rufatronics commited on
Commit
524d5e1
·
verified ·
1 Parent(s): 5b5d534

checkpoint-1750 | loss=?

Browse files
checkpoint-1750/README.md ADDED
@@ -0,0 +1,208 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: HuggingFaceTB/SmolLM2-135M-Instruct
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
7
+ - lora
8
+ - sft
9
+ - trl
10
+ ---
11
+
12
+ # Model Card for Model ID
13
+
14
+ <!-- Provide a quick summary of what the model is/does. -->
15
+
16
+
17
+
18
+ ## Model Details
19
+
20
+ ### Model Description
21
+
22
+ <!-- Provide a longer summary of what this model is. -->
23
+
24
+
25
+
26
+ - **Developed by:** [More Information Needed]
27
+ - **Funded by [optional]:** [More Information Needed]
28
+ - **Shared by [optional]:** [More Information Needed]
29
+ - **Model type:** [More Information Needed]
30
+ - **Language(s) (NLP):** [More Information Needed]
31
+ - **License:** [More Information Needed]
32
+ - **Finetuned from model [optional]:** [More Information Needed]
33
+
34
+ ### Model Sources [optional]
35
+
36
+ <!-- Provide the basic links for the model. -->
37
+
38
+ - **Repository:** [More Information Needed]
39
+ - **Paper [optional]:** [More Information Needed]
40
+ - **Demo [optional]:** [More Information Needed]
41
+
42
+ ## Uses
43
+
44
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
45
+
46
+ ### Direct Use
47
+
48
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Downstream Use [optional]
53
+
54
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
55
+
56
+ [More Information Needed]
57
+
58
+ ### Out-of-Scope Use
59
+
60
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ## Bias, Risks, and Limitations
65
+
66
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
67
+
68
+ [More Information Needed]
69
+
70
+ ### Recommendations
71
+
72
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
73
+
74
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
75
+
76
+ ## How to Get Started with the Model
77
+
78
+ Use the code below to get started with the model.
79
+
80
+ [More Information Needed]
81
+
82
+ ## Training Details
83
+
84
+ ### Training Data
85
+
86
+ <!-- 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. -->
87
+
88
+ [More Information Needed]
89
+
90
+ ### Training Procedure
91
+
92
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
93
+
94
+ #### Preprocessing [optional]
95
+
96
+ [More Information Needed]
97
+
98
+
99
+ #### Training Hyperparameters
100
+
101
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
102
+
103
+ #### Speeds, Sizes, Times [optional]
104
+
105
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
106
+
107
+ [More Information Needed]
108
+
109
+ ## Evaluation
110
+
111
+ <!-- This section describes the evaluation protocols and provides the results. -->
112
+
113
+ ### Testing Data, Factors & Metrics
114
+
115
+ #### Testing Data
116
+
117
+ <!-- This should link to a Dataset Card if possible. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Factors
122
+
123
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
124
+
125
+ [More Information Needed]
126
+
127
+ #### Metrics
128
+
129
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
130
+
131
+ [More Information Needed]
132
+
133
+ ### Results
134
+
135
+ [More Information Needed]
136
+
137
+ #### Summary
138
+
139
+
140
+
141
+ ## Model Examination [optional]
142
+
143
+ <!-- Relevant interpretability work for the model goes here -->
144
+
145
+ [More Information Needed]
146
+
147
+ ## Environmental Impact
148
+
149
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
150
+
151
+ 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).
152
+
153
+ - **Hardware Type:** [More Information Needed]
154
+ - **Hours used:** [More Information Needed]
155
+ - **Cloud Provider:** [More Information Needed]
156
+ - **Compute Region:** [More Information Needed]
157
+ - **Carbon Emitted:** [More Information Needed]
158
+
159
+ ## Technical Specifications [optional]
160
+
161
+ ### Model Architecture and Objective
162
+
163
+ [More Information Needed]
164
+
165
+ ### Compute Infrastructure
166
+
167
+ [More Information Needed]
168
+
169
+ #### Hardware
170
+
171
+ [More Information Needed]
172
+
173
+ #### Software
174
+
175
+ [More Information Needed]
176
+
177
+ ## Citation [optional]
178
+
179
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
180
+
181
+ **BibTeX:**
182
+
183
+ [More Information Needed]
184
+
185
+ **APA:**
186
+
187
+ [More Information Needed]
188
+
189
+ ## Glossary [optional]
190
+
191
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
192
+
193
+ [More Information Needed]
194
+
195
+ ## More Information [optional]
196
+
197
+ [More Information Needed]
198
+
199
+ ## Model Card Authors [optional]
200
+
201
+ [More Information Needed]
202
+
203
+ ## Model Card Contact
204
+
205
+ [More Information Needed]
206
+ ### Framework versions
207
+
208
+ - PEFT 0.19.1
checkpoint-1750/adapter_config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M-Instruct",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.05,
22
+ "lora_ga_config": null,
23
+ "megatron_config": null,
24
+ "megatron_core": "megatron.core",
25
+ "modules_to_save": null,
26
+ "peft_type": "LORA",
27
+ "peft_version": "0.19.1",
28
+ "qalora_group_size": 16,
29
+ "r": 16,
30
+ "rank_pattern": {},
31
+ "revision": null,
32
+ "target_modules": [
33
+ "k_proj",
34
+ "o_proj",
35
+ "v_proj",
36
+ "q_proj"
37
+ ],
38
+ "target_parameters": null,
39
+ "task_type": "CAUSAL_LM",
40
+ "trainable_token_indices": null,
41
+ "use_bdlora": null,
42
+ "use_dora": false,
43
+ "use_qalora": false,
44
+ "use_rslora": false
45
+ }
checkpoint-1750/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a29890cb41f53e1f5482887af0134edb716f36f8815730374fb2f1b925064d9a
3
+ size 7408448
checkpoint-1750/chat_template.jinja ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
2
+ You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
3
+ ' }}{% endif %}{{'<|im_start|>' + message['role'] + '
4
+ ' + message['content'] + '<|im_end|>' + '
5
+ '}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
6
+ ' }}{% endif %}
checkpoint-1750/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e65c11b1d3f2d4fb1504e154acce055e2a7db93ad5387c51d531bbf7cfa9d710
3
+ size 14950667
checkpoint-1750/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:17985cc82964fd9666e7fe1dcf4731bf957632fcc5bba70edb8b1e8e7062c23d
3
+ size 14645
checkpoint-1750/scaler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c3f9facd156f274d558585c8beffeda844e22f840dd0b18f6d2c35abbad24bd0
3
+ size 1383
checkpoint-1750/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6dfa19d0aafe75eafc37d0a43e7cd22139a91edaa29612fcf9645fd07e0b0caa
3
+ size 1465
checkpoint-1750/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1750/tokenizer_config.json ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": "<|im_start|>",
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|im_end|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>"
11
+ ],
12
+ "is_local": false,
13
+ "local_files_only": false,
14
+ "model_max_length": 8192,
15
+ "pad_token": "<|im_end|>",
16
+ "tokenizer_class": "GPT2Tokenizer",
17
+ "unk_token": "<|endoftext|>",
18
+ "vocab_size": 49152
19
+ }
checkpoint-1750/trainer_state.json ADDED
@@ -0,0 +1,461 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": 1750,
3
+ "best_metric": 1.0002270936965942,
4
+ "best_model_checkpoint": "/content/work/checkpoints/checkpoint-1750",
5
+ "epoch": 0.15659955257270694,
6
+ "eval_steps": 250,
7
+ "global_step": 1750,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 2.651820396184921,
14
+ "epoch": 0.0044742729306487695,
15
+ "grad_norm": 0.442557692527771,
16
+ "learning_rate": 9.8e-05,
17
+ "loss": 3.280501403808594,
18
+ "mean_token_accuracy": 0.4026365186274052,
19
+ "num_tokens": 130832.0,
20
+ "step": 50
21
+ },
22
+ {
23
+ "entropy": 2.7069850754737854,
24
+ "epoch": 0.008948545861297539,
25
+ "grad_norm": 0.41429346799850464,
26
+ "learning_rate": 0.00019800000000000002,
27
+ "loss": 2.666520690917969,
28
+ "mean_token_accuracy": 0.4684834870696068,
29
+ "num_tokens": 261056.0,
30
+ "step": 100
31
+ },
32
+ {
33
+ "entropy": 2.2673240077495573,
34
+ "epoch": 0.013422818791946308,
35
+ "grad_norm": 0.5210320353507996,
36
+ "learning_rate": 0.00019999760668343463,
37
+ "loss": 2.216226806640625,
38
+ "mean_token_accuracy": 0.5265978004038334,
39
+ "num_tokens": 391278.0,
40
+ "step": 150
41
+ },
42
+ {
43
+ "entropy": 2.015401030778885,
44
+ "epoch": 0.017897091722595078,
45
+ "grad_norm": 0.5778504610061646,
46
+ "learning_rate": 0.00019999023048426428,
47
+ "loss": 1.9369810485839845,
48
+ "mean_token_accuracy": 0.5647277806699276,
49
+ "num_tokens": 522585.0,
50
+ "step": 200
51
+ },
52
+ {
53
+ "entropy": 1.8627986872196198,
54
+ "epoch": 0.02237136465324385,
55
+ "grad_norm": 1.007725715637207,
56
+ "learning_rate": 0.00019997787077331475,
57
+ "loss": 1.7507638549804687,
58
+ "mean_token_accuracy": 0.5964532348513604,
59
+ "num_tokens": 653608.0,
60
+ "step": 250
61
+ },
62
+ {
63
+ "epoch": 0.02237136465324385,
64
+ "eval_entropy": 1.7570730288181717,
65
+ "eval_loss": 1.663292407989502,
66
+ "eval_mean_token_accuracy": 0.6118261234789669,
67
+ "eval_num_tokens": 653608.0,
68
+ "eval_runtime": 231.339,
69
+ "eval_samples_per_second": 81.352,
70
+ "eval_steps_per_second": 10.171,
71
+ "step": 250
72
+ },
73
+ {
74
+ "entropy": 2.790428400039673,
75
+ "epoch": 0.026845637583892617,
76
+ "grad_norm": 0.3708084523677826,
77
+ "learning_rate": 0.0001999605281665939,
78
+ "loss": 2.9912448120117188,
79
+ "mean_token_accuracy": 0.4324784705042839,
80
+ "num_tokens": 129363.0,
81
+ "step": 300
82
+ },
83
+ {
84
+ "entropy": 2.5292794728279113,
85
+ "epoch": 0.03131991051454139,
86
+ "grad_norm": 0.504848301410675,
87
+ "learning_rate": 0.00019993820352845704,
88
+ "loss": 2.4664991760253905,
89
+ "mean_token_accuracy": 0.4925511160492897,
90
+ "num_tokens": 259293.0,
91
+ "step": 350
92
+ },
93
+ {
94
+ "entropy": 2.227354944348335,
95
+ "epoch": 0.035794183445190156,
96
+ "grad_norm": 0.47856974601745605,
97
+ "learning_rate": 0.00019991089797156378,
98
+ "loss": 2.183813171386719,
99
+ "mean_token_accuracy": 0.5316948869824409,
100
+ "num_tokens": 389682.0,
101
+ "step": 400
102
+ },
103
+ {
104
+ "entropy": 2.036482437252998,
105
+ "epoch": 0.040268456375838924,
106
+ "grad_norm": 0.6903976202011108,
107
+ "learning_rate": 0.00019987861285682276,
108
+ "loss": 1.9621383666992187,
109
+ "mean_token_accuracy": 0.5651841352880002,
110
+ "num_tokens": 521242.0,
111
+ "step": 450
112
+ },
113
+ {
114
+ "entropy": 1.8924587434530258,
115
+ "epoch": 0.0447427293064877,
116
+ "grad_norm": 1.118765950202942,
117
+ "learning_rate": 0.00019984134979332365,
118
+ "loss": 1.7855641174316406,
119
+ "mean_token_accuracy": 0.5903938812017441,
120
+ "num_tokens": 650935.0,
121
+ "step": 500
122
+ },
123
+ {
124
+ "epoch": 0.0447427293064877,
125
+ "eval_entropy": 1.7746684214838808,
126
+ "eval_loss": 1.6794711351394653,
127
+ "eval_mean_token_accuracy": 0.608957466980877,
128
+ "eval_num_tokens": 650935.0,
129
+ "eval_runtime": 223.3119,
130
+ "eval_samples_per_second": 84.277,
131
+ "eval_steps_per_second": 10.537,
132
+ "step": 500
133
+ },
134
+ {
135
+ "entropy": 1.7242326444387437,
136
+ "epoch": 0.049217002237136466,
137
+ "grad_norm": 0.7724726796150208,
138
+ "learning_rate": 0.00019979911063825706,
139
+ "loss": 1.6321617126464845,
140
+ "mean_token_accuracy": 0.6168254449963569,
141
+ "num_tokens": 781036.0,
142
+ "step": 550
143
+ },
144
+ {
145
+ "entropy": 1.6163885343074798,
146
+ "epoch": 0.053691275167785234,
147
+ "grad_norm": 0.7551260590553284,
148
+ "learning_rate": 0.00019975189749682197,
149
+ "loss": 1.518061981201172,
150
+ "mean_token_accuracy": 0.6360310778021813,
151
+ "num_tokens": 911151.0,
152
+ "step": 600
153
+ },
154
+ {
155
+ "entropy": 1.5225655400753022,
156
+ "epoch": 0.058165548098434,
157
+ "grad_norm": 0.828091561794281,
158
+ "learning_rate": 0.00019969971272212067,
159
+ "loss": 1.42819580078125,
160
+ "mean_token_accuracy": 0.653648351430893,
161
+ "num_tokens": 1042446.0,
162
+ "step": 650
163
+ },
164
+ {
165
+ "entropy": 1.458768675327301,
166
+ "epoch": 0.06263982102908278,
167
+ "grad_norm": 0.8204870223999023,
168
+ "learning_rate": 0.00019964255891504167,
169
+ "loss": 1.365126190185547,
170
+ "mean_token_accuracy": 0.6648625639081002,
171
+ "num_tokens": 1173209.0,
172
+ "step": 700
173
+ },
174
+ {
175
+ "entropy": 1.4292363184690475,
176
+ "epoch": 0.06711409395973154,
177
+ "grad_norm": 0.7856907844543457,
178
+ "learning_rate": 0.00019958043892412996,
179
+ "loss": 1.3346669006347656,
180
+ "mean_token_accuracy": 0.6717766770720481,
181
+ "num_tokens": 1303838.0,
182
+ "step": 750
183
+ },
184
+ {
185
+ "epoch": 0.06711409395973154,
186
+ "eval_entropy": 1.397561838664352,
187
+ "eval_loss": 1.309268832206726,
188
+ "eval_mean_token_accuracy": 0.6755058221040864,
189
+ "eval_num_tokens": 1303838.0,
190
+ "eval_runtime": 222.6658,
191
+ "eval_samples_per_second": 84.521,
192
+ "eval_steps_per_second": 10.567,
193
+ "step": 750
194
+ },
195
+ {
196
+ "entropy": 1.3611693698167802,
197
+ "epoch": 0.07158836689038031,
198
+ "grad_norm": 0.8001652359962463,
199
+ "learning_rate": 0.00019951335584544508,
200
+ "loss": 1.2735862731933594,
201
+ "mean_token_accuracy": 0.6819746169447899,
202
+ "num_tokens": 1435829.0,
203
+ "step": 800
204
+ },
205
+ {
206
+ "entropy": 1.3399207586050033,
207
+ "epoch": 0.07606263982102908,
208
+ "grad_norm": 0.7596322894096375,
209
+ "learning_rate": 0.0001994413130224068,
210
+ "loss": 1.2516276550292968,
211
+ "mean_token_accuracy": 0.6869581064581871,
212
+ "num_tokens": 1566040.0,
213
+ "step": 850
214
+ },
215
+ {
216
+ "entropy": 1.2940440517663956,
217
+ "epoch": 0.08053691275167785,
218
+ "grad_norm": 0.7932069897651672,
219
+ "learning_rate": 0.0001993643140456285,
220
+ "loss": 1.2029429626464845,
221
+ "mean_token_accuracy": 0.6938760960102082,
222
+ "num_tokens": 1697705.0,
223
+ "step": 900
224
+ },
225
+ {
226
+ "entropy": 1.2705094563961028,
227
+ "epoch": 0.08501118568232663,
228
+ "grad_norm": 0.7709512710571289,
229
+ "learning_rate": 0.0001992823627527381,
230
+ "loss": 1.1883332061767578,
231
+ "mean_token_accuracy": 0.6987017118930816,
232
+ "num_tokens": 1829189.0,
233
+ "step": 950
234
+ },
235
+ {
236
+ "entropy": 1.257964146733284,
237
+ "epoch": 0.0894854586129754,
238
+ "grad_norm": 0.794852077960968,
239
+ "learning_rate": 0.00019919546322818703,
240
+ "loss": 1.1787289428710936,
241
+ "mean_token_accuracy": 0.7000182312726975,
242
+ "num_tokens": 1960750.0,
243
+ "step": 1000
244
+ },
245
+ {
246
+ "epoch": 0.0894854586129754,
247
+ "eval_entropy": 1.254803528164581,
248
+ "eval_loss": 1.1655369997024536,
249
+ "eval_mean_token_accuracy": 0.7024362733705571,
250
+ "eval_num_tokens": 1960750.0,
251
+ "eval_runtime": 216.8801,
252
+ "eval_samples_per_second": 86.776,
253
+ "eval_steps_per_second": 10.849,
254
+ "step": 1000
255
+ },
256
+ {
257
+ "entropy": 1.231136965751648,
258
+ "epoch": 0.09395973154362416,
259
+ "grad_norm": 0.8129546046257019,
260
+ "learning_rate": 0.00019910361980304646,
261
+ "loss": 1.1491018676757812,
262
+ "mean_token_accuracy": 0.7032070714235306,
263
+ "num_tokens": 2090761.0,
264
+ "step": 1050
265
+ },
266
+ {
267
+ "entropy": 1.2148332485556603,
268
+ "epoch": 0.09843400447427293,
269
+ "grad_norm": 0.7990450859069824,
270
+ "learning_rate": 0.00019900683705479147,
271
+ "loss": 1.1374418640136719,
272
+ "mean_token_accuracy": 0.7060695806145668,
273
+ "num_tokens": 2222147.0,
274
+ "step": 1100
275
+ },
276
+ {
277
+ "entropy": 1.208145708143711,
278
+ "epoch": 0.1029082774049217,
279
+ "grad_norm": 0.7982318997383118,
280
+ "learning_rate": 0.00019890511980707296,
281
+ "loss": 1.1248248291015626,
282
+ "mean_token_accuracy": 0.7086962693929673,
283
+ "num_tokens": 2353159.0,
284
+ "step": 1150
285
+ },
286
+ {
287
+ "entropy": 1.1811901706457137,
288
+ "epoch": 0.10738255033557047,
289
+ "grad_norm": 0.8226000666618347,
290
+ "learning_rate": 0.00019879847312947725,
291
+ "loss": 1.1086264038085938,
292
+ "mean_token_accuracy": 0.7128409042954444,
293
+ "num_tokens": 2484594.0,
294
+ "step": 1200
295
+ },
296
+ {
297
+ "entropy": 1.1749517038464545,
298
+ "epoch": 0.11185682326621924,
299
+ "grad_norm": 0.8299795985221863,
300
+ "learning_rate": 0.00019868690233727337,
301
+ "loss": 1.1017638397216798,
302
+ "mean_token_accuracy": 0.7125484347343445,
303
+ "num_tokens": 2615149.0,
304
+ "step": 1250
305
+ },
306
+ {
307
+ "epoch": 0.11185682326621924,
308
+ "eval_entropy": 1.1516895890995842,
309
+ "eval_loss": 1.0878593921661377,
310
+ "eval_mean_token_accuracy": 0.7167255235032431,
311
+ "eval_num_tokens": 2615149.0,
312
+ "eval_runtime": 222.8313,
313
+ "eval_samples_per_second": 84.458,
314
+ "eval_steps_per_second": 10.56,
315
+ "step": 1250
316
+ },
317
+ {
318
+ "entropy": 1.1464633461833,
319
+ "epoch": 0.116331096196868,
320
+ "grad_norm": 0.7202898859977722,
321
+ "learning_rate": 0.00019857041299114808,
322
+ "loss": 1.0679573822021484,
323
+ "mean_token_accuracy": 0.7206197878718377,
324
+ "num_tokens": 2746727.0,
325
+ "step": 1300
326
+ },
327
+ {
328
+ "entropy": 1.1379328405857085,
329
+ "epoch": 0.12080536912751678,
330
+ "grad_norm": 0.8328574895858765,
331
+ "learning_rate": 0.00019844901089692893,
332
+ "loss": 1.0696997833251953,
333
+ "mean_token_accuracy": 0.7188817489147187,
334
+ "num_tokens": 2877531.0,
335
+ "step": 1350
336
+ },
337
+ {
338
+ "entropy": 1.131972982287407,
339
+ "epoch": 0.12527964205816555,
340
+ "grad_norm": 0.8093060851097107,
341
+ "learning_rate": 0.00019832270210529466,
342
+ "loss": 1.058395538330078,
343
+ "mean_token_accuracy": 0.7203070876002312,
344
+ "num_tokens": 3008803.0,
345
+ "step": 1400
346
+ },
347
+ {
348
+ "entropy": 1.12234389513731,
349
+ "epoch": 0.1297539149888143,
350
+ "grad_norm": 0.742972195148468,
351
+ "learning_rate": 0.00019819149291147383,
352
+ "loss": 1.0534366607666015,
353
+ "mean_token_accuracy": 0.7222018876671791,
354
+ "num_tokens": 3137965.0,
355
+ "step": 1450
356
+ },
357
+ {
358
+ "entropy": 1.1006340074539185,
359
+ "epoch": 0.1342281879194631,
360
+ "grad_norm": 0.7162765860557556,
361
+ "learning_rate": 0.00019805538985493096,
362
+ "loss": 1.028353729248047,
363
+ "mean_token_accuracy": 0.7277802819013596,
364
+ "num_tokens": 3269555.0,
365
+ "step": 1500
366
+ },
367
+ {
368
+ "epoch": 0.1342281879194631,
369
+ "eval_entropy": 1.1096204250759638,
370
+ "eval_loss": 1.0371356010437012,
371
+ "eval_mean_token_accuracy": 0.7253242311354045,
372
+ "eval_num_tokens": 3269555.0,
373
+ "eval_runtime": 211.312,
374
+ "eval_samples_per_second": 89.063,
375
+ "eval_steps_per_second": 11.135,
376
+ "step": 1500
377
+ },
378
+ {
379
+ "entropy": 1.0943984407186509,
380
+ "epoch": 0.13870246085011187,
381
+ "grad_norm": 0.737978458404541,
382
+ "learning_rate": 0.00019791439971904054,
383
+ "loss": 1.0289594268798827,
384
+ "mean_token_accuracy": 0.7275757938623428,
385
+ "num_tokens": 3400488.0,
386
+ "step": 1550
387
+ },
388
+ {
389
+ "entropy": 1.0984211310744285,
390
+ "epoch": 0.14317673378076062,
391
+ "grad_norm": 0.7294973731040955,
392
+ "learning_rate": 0.00019776852953074916,
393
+ "loss": 1.0274142456054687,
394
+ "mean_token_accuracy": 0.7268362134695053,
395
+ "num_tokens": 3531555.0,
396
+ "step": 1600
397
+ },
398
+ {
399
+ "entropy": 1.08658686876297,
400
+ "epoch": 0.1476510067114094,
401
+ "grad_norm": 0.7807707190513611,
402
+ "learning_rate": 0.00019761778656022505,
403
+ "loss": 1.0233070373535156,
404
+ "mean_token_accuracy": 0.7277577331662178,
405
+ "num_tokens": 3662724.0,
406
+ "step": 1650
407
+ },
408
+ {
409
+ "entropy": 1.0737271896004676,
410
+ "epoch": 0.15212527964205816,
411
+ "grad_norm": 0.7554745078086853,
412
+ "learning_rate": 0.0001974621783204959,
413
+ "loss": 1.007247314453125,
414
+ "mean_token_accuracy": 0.7310737103223801,
415
+ "num_tokens": 3792301.0,
416
+ "step": 1700
417
+ },
418
+ {
419
+ "entropy": 1.0761739972233773,
420
+ "epoch": 0.15659955257270694,
421
+ "grad_norm": 0.7434770464897156,
422
+ "learning_rate": 0.0001973017125670744,
423
+ "loss": 1.010123519897461,
424
+ "mean_token_accuracy": 0.7281501796841622,
425
+ "num_tokens": 3923244.0,
426
+ "step": 1750
427
+ },
428
+ {
429
+ "epoch": 0.15659955257270694,
430
+ "eval_entropy": 1.0884551091291426,
431
+ "eval_loss": 1.0002270936965942,
432
+ "eval_mean_token_accuracy": 0.7324172373381362,
433
+ "eval_num_tokens": 3923244.0,
434
+ "eval_runtime": 222.3676,
435
+ "eval_samples_per_second": 84.635,
436
+ "eval_steps_per_second": 10.582,
437
+ "step": 1750
438
+ }
439
+ ],
440
+ "logging_steps": 50,
441
+ "max_steps": 22350,
442
+ "num_input_tokens_seen": 0,
443
+ "num_train_epochs": 2,
444
+ "save_steps": 250,
445
+ "stateful_callbacks": {
446
+ "TrainerControl": {
447
+ "args": {
448
+ "should_epoch_stop": false,
449
+ "should_evaluate": false,
450
+ "should_log": false,
451
+ "should_save": true,
452
+ "should_training_stop": false
453
+ },
454
+ "attributes": {}
455
+ }
456
+ },
457
+ "total_flos": 3799475352585216.0,
458
+ "train_batch_size": 8,
459
+ "trial_name": null,
460
+ "trial_params": null
461
+ }
checkpoint-1750/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:104f85937520e28216c832e9214554f46be2b0495d85b4111df01a6c6a97996b
3
+ size 5649