,
2295
+ "step": 266
2296
+ },
2297
+ {
2298
+ "epoch": 33.41379310344828,
2299
+ "grad_norm": 0.019618013277003066,
2300
+ "learning_rate": 4.676258992805755e-05,
2301
+ "loss": 0.1752,
2302
+ "step": 267
2303
+ },
2304
+ {
2305
+ "epoch": 33.55172413793103,
2306
+ "grad_norm": 0.019429892402410356,
2307
+ "learning_rate": 4.3165467625899284e-05,
2308
+ "loss": 0.1836,
2309
+ "step": 268
2310
+ },
2311
+ {
2312
+ "epoch": 33.689655172413794,
2313
+ "grad_norm": 0.02004252998480519,
2314
+ "learning_rate": 3.9568345323741e-05,
2315
+ "loss": 0.1857,
2316
+ "step": 269
2317
+ },
2318
+ {
2319
+ "epoch": 33.827586206896555,
2320
+ "grad_norm": 0.019771614320221557,
2321
+ "learning_rate": 3.597122302158273e-05,
2322
+ "loss": 0.1619,
2323
+ "step": 270
2324
+ },
2325
+ {
2326
+ "epoch": 33.827586206896555,
2327
+ "eval_loss": 0.33135661482810974,
2328
+ "eval_runtime": 8.7624,
2329
+ "eval_samples_per_second": 39.373,
2330
+ "eval_steps_per_second": 0.913,
2331
+ "step": 270
2332
+ },
2333
+ {
2334
+ "epoch": 33.96551724137931,
2335
+ "grad_norm": 0.019960028124653008,
2336
+ "learning_rate": 3.237410071942446e-05,
2337
+ "loss": 0.1705,
2338
+ "step": 271
2339
+ },
2340
+ {
2341
+ "epoch": 34.0,
2342
+ "grad_norm": 0.019960028124653008,
2343
+ "learning_rate": 2.877697841726619e-05,
2344
+ "loss": 0.1802,
2345
+ "step": 272
2346
+ },
2347
+ {
2348
+ "epoch": 34.13793103448276,
2349
+ "grad_norm": 0.04386228318206151,
2350
+ "learning_rate": 2.5179856115107914e-05,
2351
+ "loss": 0.1789,
2352
+ "step": 273
2353
+ },
2354
+ {
2355
+ "epoch": 34.275862068965516,
2356
+ "grad_norm": 0.019314259532766637,
2357
+ "learning_rate": 2.1582733812949642e-05,
2358
+ "loss": 0.1731,
2359
+ "step": 274
2360
+ },
2361
+ {
2362
+ "epoch": 34.41379310344828,
2363
+ "grad_norm": 0.018918470446421552,
2364
+ "learning_rate": 1.7985611510791367e-05,
2365
+ "loss": 0.1807,
2366
+ "step": 275
2367
+ },
2368
+ {
2369
+ "epoch": 34.41379310344828,
2370
+ "eval_loss": 0.3319057524204254,
2371
+ "eval_runtime": 8.8,
2372
+ "eval_samples_per_second": 39.205,
2373
+ "eval_steps_per_second": 0.909,
2374
+ "step": 275
2375
+ },
2376
+ {
2377
+ "epoch": 34.55172413793103,
2378
+ "grad_norm": 0.019063515768535925,
2379
+ "learning_rate": 1.4388489208633095e-05,
2380
+ "loss": 0.17,
2381
+ "step": 276
2382
+ },
2383
+ {
2384
+ "epoch": 34.689655172413794,
2385
+ "grad_norm": 0.04451575576404841,
2386
+ "learning_rate": 1.0791366906474821e-05,
2387
+ "loss": 0.1777,
2388
+ "step": 277
2389
+ },
2390
+ {
2391
+ "epoch": 34.827586206896555,
2392
+ "grad_norm": 0.020625636072458247,
2393
+ "learning_rate": 7.194244604316547e-06,
2394
+ "loss": 0.1919,
2395
+ "step": 278
2396
+ },
2397
+ {
2398
+ "epoch": 34.96551724137931,
2399
+ "grad_norm": 0.020437913007304815,
2400
+ "learning_rate": 3.5971223021582737e-06,
2401
+ "loss": 0.1656,
2402
+ "step": 279
2403
+ },
2404
+ {
2405
+ "epoch": 35.0,
2406
+ "grad_norm": 0.020437913007304815,
2407
+ "learning_rate": 0.0,
2408
+ "loss": 0.1619,
2409
+ "step": 280
2410
+ },
2411
+ {
2412
+ "epoch": 35.0,
2413
+ "eval_loss": 0.3319094479084015,
2414
+ "eval_runtime": 8.8024,
2415
+ "eval_samples_per_second": 39.194,
2416
+ "eval_steps_per_second": 0.909,
2417
+ "step": 280
2418
+ }
2419
+ ],
2420
+ "logging_steps": 1.0,
2421
+ "max_steps": 280,
2422
+ "num_input_tokens_seen": 0,
2423
+ "num_train_epochs": 40,
2424
+ "save_steps": 500,
2425
+ "stateful_callbacks": {
2426
+ "TrainerControl": {
2427
+ "args": {
2428
+ "should_epoch_stop": false,
2429
+ "should_evaluate": false,
2430
+ "should_log": false,
2431
+ "should_save": true,
2432
+ "should_training_stop": true
2433
+ },
2434
+ "attributes": {}
2435
+ }
2436
+ },
2437
+ "total_flos": 706750558765056.0,
2438
+ "train_batch_size": 12,
2439
+ "trial_name": null,
2440
+ "trial_params": null
2441
+ }
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:aa6923eb43145cb65eac82ecea6517bc399049191e13374663b70fabeb8e01d9
3
+ size 7160
zero_to_fp32.py ADDED
@@ -0,0 +1,760 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python
2
+
3
+ # Copyright (c) Microsoft Corporation.
4
+ # SPDX-License-Identifier: Apache-2.0
5
+
6
+ # DeepSpeed Team
7
+
8
+ # This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
9
+ # copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
10
+ # the future. Once extracted, the weights don't require DeepSpeed and can be used in any
11
+ # application.
12
+ #
13
+ # example:
14
+ # python zero_to_fp32.py . output_dir/
15
+ # or
16
+ # python zero_to_fp32.py . output_dir/ --safe_serialization
17
+
18
+ import argparse
19
+ import torch
20
+ import glob
21
+ import math
22
+ import os
23
+ import re
24
+ import gc
25
+ import json
26
+ import numpy as np
27
+ from tqdm import tqdm
28
+ from collections import OrderedDict
29
+ from dataclasses import dataclass
30
+
31
+ # while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
32
+ # DeepSpeed data structures it has to be available in the current python environment.
33
+ from deepspeed.utils import logger
34
+ from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
35
+ FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
36
+ FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
37
+
38
+
39
+ @dataclass
40
+ class zero_model_state:
41
+ buffers: dict()
42
+ param_shapes: dict()
43
+ shared_params: list
44
+ ds_version: int
45
+ frozen_param_shapes: dict()
46
+ frozen_param_fragments: dict()
47
+
48
+
49
+ debug = 0
50
+
51
+ # load to cpu
52
+ device = torch.device('cpu')
53
+
54
+
55
+ def atoi(text):
56
+ return int(text) if text.isdigit() else text
57
+
58
+
59
+ def natural_keys(text):
60
+ '''
61
+ alist.sort(key=natural_keys) sorts in human order
62
+ http://nedbatchelder.com/blog/200712/human_sorting.html
63
+ (See Toothy's implementation in the comments)
64
+ '''
65
+ return [atoi(c) for c in re.split(r'(\d+)', text)]
66
+
67
+
68
+ def get_model_state_file(checkpoint_dir, zero_stage):
69
+ if not os.path.isdir(checkpoint_dir):
70
+ raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
71
+
72
+ # there should be only one file
73
+ if zero_stage <= 2:
74
+ file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
75
+ elif zero_stage == 3:
76
+ file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
77
+
78
+ if not os.path.exists(file):
79
+ raise FileNotFoundError(f"can't find model states file at '{file}'")
80
+
81
+ return file
82
+
83
+
84
+ def get_checkpoint_files(checkpoint_dir, glob_pattern):
85
+ # XXX: need to test that this simple glob rule works for multi-node setup too
86
+ ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
87
+
88
+ if len(ckpt_files) == 0:
89
+ raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
90
+
91
+ return ckpt_files
92
+
93
+
94
+ def get_optim_files(checkpoint_dir):
95
+ return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
96
+
97
+
98
+ def get_model_state_files(checkpoint_dir):
99
+ return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
100
+
101
+
102
+ def parse_model_states(files):
103
+ zero_model_states = []
104
+ for file in files:
105
+ state_dict = torch.load(file, map_location=device, weights_only=False)
106
+
107
+ if BUFFER_NAMES not in state_dict:
108
+ raise ValueError(f"{file} is not a model state checkpoint")
109
+ buffer_names = state_dict[BUFFER_NAMES]
110
+ if debug:
111
+ print("Found buffers:", buffer_names)
112
+
113
+ # recover just the buffers while restoring them to fp32 if they were saved in fp16
114
+ buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
115
+ param_shapes = state_dict[PARAM_SHAPES]
116
+
117
+ # collect parameters that are included in param_shapes
118
+ param_names = []
119
+ for s in param_shapes:
120
+ for name in s.keys():
121
+ param_names.append(name)
122
+
123
+ # update with frozen parameters
124
+ frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
125
+ if frozen_param_shapes is not None:
126
+ if debug:
127
+ print(f"Found frozen_param_shapes: {frozen_param_shapes}")
128
+ param_names += list(frozen_param_shapes.keys())
129
+
130
+ # handle shared params
131
+ shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
132
+
133
+ ds_version = state_dict.get(DS_VERSION, None)
134
+
135
+ frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
136
+
137
+ z_model_state = zero_model_state(buffers=buffers,
138
+ param_shapes=param_shapes,
139
+ shared_params=shared_params,
140
+ ds_version=ds_version,
141
+ frozen_param_shapes=frozen_param_shapes,
142
+ frozen_param_fragments=frozen_param_fragments)
143
+ zero_model_states.append(z_model_state)
144
+
145
+ return zero_model_states
146
+
147
+
148
+ def parse_optim_states(files, ds_checkpoint_dir):
149
+ total_files = len(files)
150
+ state_dicts = []
151
+ for f in tqdm(files, desc='Loading checkpoint shards'):
152
+ state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
153
+ # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
154
+ # and also handle the case where it was already removed by another helper script
155
+ state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
156
+ state_dicts.append(state_dict)
157
+
158
+ if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
159
+ raise ValueError(f"{files[0]} is not a zero checkpoint")
160
+ zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
161
+ world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
162
+
163
+ # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
164
+ # parameters can be different from data parallelism for non-expert parameters. So we can just
165
+ # use the max of the partition_count to get the dp world_size.
166
+
167
+ if type(world_size) is list:
168
+ world_size = max(world_size)
169
+
170
+ if world_size != total_files:
171
+ raise ValueError(
172
+ f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
173
+ "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
174
+ )
175
+
176
+ # the groups are named differently in each stage
177
+ if zero_stage <= 2:
178
+ fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
179
+ elif zero_stage == 3:
180
+ fp32_groups_key = FP32_FLAT_GROUPS
181
+ else:
182
+ raise ValueError(f"unknown zero stage {zero_stage}")
183
+
184
+ fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
185
+ return zero_stage, world_size, fp32_flat_groups
186
+
187
+
188
+ def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
189
+ """
190
+ Returns fp32 state_dict reconstructed from ds checkpoint
191
+
192
+ Args:
193
+ - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
194
+
195
+ """
196
+ print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
197
+
198
+ optim_files = get_optim_files(ds_checkpoint_dir)
199
+ zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
200
+ print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
201
+
202
+ model_files = get_model_state_files(ds_checkpoint_dir)
203
+
204
+ zero_model_states = parse_model_states(model_files)
205
+ print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
206
+
207
+ if zero_stage <= 2:
208
+ return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
209
+ exclude_frozen_parameters)
210
+ elif zero_stage == 3:
211
+ return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
212
+ exclude_frozen_parameters)
213
+
214
+
215
+ def _zero2_merge_frozen_params(state_dict, zero_model_states):
216
+ if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
217
+ return
218
+
219
+ frozen_param_shapes = zero_model_states[0].frozen_param_shapes
220
+ frozen_param_fragments = zero_model_states[0].frozen_param_fragments
221
+
222
+ if debug:
223
+ num_elem = sum(s.numel() for s in frozen_param_shapes.values())
224
+ print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
225
+
226
+ wanted_params = len(frozen_param_shapes)
227
+ wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
228
+ avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
229
+ print(f'Frozen params: Have {avail_numel} numels to process.')
230
+ print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
231
+
232
+ total_params = 0
233
+ total_numel = 0
234
+ for name, shape in frozen_param_shapes.items():
235
+ total_params += 1
236
+ unpartitioned_numel = shape.numel()
237
+ total_numel += unpartitioned_numel
238
+
239
+ state_dict[name] = frozen_param_fragments[name]
240
+
241
+ if debug:
242
+ print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
243
+
244
+ print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
245
+
246
+
247
+ def _has_callable(obj, fn):
248
+ attr = getattr(obj, fn, None)
249
+ return callable(attr)
250
+
251
+
252
+ def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
253
+ param_shapes = zero_model_states[0].param_shapes
254
+
255
+ # Reconstruction protocol:
256
+ #
257
+ # XXX: document this
258
+
259
+ if debug:
260
+ for i in range(world_size):
261
+ for j in range(len(fp32_flat_groups[0])):
262
+ print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
263
+
264
+ # XXX: memory usage doubles here (zero2)
265
+ num_param_groups = len(fp32_flat_groups[0])
266
+ merged_single_partition_of_fp32_groups = []
267
+ for i in range(num_param_groups):
268
+ merged_partitions = [sd[i] for sd in fp32_flat_groups]
269
+ full_single_fp32_vector = torch.cat(merged_partitions, 0)
270
+ merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
271
+ avail_numel = sum(
272
+ [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
273
+
274
+ if debug:
275
+ wanted_params = sum([len(shapes) for shapes in param_shapes])
276
+ wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
277
+ # not asserting if there is a mismatch due to possible padding
278
+ print(f"Have {avail_numel} numels to process.")
279
+ print(f"Need {wanted_numel} numels in {wanted_params} params.")
280
+
281
+ # params
282
+ # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
283
+ # out-of-core computing solution
284
+ total_numel = 0
285
+ total_params = 0
286
+ for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
287
+ offset = 0
288
+ avail_numel = full_single_fp32_vector.numel()
289
+ for name, shape in shapes.items():
290
+
291
+ unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
292
+ total_numel += unpartitioned_numel
293
+ total_params += 1
294
+
295
+ if debug:
296
+ print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
297
+ state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
298
+ offset += unpartitioned_numel
299
+
300
+ # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
301
+ # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
302
+ # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
303
+ # live optimizer object, so we are checking that the numbers are within the right range
304
+ align_to = 2 * world_size
305
+
306
+ def zero2_align(x):
307
+ return align_to * math.ceil(x / align_to)
308
+
309
+ if debug:
310
+ print(f"original offset={offset}, avail_numel={avail_numel}")
311
+
312
+ offset = zero2_align(offset)
313
+ avail_numel = zero2_align(avail_numel)
314
+
315
+ if debug:
316
+ print(f"aligned offset={offset}, avail_numel={avail_numel}")
317
+
318
+ # Sanity check
319
+ if offset != avail_numel:
320
+ raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
321
+
322
+ print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
323
+
324
+
325
+ def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
326
+ exclude_frozen_parameters):
327
+ state_dict = OrderedDict()
328
+
329
+ # buffers
330
+ buffers = zero_model_states[0].buffers
331
+ state_dict.update(buffers)
332
+ if debug:
333
+ print(f"added {len(buffers)} buffers")
334
+
335
+ if not exclude_frozen_parameters:
336
+ _zero2_merge_frozen_params(state_dict, zero_model_states)
337
+
338
+ _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
339
+
340
+ # recover shared parameters
341
+ for pair in zero_model_states[0].shared_params:
342
+ if pair[1] in state_dict:
343
+ state_dict[pair[0]] = state_dict[pair[1]]
344
+
345
+ return state_dict
346
+
347
+
348
+ def zero3_partitioned_param_info(unpartitioned_numel, world_size):
349
+ remainder = unpartitioned_numel % world_size
350
+ padding_numel = (world_size - remainder) if remainder else 0
351
+ partitioned_numel = math.ceil(unpartitioned_numel / world_size)
352
+ return partitioned_numel, padding_numel
353
+
354
+
355
+ def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
356
+ if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
357
+ return
358
+
359
+ if debug:
360
+ for i in range(world_size):
361
+ num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
362
+ print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
363
+
364
+ frozen_param_shapes = zero_model_states[0].frozen_param_shapes
365
+ wanted_params = len(frozen_param_shapes)
366
+ wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
367
+ avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
368
+ print(f'Frozen params: Have {avail_numel} numels to process.')
369
+ print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
370
+
371
+ total_params = 0
372
+ total_numel = 0
373
+ for name, shape in zero_model_states[0].frozen_param_shapes.items():
374
+ total_params += 1
375
+ unpartitioned_numel = shape.numel()
376
+ total_numel += unpartitioned_numel
377
+
378
+ param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
379
+ state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
380
+
381
+ partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
382
+
383
+ if debug:
384
+ print(
385
+ f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
386
+ )
387
+
388
+ print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
389
+
390
+
391
+ class GatheredTensor:
392
+ """
393
+ A pseudo tensor that collects partitioned weights.
394
+ It is more memory efficient when there are multiple groups.
395
+ """
396
+
397
+ def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
398
+ self.flat_groups = flat_groups
399
+ self.flat_groups_offset = flat_groups_offset
400
+ self.offset = offset
401
+ self.partitioned_numel = partitioned_numel
402
+ self.shape = shape
403
+ self.dtype = self.flat_groups[0][0].dtype
404
+
405
+ def contiguous(self):
406
+ """
407
+ Merge partitioned weights from flat_groups into a single tensor.
408
+ """
409
+ end_idx = self.offset + self.partitioned_numel
410
+ world_size = len(self.flat_groups)
411
+ pad_flat_param_chunks = []
412
+
413
+ for rank_i in range(world_size):
414
+ # for each rank, we need to collect weights from related group/groups
415
+ flat_groups_at_rank_i = self.flat_groups[rank_i]
416
+ start_group_id = None
417
+ end_group_id = None
418
+ for group_id in range(len(self.flat_groups_offset)):
419
+ if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
420
+ start_group_id = group_id
421
+ if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
422
+ end_group_id = group_id
423
+ break
424
+ # collect weights from related group/groups
425
+ for group_id in range(start_group_id, end_group_id + 1):
426
+ flat_tensor = flat_groups_at_rank_i[group_id]
427
+ start_offset = self.offset - self.flat_groups_offset[group_id]
428
+ end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
429
+ pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
430
+
431
+ # collect weights from all ranks
432
+ pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
433
+ param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
434
+ return param
435
+
436
+
437
+ def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
438
+ param_shapes = zero_model_states[0].param_shapes
439
+ avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
440
+
441
+ # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
442
+ # param, re-consolidating each param, while dealing with padding if any
443
+
444
+ # merge list of dicts, preserving order
445
+ param_shapes = {k: v for d in param_shapes for k, v in d.items()}
446
+
447
+ if debug:
448
+ for i in range(world_size):
449
+ print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
450
+
451
+ wanted_params = len(param_shapes)
452
+ wanted_numel = sum(shape.numel() for shape in param_shapes.values())
453
+ # not asserting if there is a mismatch due to possible padding
454
+ avail_numel = fp32_flat_groups[0].numel() * world_size
455
+ print(f"Trainable params: Have {avail_numel} numels to process.")
456
+ print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
457
+
458
+ # params
459
+ # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
460
+ # out-of-core computing solution
461
+ offset = 0
462
+ total_numel = 0
463
+ total_params = 0
464
+ flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
465
+ for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
466
+ unpartitioned_numel = shape.numel()
467
+ total_numel += unpartitioned_numel
468
+ total_params += 1
469
+ partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
470
+
471
+ if debug:
472
+ print(
473
+ f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
474
+ )
475
+
476
+ # memory efficient tensor
477
+ tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
478
+ state_dict[name] = tensor
479
+ offset += partitioned_numel
480
+
481
+ offset *= world_size
482
+
483
+ # Sanity check
484
+ if offset != avail_numel:
485
+ raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
486
+
487
+ print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
488
+
489
+
490
+ def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
491
+ exclude_frozen_parameters):
492
+ state_dict = OrderedDict()
493
+
494
+ # buffers
495
+ buffers = zero_model_states[0].buffers
496
+ state_dict.update(buffers)
497
+ if debug:
498
+ print(f"added {len(buffers)} buffers")
499
+
500
+ if not exclude_frozen_parameters:
501
+ _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
502
+
503
+ _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
504
+
505
+ # recover shared parameters
506
+ for pair in zero_model_states[0].shared_params:
507
+ if pair[1] in state_dict:
508
+ state_dict[pair[0]] = state_dict[pair[1]]
509
+
510
+ return state_dict
511
+
512
+
513
+ def to_torch_tensor(state_dict, return_empty_tensor=False):
514
+ """
515
+ Convert state_dict of GatheredTensor to torch tensor
516
+ """
517
+ torch_state_dict = {}
518
+ converted_tensors = {}
519
+ for name, tensor in state_dict.items():
520
+ tensor_id = id(tensor)
521
+ if tensor_id in converted_tensors: # shared tensors
522
+ shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
523
+ torch_state_dict[name] = shared_tensor
524
+ else:
525
+ converted_tensors[tensor_id] = name
526
+ if return_empty_tensor:
527
+ torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
528
+ else:
529
+ torch_state_dict[name] = tensor.contiguous()
530
+ return torch_state_dict
531
+
532
+
533
+ def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
534
+ tag=None,
535
+ exclude_frozen_parameters=False,
536
+ lazy_mode=False):
537
+ """
538
+ Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
539
+ ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
540
+ via a model hub.
541
+
542
+ Args:
543
+ - ``checkpoint_dir``: path to the desired checkpoint folder
544
+ - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
545
+ - ``exclude_frozen_parameters``: exclude frozen parameters
546
+ - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
547
+ Convert the pesduo tensor to torch tensor by ``.contiguous()``
548
+
549
+ Returns:
550
+ - pytorch ``state_dict``
551
+
552
+ A typical usage might be ::
553
+
554
+ from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
555
+ # do the training and checkpoint saving
556
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
557
+ model = model.cpu() # move to cpu
558
+ model.load_state_dict(state_dict)
559
+ # submit to model hub or save the model to share with others
560
+
561
+ In this example the ``model`` will no longer be usable in the deepspeed context of the same
562
+ application. i.e. you will need to re-initialize the deepspeed engine, since
563
+ ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
564
+
565
+ If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
566
+
567
+ Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
568
+ You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
569
+ the checkpoint. Or you can load state_dict in lazy mode ::
570
+
571
+ from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
572
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
573
+ for name, lazy_tensor in state_dict.item():
574
+ tensor = lazy_tensor.contiguous() # to cpu
575
+ print(name, tensor)
576
+ # del tensor to release memory if it no longer in use
577
+ """
578
+ if tag is None:
579
+ latest_path = os.path.join(checkpoint_dir, 'latest')
580
+ if os.path.isfile(latest_path):
581
+ with open(latest_path, 'r') as fd:
582
+ tag = fd.read().strip()
583
+ else:
584
+ raise ValueError(f"Unable to find 'latest' file at {latest_path}")
585
+
586
+ ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
587
+
588
+ if not os.path.isdir(ds_checkpoint_dir):
589
+ raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
590
+
591
+ state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
592
+ if lazy_mode:
593
+ return state_dict
594
+ else:
595
+ return to_torch_tensor(state_dict)
596
+
597
+
598
+ def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
599
+ output_dir,
600
+ max_shard_size="5GB",
601
+ safe_serialization=False,
602
+ tag=None,
603
+ exclude_frozen_parameters=False):
604
+ """
605
+ Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
606
+ loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
607
+
608
+ Args:
609
+ - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
610
+ - ``output_dir``: directory to the pytorch fp32 state_dict output files
611
+ - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
612
+ - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
613
+ - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
614
+ - ``exclude_frozen_parameters``: exclude frozen parameters
615
+ """
616
+
617
+ # Dependency pre-check
618
+ if safe_serialization:
619
+ try:
620
+ from safetensors.torch import save_file
621
+ except ImportError:
622
+ print('If you want to use `safe_serialization`, please `pip install safetensors`')
623
+ raise
624
+ if max_shard_size is not None:
625
+ try:
626
+ from huggingface_hub import split_torch_state_dict_into_shards
627
+ except ImportError:
628
+ print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
629
+ raise
630
+
631
+ # Convert zero checkpoint to state_dict
632
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
633
+ tag,
634
+ exclude_frozen_parameters,
635
+ lazy_mode=True)
636
+
637
+ # Shard the model if it is too big.
638
+ weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
639
+ if max_shard_size is not None:
640
+ filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
641
+ # an memory-efficient approach for sharding
642
+ empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
643
+ state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
644
+ filename_pattern=filename_pattern,
645
+ max_shard_size=max_shard_size)
646
+ else:
647
+ from collections import namedtuple
648
+ StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
649
+ state_dict_split = StateDictSplit(is_sharded=False,
650
+ filename_to_tensors={weights_name: list(state_dict.keys())})
651
+
652
+ # Save the model by shard
653
+ os.makedirs(output_dir, exist_ok=True)
654
+ filename_to_tensors = state_dict_split.filename_to_tensors.items()
655
+ for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
656
+ shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
657
+ shard_state_dict = to_torch_tensor(shard_state_dict)
658
+ output_path = os.path.join(output_dir, shard_file)
659
+ if safe_serialization:
660
+ save_file(shard_state_dict, output_path, metadata={"format": "pt"})
661
+ else:
662
+ torch.save(shard_state_dict, output_path)
663
+ # release the memory of current shard
664
+ for tensor_name in list(shard_state_dict.keys()):
665
+ del state_dict[tensor_name]
666
+ del shard_state_dict[tensor_name]
667
+ del shard_state_dict
668
+ gc.collect()
669
+
670
+ # Save index if sharded
671
+ if state_dict_split.is_sharded:
672
+ index = {
673
+ "metadata": state_dict_split.metadata,
674
+ "weight_map": state_dict_split.tensor_to_filename,
675
+ }
676
+ save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
677
+ save_index_file = os.path.join(output_dir, save_index_file)
678
+ with open(save_index_file, "w", encoding="utf-8") as f:
679
+ content = json.dumps(index, indent=2, sort_keys=True) + "\n"
680
+ f.write(content)
681
+
682
+
683
+ def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
684
+ """
685
+ 1. Put the provided model to cpu
686
+ 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
687
+ 3. Load it into the provided model
688
+
689
+ Args:
690
+ - ``model``: the model object to update
691
+ - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
692
+ - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
693
+
694
+ Returns:
695
+ - ``model`: modified model
696
+
697
+ Make sure you have plenty of CPU memory available before you call this function. If you don't
698
+ have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
699
+ conveniently placed for you in the checkpoint folder.
700
+
701
+ A typical usage might be ::
702
+
703
+ from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
704
+ model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
705
+ # submit to model hub or save the model to share with others
706
+
707
+ Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
708
+ of the same application. i.e. you will need to re-initialize the deepspeed engine, since
709
+ ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
710
+
711
+ """
712
+ logger.info(f"Extracting fp32 weights")
713
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
714
+
715
+ logger.info(f"Overwriting model with fp32 weights")
716
+ model = model.cpu()
717
+ model.load_state_dict(state_dict, strict=False)
718
+
719
+ return model
720
+
721
+
722
+ if __name__ == "__main__":
723
+ parser = argparse.ArgumentParser()
724
+ parser.add_argument("checkpoint_dir",
725
+ type=str,
726
+ help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
727
+ parser.add_argument("output_dir",
728
+ type=str,
729
+ help="directory to the pytorch fp32 state_dict output files"
730
+ "(e.g. path/checkpoint-12-output/)")
731
+ parser.add_argument(
732
+ "--max_shard_size",
733
+ type=str,
734
+ default="5GB",
735
+ help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
736
+ "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
737
+ "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
738
+ "without CPU OOM issues.")
739
+ parser.add_argument(
740
+ "--safe_serialization",
741
+ default=False,
742
+ action='store_true',
743
+ help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
744
+ parser.add_argument("-t",
745
+ "--tag",
746
+ type=str,
747
+ default=None,
748
+ help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
749
+ parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
750
+ parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
751
+ args = parser.parse_args()
752
+
753
+ debug = args.debug
754
+
755
+ convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
756
+ args.output_dir,
757
+ max_shard_size=args.max_shard_size,
758
+ safe_serialization=args.safe_serialization,
759
+ tag=args.tag,
760
+ exclude_frozen_parameters=args.exclude_frozen_parameters)