cyan2k commited on
Commit
27fa81d
·
verified ·
1 Parent(s): 76fd9f7

Add files using upload-large-folder tool

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. .gitattributes +6 -0
  2. outputs/.ipynb_checkpoints/README-checkpoint.md +59 -0
  3. outputs/README.md +59 -0
  4. outputs/checkpoint-1000/README.md +209 -0
  5. outputs/checkpoint-1000/adapter_config.json +45 -0
  6. outputs/checkpoint-1000/chat_template.jinja +315 -0
  7. outputs/checkpoint-1000/special_tokens_map.json +23 -0
  8. outputs/checkpoint-1000/tokenizer.json +3 -0
  9. outputs/checkpoint-1000/tokenizer_config.json +185 -0
  10. outputs/checkpoint-1000/trainer_state.json +0 -0
  11. outputs/checkpoint-1500/README.md +209 -0
  12. outputs/checkpoint-1500/adapter_config.json +45 -0
  13. outputs/checkpoint-1500/chat_template.jinja +315 -0
  14. outputs/checkpoint-1500/optimizer.pt +3 -0
  15. outputs/checkpoint-1500/special_tokens_map.json +23 -0
  16. outputs/checkpoint-1500/tokenizer.json +3 -0
  17. outputs/checkpoint-1500/tokenizer_config.json +185 -0
  18. outputs/checkpoint-1500/trainer_state.json +0 -0
  19. outputs/checkpoint-2000/README.md +209 -0
  20. outputs/checkpoint-2000/adapter_config.json +45 -0
  21. outputs/checkpoint-2000/chat_template.jinja +315 -0
  22. outputs/checkpoint-2000/special_tokens_map.json +23 -0
  23. outputs/checkpoint-2000/tokenizer.json +3 -0
  24. outputs/checkpoint-2000/tokenizer_config.json +185 -0
  25. outputs/checkpoint-2000/trainer_state.json +0 -0
  26. outputs/checkpoint-2500/README.md +209 -0
  27. outputs/checkpoint-2500/adapter_config.json +45 -0
  28. outputs/checkpoint-2500/chat_template.jinja +315 -0
  29. outputs/checkpoint-2500/optimizer.pt +3 -0
  30. outputs/checkpoint-2500/rng_state.pth +3 -0
  31. outputs/checkpoint-2500/special_tokens_map.json +23 -0
  32. outputs/checkpoint-2500/tokenizer.json +3 -0
  33. outputs/checkpoint-2500/tokenizer_config.json +185 -0
  34. outputs/checkpoint-2500/trainer_state.json +0 -0
  35. outputs/checkpoint-313/README.md +209 -0
  36. outputs/checkpoint-313/adapter_config.json +45 -0
  37. outputs/checkpoint-313/chat_template.jinja +315 -0
  38. outputs/checkpoint-313/special_tokens_map.json +23 -0
  39. outputs/checkpoint-313/tokenizer.json +3 -0
  40. outputs/checkpoint-313/tokenizer_config.json +185 -0
  41. outputs/checkpoint-313/trainer_state.json +2225 -0
  42. outputs/checkpoint-500/README.md +209 -0
  43. outputs/checkpoint-500/adapter_config.json +45 -0
  44. outputs/checkpoint-500/chat_template.jinja +315 -0
  45. outputs/checkpoint-500/optimizer.pt +3 -0
  46. outputs/checkpoint-500/rng_state.pth +3 -0
  47. outputs/checkpoint-500/special_tokens_map.json +23 -0
  48. outputs/checkpoint-500/tokenizer.json +3 -0
  49. outputs/checkpoint-500/tokenizer_config.json +185 -0
  50. outputs/checkpoint-500/trainer_state.json +3534 -0
.gitattributes CHANGED
@@ -33,3 +33,9 @@ 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
+ outputs/checkpoint-500/tokenizer.json filter=lfs diff=lfs merge=lfs -text
37
+ outputs/checkpoint-1500/tokenizer.json filter=lfs diff=lfs merge=lfs -text
38
+ outputs/checkpoint-1000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
39
+ outputs/checkpoint-2000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
40
+ outputs/checkpoint-313/tokenizer.json filter=lfs diff=lfs merge=lfs -text
41
+ outputs/checkpoint-2500/tokenizer.json filter=lfs diff=lfs merge=lfs -text
outputs/.ipynb_checkpoints/README-checkpoint.md ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
3
+ library_name: transformers
4
+ model_name: outputs
5
+ tags:
6
+ - generated_from_trainer
7
+ - trl
8
+ - sft
9
+ - unsloth
10
+ licence: license
11
+ ---
12
+
13
+ # Model Card for outputs
14
+
15
+ This model is a fine-tuned version of [unsloth/gpt-oss-20b-unsloth-bnb-4bit](https://huggingface.co/unsloth/gpt-oss-20b-unsloth-bnb-4bit).
16
+ It has been trained using [TRL](https://github.com/huggingface/trl).
17
+
18
+ ## Quick start
19
+
20
+ ```python
21
+ from transformers import pipeline
22
+
23
+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
24
+ generator = pipeline("text-generation", model="None", device="cuda")
25
+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
26
+ print(output["generated_text"])
27
+ ```
28
+
29
+ ## Training procedure
30
+
31
+
32
+
33
+
34
+ This model was trained with SFT.
35
+
36
+ ### Framework versions
37
+
38
+ - TRL: 0.22.2
39
+ - Transformers: 4.55.4
40
+ - Pytorch: 2.8.0+cu128
41
+ - Datasets: 3.6.0
42
+ - Tokenizers: 0.21.4
43
+
44
+ ## Citations
45
+
46
+
47
+
48
+ Cite TRL as:
49
+
50
+ ```bibtex
51
+ @misc{vonwerra2022trl,
52
+ title = {{TRL: Transformer Reinforcement Learning}},
53
+ author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
54
+ year = 2020,
55
+ journal = {GitHub repository},
56
+ publisher = {GitHub},
57
+ howpublished = {\url{https://github.com/huggingface/trl}}
58
+ }
59
+ ```
outputs/README.md ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
3
+ library_name: transformers
4
+ model_name: outputs
5
+ tags:
6
+ - generated_from_trainer
7
+ - unsloth
8
+ - sft
9
+ - trl
10
+ licence: license
11
+ ---
12
+
13
+ # Model Card for outputs
14
+
15
+ This model is a fine-tuned version of [unsloth/gpt-oss-20b-unsloth-bnb-4bit](https://huggingface.co/unsloth/gpt-oss-20b-unsloth-bnb-4bit).
16
+ It has been trained using [TRL](https://github.com/huggingface/trl).
17
+
18
+ ## Quick start
19
+
20
+ ```python
21
+ from transformers import pipeline
22
+
23
+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
24
+ generator = pipeline("text-generation", model="None", device="cuda")
25
+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
26
+ print(output["generated_text"])
27
+ ```
28
+
29
+ ## Training procedure
30
+
31
+
32
+
33
+
34
+ This model was trained with SFT.
35
+
36
+ ### Framework versions
37
+
38
+ - TRL: 0.22.2
39
+ - Transformers: 4.55.4
40
+ - Pytorch: 2.8.0
41
+ - Datasets: 3.6.0
42
+ - Tokenizers: 0.21.4
43
+
44
+ ## Citations
45
+
46
+
47
+
48
+ Cite TRL as:
49
+
50
+ ```bibtex
51
+ @misc{vonwerra2022trl,
52
+ title = {{TRL: Transformer Reinforcement Learning}},
53
+ author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
54
+ year = 2020,
55
+ journal = {GitHub repository},
56
+ publisher = {GitHub},
57
+ howpublished = {\url{https://github.com/huggingface/trl}}
58
+ }
59
+ ```
outputs/checkpoint-1000/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
3
+ library_name: peft
4
+ tags:
5
+ - base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit
6
+ - lora
7
+ - sft
8
+ - transformers
9
+ - trl
10
+ - unsloth
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.17.1
outputs/checkpoint-1000/adapter_config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": {
4
+ "base_model_class": "GptOssForCausalLM",
5
+ "parent_library": "transformers.models.gpt_oss.modeling_gpt_oss"
6
+ },
7
+ "base_model_name_or_path": "unsloth/gpt-oss-20b-unsloth-bnb-4bit",
8
+ "bias": "none",
9
+ "corda_config": null,
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": 0.0,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "qalora_group_size": 16,
27
+ "r": 16,
28
+ "rank_pattern": {},
29
+ "revision": null,
30
+ "target_modules": [
31
+ "o_proj",
32
+ "v_proj",
33
+ "up_proj",
34
+ "down_proj",
35
+ "gate_proj",
36
+ "k_proj",
37
+ "q_proj"
38
+ ],
39
+ "target_parameters": null,
40
+ "task_type": null,
41
+ "trainable_token_indices": null,
42
+ "use_dora": false,
43
+ "use_qalora": false,
44
+ "use_rslora": false
45
+ }
outputs/checkpoint-1000/chat_template.jinja ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
2
+ {#-
3
+ In addition to the normal inputs of `messages` and `tools`, this template also accepts the
4
+ following kwargs:
5
+ - "builtin_tools": A list, can contain "browser" and/or "python".
6
+ - "model_identity": A string that optionally describes the model identity.
7
+ - "reasoning_effort": A string that describes the reasoning effort, defaults to "medium".
8
+ #}
9
+
10
+ {#- Tool Definition Rendering ============================================== #}
11
+ {%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}
12
+ {%- if param_spec.type == "array" -%}
13
+ {%- if param_spec['items'] -%}
14
+ {%- if param_spec['items']['type'] == "string" -%}
15
+ {{- "string[]" }}
16
+ {%- elif param_spec['items']['type'] == "number" -%}
17
+ {{- "number[]" }}
18
+ {%- elif param_spec['items']['type'] == "integer" -%}
19
+ {{- "number[]" }}
20
+ {%- elif param_spec['items']['type'] == "boolean" -%}
21
+ {{- "boolean[]" }}
22
+ {%- else -%}
23
+ {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}
24
+ {%- if inner_type == "object | object" or inner_type|length > 50 -%}
25
+ {{- "any[]" }}
26
+ {%- else -%}
27
+ {{- inner_type + "[]" }}
28
+ {%- endif -%}
29
+ {%- endif -%}
30
+ {%- if param_spec.nullable -%}
31
+ {{- " | null" }}
32
+ {%- endif -%}
33
+ {%- else -%}
34
+ {{- "any[]" }}
35
+ {%- if param_spec.nullable -%}
36
+ {{- " | null" }}
37
+ {%- endif -%}
38
+ {%- endif -%}
39
+ {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}
40
+ {#- Handle array of types like ["object", "object"] from Union[dict, list] #}
41
+ {%- if param_spec.type | length > 1 -%}
42
+ {{- param_spec.type | join(" | ") }}
43
+ {%- else -%}
44
+ {{- param_spec.type[0] }}
45
+ {%- endif -%}
46
+ {%- elif param_spec.oneOf -%}
47
+ {#- Handle oneOf schemas - check for complex unions and fallback to any #}
48
+ {%- set has_object_variants = false -%}
49
+ {%- for variant in param_spec.oneOf -%}
50
+ {%- if variant.type == "object" -%}
51
+ {%- set has_object_variants = true -%}
52
+ {%- endif -%}
53
+ {%- endfor -%}
54
+ {%- if has_object_variants and param_spec.oneOf|length > 1 -%}
55
+ {{- "any" }}
56
+ {%- else -%}
57
+ {%- for variant in param_spec.oneOf -%}
58
+ {{- render_typescript_type(variant, required_params) -}}
59
+ {%- if variant.description %}
60
+ {{- "// " + variant.description }}
61
+ {%- endif -%}
62
+ {%- if variant.default is defined %}
63
+ {{ "// default: " + variant.default|tojson }}
64
+ {%- endif -%}
65
+ {%- if not loop.last %}
66
+ {{- " | " }}
67
+ {% endif -%}
68
+ {%- endfor -%}
69
+ {%- endif -%}
70
+ {%- elif param_spec.type == "string" -%}
71
+ {%- if param_spec.enum -%}
72
+ {{- '"' + param_spec.enum|join('" | "') + '"' -}}
73
+ {%- else -%}
74
+ {{- "string" }}
75
+ {%- if param_spec.nullable %}
76
+ {{- " | null" }}
77
+ {%- endif -%}
78
+ {%- endif -%}
79
+ {%- elif param_spec.type == "number" -%}
80
+ {{- "number" }}
81
+ {%- elif param_spec.type == "integer" -%}
82
+ {{- "number" }}
83
+ {%- elif param_spec.type == "boolean" -%}
84
+ {{- "boolean" }}
85
+
86
+ {%- elif param_spec.type == "object" -%}
87
+ {%- if param_spec.properties -%}
88
+ {{- "{\n" }}
89
+ {%- for prop_name, prop_spec in param_spec.properties.items() -%}
90
+ {{- prop_name -}}
91
+ {%- if prop_name not in (param_spec.required or []) -%}
92
+ {{- "?" }}
93
+ {%- endif -%}
94
+ {{- ": " }}
95
+ {{ render_typescript_type(prop_spec, param_spec.required or []) }}
96
+ {%- if not loop.last -%}
97
+ {{-", " }}
98
+ {%- endif -%}
99
+ {%- endfor -%}
100
+ {{- "}" }}
101
+ {%- else -%}
102
+ {{- "object" }}
103
+ {%- endif -%}
104
+ {%- else -%}
105
+ {{- "any" }}
106
+ {%- endif -%}
107
+ {%- endmacro -%}
108
+
109
+ {%- macro render_tool_namespace(namespace_name, tools) -%}
110
+ {{- "## " + namespace_name + "\n\n" }}
111
+ {{- "namespace " + namespace_name + " {\n\n" }}
112
+ {%- for tool in tools %}
113
+ {%- set tool = tool.function %}
114
+ {{- "// " + tool.description + "\n" }}
115
+ {{- "type "+ tool.name + " = " }}
116
+ {%- if tool.parameters and tool.parameters.properties -%}
117
+ {{- "(_: " }}
118
+ {{- "{\n" }}
119
+ {%- for param_name, param_spec in tool.parameters.properties.items() %}
120
+ {{- "// " + param_spec.description + "\n" }}
121
+ {{- param_name }}
122
+ {%- if param_name not in (tool.parameters.required or []) -%}
123
+ {{- "?" }}
124
+ {%- endif -%}
125
+ {{- ": " }}
126
+ {{- render_typescript_type(param_spec, tool.parameters.required or []) }}
127
+ {%- if param_spec.default is defined -%}
128
+ {%- if param_spec.enum %}
129
+ {{- ", // default: " + param_spec.default }}
130
+ {%- elif param_spec.oneOf %}
131
+ {{- "// default: " + param_spec.default }}
132
+ {%- else %}
133
+ {{- ", // default: " + param_spec.default|tojson }}
134
+ {%- endif -%}
135
+ {%- endif -%}
136
+ {%- if not loop.last %}
137
+ {{- ",\n" }}
138
+ {%- else %}
139
+ {{- "\n" }}
140
+ {%- endif -%}
141
+ {%- endfor %}
142
+ {{- "}) => any;\n\n" }}
143
+ {%- else -%}
144
+ {{- "() => any;\n\n" }}
145
+ {%- endif -%}
146
+ {%- endfor %}
147
+ {{- "} // namespace " + namespace_name }}
148
+ {%- endmacro -%}
149
+
150
+ {%- macro render_builtin_tools(browser_tool, python_tool) -%}
151
+ {%- if browser_tool %}
152
+ {{- "## browser\n\n" }}
153
+ {{- "// Tool for browsing.\n" }}
154
+ {{- "// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.\n" }}
155
+ {{- "// Cite information from the tool using the following format:\n" }}
156
+ {{- "// `【{cursor}†L{line_start}(-L{line_end})?】`, for example: `【6†L9-L11】` or `【8†L3】`.\n" }}
157
+ {{- "// Do not quote more than 10 words directly from the tool output.\n" }}
158
+ {{- "// sources=web (default: web)\n" }}
159
+ {{- "namespace browser {\n\n" }}
160
+ {{- "// Searches for information related to `query` and displays `topn` results.\n" }}
161
+ {{- "type search = (_: {\n" }}
162
+ {{- "query: string,\n" }}
163
+ {{- "topn?: number, // default: 10\n" }}
164
+ {{- "source?: string,\n" }}
165
+ {{- "}) => any;\n\n" }}
166
+ {{- "// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.\n" }}
167
+ {{- "// Valid link ids are displayed with the formatting: `【{id}†.*】`.\n" }}
168
+ {{- "// If `cursor` is not provided, the most recent page is implied.\n" }}
169
+ {{- "// If `id` is a string, it is treated as a fully qualified URL associated with `source`.\n" }}
170
+ {{- "// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.\n" }}
171
+ {{- "// Use this function without `id` to scroll to a new location of an opened page.\n" }}
172
+ {{- "type open = (_: {\n" }}
173
+ {{- "id?: number | string, // default: -1\n" }}
174
+ {{- "cursor?: number, // default: -1\n" }}
175
+ {{- "loc?: number, // default: -1\n" }}
176
+ {{- "num_lines?: number, // default: -1\n" }}
177
+ {{- "view_source?: boolean, // default: false\n" }}
178
+ {{- "source?: string,\n" }}
179
+ {{- "}) => any;\n\n" }}
180
+ {{- "// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.\n" }}
181
+ {{- "type find = (_: {\n" }}
182
+ {{- "pattern: string,\n" }}
183
+ {{- "cursor?: number, // default: -1\n" }}
184
+ {{- "}) => any;\n\n" }}
185
+ {{- "} // namespace browser\n\n" }}
186
+ {%- endif -%}
187
+
188
+ {%- if python_tool %}
189
+ {{- "## python\n\n" }}
190
+ {{- "Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).\n\n" }}
191
+ {{- "When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.\n\n" }}
192
+ {%- endif -%}
193
+ {%- endmacro -%}
194
+
195
+ {#- System Message Construction ============================================ #}
196
+ {%- macro build_system_message() -%}
197
+ {%- if model_identity is not defined %}
198
+ {{- "You are ChatGPT, a large language model trained by OpenAI.\n" -}}
199
+ {%- else %}
200
+ {{- model_identity }}
201
+ {%- endif %}
202
+ {{- "Knowledge cutoff: 2024-06\n" }}
203
+ {{- "Current date: " + strftime_now("%Y-%m-%d") + "\n\n" }}
204
+ {%- if reasoning_effort is not defined %}
205
+ {%- set reasoning_effort = "medium" %}
206
+ {%- endif %}
207
+ {{- "Reasoning: " + reasoning_effort + "\n\n" }}
208
+ {%- if builtin_tools is defined %}
209
+ {{- "# Tools\n\n" }}
210
+ {%- set available_builtin_tools = namespace(browser=false, python=false) %}
211
+ {%- for tool in builtin_tools %}
212
+ {%- if tool == "browser" %}
213
+ {%- set available_builtin_tools.browser = true %}
214
+ {%- elif tool == "python" %}
215
+ {%- set available_builtin_tools.python = true %}
216
+ {%- endif %}
217
+ {%- endfor %}
218
+ {{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}
219
+ {%- endif -%}
220
+ {{- "# Valid channels: analysis, commentary, final. Channel must be included for every message." }}
221
+ {%- if tools is defined -%}
222
+ {{- "\nCalls to these tools must go to the commentary channel: 'functions'." }}
223
+ {%- endif -%}
224
+ {%- endmacro -%}
225
+
226
+ {#- Main Template Logic ================================================= #}
227
+ {#- Set defaults #}
228
+
229
+ {#- Render system message #}
230
+ {{- "<|start|>system<|message|>" }}
231
+ {{- build_system_message() }}
232
+ {{- "<|end|>" }}
233
+
234
+ {#- Extract developer message #}
235
+ {%- if messages[0].role == "developer" or messages[0].role == "system" %}
236
+ {%- set developer_message = messages[0].content %}
237
+ {%- set loop_messages = messages[1:] %}
238
+ {%- else %}
239
+ {%- set developer_message = "" %}
240
+ {%- set loop_messages = messages %}
241
+ {%- endif %}
242
+
243
+ {#- Render developer message #}
244
+ {%- if developer_message or tools %}
245
+ {{- "<|start|>developer<|message|>" }}
246
+ {%- if developer_message %}
247
+ {{- "# Instructions\n\n" }}
248
+ {{- developer_message }}
249
+ {%- endif %}
250
+ {%- if tools -%}
251
+ {{- "\n\n" }}
252
+ {{- "# Tools\n\n" }}
253
+ {{- render_tool_namespace("functions", tools) }}
254
+ {%- endif -%}
255
+ {{- "<|end|>" }}
256
+ {%- endif %}
257
+
258
+ {#- Render messages #}
259
+ {%- set last_tool_call = namespace(name=none) %}
260
+ {%- for message in loop_messages -%}
261
+ {#- At this point only assistant/user/tool messages should remain #}
262
+ {%- if message.role == 'assistant' -%}
263
+ {%- if "tool_calls" in message %}
264
+ {#- We assume max 1 tool call per message, and so we infer the tool call name #}
265
+ {#- in "tool" messages from the most recent assistant tool call name #}
266
+ {%- set tool_call = message.tool_calls[0] %}
267
+ {%- if tool_call.function %}
268
+ {%- set tool_call = tool_call.function %}
269
+ {%- endif %}
270
+ {%- if message.content %}
271
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.content + "<|end|>" }}
272
+ {%- endif %}
273
+ {{- "<|start|>assistant to=" }}
274
+ {{- "functions." + tool_call.name + "<|channel|>commentary json<|message|>" }}
275
+ {{- tool_call.arguments|tojson }}
276
+ {{- "<|call|>" }}
277
+ {%- set last_tool_call.name = tool_call.name %}
278
+ {%- elif "thinking" in message and loop.last and not add_generation_prompt %}
279
+ {#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}
280
+ {#- This is a situation that should only occur in training, never in inference. #}
281
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
282
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
283
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
284
+ {#- when training, so the model learns to emit it. #}
285
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|return|>" }}
286
+ {%- set last_tool_call.name = none %}
287
+ {%- elif "thinking" in message %}
288
+ {#- CoT is dropped during all previous turns, so we never render it for inference #}
289
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|end|>" }}
290
+ {%- set last_tool_call.name = none %}
291
+ {%- elif loop.last and not add_generation_prompt %}
292
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
293
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
294
+ {#- when training, so the model learns to emit it. #}
295
+ {{- "<|start|>assistant<|message|>" + message.content + "<|return|>" }}
296
+ {%- else %}
297
+ {{- "<|start|>assistant<|message|>" + message.content + "<|end|>" }}
298
+ {%- set last_tool_call.name = none %}
299
+ {%- endif %}
300
+ {%- elif message.role == 'tool' -%}
301
+ {%- if last_tool_call.name is none %}
302
+ {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
303
+ {%- endif %}
304
+ {{- "<|start|>functions." + last_tool_call.name }}
305
+ {{- " to=assistant<|channel|>commentary<|message|>" + message.content|tojson + "<|end|>" }}
306
+ {%- else -%}
307
+ {{- "<|start|>user<|message|>" + message.content + "<|end|>" }}
308
+ {%- endif -%}
309
+ {%- endfor -%}
310
+
311
+ {#- Generation prompt #}
312
+ {%- if add_generation_prompt -%}
313
+ <|start|>assistant
314
+ {%- endif -%}
315
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
outputs/checkpoint-1000/special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|return|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<|reserved_200017|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
outputs/checkpoint-1000/tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0614fe83cadab421296e664e1f48f4261fa8fef6e03e63bb75c20f38e37d07d3
3
+ size 27868174
outputs/checkpoint-1000/tokenizer_config.json ADDED
@@ -0,0 +1,185 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "199998": {
4
+ "content": "<|startoftext|>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "199999": {
12
+ "content": "<|endoftext|>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "200000": {
20
+ "content": "<|reserved_200000|>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "200001": {
28
+ "content": "<|reserved_200001|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "200002": {
36
+ "content": "<|return|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "200003": {
44
+ "content": "<|constrain|>",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "200004": {
52
+ "content": "<|reserved_200004|>",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "200005": {
60
+ "content": "<|channel|>",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "200006": {
68
+ "content": "<|start|>",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "200007": {
76
+ "content": "<|end|>",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "200008": {
84
+ "content": "<|message|>",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "200009": {
92
+ "content": "<|reserved_200009|>",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
+ "200010": {
100
+ "content": "<|reserved_200010|>",
101
+ "lstrip": false,
102
+ "normalized": false,
103
+ "rstrip": false,
104
+ "single_word": false,
105
+ "special": true
106
+ },
107
+ "200011": {
108
+ "content": "<|reserved_200011|>",
109
+ "lstrip": false,
110
+ "normalized": false,
111
+ "rstrip": false,
112
+ "single_word": false,
113
+ "special": true
114
+ },
115
+ "200012": {
116
+ "content": "<|call|>",
117
+ "lstrip": false,
118
+ "normalized": false,
119
+ "rstrip": false,
120
+ "single_word": false,
121
+ "special": true
122
+ },
123
+ "200013": {
124
+ "content": "<|reserved_200013|>",
125
+ "lstrip": false,
126
+ "normalized": false,
127
+ "rstrip": false,
128
+ "single_word": false,
129
+ "special": true
130
+ },
131
+ "200014": {
132
+ "content": "<|reserved_200014|>",
133
+ "lstrip": false,
134
+ "normalized": false,
135
+ "rstrip": false,
136
+ "single_word": false,
137
+ "special": true
138
+ },
139
+ "200015": {
140
+ "content": "<|reserved_200015|>",
141
+ "lstrip": false,
142
+ "normalized": false,
143
+ "rstrip": false,
144
+ "single_word": false,
145
+ "special": true
146
+ },
147
+ "200016": {
148
+ "content": "<|reserved_200016|>",
149
+ "lstrip": false,
150
+ "normalized": false,
151
+ "rstrip": false,
152
+ "single_word": false,
153
+ "special": true
154
+ },
155
+ "200017": {
156
+ "content": "<|reserved_200017|>",
157
+ "lstrip": false,
158
+ "normalized": false,
159
+ "rstrip": false,
160
+ "single_word": false,
161
+ "special": true
162
+ },
163
+ "200018": {
164
+ "content": "<|endofprompt|>",
165
+ "lstrip": false,
166
+ "normalized": false,
167
+ "rstrip": false,
168
+ "single_word": false,
169
+ "special": true
170
+ }
171
+ },
172
+ "bos_token": "<|startoftext|>",
173
+ "clean_up_tokenization_spaces": false,
174
+ "eos_token": "<|return|>",
175
+ "extra_special_tokens": {},
176
+ "model_input_names": [
177
+ "input_ids",
178
+ "attention_mask"
179
+ ],
180
+ "model_max_length": 131072,
181
+ "pad_token": "<|reserved_200017|>",
182
+ "padding_side": "right",
183
+ "tokenizer_class": "PreTrainedTokenizerFast",
184
+ "unk_token": null
185
+ }
outputs/checkpoint-1000/trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
outputs/checkpoint-1500/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
3
+ library_name: peft
4
+ tags:
5
+ - base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit
6
+ - lora
7
+ - sft
8
+ - transformers
9
+ - trl
10
+ - unsloth
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.17.1
outputs/checkpoint-1500/adapter_config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": {
4
+ "base_model_class": "GptOssForCausalLM",
5
+ "parent_library": "transformers.models.gpt_oss.modeling_gpt_oss"
6
+ },
7
+ "base_model_name_or_path": "unsloth/gpt-oss-20b-unsloth-bnb-4bit",
8
+ "bias": "none",
9
+ "corda_config": null,
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": 0.0,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "qalora_group_size": 16,
27
+ "r": 16,
28
+ "rank_pattern": {},
29
+ "revision": null,
30
+ "target_modules": [
31
+ "o_proj",
32
+ "v_proj",
33
+ "up_proj",
34
+ "down_proj",
35
+ "gate_proj",
36
+ "k_proj",
37
+ "q_proj"
38
+ ],
39
+ "target_parameters": null,
40
+ "task_type": null,
41
+ "trainable_token_indices": null,
42
+ "use_dora": false,
43
+ "use_qalora": false,
44
+ "use_rslora": false
45
+ }
outputs/checkpoint-1500/chat_template.jinja ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
2
+ {#-
3
+ In addition to the normal inputs of `messages` and `tools`, this template also accepts the
4
+ following kwargs:
5
+ - "builtin_tools": A list, can contain "browser" and/or "python".
6
+ - "model_identity": A string that optionally describes the model identity.
7
+ - "reasoning_effort": A string that describes the reasoning effort, defaults to "medium".
8
+ #}
9
+
10
+ {#- Tool Definition Rendering ============================================== #}
11
+ {%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}
12
+ {%- if param_spec.type == "array" -%}
13
+ {%- if param_spec['items'] -%}
14
+ {%- if param_spec['items']['type'] == "string" -%}
15
+ {{- "string[]" }}
16
+ {%- elif param_spec['items']['type'] == "number" -%}
17
+ {{- "number[]" }}
18
+ {%- elif param_spec['items']['type'] == "integer" -%}
19
+ {{- "number[]" }}
20
+ {%- elif param_spec['items']['type'] == "boolean" -%}
21
+ {{- "boolean[]" }}
22
+ {%- else -%}
23
+ {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}
24
+ {%- if inner_type == "object | object" or inner_type|length > 50 -%}
25
+ {{- "any[]" }}
26
+ {%- else -%}
27
+ {{- inner_type + "[]" }}
28
+ {%- endif -%}
29
+ {%- endif -%}
30
+ {%- if param_spec.nullable -%}
31
+ {{- " | null" }}
32
+ {%- endif -%}
33
+ {%- else -%}
34
+ {{- "any[]" }}
35
+ {%- if param_spec.nullable -%}
36
+ {{- " | null" }}
37
+ {%- endif -%}
38
+ {%- endif -%}
39
+ {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}
40
+ {#- Handle array of types like ["object", "object"] from Union[dict, list] #}
41
+ {%- if param_spec.type | length > 1 -%}
42
+ {{- param_spec.type | join(" | ") }}
43
+ {%- else -%}
44
+ {{- param_spec.type[0] }}
45
+ {%- endif -%}
46
+ {%- elif param_spec.oneOf -%}
47
+ {#- Handle oneOf schemas - check for complex unions and fallback to any #}
48
+ {%- set has_object_variants = false -%}
49
+ {%- for variant in param_spec.oneOf -%}
50
+ {%- if variant.type == "object" -%}
51
+ {%- set has_object_variants = true -%}
52
+ {%- endif -%}
53
+ {%- endfor -%}
54
+ {%- if has_object_variants and param_spec.oneOf|length > 1 -%}
55
+ {{- "any" }}
56
+ {%- else -%}
57
+ {%- for variant in param_spec.oneOf -%}
58
+ {{- render_typescript_type(variant, required_params) -}}
59
+ {%- if variant.description %}
60
+ {{- "// " + variant.description }}
61
+ {%- endif -%}
62
+ {%- if variant.default is defined %}
63
+ {{ "// default: " + variant.default|tojson }}
64
+ {%- endif -%}
65
+ {%- if not loop.last %}
66
+ {{- " | " }}
67
+ {% endif -%}
68
+ {%- endfor -%}
69
+ {%- endif -%}
70
+ {%- elif param_spec.type == "string" -%}
71
+ {%- if param_spec.enum -%}
72
+ {{- '"' + param_spec.enum|join('" | "') + '"' -}}
73
+ {%- else -%}
74
+ {{- "string" }}
75
+ {%- if param_spec.nullable %}
76
+ {{- " | null" }}
77
+ {%- endif -%}
78
+ {%- endif -%}
79
+ {%- elif param_spec.type == "number" -%}
80
+ {{- "number" }}
81
+ {%- elif param_spec.type == "integer" -%}
82
+ {{- "number" }}
83
+ {%- elif param_spec.type == "boolean" -%}
84
+ {{- "boolean" }}
85
+
86
+ {%- elif param_spec.type == "object" -%}
87
+ {%- if param_spec.properties -%}
88
+ {{- "{\n" }}
89
+ {%- for prop_name, prop_spec in param_spec.properties.items() -%}
90
+ {{- prop_name -}}
91
+ {%- if prop_name not in (param_spec.required or []) -%}
92
+ {{- "?" }}
93
+ {%- endif -%}
94
+ {{- ": " }}
95
+ {{ render_typescript_type(prop_spec, param_spec.required or []) }}
96
+ {%- if not loop.last -%}
97
+ {{-", " }}
98
+ {%- endif -%}
99
+ {%- endfor -%}
100
+ {{- "}" }}
101
+ {%- else -%}
102
+ {{- "object" }}
103
+ {%- endif -%}
104
+ {%- else -%}
105
+ {{- "any" }}
106
+ {%- endif -%}
107
+ {%- endmacro -%}
108
+
109
+ {%- macro render_tool_namespace(namespace_name, tools) -%}
110
+ {{- "## " + namespace_name + "\n\n" }}
111
+ {{- "namespace " + namespace_name + " {\n\n" }}
112
+ {%- for tool in tools %}
113
+ {%- set tool = tool.function %}
114
+ {{- "// " + tool.description + "\n" }}
115
+ {{- "type "+ tool.name + " = " }}
116
+ {%- if tool.parameters and tool.parameters.properties -%}
117
+ {{- "(_: " }}
118
+ {{- "{\n" }}
119
+ {%- for param_name, param_spec in tool.parameters.properties.items() %}
120
+ {{- "// " + param_spec.description + "\n" }}
121
+ {{- param_name }}
122
+ {%- if param_name not in (tool.parameters.required or []) -%}
123
+ {{- "?" }}
124
+ {%- endif -%}
125
+ {{- ": " }}
126
+ {{- render_typescript_type(param_spec, tool.parameters.required or []) }}
127
+ {%- if param_spec.default is defined -%}
128
+ {%- if param_spec.enum %}
129
+ {{- ", // default: " + param_spec.default }}
130
+ {%- elif param_spec.oneOf %}
131
+ {{- "// default: " + param_spec.default }}
132
+ {%- else %}
133
+ {{- ", // default: " + param_spec.default|tojson }}
134
+ {%- endif -%}
135
+ {%- endif -%}
136
+ {%- if not loop.last %}
137
+ {{- ",\n" }}
138
+ {%- else %}
139
+ {{- "\n" }}
140
+ {%- endif -%}
141
+ {%- endfor %}
142
+ {{- "}) => any;\n\n" }}
143
+ {%- else -%}
144
+ {{- "() => any;\n\n" }}
145
+ {%- endif -%}
146
+ {%- endfor %}
147
+ {{- "} // namespace " + namespace_name }}
148
+ {%- endmacro -%}
149
+
150
+ {%- macro render_builtin_tools(browser_tool, python_tool) -%}
151
+ {%- if browser_tool %}
152
+ {{- "## browser\n\n" }}
153
+ {{- "// Tool for browsing.\n" }}
154
+ {{- "// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.\n" }}
155
+ {{- "// Cite information from the tool using the following format:\n" }}
156
+ {{- "// `【{cursor}†L{line_start}(-L{line_end})?】`, for example: `【6†L9-L11】` or `【8†L3】`.\n" }}
157
+ {{- "// Do not quote more than 10 words directly from the tool output.\n" }}
158
+ {{- "// sources=web (default: web)\n" }}
159
+ {{- "namespace browser {\n\n" }}
160
+ {{- "// Searches for information related to `query` and displays `topn` results.\n" }}
161
+ {{- "type search = (_: {\n" }}
162
+ {{- "query: string,\n" }}
163
+ {{- "topn?: number, // default: 10\n" }}
164
+ {{- "source?: string,\n" }}
165
+ {{- "}) => any;\n\n" }}
166
+ {{- "// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.\n" }}
167
+ {{- "// Valid link ids are displayed with the formatting: `【{id}†.*】`.\n" }}
168
+ {{- "// If `cursor` is not provided, the most recent page is implied.\n" }}
169
+ {{- "// If `id` is a string, it is treated as a fully qualified URL associated with `source`.\n" }}
170
+ {{- "// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.\n" }}
171
+ {{- "// Use this function without `id` to scroll to a new location of an opened page.\n" }}
172
+ {{- "type open = (_: {\n" }}
173
+ {{- "id?: number | string, // default: -1\n" }}
174
+ {{- "cursor?: number, // default: -1\n" }}
175
+ {{- "loc?: number, // default: -1\n" }}
176
+ {{- "num_lines?: number, // default: -1\n" }}
177
+ {{- "view_source?: boolean, // default: false\n" }}
178
+ {{- "source?: string,\n" }}
179
+ {{- "}) => any;\n\n" }}
180
+ {{- "// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.\n" }}
181
+ {{- "type find = (_: {\n" }}
182
+ {{- "pattern: string,\n" }}
183
+ {{- "cursor?: number, // default: -1\n" }}
184
+ {{- "}) => any;\n\n" }}
185
+ {{- "} // namespace browser\n\n" }}
186
+ {%- endif -%}
187
+
188
+ {%- if python_tool %}
189
+ {{- "## python\n\n" }}
190
+ {{- "Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).\n\n" }}
191
+ {{- "When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.\n\n" }}
192
+ {%- endif -%}
193
+ {%- endmacro -%}
194
+
195
+ {#- System Message Construction ============================================ #}
196
+ {%- macro build_system_message() -%}
197
+ {%- if model_identity is not defined %}
198
+ {{- "You are ChatGPT, a large language model trained by OpenAI.\n" -}}
199
+ {%- else %}
200
+ {{- model_identity }}
201
+ {%- endif %}
202
+ {{- "Knowledge cutoff: 2024-06\n" }}
203
+ {{- "Current date: " + strftime_now("%Y-%m-%d") + "\n\n" }}
204
+ {%- if reasoning_effort is not defined %}
205
+ {%- set reasoning_effort = "medium" %}
206
+ {%- endif %}
207
+ {{- "Reasoning: " + reasoning_effort + "\n\n" }}
208
+ {%- if builtin_tools is defined %}
209
+ {{- "# Tools\n\n" }}
210
+ {%- set available_builtin_tools = namespace(browser=false, python=false) %}
211
+ {%- for tool in builtin_tools %}
212
+ {%- if tool == "browser" %}
213
+ {%- set available_builtin_tools.browser = true %}
214
+ {%- elif tool == "python" %}
215
+ {%- set available_builtin_tools.python = true %}
216
+ {%- endif %}
217
+ {%- endfor %}
218
+ {{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}
219
+ {%- endif -%}
220
+ {{- "# Valid channels: analysis, commentary, final. Channel must be included for every message." }}
221
+ {%- if tools is defined -%}
222
+ {{- "\nCalls to these tools must go to the commentary channel: 'functions'." }}
223
+ {%- endif -%}
224
+ {%- endmacro -%}
225
+
226
+ {#- Main Template Logic ================================================= #}
227
+ {#- Set defaults #}
228
+
229
+ {#- Render system message #}
230
+ {{- "<|start|>system<|message|>" }}
231
+ {{- build_system_message() }}
232
+ {{- "<|end|>" }}
233
+
234
+ {#- Extract developer message #}
235
+ {%- if messages[0].role == "developer" or messages[0].role == "system" %}
236
+ {%- set developer_message = messages[0].content %}
237
+ {%- set loop_messages = messages[1:] %}
238
+ {%- else %}
239
+ {%- set developer_message = "" %}
240
+ {%- set loop_messages = messages %}
241
+ {%- endif %}
242
+
243
+ {#- Render developer message #}
244
+ {%- if developer_message or tools %}
245
+ {{- "<|start|>developer<|message|>" }}
246
+ {%- if developer_message %}
247
+ {{- "# Instructions\n\n" }}
248
+ {{- developer_message }}
249
+ {%- endif %}
250
+ {%- if tools -%}
251
+ {{- "\n\n" }}
252
+ {{- "# Tools\n\n" }}
253
+ {{- render_tool_namespace("functions", tools) }}
254
+ {%- endif -%}
255
+ {{- "<|end|>" }}
256
+ {%- endif %}
257
+
258
+ {#- Render messages #}
259
+ {%- set last_tool_call = namespace(name=none) %}
260
+ {%- for message in loop_messages -%}
261
+ {#- At this point only assistant/user/tool messages should remain #}
262
+ {%- if message.role == 'assistant' -%}
263
+ {%- if "tool_calls" in message %}
264
+ {#- We assume max 1 tool call per message, and so we infer the tool call name #}
265
+ {#- in "tool" messages from the most recent assistant tool call name #}
266
+ {%- set tool_call = message.tool_calls[0] %}
267
+ {%- if tool_call.function %}
268
+ {%- set tool_call = tool_call.function %}
269
+ {%- endif %}
270
+ {%- if message.content %}
271
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.content + "<|end|>" }}
272
+ {%- endif %}
273
+ {{- "<|start|>assistant to=" }}
274
+ {{- "functions." + tool_call.name + "<|channel|>commentary json<|message|>" }}
275
+ {{- tool_call.arguments|tojson }}
276
+ {{- "<|call|>" }}
277
+ {%- set last_tool_call.name = tool_call.name %}
278
+ {%- elif "thinking" in message and loop.last and not add_generation_prompt %}
279
+ {#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}
280
+ {#- This is a situation that should only occur in training, never in inference. #}
281
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
282
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
283
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
284
+ {#- when training, so the model learns to emit it. #}
285
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|return|>" }}
286
+ {%- set last_tool_call.name = none %}
287
+ {%- elif "thinking" in message %}
288
+ {#- CoT is dropped during all previous turns, so we never render it for inference #}
289
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|end|>" }}
290
+ {%- set last_tool_call.name = none %}
291
+ {%- elif loop.last and not add_generation_prompt %}
292
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
293
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
294
+ {#- when training, so the model learns to emit it. #}
295
+ {{- "<|start|>assistant<|message|>" + message.content + "<|return|>" }}
296
+ {%- else %}
297
+ {{- "<|start|>assistant<|message|>" + message.content + "<|end|>" }}
298
+ {%- set last_tool_call.name = none %}
299
+ {%- endif %}
300
+ {%- elif message.role == 'tool' -%}
301
+ {%- if last_tool_call.name is none %}
302
+ {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
303
+ {%- endif %}
304
+ {{- "<|start|>functions." + last_tool_call.name }}
305
+ {{- " to=assistant<|channel|>commentary<|message|>" + message.content|tojson + "<|end|>" }}
306
+ {%- else -%}
307
+ {{- "<|start|>user<|message|>" + message.content + "<|end|>" }}
308
+ {%- endif -%}
309
+ {%- endfor -%}
310
+
311
+ {#- Generation prompt #}
312
+ {%- if add_generation_prompt -%}
313
+ <|start|>assistant
314
+ {%- endif -%}
315
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
outputs/checkpoint-1500/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d7f1f22a5f354441b5f815d259903c11b98274ed999c6581547affb39792f494
3
+ size 16894883
outputs/checkpoint-1500/special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|return|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<|reserved_200017|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
outputs/checkpoint-1500/tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0614fe83cadab421296e664e1f48f4261fa8fef6e03e63bb75c20f38e37d07d3
3
+ size 27868174
outputs/checkpoint-1500/tokenizer_config.json ADDED
@@ -0,0 +1,185 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "199998": {
4
+ "content": "<|startoftext|>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "199999": {
12
+ "content": "<|endoftext|>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "200000": {
20
+ "content": "<|reserved_200000|>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "200001": {
28
+ "content": "<|reserved_200001|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "200002": {
36
+ "content": "<|return|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "200003": {
44
+ "content": "<|constrain|>",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "200004": {
52
+ "content": "<|reserved_200004|>",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "200005": {
60
+ "content": "<|channel|>",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "200006": {
68
+ "content": "<|start|>",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "200007": {
76
+ "content": "<|end|>",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "200008": {
84
+ "content": "<|message|>",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "200009": {
92
+ "content": "<|reserved_200009|>",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
+ "200010": {
100
+ "content": "<|reserved_200010|>",
101
+ "lstrip": false,
102
+ "normalized": false,
103
+ "rstrip": false,
104
+ "single_word": false,
105
+ "special": true
106
+ },
107
+ "200011": {
108
+ "content": "<|reserved_200011|>",
109
+ "lstrip": false,
110
+ "normalized": false,
111
+ "rstrip": false,
112
+ "single_word": false,
113
+ "special": true
114
+ },
115
+ "200012": {
116
+ "content": "<|call|>",
117
+ "lstrip": false,
118
+ "normalized": false,
119
+ "rstrip": false,
120
+ "single_word": false,
121
+ "special": true
122
+ },
123
+ "200013": {
124
+ "content": "<|reserved_200013|>",
125
+ "lstrip": false,
126
+ "normalized": false,
127
+ "rstrip": false,
128
+ "single_word": false,
129
+ "special": true
130
+ },
131
+ "200014": {
132
+ "content": "<|reserved_200014|>",
133
+ "lstrip": false,
134
+ "normalized": false,
135
+ "rstrip": false,
136
+ "single_word": false,
137
+ "special": true
138
+ },
139
+ "200015": {
140
+ "content": "<|reserved_200015|>",
141
+ "lstrip": false,
142
+ "normalized": false,
143
+ "rstrip": false,
144
+ "single_word": false,
145
+ "special": true
146
+ },
147
+ "200016": {
148
+ "content": "<|reserved_200016|>",
149
+ "lstrip": false,
150
+ "normalized": false,
151
+ "rstrip": false,
152
+ "single_word": false,
153
+ "special": true
154
+ },
155
+ "200017": {
156
+ "content": "<|reserved_200017|>",
157
+ "lstrip": false,
158
+ "normalized": false,
159
+ "rstrip": false,
160
+ "single_word": false,
161
+ "special": true
162
+ },
163
+ "200018": {
164
+ "content": "<|endofprompt|>",
165
+ "lstrip": false,
166
+ "normalized": false,
167
+ "rstrip": false,
168
+ "single_word": false,
169
+ "special": true
170
+ }
171
+ },
172
+ "bos_token": "<|startoftext|>",
173
+ "clean_up_tokenization_spaces": false,
174
+ "eos_token": "<|return|>",
175
+ "extra_special_tokens": {},
176
+ "model_input_names": [
177
+ "input_ids",
178
+ "attention_mask"
179
+ ],
180
+ "model_max_length": 131072,
181
+ "pad_token": "<|reserved_200017|>",
182
+ "padding_side": "right",
183
+ "tokenizer_class": "PreTrainedTokenizerFast",
184
+ "unk_token": null
185
+ }
outputs/checkpoint-1500/trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
outputs/checkpoint-2000/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
3
+ library_name: peft
4
+ tags:
5
+ - base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit
6
+ - lora
7
+ - sft
8
+ - transformers
9
+ - trl
10
+ - unsloth
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.17.1
outputs/checkpoint-2000/adapter_config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": {
4
+ "base_model_class": "GptOssForCausalLM",
5
+ "parent_library": "transformers.models.gpt_oss.modeling_gpt_oss"
6
+ },
7
+ "base_model_name_or_path": "unsloth/gpt-oss-20b-unsloth-bnb-4bit",
8
+ "bias": "none",
9
+ "corda_config": null,
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": 0.0,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "qalora_group_size": 16,
27
+ "r": 16,
28
+ "rank_pattern": {},
29
+ "revision": null,
30
+ "target_modules": [
31
+ "o_proj",
32
+ "v_proj",
33
+ "up_proj",
34
+ "down_proj",
35
+ "gate_proj",
36
+ "k_proj",
37
+ "q_proj"
38
+ ],
39
+ "target_parameters": null,
40
+ "task_type": null,
41
+ "trainable_token_indices": null,
42
+ "use_dora": false,
43
+ "use_qalora": false,
44
+ "use_rslora": false
45
+ }
outputs/checkpoint-2000/chat_template.jinja ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
2
+ {#-
3
+ In addition to the normal inputs of `messages` and `tools`, this template also accepts the
4
+ following kwargs:
5
+ - "builtin_tools": A list, can contain "browser" and/or "python".
6
+ - "model_identity": A string that optionally describes the model identity.
7
+ - "reasoning_effort": A string that describes the reasoning effort, defaults to "medium".
8
+ #}
9
+
10
+ {#- Tool Definition Rendering ============================================== #}
11
+ {%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}
12
+ {%- if param_spec.type == "array" -%}
13
+ {%- if param_spec['items'] -%}
14
+ {%- if param_spec['items']['type'] == "string" -%}
15
+ {{- "string[]" }}
16
+ {%- elif param_spec['items']['type'] == "number" -%}
17
+ {{- "number[]" }}
18
+ {%- elif param_spec['items']['type'] == "integer" -%}
19
+ {{- "number[]" }}
20
+ {%- elif param_spec['items']['type'] == "boolean" -%}
21
+ {{- "boolean[]" }}
22
+ {%- else -%}
23
+ {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}
24
+ {%- if inner_type == "object | object" or inner_type|length > 50 -%}
25
+ {{- "any[]" }}
26
+ {%- else -%}
27
+ {{- inner_type + "[]" }}
28
+ {%- endif -%}
29
+ {%- endif -%}
30
+ {%- if param_spec.nullable -%}
31
+ {{- " | null" }}
32
+ {%- endif -%}
33
+ {%- else -%}
34
+ {{- "any[]" }}
35
+ {%- if param_spec.nullable -%}
36
+ {{- " | null" }}
37
+ {%- endif -%}
38
+ {%- endif -%}
39
+ {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}
40
+ {#- Handle array of types like ["object", "object"] from Union[dict, list] #}
41
+ {%- if param_spec.type | length > 1 -%}
42
+ {{- param_spec.type | join(" | ") }}
43
+ {%- else -%}
44
+ {{- param_spec.type[0] }}
45
+ {%- endif -%}
46
+ {%- elif param_spec.oneOf -%}
47
+ {#- Handle oneOf schemas - check for complex unions and fallback to any #}
48
+ {%- set has_object_variants = false -%}
49
+ {%- for variant in param_spec.oneOf -%}
50
+ {%- if variant.type == "object" -%}
51
+ {%- set has_object_variants = true -%}
52
+ {%- endif -%}
53
+ {%- endfor -%}
54
+ {%- if has_object_variants and param_spec.oneOf|length > 1 -%}
55
+ {{- "any" }}
56
+ {%- else -%}
57
+ {%- for variant in param_spec.oneOf -%}
58
+ {{- render_typescript_type(variant, required_params) -}}
59
+ {%- if variant.description %}
60
+ {{- "// " + variant.description }}
61
+ {%- endif -%}
62
+ {%- if variant.default is defined %}
63
+ {{ "// default: " + variant.default|tojson }}
64
+ {%- endif -%}
65
+ {%- if not loop.last %}
66
+ {{- " | " }}
67
+ {% endif -%}
68
+ {%- endfor -%}
69
+ {%- endif -%}
70
+ {%- elif param_spec.type == "string" -%}
71
+ {%- if param_spec.enum -%}
72
+ {{- '"' + param_spec.enum|join('" | "') + '"' -}}
73
+ {%- else -%}
74
+ {{- "string" }}
75
+ {%- if param_spec.nullable %}
76
+ {{- " | null" }}
77
+ {%- endif -%}
78
+ {%- endif -%}
79
+ {%- elif param_spec.type == "number" -%}
80
+ {{- "number" }}
81
+ {%- elif param_spec.type == "integer" -%}
82
+ {{- "number" }}
83
+ {%- elif param_spec.type == "boolean" -%}
84
+ {{- "boolean" }}
85
+
86
+ {%- elif param_spec.type == "object" -%}
87
+ {%- if param_spec.properties -%}
88
+ {{- "{\n" }}
89
+ {%- for prop_name, prop_spec in param_spec.properties.items() -%}
90
+ {{- prop_name -}}
91
+ {%- if prop_name not in (param_spec.required or []) -%}
92
+ {{- "?" }}
93
+ {%- endif -%}
94
+ {{- ": " }}
95
+ {{ render_typescript_type(prop_spec, param_spec.required or []) }}
96
+ {%- if not loop.last -%}
97
+ {{-", " }}
98
+ {%- endif -%}
99
+ {%- endfor -%}
100
+ {{- "}" }}
101
+ {%- else -%}
102
+ {{- "object" }}
103
+ {%- endif -%}
104
+ {%- else -%}
105
+ {{- "any" }}
106
+ {%- endif -%}
107
+ {%- endmacro -%}
108
+
109
+ {%- macro render_tool_namespace(namespace_name, tools) -%}
110
+ {{- "## " + namespace_name + "\n\n" }}
111
+ {{- "namespace " + namespace_name + " {\n\n" }}
112
+ {%- for tool in tools %}
113
+ {%- set tool = tool.function %}
114
+ {{- "// " + tool.description + "\n" }}
115
+ {{- "type "+ tool.name + " = " }}
116
+ {%- if tool.parameters and tool.parameters.properties -%}
117
+ {{- "(_: " }}
118
+ {{- "{\n" }}
119
+ {%- for param_name, param_spec in tool.parameters.properties.items() %}
120
+ {{- "// " + param_spec.description + "\n" }}
121
+ {{- param_name }}
122
+ {%- if param_name not in (tool.parameters.required or []) -%}
123
+ {{- "?" }}
124
+ {%- endif -%}
125
+ {{- ": " }}
126
+ {{- render_typescript_type(param_spec, tool.parameters.required or []) }}
127
+ {%- if param_spec.default is defined -%}
128
+ {%- if param_spec.enum %}
129
+ {{- ", // default: " + param_spec.default }}
130
+ {%- elif param_spec.oneOf %}
131
+ {{- "// default: " + param_spec.default }}
132
+ {%- else %}
133
+ {{- ", // default: " + param_spec.default|tojson }}
134
+ {%- endif -%}
135
+ {%- endif -%}
136
+ {%- if not loop.last %}
137
+ {{- ",\n" }}
138
+ {%- else %}
139
+ {{- "\n" }}
140
+ {%- endif -%}
141
+ {%- endfor %}
142
+ {{- "}) => any;\n\n" }}
143
+ {%- else -%}
144
+ {{- "() => any;\n\n" }}
145
+ {%- endif -%}
146
+ {%- endfor %}
147
+ {{- "} // namespace " + namespace_name }}
148
+ {%- endmacro -%}
149
+
150
+ {%- macro render_builtin_tools(browser_tool, python_tool) -%}
151
+ {%- if browser_tool %}
152
+ {{- "## browser\n\n" }}
153
+ {{- "// Tool for browsing.\n" }}
154
+ {{- "// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.\n" }}
155
+ {{- "// Cite information from the tool using the following format:\n" }}
156
+ {{- "// `【{cursor}†L{line_start}(-L{line_end})?】`, for example: `【6†L9-L11】` or `【8†L3】`.\n" }}
157
+ {{- "// Do not quote more than 10 words directly from the tool output.\n" }}
158
+ {{- "// sources=web (default: web)\n" }}
159
+ {{- "namespace browser {\n\n" }}
160
+ {{- "// Searches for information related to `query` and displays `topn` results.\n" }}
161
+ {{- "type search = (_: {\n" }}
162
+ {{- "query: string,\n" }}
163
+ {{- "topn?: number, // default: 10\n" }}
164
+ {{- "source?: string,\n" }}
165
+ {{- "}) => any;\n\n" }}
166
+ {{- "// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.\n" }}
167
+ {{- "// Valid link ids are displayed with the formatting: `【{id}†.*】`.\n" }}
168
+ {{- "// If `cursor` is not provided, the most recent page is implied.\n" }}
169
+ {{- "// If `id` is a string, it is treated as a fully qualified URL associated with `source`.\n" }}
170
+ {{- "// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.\n" }}
171
+ {{- "// Use this function without `id` to scroll to a new location of an opened page.\n" }}
172
+ {{- "type open = (_: {\n" }}
173
+ {{- "id?: number | string, // default: -1\n" }}
174
+ {{- "cursor?: number, // default: -1\n" }}
175
+ {{- "loc?: number, // default: -1\n" }}
176
+ {{- "num_lines?: number, // default: -1\n" }}
177
+ {{- "view_source?: boolean, // default: false\n" }}
178
+ {{- "source?: string,\n" }}
179
+ {{- "}) => any;\n\n" }}
180
+ {{- "// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.\n" }}
181
+ {{- "type find = (_: {\n" }}
182
+ {{- "pattern: string,\n" }}
183
+ {{- "cursor?: number, // default: -1\n" }}
184
+ {{- "}) => any;\n\n" }}
185
+ {{- "} // namespace browser\n\n" }}
186
+ {%- endif -%}
187
+
188
+ {%- if python_tool %}
189
+ {{- "## python\n\n" }}
190
+ {{- "Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).\n\n" }}
191
+ {{- "When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.\n\n" }}
192
+ {%- endif -%}
193
+ {%- endmacro -%}
194
+
195
+ {#- System Message Construction ============================================ #}
196
+ {%- macro build_system_message() -%}
197
+ {%- if model_identity is not defined %}
198
+ {{- "You are ChatGPT, a large language model trained by OpenAI.\n" -}}
199
+ {%- else %}
200
+ {{- model_identity }}
201
+ {%- endif %}
202
+ {{- "Knowledge cutoff: 2024-06\n" }}
203
+ {{- "Current date: " + strftime_now("%Y-%m-%d") + "\n\n" }}
204
+ {%- if reasoning_effort is not defined %}
205
+ {%- set reasoning_effort = "medium" %}
206
+ {%- endif %}
207
+ {{- "Reasoning: " + reasoning_effort + "\n\n" }}
208
+ {%- if builtin_tools is defined %}
209
+ {{- "# Tools\n\n" }}
210
+ {%- set available_builtin_tools = namespace(browser=false, python=false) %}
211
+ {%- for tool in builtin_tools %}
212
+ {%- if tool == "browser" %}
213
+ {%- set available_builtin_tools.browser = true %}
214
+ {%- elif tool == "python" %}
215
+ {%- set available_builtin_tools.python = true %}
216
+ {%- endif %}
217
+ {%- endfor %}
218
+ {{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}
219
+ {%- endif -%}
220
+ {{- "# Valid channels: analysis, commentary, final. Channel must be included for every message." }}
221
+ {%- if tools is defined -%}
222
+ {{- "\nCalls to these tools must go to the commentary channel: 'functions'." }}
223
+ {%- endif -%}
224
+ {%- endmacro -%}
225
+
226
+ {#- Main Template Logic ================================================= #}
227
+ {#- Set defaults #}
228
+
229
+ {#- Render system message #}
230
+ {{- "<|start|>system<|message|>" }}
231
+ {{- build_system_message() }}
232
+ {{- "<|end|>" }}
233
+
234
+ {#- Extract developer message #}
235
+ {%- if messages[0].role == "developer" or messages[0].role == "system" %}
236
+ {%- set developer_message = messages[0].content %}
237
+ {%- set loop_messages = messages[1:] %}
238
+ {%- else %}
239
+ {%- set developer_message = "" %}
240
+ {%- set loop_messages = messages %}
241
+ {%- endif %}
242
+
243
+ {#- Render developer message #}
244
+ {%- if developer_message or tools %}
245
+ {{- "<|start|>developer<|message|>" }}
246
+ {%- if developer_message %}
247
+ {{- "# Instructions\n\n" }}
248
+ {{- developer_message }}
249
+ {%- endif %}
250
+ {%- if tools -%}
251
+ {{- "\n\n" }}
252
+ {{- "# Tools\n\n" }}
253
+ {{- render_tool_namespace("functions", tools) }}
254
+ {%- endif -%}
255
+ {{- "<|end|>" }}
256
+ {%- endif %}
257
+
258
+ {#- Render messages #}
259
+ {%- set last_tool_call = namespace(name=none) %}
260
+ {%- for message in loop_messages -%}
261
+ {#- At this point only assistant/user/tool messages should remain #}
262
+ {%- if message.role == 'assistant' -%}
263
+ {%- if "tool_calls" in message %}
264
+ {#- We assume max 1 tool call per message, and so we infer the tool call name #}
265
+ {#- in "tool" messages from the most recent assistant tool call name #}
266
+ {%- set tool_call = message.tool_calls[0] %}
267
+ {%- if tool_call.function %}
268
+ {%- set tool_call = tool_call.function %}
269
+ {%- endif %}
270
+ {%- if message.content %}
271
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.content + "<|end|>" }}
272
+ {%- endif %}
273
+ {{- "<|start|>assistant to=" }}
274
+ {{- "functions." + tool_call.name + "<|channel|>commentary json<|message|>" }}
275
+ {{- tool_call.arguments|tojson }}
276
+ {{- "<|call|>" }}
277
+ {%- set last_tool_call.name = tool_call.name %}
278
+ {%- elif "thinking" in message and loop.last and not add_generation_prompt %}
279
+ {#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}
280
+ {#- This is a situation that should only occur in training, never in inference. #}
281
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
282
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
283
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
284
+ {#- when training, so the model learns to emit it. #}
285
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|return|>" }}
286
+ {%- set last_tool_call.name = none %}
287
+ {%- elif "thinking" in message %}
288
+ {#- CoT is dropped during all previous turns, so we never render it for inference #}
289
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|end|>" }}
290
+ {%- set last_tool_call.name = none %}
291
+ {%- elif loop.last and not add_generation_prompt %}
292
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
293
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
294
+ {#- when training, so the model learns to emit it. #}
295
+ {{- "<|start|>assistant<|message|>" + message.content + "<|return|>" }}
296
+ {%- else %}
297
+ {{- "<|start|>assistant<|message|>" + message.content + "<|end|>" }}
298
+ {%- set last_tool_call.name = none %}
299
+ {%- endif %}
300
+ {%- elif message.role == 'tool' -%}
301
+ {%- if last_tool_call.name is none %}
302
+ {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
303
+ {%- endif %}
304
+ {{- "<|start|>functions." + last_tool_call.name }}
305
+ {{- " to=assistant<|channel|>commentary<|message|>" + message.content|tojson + "<|end|>" }}
306
+ {%- else -%}
307
+ {{- "<|start|>user<|message|>" + message.content + "<|end|>" }}
308
+ {%- endif -%}
309
+ {%- endfor -%}
310
+
311
+ {#- Generation prompt #}
312
+ {%- if add_generation_prompt -%}
313
+ <|start|>assistant
314
+ {%- endif -%}
315
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
outputs/checkpoint-2000/special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|return|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<|reserved_200017|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
outputs/checkpoint-2000/tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0614fe83cadab421296e664e1f48f4261fa8fef6e03e63bb75c20f38e37d07d3
3
+ size 27868174
outputs/checkpoint-2000/tokenizer_config.json ADDED
@@ -0,0 +1,185 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "199998": {
4
+ "content": "<|startoftext|>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "199999": {
12
+ "content": "<|endoftext|>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "200000": {
20
+ "content": "<|reserved_200000|>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "200001": {
28
+ "content": "<|reserved_200001|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "200002": {
36
+ "content": "<|return|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "200003": {
44
+ "content": "<|constrain|>",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "200004": {
52
+ "content": "<|reserved_200004|>",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "200005": {
60
+ "content": "<|channel|>",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "200006": {
68
+ "content": "<|start|>",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "200007": {
76
+ "content": "<|end|>",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "200008": {
84
+ "content": "<|message|>",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "200009": {
92
+ "content": "<|reserved_200009|>",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
+ "200010": {
100
+ "content": "<|reserved_200010|>",
101
+ "lstrip": false,
102
+ "normalized": false,
103
+ "rstrip": false,
104
+ "single_word": false,
105
+ "special": true
106
+ },
107
+ "200011": {
108
+ "content": "<|reserved_200011|>",
109
+ "lstrip": false,
110
+ "normalized": false,
111
+ "rstrip": false,
112
+ "single_word": false,
113
+ "special": true
114
+ },
115
+ "200012": {
116
+ "content": "<|call|>",
117
+ "lstrip": false,
118
+ "normalized": false,
119
+ "rstrip": false,
120
+ "single_word": false,
121
+ "special": true
122
+ },
123
+ "200013": {
124
+ "content": "<|reserved_200013|>",
125
+ "lstrip": false,
126
+ "normalized": false,
127
+ "rstrip": false,
128
+ "single_word": false,
129
+ "special": true
130
+ },
131
+ "200014": {
132
+ "content": "<|reserved_200014|>",
133
+ "lstrip": false,
134
+ "normalized": false,
135
+ "rstrip": false,
136
+ "single_word": false,
137
+ "special": true
138
+ },
139
+ "200015": {
140
+ "content": "<|reserved_200015|>",
141
+ "lstrip": false,
142
+ "normalized": false,
143
+ "rstrip": false,
144
+ "single_word": false,
145
+ "special": true
146
+ },
147
+ "200016": {
148
+ "content": "<|reserved_200016|>",
149
+ "lstrip": false,
150
+ "normalized": false,
151
+ "rstrip": false,
152
+ "single_word": false,
153
+ "special": true
154
+ },
155
+ "200017": {
156
+ "content": "<|reserved_200017|>",
157
+ "lstrip": false,
158
+ "normalized": false,
159
+ "rstrip": false,
160
+ "single_word": false,
161
+ "special": true
162
+ },
163
+ "200018": {
164
+ "content": "<|endofprompt|>",
165
+ "lstrip": false,
166
+ "normalized": false,
167
+ "rstrip": false,
168
+ "single_word": false,
169
+ "special": true
170
+ }
171
+ },
172
+ "bos_token": "<|startoftext|>",
173
+ "clean_up_tokenization_spaces": false,
174
+ "eos_token": "<|return|>",
175
+ "extra_special_tokens": {},
176
+ "model_input_names": [
177
+ "input_ids",
178
+ "attention_mask"
179
+ ],
180
+ "model_max_length": 131072,
181
+ "pad_token": "<|reserved_200017|>",
182
+ "padding_side": "right",
183
+ "tokenizer_class": "PreTrainedTokenizerFast",
184
+ "unk_token": null
185
+ }
outputs/checkpoint-2000/trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
outputs/checkpoint-2500/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
3
+ library_name: peft
4
+ tags:
5
+ - base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit
6
+ - lora
7
+ - sft
8
+ - transformers
9
+ - trl
10
+ - unsloth
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.17.1
outputs/checkpoint-2500/adapter_config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": {
4
+ "base_model_class": "GptOssForCausalLM",
5
+ "parent_library": "transformers.models.gpt_oss.modeling_gpt_oss"
6
+ },
7
+ "base_model_name_or_path": "unsloth/gpt-oss-20b-unsloth-bnb-4bit",
8
+ "bias": "none",
9
+ "corda_config": null,
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": 0.0,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "qalora_group_size": 16,
27
+ "r": 16,
28
+ "rank_pattern": {},
29
+ "revision": null,
30
+ "target_modules": [
31
+ "o_proj",
32
+ "v_proj",
33
+ "up_proj",
34
+ "down_proj",
35
+ "gate_proj",
36
+ "k_proj",
37
+ "q_proj"
38
+ ],
39
+ "target_parameters": null,
40
+ "task_type": null,
41
+ "trainable_token_indices": null,
42
+ "use_dora": false,
43
+ "use_qalora": false,
44
+ "use_rslora": false
45
+ }
outputs/checkpoint-2500/chat_template.jinja ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
2
+ {#-
3
+ In addition to the normal inputs of `messages` and `tools`, this template also accepts the
4
+ following kwargs:
5
+ - "builtin_tools": A list, can contain "browser" and/or "python".
6
+ - "model_identity": A string that optionally describes the model identity.
7
+ - "reasoning_effort": A string that describes the reasoning effort, defaults to "medium".
8
+ #}
9
+
10
+ {#- Tool Definition Rendering ============================================== #}
11
+ {%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}
12
+ {%- if param_spec.type == "array" -%}
13
+ {%- if param_spec['items'] -%}
14
+ {%- if param_spec['items']['type'] == "string" -%}
15
+ {{- "string[]" }}
16
+ {%- elif param_spec['items']['type'] == "number" -%}
17
+ {{- "number[]" }}
18
+ {%- elif param_spec['items']['type'] == "integer" -%}
19
+ {{- "number[]" }}
20
+ {%- elif param_spec['items']['type'] == "boolean" -%}
21
+ {{- "boolean[]" }}
22
+ {%- else -%}
23
+ {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}
24
+ {%- if inner_type == "object | object" or inner_type|length > 50 -%}
25
+ {{- "any[]" }}
26
+ {%- else -%}
27
+ {{- inner_type + "[]" }}
28
+ {%- endif -%}
29
+ {%- endif -%}
30
+ {%- if param_spec.nullable -%}
31
+ {{- " | null" }}
32
+ {%- endif -%}
33
+ {%- else -%}
34
+ {{- "any[]" }}
35
+ {%- if param_spec.nullable -%}
36
+ {{- " | null" }}
37
+ {%- endif -%}
38
+ {%- endif -%}
39
+ {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}
40
+ {#- Handle array of types like ["object", "object"] from Union[dict, list] #}
41
+ {%- if param_spec.type | length > 1 -%}
42
+ {{- param_spec.type | join(" | ") }}
43
+ {%- else -%}
44
+ {{- param_spec.type[0] }}
45
+ {%- endif -%}
46
+ {%- elif param_spec.oneOf -%}
47
+ {#- Handle oneOf schemas - check for complex unions and fallback to any #}
48
+ {%- set has_object_variants = false -%}
49
+ {%- for variant in param_spec.oneOf -%}
50
+ {%- if variant.type == "object" -%}
51
+ {%- set has_object_variants = true -%}
52
+ {%- endif -%}
53
+ {%- endfor -%}
54
+ {%- if has_object_variants and param_spec.oneOf|length > 1 -%}
55
+ {{- "any" }}
56
+ {%- else -%}
57
+ {%- for variant in param_spec.oneOf -%}
58
+ {{- render_typescript_type(variant, required_params) -}}
59
+ {%- if variant.description %}
60
+ {{- "// " + variant.description }}
61
+ {%- endif -%}
62
+ {%- if variant.default is defined %}
63
+ {{ "// default: " + variant.default|tojson }}
64
+ {%- endif -%}
65
+ {%- if not loop.last %}
66
+ {{- " | " }}
67
+ {% endif -%}
68
+ {%- endfor -%}
69
+ {%- endif -%}
70
+ {%- elif param_spec.type == "string" -%}
71
+ {%- if param_spec.enum -%}
72
+ {{- '"' + param_spec.enum|join('" | "') + '"' -}}
73
+ {%- else -%}
74
+ {{- "string" }}
75
+ {%- if param_spec.nullable %}
76
+ {{- " | null" }}
77
+ {%- endif -%}
78
+ {%- endif -%}
79
+ {%- elif param_spec.type == "number" -%}
80
+ {{- "number" }}
81
+ {%- elif param_spec.type == "integer" -%}
82
+ {{- "number" }}
83
+ {%- elif param_spec.type == "boolean" -%}
84
+ {{- "boolean" }}
85
+
86
+ {%- elif param_spec.type == "object" -%}
87
+ {%- if param_spec.properties -%}
88
+ {{- "{\n" }}
89
+ {%- for prop_name, prop_spec in param_spec.properties.items() -%}
90
+ {{- prop_name -}}
91
+ {%- if prop_name not in (param_spec.required or []) -%}
92
+ {{- "?" }}
93
+ {%- endif -%}
94
+ {{- ": " }}
95
+ {{ render_typescript_type(prop_spec, param_spec.required or []) }}
96
+ {%- if not loop.last -%}
97
+ {{-", " }}
98
+ {%- endif -%}
99
+ {%- endfor -%}
100
+ {{- "}" }}
101
+ {%- else -%}
102
+ {{- "object" }}
103
+ {%- endif -%}
104
+ {%- else -%}
105
+ {{- "any" }}
106
+ {%- endif -%}
107
+ {%- endmacro -%}
108
+
109
+ {%- macro render_tool_namespace(namespace_name, tools) -%}
110
+ {{- "## " + namespace_name + "\n\n" }}
111
+ {{- "namespace " + namespace_name + " {\n\n" }}
112
+ {%- for tool in tools %}
113
+ {%- set tool = tool.function %}
114
+ {{- "// " + tool.description + "\n" }}
115
+ {{- "type "+ tool.name + " = " }}
116
+ {%- if tool.parameters and tool.parameters.properties -%}
117
+ {{- "(_: " }}
118
+ {{- "{\n" }}
119
+ {%- for param_name, param_spec in tool.parameters.properties.items() %}
120
+ {{- "// " + param_spec.description + "\n" }}
121
+ {{- param_name }}
122
+ {%- if param_name not in (tool.parameters.required or []) -%}
123
+ {{- "?" }}
124
+ {%- endif -%}
125
+ {{- ": " }}
126
+ {{- render_typescript_type(param_spec, tool.parameters.required or []) }}
127
+ {%- if param_spec.default is defined -%}
128
+ {%- if param_spec.enum %}
129
+ {{- ", // default: " + param_spec.default }}
130
+ {%- elif param_spec.oneOf %}
131
+ {{- "// default: " + param_spec.default }}
132
+ {%- else %}
133
+ {{- ", // default: " + param_spec.default|tojson }}
134
+ {%- endif -%}
135
+ {%- endif -%}
136
+ {%- if not loop.last %}
137
+ {{- ",\n" }}
138
+ {%- else %}
139
+ {{- "\n" }}
140
+ {%- endif -%}
141
+ {%- endfor %}
142
+ {{- "}) => any;\n\n" }}
143
+ {%- else -%}
144
+ {{- "() => any;\n\n" }}
145
+ {%- endif -%}
146
+ {%- endfor %}
147
+ {{- "} // namespace " + namespace_name }}
148
+ {%- endmacro -%}
149
+
150
+ {%- macro render_builtin_tools(browser_tool, python_tool) -%}
151
+ {%- if browser_tool %}
152
+ {{- "## browser\n\n" }}
153
+ {{- "// Tool for browsing.\n" }}
154
+ {{- "// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.\n" }}
155
+ {{- "// Cite information from the tool using the following format:\n" }}
156
+ {{- "// `【{cursor}†L{line_start}(-L{line_end})?】`, for example: `【6†L9-L11】` or `【8†L3】`.\n" }}
157
+ {{- "// Do not quote more than 10 words directly from the tool output.\n" }}
158
+ {{- "// sources=web (default: web)\n" }}
159
+ {{- "namespace browser {\n\n" }}
160
+ {{- "// Searches for information related to `query` and displays `topn` results.\n" }}
161
+ {{- "type search = (_: {\n" }}
162
+ {{- "query: string,\n" }}
163
+ {{- "topn?: number, // default: 10\n" }}
164
+ {{- "source?: string,\n" }}
165
+ {{- "}) => any;\n\n" }}
166
+ {{- "// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.\n" }}
167
+ {{- "// Valid link ids are displayed with the formatting: `【{id}†.*】`.\n" }}
168
+ {{- "// If `cursor` is not provided, the most recent page is implied.\n" }}
169
+ {{- "// If `id` is a string, it is treated as a fully qualified URL associated with `source`.\n" }}
170
+ {{- "// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.\n" }}
171
+ {{- "// Use this function without `id` to scroll to a new location of an opened page.\n" }}
172
+ {{- "type open = (_: {\n" }}
173
+ {{- "id?: number | string, // default: -1\n" }}
174
+ {{- "cursor?: number, // default: -1\n" }}
175
+ {{- "loc?: number, // default: -1\n" }}
176
+ {{- "num_lines?: number, // default: -1\n" }}
177
+ {{- "view_source?: boolean, // default: false\n" }}
178
+ {{- "source?: string,\n" }}
179
+ {{- "}) => any;\n\n" }}
180
+ {{- "// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.\n" }}
181
+ {{- "type find = (_: {\n" }}
182
+ {{- "pattern: string,\n" }}
183
+ {{- "cursor?: number, // default: -1\n" }}
184
+ {{- "}) => any;\n\n" }}
185
+ {{- "} // namespace browser\n\n" }}
186
+ {%- endif -%}
187
+
188
+ {%- if python_tool %}
189
+ {{- "## python\n\n" }}
190
+ {{- "Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).\n\n" }}
191
+ {{- "When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.\n\n" }}
192
+ {%- endif -%}
193
+ {%- endmacro -%}
194
+
195
+ {#- System Message Construction ============================================ #}
196
+ {%- macro build_system_message() -%}
197
+ {%- if model_identity is not defined %}
198
+ {{- "You are ChatGPT, a large language model trained by OpenAI.\n" -}}
199
+ {%- else %}
200
+ {{- model_identity }}
201
+ {%- endif %}
202
+ {{- "Knowledge cutoff: 2024-06\n" }}
203
+ {{- "Current date: " + strftime_now("%Y-%m-%d") + "\n\n" }}
204
+ {%- if reasoning_effort is not defined %}
205
+ {%- set reasoning_effort = "medium" %}
206
+ {%- endif %}
207
+ {{- "Reasoning: " + reasoning_effort + "\n\n" }}
208
+ {%- if builtin_tools is defined %}
209
+ {{- "# Tools\n\n" }}
210
+ {%- set available_builtin_tools = namespace(browser=false, python=false) %}
211
+ {%- for tool in builtin_tools %}
212
+ {%- if tool == "browser" %}
213
+ {%- set available_builtin_tools.browser = true %}
214
+ {%- elif tool == "python" %}
215
+ {%- set available_builtin_tools.python = true %}
216
+ {%- endif %}
217
+ {%- endfor %}
218
+ {{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}
219
+ {%- endif -%}
220
+ {{- "# Valid channels: analysis, commentary, final. Channel must be included for every message." }}
221
+ {%- if tools is defined -%}
222
+ {{- "\nCalls to these tools must go to the commentary channel: 'functions'." }}
223
+ {%- endif -%}
224
+ {%- endmacro -%}
225
+
226
+ {#- Main Template Logic ================================================= #}
227
+ {#- Set defaults #}
228
+
229
+ {#- Render system message #}
230
+ {{- "<|start|>system<|message|>" }}
231
+ {{- build_system_message() }}
232
+ {{- "<|end|>" }}
233
+
234
+ {#- Extract developer message #}
235
+ {%- if messages[0].role == "developer" or messages[0].role == "system" %}
236
+ {%- set developer_message = messages[0].content %}
237
+ {%- set loop_messages = messages[1:] %}
238
+ {%- else %}
239
+ {%- set developer_message = "" %}
240
+ {%- set loop_messages = messages %}
241
+ {%- endif %}
242
+
243
+ {#- Render developer message #}
244
+ {%- if developer_message or tools %}
245
+ {{- "<|start|>developer<|message|>" }}
246
+ {%- if developer_message %}
247
+ {{- "# Instructions\n\n" }}
248
+ {{- developer_message }}
249
+ {%- endif %}
250
+ {%- if tools -%}
251
+ {{- "\n\n" }}
252
+ {{- "# Tools\n\n" }}
253
+ {{- render_tool_namespace("functions", tools) }}
254
+ {%- endif -%}
255
+ {{- "<|end|>" }}
256
+ {%- endif %}
257
+
258
+ {#- Render messages #}
259
+ {%- set last_tool_call = namespace(name=none) %}
260
+ {%- for message in loop_messages -%}
261
+ {#- At this point only assistant/user/tool messages should remain #}
262
+ {%- if message.role == 'assistant' -%}
263
+ {%- if "tool_calls" in message %}
264
+ {#- We assume max 1 tool call per message, and so we infer the tool call name #}
265
+ {#- in "tool" messages from the most recent assistant tool call name #}
266
+ {%- set tool_call = message.tool_calls[0] %}
267
+ {%- if tool_call.function %}
268
+ {%- set tool_call = tool_call.function %}
269
+ {%- endif %}
270
+ {%- if message.content %}
271
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.content + "<|end|>" }}
272
+ {%- endif %}
273
+ {{- "<|start|>assistant to=" }}
274
+ {{- "functions." + tool_call.name + "<|channel|>commentary json<|message|>" }}
275
+ {{- tool_call.arguments|tojson }}
276
+ {{- "<|call|>" }}
277
+ {%- set last_tool_call.name = tool_call.name %}
278
+ {%- elif "thinking" in message and loop.last and not add_generation_prompt %}
279
+ {#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}
280
+ {#- This is a situation that should only occur in training, never in inference. #}
281
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
282
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
283
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
284
+ {#- when training, so the model learns to emit it. #}
285
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|return|>" }}
286
+ {%- set last_tool_call.name = none %}
287
+ {%- elif "thinking" in message %}
288
+ {#- CoT is dropped during all previous turns, so we never render it for inference #}
289
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|end|>" }}
290
+ {%- set last_tool_call.name = none %}
291
+ {%- elif loop.last and not add_generation_prompt %}
292
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
293
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
294
+ {#- when training, so the model learns to emit it. #}
295
+ {{- "<|start|>assistant<|message|>" + message.content + "<|return|>" }}
296
+ {%- else %}
297
+ {{- "<|start|>assistant<|message|>" + message.content + "<|end|>" }}
298
+ {%- set last_tool_call.name = none %}
299
+ {%- endif %}
300
+ {%- elif message.role == 'tool' -%}
301
+ {%- if last_tool_call.name is none %}
302
+ {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
303
+ {%- endif %}
304
+ {{- "<|start|>functions." + last_tool_call.name }}
305
+ {{- " to=assistant<|channel|>commentary<|message|>" + message.content|tojson + "<|end|>" }}
306
+ {%- else -%}
307
+ {{- "<|start|>user<|message|>" + message.content + "<|end|>" }}
308
+ {%- endif -%}
309
+ {%- endfor -%}
310
+
311
+ {#- Generation prompt #}
312
+ {%- if add_generation_prompt -%}
313
+ <|start|>assistant
314
+ {%- endif -%}
315
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
outputs/checkpoint-2500/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e7e45070f968f70d7a53d726615310d86734504e3bce33d45c6aeee13b2a6a00
3
+ size 16894883
outputs/checkpoint-2500/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f1d565802a8e26c4e8a31328752b7a7fdc186d9401aa008e65697d0ad8c22e33
3
+ size 14645
outputs/checkpoint-2500/special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|return|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<|reserved_200017|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
outputs/checkpoint-2500/tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0614fe83cadab421296e664e1f48f4261fa8fef6e03e63bb75c20f38e37d07d3
3
+ size 27868174
outputs/checkpoint-2500/tokenizer_config.json ADDED
@@ -0,0 +1,185 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "199998": {
4
+ "content": "<|startoftext|>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "199999": {
12
+ "content": "<|endoftext|>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "200000": {
20
+ "content": "<|reserved_200000|>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "200001": {
28
+ "content": "<|reserved_200001|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "200002": {
36
+ "content": "<|return|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "200003": {
44
+ "content": "<|constrain|>",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "200004": {
52
+ "content": "<|reserved_200004|>",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "200005": {
60
+ "content": "<|channel|>",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "200006": {
68
+ "content": "<|start|>",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "200007": {
76
+ "content": "<|end|>",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "200008": {
84
+ "content": "<|message|>",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "200009": {
92
+ "content": "<|reserved_200009|>",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
+ "200010": {
100
+ "content": "<|reserved_200010|>",
101
+ "lstrip": false,
102
+ "normalized": false,
103
+ "rstrip": false,
104
+ "single_word": false,
105
+ "special": true
106
+ },
107
+ "200011": {
108
+ "content": "<|reserved_200011|>",
109
+ "lstrip": false,
110
+ "normalized": false,
111
+ "rstrip": false,
112
+ "single_word": false,
113
+ "special": true
114
+ },
115
+ "200012": {
116
+ "content": "<|call|>",
117
+ "lstrip": false,
118
+ "normalized": false,
119
+ "rstrip": false,
120
+ "single_word": false,
121
+ "special": true
122
+ },
123
+ "200013": {
124
+ "content": "<|reserved_200013|>",
125
+ "lstrip": false,
126
+ "normalized": false,
127
+ "rstrip": false,
128
+ "single_word": false,
129
+ "special": true
130
+ },
131
+ "200014": {
132
+ "content": "<|reserved_200014|>",
133
+ "lstrip": false,
134
+ "normalized": false,
135
+ "rstrip": false,
136
+ "single_word": false,
137
+ "special": true
138
+ },
139
+ "200015": {
140
+ "content": "<|reserved_200015|>",
141
+ "lstrip": false,
142
+ "normalized": false,
143
+ "rstrip": false,
144
+ "single_word": false,
145
+ "special": true
146
+ },
147
+ "200016": {
148
+ "content": "<|reserved_200016|>",
149
+ "lstrip": false,
150
+ "normalized": false,
151
+ "rstrip": false,
152
+ "single_word": false,
153
+ "special": true
154
+ },
155
+ "200017": {
156
+ "content": "<|reserved_200017|>",
157
+ "lstrip": false,
158
+ "normalized": false,
159
+ "rstrip": false,
160
+ "single_word": false,
161
+ "special": true
162
+ },
163
+ "200018": {
164
+ "content": "<|endofprompt|>",
165
+ "lstrip": false,
166
+ "normalized": false,
167
+ "rstrip": false,
168
+ "single_word": false,
169
+ "special": true
170
+ }
171
+ },
172
+ "bos_token": "<|startoftext|>",
173
+ "clean_up_tokenization_spaces": false,
174
+ "eos_token": "<|return|>",
175
+ "extra_special_tokens": {},
176
+ "model_input_names": [
177
+ "input_ids",
178
+ "attention_mask"
179
+ ],
180
+ "model_max_length": 131072,
181
+ "pad_token": "<|reserved_200017|>",
182
+ "padding_side": "right",
183
+ "tokenizer_class": "PreTrainedTokenizerFast",
184
+ "unk_token": null
185
+ }
outputs/checkpoint-2500/trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
outputs/checkpoint-313/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
3
+ library_name: peft
4
+ tags:
5
+ - base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit
6
+ - lora
7
+ - sft
8
+ - transformers
9
+ - trl
10
+ - unsloth
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.17.1
outputs/checkpoint-313/adapter_config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": {
4
+ "base_model_class": "GptOssForCausalLM",
5
+ "parent_library": "transformers.models.gpt_oss.modeling_gpt_oss"
6
+ },
7
+ "base_model_name_or_path": "unsloth/gpt-oss-20b-unsloth-bnb-4bit",
8
+ "bias": "none",
9
+ "corda_config": null,
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": 0.0,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "qalora_group_size": 16,
27
+ "r": 16,
28
+ "rank_pattern": {},
29
+ "revision": null,
30
+ "target_modules": [
31
+ "up_proj",
32
+ "down_proj",
33
+ "o_proj",
34
+ "v_proj",
35
+ "q_proj",
36
+ "k_proj",
37
+ "gate_proj"
38
+ ],
39
+ "target_parameters": null,
40
+ "task_type": null,
41
+ "trainable_token_indices": null,
42
+ "use_dora": false,
43
+ "use_qalora": false,
44
+ "use_rslora": false
45
+ }
outputs/checkpoint-313/chat_template.jinja ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
2
+ {#-
3
+ In addition to the normal inputs of `messages` and `tools`, this template also accepts the
4
+ following kwargs:
5
+ - "builtin_tools": A list, can contain "browser" and/or "python".
6
+ - "model_identity": A string that optionally describes the model identity.
7
+ - "reasoning_effort": A string that describes the reasoning effort, defaults to "medium".
8
+ #}
9
+
10
+ {#- Tool Definition Rendering ============================================== #}
11
+ {%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}
12
+ {%- if param_spec.type == "array" -%}
13
+ {%- if param_spec['items'] -%}
14
+ {%- if param_spec['items']['type'] == "string" -%}
15
+ {{- "string[]" }}
16
+ {%- elif param_spec['items']['type'] == "number" -%}
17
+ {{- "number[]" }}
18
+ {%- elif param_spec['items']['type'] == "integer" -%}
19
+ {{- "number[]" }}
20
+ {%- elif param_spec['items']['type'] == "boolean" -%}
21
+ {{- "boolean[]" }}
22
+ {%- else -%}
23
+ {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}
24
+ {%- if inner_type == "object | object" or inner_type|length > 50 -%}
25
+ {{- "any[]" }}
26
+ {%- else -%}
27
+ {{- inner_type + "[]" }}
28
+ {%- endif -%}
29
+ {%- endif -%}
30
+ {%- if param_spec.nullable -%}
31
+ {{- " | null" }}
32
+ {%- endif -%}
33
+ {%- else -%}
34
+ {{- "any[]" }}
35
+ {%- if param_spec.nullable -%}
36
+ {{- " | null" }}
37
+ {%- endif -%}
38
+ {%- endif -%}
39
+ {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}
40
+ {#- Handle array of types like ["object", "object"] from Union[dict, list] #}
41
+ {%- if param_spec.type | length > 1 -%}
42
+ {{- param_spec.type | join(" | ") }}
43
+ {%- else -%}
44
+ {{- param_spec.type[0] }}
45
+ {%- endif -%}
46
+ {%- elif param_spec.oneOf -%}
47
+ {#- Handle oneOf schemas - check for complex unions and fallback to any #}
48
+ {%- set has_object_variants = false -%}
49
+ {%- for variant in param_spec.oneOf -%}
50
+ {%- if variant.type == "object" -%}
51
+ {%- set has_object_variants = true -%}
52
+ {%- endif -%}
53
+ {%- endfor -%}
54
+ {%- if has_object_variants and param_spec.oneOf|length > 1 -%}
55
+ {{- "any" }}
56
+ {%- else -%}
57
+ {%- for variant in param_spec.oneOf -%}
58
+ {{- render_typescript_type(variant, required_params) -}}
59
+ {%- if variant.description %}
60
+ {{- "// " + variant.description }}
61
+ {%- endif -%}
62
+ {%- if variant.default is defined %}
63
+ {{ "// default: " + variant.default|tojson }}
64
+ {%- endif -%}
65
+ {%- if not loop.last %}
66
+ {{- " | " }}
67
+ {% endif -%}
68
+ {%- endfor -%}
69
+ {%- endif -%}
70
+ {%- elif param_spec.type == "string" -%}
71
+ {%- if param_spec.enum -%}
72
+ {{- '"' + param_spec.enum|join('" | "') + '"' -}}
73
+ {%- else -%}
74
+ {{- "string" }}
75
+ {%- if param_spec.nullable %}
76
+ {{- " | null" }}
77
+ {%- endif -%}
78
+ {%- endif -%}
79
+ {%- elif param_spec.type == "number" -%}
80
+ {{- "number" }}
81
+ {%- elif param_spec.type == "integer" -%}
82
+ {{- "number" }}
83
+ {%- elif param_spec.type == "boolean" -%}
84
+ {{- "boolean" }}
85
+
86
+ {%- elif param_spec.type == "object" -%}
87
+ {%- if param_spec.properties -%}
88
+ {{- "{\n" }}
89
+ {%- for prop_name, prop_spec in param_spec.properties.items() -%}
90
+ {{- prop_name -}}
91
+ {%- if prop_name not in (param_spec.required or []) -%}
92
+ {{- "?" }}
93
+ {%- endif -%}
94
+ {{- ": " }}
95
+ {{ render_typescript_type(prop_spec, param_spec.required or []) }}
96
+ {%- if not loop.last -%}
97
+ {{-", " }}
98
+ {%- endif -%}
99
+ {%- endfor -%}
100
+ {{- "}" }}
101
+ {%- else -%}
102
+ {{- "object" }}
103
+ {%- endif -%}
104
+ {%- else -%}
105
+ {{- "any" }}
106
+ {%- endif -%}
107
+ {%- endmacro -%}
108
+
109
+ {%- macro render_tool_namespace(namespace_name, tools) -%}
110
+ {{- "## " + namespace_name + "\n\n" }}
111
+ {{- "namespace " + namespace_name + " {\n\n" }}
112
+ {%- for tool in tools %}
113
+ {%- set tool = tool.function %}
114
+ {{- "// " + tool.description + "\n" }}
115
+ {{- "type "+ tool.name + " = " }}
116
+ {%- if tool.parameters and tool.parameters.properties -%}
117
+ {{- "(_: " }}
118
+ {{- "{\n" }}
119
+ {%- for param_name, param_spec in tool.parameters.properties.items() %}
120
+ {{- "// " + param_spec.description + "\n" }}
121
+ {{- param_name }}
122
+ {%- if param_name not in (tool.parameters.required or []) -%}
123
+ {{- "?" }}
124
+ {%- endif -%}
125
+ {{- ": " }}
126
+ {{- render_typescript_type(param_spec, tool.parameters.required or []) }}
127
+ {%- if param_spec.default is defined -%}
128
+ {%- if param_spec.enum %}
129
+ {{- ", // default: " + param_spec.default }}
130
+ {%- elif param_spec.oneOf %}
131
+ {{- "// default: " + param_spec.default }}
132
+ {%- else %}
133
+ {{- ", // default: " + param_spec.default|tojson }}
134
+ {%- endif -%}
135
+ {%- endif -%}
136
+ {%- if not loop.last %}
137
+ {{- ",\n" }}
138
+ {%- else %}
139
+ {{- "\n" }}
140
+ {%- endif -%}
141
+ {%- endfor %}
142
+ {{- "}) => any;\n\n" }}
143
+ {%- else -%}
144
+ {{- "() => any;\n\n" }}
145
+ {%- endif -%}
146
+ {%- endfor %}
147
+ {{- "} // namespace " + namespace_name }}
148
+ {%- endmacro -%}
149
+
150
+ {%- macro render_builtin_tools(browser_tool, python_tool) -%}
151
+ {%- if browser_tool %}
152
+ {{- "## browser\n\n" }}
153
+ {{- "// Tool for browsing.\n" }}
154
+ {{- "// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.\n" }}
155
+ {{- "// Cite information from the tool using the following format:\n" }}
156
+ {{- "// `【{cursor}†L{line_start}(-L{line_end})?】`, for example: `【6†L9-L11】` or `【8†L3】`.\n" }}
157
+ {{- "// Do not quote more than 10 words directly from the tool output.\n" }}
158
+ {{- "// sources=web (default: web)\n" }}
159
+ {{- "namespace browser {\n\n" }}
160
+ {{- "// Searches for information related to `query` and displays `topn` results.\n" }}
161
+ {{- "type search = (_: {\n" }}
162
+ {{- "query: string,\n" }}
163
+ {{- "topn?: number, // default: 10\n" }}
164
+ {{- "source?: string,\n" }}
165
+ {{- "}) => any;\n\n" }}
166
+ {{- "// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.\n" }}
167
+ {{- "// Valid link ids are displayed with the formatting: `【{id}†.*】`.\n" }}
168
+ {{- "// If `cursor` is not provided, the most recent page is implied.\n" }}
169
+ {{- "// If `id` is a string, it is treated as a fully qualified URL associated with `source`.\n" }}
170
+ {{- "// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.\n" }}
171
+ {{- "// Use this function without `id` to scroll to a new location of an opened page.\n" }}
172
+ {{- "type open = (_: {\n" }}
173
+ {{- "id?: number | string, // default: -1\n" }}
174
+ {{- "cursor?: number, // default: -1\n" }}
175
+ {{- "loc?: number, // default: -1\n" }}
176
+ {{- "num_lines?: number, // default: -1\n" }}
177
+ {{- "view_source?: boolean, // default: false\n" }}
178
+ {{- "source?: string,\n" }}
179
+ {{- "}) => any;\n\n" }}
180
+ {{- "// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.\n" }}
181
+ {{- "type find = (_: {\n" }}
182
+ {{- "pattern: string,\n" }}
183
+ {{- "cursor?: number, // default: -1\n" }}
184
+ {{- "}) => any;\n\n" }}
185
+ {{- "} // namespace browser\n\n" }}
186
+ {%- endif -%}
187
+
188
+ {%- if python_tool %}
189
+ {{- "## python\n\n" }}
190
+ {{- "Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).\n\n" }}
191
+ {{- "When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.\n\n" }}
192
+ {%- endif -%}
193
+ {%- endmacro -%}
194
+
195
+ {#- System Message Construction ============================================ #}
196
+ {%- macro build_system_message() -%}
197
+ {%- if model_identity is not defined %}
198
+ {{- "You are ChatGPT, a large language model trained by OpenAI.\n" -}}
199
+ {%- else %}
200
+ {{- model_identity }}
201
+ {%- endif %}
202
+ {{- "Knowledge cutoff: 2024-06\n" }}
203
+ {{- "Current date: " + strftime_now("%Y-%m-%d") + "\n\n" }}
204
+ {%- if reasoning_effort is not defined %}
205
+ {%- set reasoning_effort = "medium" %}
206
+ {%- endif %}
207
+ {{- "Reasoning: " + reasoning_effort + "\n\n" }}
208
+ {%- if builtin_tools is defined %}
209
+ {{- "# Tools\n\n" }}
210
+ {%- set available_builtin_tools = namespace(browser=false, python=false) %}
211
+ {%- for tool in builtin_tools %}
212
+ {%- if tool == "browser" %}
213
+ {%- set available_builtin_tools.browser = true %}
214
+ {%- elif tool == "python" %}
215
+ {%- set available_builtin_tools.python = true %}
216
+ {%- endif %}
217
+ {%- endfor %}
218
+ {{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}
219
+ {%- endif -%}
220
+ {{- "# Valid channels: analysis, commentary, final. Channel must be included for every message." }}
221
+ {%- if tools is defined -%}
222
+ {{- "\nCalls to these tools must go to the commentary channel: 'functions'." }}
223
+ {%- endif -%}
224
+ {%- endmacro -%}
225
+
226
+ {#- Main Template Logic ================================================= #}
227
+ {#- Set defaults #}
228
+
229
+ {#- Render system message #}
230
+ {{- "<|start|>system<|message|>" }}
231
+ {{- build_system_message() }}
232
+ {{- "<|end|>" }}
233
+
234
+ {#- Extract developer message #}
235
+ {%- if messages[0].role == "developer" or messages[0].role == "system" %}
236
+ {%- set developer_message = messages[0].content %}
237
+ {%- set loop_messages = messages[1:] %}
238
+ {%- else %}
239
+ {%- set developer_message = "" %}
240
+ {%- set loop_messages = messages %}
241
+ {%- endif %}
242
+
243
+ {#- Render developer message #}
244
+ {%- if developer_message or tools %}
245
+ {{- "<|start|>developer<|message|>" }}
246
+ {%- if developer_message %}
247
+ {{- "# Instructions\n\n" }}
248
+ {{- developer_message }}
249
+ {%- endif %}
250
+ {%- if tools -%}
251
+ {{- "\n\n" }}
252
+ {{- "# Tools\n\n" }}
253
+ {{- render_tool_namespace("functions", tools) }}
254
+ {%- endif -%}
255
+ {{- "<|end|>" }}
256
+ {%- endif %}
257
+
258
+ {#- Render messages #}
259
+ {%- set last_tool_call = namespace(name=none) %}
260
+ {%- for message in loop_messages -%}
261
+ {#- At this point only assistant/user/tool messages should remain #}
262
+ {%- if message.role == 'assistant' -%}
263
+ {%- if "tool_calls" in message %}
264
+ {#- We assume max 1 tool call per message, and so we infer the tool call name #}
265
+ {#- in "tool" messages from the most recent assistant tool call name #}
266
+ {%- set tool_call = message.tool_calls[0] %}
267
+ {%- if tool_call.function %}
268
+ {%- set tool_call = tool_call.function %}
269
+ {%- endif %}
270
+ {%- if message.content %}
271
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.content + "<|end|>" }}
272
+ {%- endif %}
273
+ {{- "<|start|>assistant to=" }}
274
+ {{- "functions." + tool_call.name + "<|channel|>commentary json<|message|>" }}
275
+ {{- tool_call.arguments|tojson }}
276
+ {{- "<|call|>" }}
277
+ {%- set last_tool_call.name = tool_call.name %}
278
+ {%- elif "thinking" in message and loop.last and not add_generation_prompt %}
279
+ {#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}
280
+ {#- This is a situation that should only occur in training, never in inference. #}
281
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
282
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
283
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
284
+ {#- when training, so the model learns to emit it. #}
285
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|return|>" }}
286
+ {%- set last_tool_call.name = none %}
287
+ {%- elif "thinking" in message %}
288
+ {#- CoT is dropped during all previous turns, so we never render it for inference #}
289
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|end|>" }}
290
+ {%- set last_tool_call.name = none %}
291
+ {%- elif loop.last and not add_generation_prompt %}
292
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
293
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
294
+ {#- when training, so the model learns to emit it. #}
295
+ {{- "<|start|>assistant<|message|>" + message.content + "<|return|>" }}
296
+ {%- else %}
297
+ {{- "<|start|>assistant<|message|>" + message.content + "<|end|>" }}
298
+ {%- set last_tool_call.name = none %}
299
+ {%- endif %}
300
+ {%- elif message.role == 'tool' -%}
301
+ {%- if last_tool_call.name is none %}
302
+ {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
303
+ {%- endif %}
304
+ {{- "<|start|>functions." + last_tool_call.name }}
305
+ {{- " to=assistant<|channel|>commentary<|message|>" + message.content|tojson + "<|end|>" }}
306
+ {%- else -%}
307
+ {{- "<|start|>user<|message|>" + message.content + "<|end|>" }}
308
+ {%- endif -%}
309
+ {%- endfor -%}
310
+
311
+ {#- Generation prompt #}
312
+ {%- if add_generation_prompt -%}
313
+ <|start|>assistant
314
+ {%- endif -%}
315
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
outputs/checkpoint-313/special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|return|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<|reserved_200017|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
outputs/checkpoint-313/tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0614fe83cadab421296e664e1f48f4261fa8fef6e03e63bb75c20f38e37d07d3
3
+ size 27868174
outputs/checkpoint-313/tokenizer_config.json ADDED
@@ -0,0 +1,185 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "199998": {
4
+ "content": "<|startoftext|>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "199999": {
12
+ "content": "<|endoftext|>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "200000": {
20
+ "content": "<|reserved_200000|>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "200001": {
28
+ "content": "<|reserved_200001|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "200002": {
36
+ "content": "<|return|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "200003": {
44
+ "content": "<|constrain|>",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "200004": {
52
+ "content": "<|reserved_200004|>",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "200005": {
60
+ "content": "<|channel|>",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "200006": {
68
+ "content": "<|start|>",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "200007": {
76
+ "content": "<|end|>",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "200008": {
84
+ "content": "<|message|>",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "200009": {
92
+ "content": "<|reserved_200009|>",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
+ "200010": {
100
+ "content": "<|reserved_200010|>",
101
+ "lstrip": false,
102
+ "normalized": false,
103
+ "rstrip": false,
104
+ "single_word": false,
105
+ "special": true
106
+ },
107
+ "200011": {
108
+ "content": "<|reserved_200011|>",
109
+ "lstrip": false,
110
+ "normalized": false,
111
+ "rstrip": false,
112
+ "single_word": false,
113
+ "special": true
114
+ },
115
+ "200012": {
116
+ "content": "<|call|>",
117
+ "lstrip": false,
118
+ "normalized": false,
119
+ "rstrip": false,
120
+ "single_word": false,
121
+ "special": true
122
+ },
123
+ "200013": {
124
+ "content": "<|reserved_200013|>",
125
+ "lstrip": false,
126
+ "normalized": false,
127
+ "rstrip": false,
128
+ "single_word": false,
129
+ "special": true
130
+ },
131
+ "200014": {
132
+ "content": "<|reserved_200014|>",
133
+ "lstrip": false,
134
+ "normalized": false,
135
+ "rstrip": false,
136
+ "single_word": false,
137
+ "special": true
138
+ },
139
+ "200015": {
140
+ "content": "<|reserved_200015|>",
141
+ "lstrip": false,
142
+ "normalized": false,
143
+ "rstrip": false,
144
+ "single_word": false,
145
+ "special": true
146
+ },
147
+ "200016": {
148
+ "content": "<|reserved_200016|>",
149
+ "lstrip": false,
150
+ "normalized": false,
151
+ "rstrip": false,
152
+ "single_word": false,
153
+ "special": true
154
+ },
155
+ "200017": {
156
+ "content": "<|reserved_200017|>",
157
+ "lstrip": false,
158
+ "normalized": false,
159
+ "rstrip": false,
160
+ "single_word": false,
161
+ "special": true
162
+ },
163
+ "200018": {
164
+ "content": "<|endofprompt|>",
165
+ "lstrip": false,
166
+ "normalized": false,
167
+ "rstrip": false,
168
+ "single_word": false,
169
+ "special": true
170
+ }
171
+ },
172
+ "bos_token": "<|startoftext|>",
173
+ "clean_up_tokenization_spaces": false,
174
+ "eos_token": "<|return|>",
175
+ "extra_special_tokens": {},
176
+ "model_input_names": [
177
+ "input_ids",
178
+ "attention_mask"
179
+ ],
180
+ "model_max_length": 131072,
181
+ "pad_token": "<|reserved_200017|>",
182
+ "padding_side": "right",
183
+ "tokenizer_class": "PreTrainedTokenizerFast",
184
+ "unk_token": null
185
+ }
outputs/checkpoint-313/trainer_state.json ADDED
@@ -0,0 +1,2225 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 1.0,
6
+ "eval_steps": 500,
7
+ "global_step": 313,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "epoch": 0.0032,
14
+ "grad_norm": 13.684800148010254,
15
+ "learning_rate": 0.0,
16
+ "loss": 2.3276,
17
+ "step": 1
18
+ },
19
+ {
20
+ "epoch": 0.0064,
21
+ "grad_norm": 13.660787582397461,
22
+ "learning_rate": 4e-05,
23
+ "loss": 2.2792,
24
+ "step": 2
25
+ },
26
+ {
27
+ "epoch": 0.0096,
28
+ "grad_norm": 13.35280704498291,
29
+ "learning_rate": 8e-05,
30
+ "loss": 2.4151,
31
+ "step": 3
32
+ },
33
+ {
34
+ "epoch": 0.0128,
35
+ "grad_norm": 6.15027379989624,
36
+ "learning_rate": 0.00012,
37
+ "loss": 1.7812,
38
+ "step": 4
39
+ },
40
+ {
41
+ "epoch": 0.016,
42
+ "grad_norm": 1.3168226480484009,
43
+ "learning_rate": 0.00016,
44
+ "loss": 1.4536,
45
+ "step": 5
46
+ },
47
+ {
48
+ "epoch": 0.0192,
49
+ "grad_norm": 0.9872580170631409,
50
+ "learning_rate": 0.0002,
51
+ "loss": 1.4171,
52
+ "step": 6
53
+ },
54
+ {
55
+ "epoch": 0.0224,
56
+ "grad_norm": 0.7496100664138794,
57
+ "learning_rate": 0.00019935064935064936,
58
+ "loss": 1.4168,
59
+ "step": 7
60
+ },
61
+ {
62
+ "epoch": 0.0256,
63
+ "grad_norm": 0.7376005053520203,
64
+ "learning_rate": 0.00019870129870129872,
65
+ "loss": 1.3659,
66
+ "step": 8
67
+ },
68
+ {
69
+ "epoch": 0.0288,
70
+ "grad_norm": 0.5281137824058533,
71
+ "learning_rate": 0.00019805194805194807,
72
+ "loss": 1.2566,
73
+ "step": 9
74
+ },
75
+ {
76
+ "epoch": 0.032,
77
+ "grad_norm": 0.5485746264457703,
78
+ "learning_rate": 0.00019740259740259742,
79
+ "loss": 1.3761,
80
+ "step": 10
81
+ },
82
+ {
83
+ "epoch": 0.0352,
84
+ "grad_norm": 0.5506592392921448,
85
+ "learning_rate": 0.00019675324675324675,
86
+ "loss": 1.3327,
87
+ "step": 11
88
+ },
89
+ {
90
+ "epoch": 0.0384,
91
+ "grad_norm": 0.49382686614990234,
92
+ "learning_rate": 0.00019610389610389613,
93
+ "loss": 1.3727,
94
+ "step": 12
95
+ },
96
+ {
97
+ "epoch": 0.0416,
98
+ "grad_norm": 0.36203011870384216,
99
+ "learning_rate": 0.00019545454545454548,
100
+ "loss": 1.1515,
101
+ "step": 13
102
+ },
103
+ {
104
+ "epoch": 0.0448,
105
+ "grad_norm": 0.3528599739074707,
106
+ "learning_rate": 0.0001948051948051948,
107
+ "loss": 1.2636,
108
+ "step": 14
109
+ },
110
+ {
111
+ "epoch": 0.048,
112
+ "grad_norm": 0.31244418025016785,
113
+ "learning_rate": 0.00019415584415584416,
114
+ "loss": 1.1873,
115
+ "step": 15
116
+ },
117
+ {
118
+ "epoch": 0.0512,
119
+ "grad_norm": 0.3379523754119873,
120
+ "learning_rate": 0.00019350649350649354,
121
+ "loss": 1.2657,
122
+ "step": 16
123
+ },
124
+ {
125
+ "epoch": 0.0544,
126
+ "grad_norm": 0.3025083839893341,
127
+ "learning_rate": 0.00019285714285714286,
128
+ "loss": 1.2846,
129
+ "step": 17
130
+ },
131
+ {
132
+ "epoch": 0.0576,
133
+ "grad_norm": 0.2560190260410309,
134
+ "learning_rate": 0.00019220779220779222,
135
+ "loss": 1.1587,
136
+ "step": 18
137
+ },
138
+ {
139
+ "epoch": 0.0608,
140
+ "grad_norm": 0.2554129958152771,
141
+ "learning_rate": 0.00019155844155844157,
142
+ "loss": 1.2812,
143
+ "step": 19
144
+ },
145
+ {
146
+ "epoch": 0.064,
147
+ "grad_norm": 0.22662702202796936,
148
+ "learning_rate": 0.00019090909090909092,
149
+ "loss": 1.1664,
150
+ "step": 20
151
+ },
152
+ {
153
+ "epoch": 0.0672,
154
+ "grad_norm": 0.2515714168548584,
155
+ "learning_rate": 0.00019025974025974027,
156
+ "loss": 1.2177,
157
+ "step": 21
158
+ },
159
+ {
160
+ "epoch": 0.0704,
161
+ "grad_norm": 0.24396637082099915,
162
+ "learning_rate": 0.00018961038961038963,
163
+ "loss": 1.2053,
164
+ "step": 22
165
+ },
166
+ {
167
+ "epoch": 0.0736,
168
+ "grad_norm": 0.24488303065299988,
169
+ "learning_rate": 0.00018896103896103895,
170
+ "loss": 1.2074,
171
+ "step": 23
172
+ },
173
+ {
174
+ "epoch": 0.0768,
175
+ "grad_norm": 0.2168620079755783,
176
+ "learning_rate": 0.00018831168831168833,
177
+ "loss": 1.1284,
178
+ "step": 24
179
+ },
180
+ {
181
+ "epoch": 0.08,
182
+ "grad_norm": 0.24021224677562714,
183
+ "learning_rate": 0.00018766233766233769,
184
+ "loss": 1.2169,
185
+ "step": 25
186
+ },
187
+ {
188
+ "epoch": 0.0832,
189
+ "grad_norm": 0.20057056844234467,
190
+ "learning_rate": 0.000187012987012987,
191
+ "loss": 1.1031,
192
+ "step": 26
193
+ },
194
+ {
195
+ "epoch": 0.0864,
196
+ "grad_norm": 0.19900795817375183,
197
+ "learning_rate": 0.00018636363636363636,
198
+ "loss": 1.1004,
199
+ "step": 27
200
+ },
201
+ {
202
+ "epoch": 0.0896,
203
+ "grad_norm": 0.2019268423318863,
204
+ "learning_rate": 0.00018571428571428572,
205
+ "loss": 1.1476,
206
+ "step": 28
207
+ },
208
+ {
209
+ "epoch": 0.0928,
210
+ "grad_norm": 0.1996479034423828,
211
+ "learning_rate": 0.00018506493506493507,
212
+ "loss": 1.1455,
213
+ "step": 29
214
+ },
215
+ {
216
+ "epoch": 0.096,
217
+ "grad_norm": 0.25262022018432617,
218
+ "learning_rate": 0.00018441558441558442,
219
+ "loss": 1.1025,
220
+ "step": 30
221
+ },
222
+ {
223
+ "epoch": 0.0992,
224
+ "grad_norm": 0.225438192486763,
225
+ "learning_rate": 0.00018376623376623378,
226
+ "loss": 1.1954,
227
+ "step": 31
228
+ },
229
+ {
230
+ "epoch": 0.1024,
231
+ "grad_norm": 0.17834505438804626,
232
+ "learning_rate": 0.00018311688311688313,
233
+ "loss": 1.0934,
234
+ "step": 32
235
+ },
236
+ {
237
+ "epoch": 0.1056,
238
+ "grad_norm": 0.20071206986904144,
239
+ "learning_rate": 0.00018246753246753248,
240
+ "loss": 1.0488,
241
+ "step": 33
242
+ },
243
+ {
244
+ "epoch": 0.1088,
245
+ "grad_norm": 0.1920139640569687,
246
+ "learning_rate": 0.00018181818181818183,
247
+ "loss": 1.123,
248
+ "step": 34
249
+ },
250
+ {
251
+ "epoch": 0.112,
252
+ "grad_norm": 0.18714852631092072,
253
+ "learning_rate": 0.0001811688311688312,
254
+ "loss": 1.0798,
255
+ "step": 35
256
+ },
257
+ {
258
+ "epoch": 0.1152,
259
+ "grad_norm": 0.18315713107585907,
260
+ "learning_rate": 0.00018051948051948054,
261
+ "loss": 1.1107,
262
+ "step": 36
263
+ },
264
+ {
265
+ "epoch": 0.1184,
266
+ "grad_norm": 0.19156870245933533,
267
+ "learning_rate": 0.00017987012987012987,
268
+ "loss": 1.1125,
269
+ "step": 37
270
+ },
271
+ {
272
+ "epoch": 0.1216,
273
+ "grad_norm": 0.21527768671512604,
274
+ "learning_rate": 0.00017922077922077922,
275
+ "loss": 1.1346,
276
+ "step": 38
277
+ },
278
+ {
279
+ "epoch": 0.1248,
280
+ "grad_norm": 0.1871163249015808,
281
+ "learning_rate": 0.0001785714285714286,
282
+ "loss": 1.0742,
283
+ "step": 39
284
+ },
285
+ {
286
+ "epoch": 0.128,
287
+ "grad_norm": 0.17750784754753113,
288
+ "learning_rate": 0.00017792207792207792,
289
+ "loss": 1.1323,
290
+ "step": 40
291
+ },
292
+ {
293
+ "epoch": 0.1312,
294
+ "grad_norm": 0.177419051527977,
295
+ "learning_rate": 0.00017727272727272728,
296
+ "loss": 1.1405,
297
+ "step": 41
298
+ },
299
+ {
300
+ "epoch": 0.1344,
301
+ "grad_norm": 0.16714292764663696,
302
+ "learning_rate": 0.00017662337662337663,
303
+ "loss": 1.1084,
304
+ "step": 42
305
+ },
306
+ {
307
+ "epoch": 0.1376,
308
+ "grad_norm": 0.1610356718301773,
309
+ "learning_rate": 0.00017597402597402598,
310
+ "loss": 1.1125,
311
+ "step": 43
312
+ },
313
+ {
314
+ "epoch": 0.1408,
315
+ "grad_norm": 0.2548656761646271,
316
+ "learning_rate": 0.00017532467532467534,
317
+ "loss": 1.1114,
318
+ "step": 44
319
+ },
320
+ {
321
+ "epoch": 0.144,
322
+ "grad_norm": 0.1731044203042984,
323
+ "learning_rate": 0.0001746753246753247,
324
+ "loss": 1.1197,
325
+ "step": 45
326
+ },
327
+ {
328
+ "epoch": 0.1472,
329
+ "grad_norm": 0.1739533394575119,
330
+ "learning_rate": 0.00017402597402597401,
331
+ "loss": 1.1777,
332
+ "step": 46
333
+ },
334
+ {
335
+ "epoch": 0.1504,
336
+ "grad_norm": 0.2178352177143097,
337
+ "learning_rate": 0.0001733766233766234,
338
+ "loss": 1.1111,
339
+ "step": 47
340
+ },
341
+ {
342
+ "epoch": 0.1536,
343
+ "grad_norm": 0.17247150838375092,
344
+ "learning_rate": 0.00017272727272727275,
345
+ "loss": 1.1253,
346
+ "step": 48
347
+ },
348
+ {
349
+ "epoch": 0.1568,
350
+ "grad_norm": 0.18075324594974518,
351
+ "learning_rate": 0.00017207792207792207,
352
+ "loss": 1.1358,
353
+ "step": 49
354
+ },
355
+ {
356
+ "epoch": 0.16,
357
+ "grad_norm": 0.15898071229457855,
358
+ "learning_rate": 0.00017142857142857143,
359
+ "loss": 1.0606,
360
+ "step": 50
361
+ },
362
+ {
363
+ "epoch": 0.1632,
364
+ "grad_norm": 0.16518613696098328,
365
+ "learning_rate": 0.0001707792207792208,
366
+ "loss": 1.0944,
367
+ "step": 51
368
+ },
369
+ {
370
+ "epoch": 0.1664,
371
+ "grad_norm": 0.16035063564777374,
372
+ "learning_rate": 0.00017012987012987013,
373
+ "loss": 1.0554,
374
+ "step": 52
375
+ },
376
+ {
377
+ "epoch": 0.1696,
378
+ "grad_norm": 0.1686483472585678,
379
+ "learning_rate": 0.00016948051948051948,
380
+ "loss": 1.0384,
381
+ "step": 53
382
+ },
383
+ {
384
+ "epoch": 0.1728,
385
+ "grad_norm": 0.16575631499290466,
386
+ "learning_rate": 0.00016883116883116884,
387
+ "loss": 1.0243,
388
+ "step": 54
389
+ },
390
+ {
391
+ "epoch": 0.176,
392
+ "grad_norm": 0.16840039193630219,
393
+ "learning_rate": 0.0001681818181818182,
394
+ "loss": 1.117,
395
+ "step": 55
396
+ },
397
+ {
398
+ "epoch": 0.1792,
399
+ "grad_norm": 0.17616064846515656,
400
+ "learning_rate": 0.00016753246753246754,
401
+ "loss": 1.0743,
402
+ "step": 56
403
+ },
404
+ {
405
+ "epoch": 0.1824,
406
+ "grad_norm": 0.168218195438385,
407
+ "learning_rate": 0.0001668831168831169,
408
+ "loss": 1.0627,
409
+ "step": 57
410
+ },
411
+ {
412
+ "epoch": 0.1856,
413
+ "grad_norm": 0.17026656866073608,
414
+ "learning_rate": 0.00016623376623376625,
415
+ "loss": 1.0059,
416
+ "step": 58
417
+ },
418
+ {
419
+ "epoch": 0.1888,
420
+ "grad_norm": 0.16454458236694336,
421
+ "learning_rate": 0.0001655844155844156,
422
+ "loss": 0.9943,
423
+ "step": 59
424
+ },
425
+ {
426
+ "epoch": 0.192,
427
+ "grad_norm": 0.17185136675834656,
428
+ "learning_rate": 0.00016493506493506495,
429
+ "loss": 1.1545,
430
+ "step": 60
431
+ },
432
+ {
433
+ "epoch": 0.1952,
434
+ "grad_norm": 0.17822986841201782,
435
+ "learning_rate": 0.00016428571428571428,
436
+ "loss": 1.073,
437
+ "step": 61
438
+ },
439
+ {
440
+ "epoch": 0.1984,
441
+ "grad_norm": 0.1676608771085739,
442
+ "learning_rate": 0.00016363636363636366,
443
+ "loss": 1.0886,
444
+ "step": 62
445
+ },
446
+ {
447
+ "epoch": 0.2016,
448
+ "grad_norm": 0.1727771908044815,
449
+ "learning_rate": 0.000162987012987013,
450
+ "loss": 1.0432,
451
+ "step": 63
452
+ },
453
+ {
454
+ "epoch": 0.2048,
455
+ "grad_norm": 0.17827573418617249,
456
+ "learning_rate": 0.00016233766233766234,
457
+ "loss": 1.083,
458
+ "step": 64
459
+ },
460
+ {
461
+ "epoch": 0.208,
462
+ "grad_norm": 0.19807517528533936,
463
+ "learning_rate": 0.0001616883116883117,
464
+ "loss": 1.1208,
465
+ "step": 65
466
+ },
467
+ {
468
+ "epoch": 0.2112,
469
+ "grad_norm": 0.17693684995174408,
470
+ "learning_rate": 0.00016103896103896104,
471
+ "loss": 1.089,
472
+ "step": 66
473
+ },
474
+ {
475
+ "epoch": 0.2144,
476
+ "grad_norm": 0.15489234030246735,
477
+ "learning_rate": 0.0001603896103896104,
478
+ "loss": 0.9707,
479
+ "step": 67
480
+ },
481
+ {
482
+ "epoch": 0.2176,
483
+ "grad_norm": 0.16443990170955658,
484
+ "learning_rate": 0.00015974025974025975,
485
+ "loss": 1.0643,
486
+ "step": 68
487
+ },
488
+ {
489
+ "epoch": 0.2208,
490
+ "grad_norm": 0.2051103413105011,
491
+ "learning_rate": 0.0001590909090909091,
492
+ "loss": 1.1246,
493
+ "step": 69
494
+ },
495
+ {
496
+ "epoch": 0.224,
497
+ "grad_norm": 0.18824075162410736,
498
+ "learning_rate": 0.00015844155844155845,
499
+ "loss": 1.0855,
500
+ "step": 70
501
+ },
502
+ {
503
+ "epoch": 0.2272,
504
+ "grad_norm": 0.18659448623657227,
505
+ "learning_rate": 0.0001577922077922078,
506
+ "loss": 1.1412,
507
+ "step": 71
508
+ },
509
+ {
510
+ "epoch": 0.2304,
511
+ "grad_norm": 0.1854114979505539,
512
+ "learning_rate": 0.00015714285714285716,
513
+ "loss": 1.0249,
514
+ "step": 72
515
+ },
516
+ {
517
+ "epoch": 0.2336,
518
+ "grad_norm": 0.1876193732023239,
519
+ "learning_rate": 0.00015649350649350649,
520
+ "loss": 1.1029,
521
+ "step": 73
522
+ },
523
+ {
524
+ "epoch": 0.2368,
525
+ "grad_norm": 0.1888684630393982,
526
+ "learning_rate": 0.00015584415584415587,
527
+ "loss": 1.0789,
528
+ "step": 74
529
+ },
530
+ {
531
+ "epoch": 0.24,
532
+ "grad_norm": 0.20240606367588043,
533
+ "learning_rate": 0.0001551948051948052,
534
+ "loss": 1.0495,
535
+ "step": 75
536
+ },
537
+ {
538
+ "epoch": 0.2432,
539
+ "grad_norm": 0.232120081782341,
540
+ "learning_rate": 0.00015454545454545454,
541
+ "loss": 1.0735,
542
+ "step": 76
543
+ },
544
+ {
545
+ "epoch": 0.2464,
546
+ "grad_norm": 0.16897843778133392,
547
+ "learning_rate": 0.0001538961038961039,
548
+ "loss": 1.0164,
549
+ "step": 77
550
+ },
551
+ {
552
+ "epoch": 0.2496,
553
+ "grad_norm": 0.18796634674072266,
554
+ "learning_rate": 0.00015324675324675325,
555
+ "loss": 1.0676,
556
+ "step": 78
557
+ },
558
+ {
559
+ "epoch": 0.2528,
560
+ "grad_norm": 0.19574032723903656,
561
+ "learning_rate": 0.0001525974025974026,
562
+ "loss": 1.0456,
563
+ "step": 79
564
+ },
565
+ {
566
+ "epoch": 0.256,
567
+ "grad_norm": 0.18007811903953552,
568
+ "learning_rate": 0.00015194805194805196,
569
+ "loss": 1.0894,
570
+ "step": 80
571
+ },
572
+ {
573
+ "epoch": 0.2592,
574
+ "grad_norm": 0.18932929635047913,
575
+ "learning_rate": 0.0001512987012987013,
576
+ "loss": 1.0729,
577
+ "step": 81
578
+ },
579
+ {
580
+ "epoch": 0.2624,
581
+ "grad_norm": 0.20614288747310638,
582
+ "learning_rate": 0.00015064935064935066,
583
+ "loss": 1.0854,
584
+ "step": 82
585
+ },
586
+ {
587
+ "epoch": 0.2656,
588
+ "grad_norm": 0.19291089475154877,
589
+ "learning_rate": 0.00015000000000000001,
590
+ "loss": 1.1217,
591
+ "step": 83
592
+ },
593
+ {
594
+ "epoch": 0.2688,
595
+ "grad_norm": 0.18916529417037964,
596
+ "learning_rate": 0.00014935064935064934,
597
+ "loss": 1.0963,
598
+ "step": 84
599
+ },
600
+ {
601
+ "epoch": 0.272,
602
+ "grad_norm": 0.20306220650672913,
603
+ "learning_rate": 0.00014870129870129872,
604
+ "loss": 1.0898,
605
+ "step": 85
606
+ },
607
+ {
608
+ "epoch": 0.2752,
609
+ "grad_norm": 0.17870067059993744,
610
+ "learning_rate": 0.00014805194805194807,
611
+ "loss": 1.0213,
612
+ "step": 86
613
+ },
614
+ {
615
+ "epoch": 0.2784,
616
+ "grad_norm": 0.18411923944950104,
617
+ "learning_rate": 0.0001474025974025974,
618
+ "loss": 1.0844,
619
+ "step": 87
620
+ },
621
+ {
622
+ "epoch": 0.2816,
623
+ "grad_norm": 0.18788227438926697,
624
+ "learning_rate": 0.00014675324675324675,
625
+ "loss": 1.0338,
626
+ "step": 88
627
+ },
628
+ {
629
+ "epoch": 0.2848,
630
+ "grad_norm": 0.23874884843826294,
631
+ "learning_rate": 0.00014610389610389613,
632
+ "loss": 1.1118,
633
+ "step": 89
634
+ },
635
+ {
636
+ "epoch": 0.288,
637
+ "grad_norm": 0.19380499422550201,
638
+ "learning_rate": 0.00014545454545454546,
639
+ "loss": 1.0464,
640
+ "step": 90
641
+ },
642
+ {
643
+ "epoch": 0.2912,
644
+ "grad_norm": 0.18968750536441803,
645
+ "learning_rate": 0.0001448051948051948,
646
+ "loss": 1.0569,
647
+ "step": 91
648
+ },
649
+ {
650
+ "epoch": 0.2944,
651
+ "grad_norm": 0.19545753300189972,
652
+ "learning_rate": 0.00014415584415584416,
653
+ "loss": 1.1225,
654
+ "step": 92
655
+ },
656
+ {
657
+ "epoch": 0.2976,
658
+ "grad_norm": 0.19170494377613068,
659
+ "learning_rate": 0.00014350649350649352,
660
+ "loss": 1.0602,
661
+ "step": 93
662
+ },
663
+ {
664
+ "epoch": 0.3008,
665
+ "grad_norm": 0.17953918874263763,
666
+ "learning_rate": 0.00014285714285714287,
667
+ "loss": 1.032,
668
+ "step": 94
669
+ },
670
+ {
671
+ "epoch": 0.304,
672
+ "grad_norm": 0.1822536289691925,
673
+ "learning_rate": 0.00014220779220779222,
674
+ "loss": 1.0559,
675
+ "step": 95
676
+ },
677
+ {
678
+ "epoch": 0.3072,
679
+ "grad_norm": 0.18591298162937164,
680
+ "learning_rate": 0.00014155844155844155,
681
+ "loss": 1.031,
682
+ "step": 96
683
+ },
684
+ {
685
+ "epoch": 0.3104,
686
+ "grad_norm": 0.2129002958536148,
687
+ "learning_rate": 0.00014090909090909093,
688
+ "loss": 1.1391,
689
+ "step": 97
690
+ },
691
+ {
692
+ "epoch": 0.3136,
693
+ "grad_norm": 0.18386681377887726,
694
+ "learning_rate": 0.00014025974025974028,
695
+ "loss": 0.9919,
696
+ "step": 98
697
+ },
698
+ {
699
+ "epoch": 0.3168,
700
+ "grad_norm": 0.18314239382743835,
701
+ "learning_rate": 0.0001396103896103896,
702
+ "loss": 1.0445,
703
+ "step": 99
704
+ },
705
+ {
706
+ "epoch": 0.32,
707
+ "grad_norm": 0.1999066174030304,
708
+ "learning_rate": 0.00013896103896103896,
709
+ "loss": 1.0538,
710
+ "step": 100
711
+ },
712
+ {
713
+ "epoch": 0.3232,
714
+ "grad_norm": 0.18741188943386078,
715
+ "learning_rate": 0.00013831168831168834,
716
+ "loss": 1.0722,
717
+ "step": 101
718
+ },
719
+ {
720
+ "epoch": 0.3264,
721
+ "grad_norm": 0.19351010024547577,
722
+ "learning_rate": 0.00013766233766233766,
723
+ "loss": 1.0491,
724
+ "step": 102
725
+ },
726
+ {
727
+ "epoch": 0.3296,
728
+ "grad_norm": 0.18859203159809113,
729
+ "learning_rate": 0.00013701298701298702,
730
+ "loss": 1.0593,
731
+ "step": 103
732
+ },
733
+ {
734
+ "epoch": 0.3328,
735
+ "grad_norm": 0.1962767392396927,
736
+ "learning_rate": 0.00013636363636363637,
737
+ "loss": 1.1344,
738
+ "step": 104
739
+ },
740
+ {
741
+ "epoch": 0.336,
742
+ "grad_norm": 0.20819440484046936,
743
+ "learning_rate": 0.00013571428571428572,
744
+ "loss": 1.1137,
745
+ "step": 105
746
+ },
747
+ {
748
+ "epoch": 0.3392,
749
+ "grad_norm": 0.19590184092521667,
750
+ "learning_rate": 0.00013506493506493507,
751
+ "loss": 1.0624,
752
+ "step": 106
753
+ },
754
+ {
755
+ "epoch": 0.3424,
756
+ "grad_norm": 0.18631424009799957,
757
+ "learning_rate": 0.00013441558441558443,
758
+ "loss": 1.0587,
759
+ "step": 107
760
+ },
761
+ {
762
+ "epoch": 0.3456,
763
+ "grad_norm": 0.19572143256664276,
764
+ "learning_rate": 0.00013376623376623375,
765
+ "loss": 1.0494,
766
+ "step": 108
767
+ },
768
+ {
769
+ "epoch": 0.3488,
770
+ "grad_norm": 0.1910988837480545,
771
+ "learning_rate": 0.00013311688311688313,
772
+ "loss": 1.0481,
773
+ "step": 109
774
+ },
775
+ {
776
+ "epoch": 0.352,
777
+ "grad_norm": 0.19455869495868683,
778
+ "learning_rate": 0.00013246753246753249,
779
+ "loss": 1.029,
780
+ "step": 110
781
+ },
782
+ {
783
+ "epoch": 0.3552,
784
+ "grad_norm": 0.18669827282428741,
785
+ "learning_rate": 0.0001318181818181818,
786
+ "loss": 1.0513,
787
+ "step": 111
788
+ },
789
+ {
790
+ "epoch": 0.3584,
791
+ "grad_norm": 0.17523664236068726,
792
+ "learning_rate": 0.0001311688311688312,
793
+ "loss": 1.0126,
794
+ "step": 112
795
+ },
796
+ {
797
+ "epoch": 0.3616,
798
+ "grad_norm": 0.17929129302501678,
799
+ "learning_rate": 0.00013051948051948052,
800
+ "loss": 1.0717,
801
+ "step": 113
802
+ },
803
+ {
804
+ "epoch": 0.3648,
805
+ "grad_norm": 0.19380168616771698,
806
+ "learning_rate": 0.00012987012987012987,
807
+ "loss": 1.0324,
808
+ "step": 114
809
+ },
810
+ {
811
+ "epoch": 0.368,
812
+ "grad_norm": 0.18090228736400604,
813
+ "learning_rate": 0.00012922077922077922,
814
+ "loss": 1.0515,
815
+ "step": 115
816
+ },
817
+ {
818
+ "epoch": 0.3712,
819
+ "grad_norm": 0.2067340910434723,
820
+ "learning_rate": 0.00012857142857142858,
821
+ "loss": 1.0939,
822
+ "step": 116
823
+ },
824
+ {
825
+ "epoch": 0.3744,
826
+ "grad_norm": 0.1880485862493515,
827
+ "learning_rate": 0.00012792207792207793,
828
+ "loss": 1.0986,
829
+ "step": 117
830
+ },
831
+ {
832
+ "epoch": 0.3776,
833
+ "grad_norm": 0.182168647646904,
834
+ "learning_rate": 0.00012727272727272728,
835
+ "loss": 1.0109,
836
+ "step": 118
837
+ },
838
+ {
839
+ "epoch": 0.3808,
840
+ "grad_norm": 0.20187129080295563,
841
+ "learning_rate": 0.00012662337662337663,
842
+ "loss": 1.0668,
843
+ "step": 119
844
+ },
845
+ {
846
+ "epoch": 0.384,
847
+ "grad_norm": 0.2082669734954834,
848
+ "learning_rate": 0.000125974025974026,
849
+ "loss": 1.054,
850
+ "step": 120
851
+ },
852
+ {
853
+ "epoch": 0.3872,
854
+ "grad_norm": 0.18294434249401093,
855
+ "learning_rate": 0.00012532467532467534,
856
+ "loss": 1.0397,
857
+ "step": 121
858
+ },
859
+ {
860
+ "epoch": 0.3904,
861
+ "grad_norm": 0.20515067875385284,
862
+ "learning_rate": 0.00012467532467532467,
863
+ "loss": 1.1092,
864
+ "step": 122
865
+ },
866
+ {
867
+ "epoch": 0.3936,
868
+ "grad_norm": 0.1758790761232376,
869
+ "learning_rate": 0.00012402597402597402,
870
+ "loss": 0.9755,
871
+ "step": 123
872
+ },
873
+ {
874
+ "epoch": 0.3968,
875
+ "grad_norm": 0.2170792669057846,
876
+ "learning_rate": 0.0001233766233766234,
877
+ "loss": 1.0434,
878
+ "step": 124
879
+ },
880
+ {
881
+ "epoch": 0.4,
882
+ "grad_norm": 0.202157124876976,
883
+ "learning_rate": 0.00012272727272727272,
884
+ "loss": 1.1129,
885
+ "step": 125
886
+ },
887
+ {
888
+ "epoch": 0.4032,
889
+ "grad_norm": 0.18556398153305054,
890
+ "learning_rate": 0.00012207792207792208,
891
+ "loss": 1.0665,
892
+ "step": 126
893
+ },
894
+ {
895
+ "epoch": 0.4064,
896
+ "grad_norm": 0.20196087658405304,
897
+ "learning_rate": 0.00012142857142857143,
898
+ "loss": 1.1,
899
+ "step": 127
900
+ },
901
+ {
902
+ "epoch": 0.4096,
903
+ "grad_norm": 0.1921566128730774,
904
+ "learning_rate": 0.0001207792207792208,
905
+ "loss": 1.0918,
906
+ "step": 128
907
+ },
908
+ {
909
+ "epoch": 0.4128,
910
+ "grad_norm": 0.18866224586963654,
911
+ "learning_rate": 0.00012012987012987014,
912
+ "loss": 1.0014,
913
+ "step": 129
914
+ },
915
+ {
916
+ "epoch": 0.416,
917
+ "grad_norm": 0.207601398229599,
918
+ "learning_rate": 0.00011948051948051949,
919
+ "loss": 1.0726,
920
+ "step": 130
921
+ },
922
+ {
923
+ "epoch": 0.4192,
924
+ "grad_norm": 0.21592366695404053,
925
+ "learning_rate": 0.00011883116883116883,
926
+ "loss": 1.1379,
927
+ "step": 131
928
+ },
929
+ {
930
+ "epoch": 0.4224,
931
+ "grad_norm": 0.2016124576330185,
932
+ "learning_rate": 0.0001181818181818182,
933
+ "loss": 1.1428,
934
+ "step": 132
935
+ },
936
+ {
937
+ "epoch": 0.4256,
938
+ "grad_norm": 0.20478437840938568,
939
+ "learning_rate": 0.00011753246753246753,
940
+ "loss": 1.121,
941
+ "step": 133
942
+ },
943
+ {
944
+ "epoch": 0.4288,
945
+ "grad_norm": 0.22730594873428345,
946
+ "learning_rate": 0.00011688311688311689,
947
+ "loss": 1.0319,
948
+ "step": 134
949
+ },
950
+ {
951
+ "epoch": 0.432,
952
+ "grad_norm": 0.22592711448669434,
953
+ "learning_rate": 0.00011623376623376625,
954
+ "loss": 1.1264,
955
+ "step": 135
956
+ },
957
+ {
958
+ "epoch": 0.4352,
959
+ "grad_norm": 0.20035041868686676,
960
+ "learning_rate": 0.00011558441558441559,
961
+ "loss": 1.0686,
962
+ "step": 136
963
+ },
964
+ {
965
+ "epoch": 0.4384,
966
+ "grad_norm": 0.20648567378520966,
967
+ "learning_rate": 0.00011493506493506494,
968
+ "loss": 1.0817,
969
+ "step": 137
970
+ },
971
+ {
972
+ "epoch": 0.4416,
973
+ "grad_norm": 0.21222743391990662,
974
+ "learning_rate": 0.00011428571428571428,
975
+ "loss": 1.0678,
976
+ "step": 138
977
+ },
978
+ {
979
+ "epoch": 0.4448,
980
+ "grad_norm": 0.2075391560792923,
981
+ "learning_rate": 0.00011363636363636365,
982
+ "loss": 1.0897,
983
+ "step": 139
984
+ },
985
+ {
986
+ "epoch": 0.448,
987
+ "grad_norm": 0.1964101791381836,
988
+ "learning_rate": 0.000112987012987013,
989
+ "loss": 1.0906,
990
+ "step": 140
991
+ },
992
+ {
993
+ "epoch": 0.4512,
994
+ "grad_norm": 0.22406511008739471,
995
+ "learning_rate": 0.00011233766233766234,
996
+ "loss": 1.0594,
997
+ "step": 141
998
+ },
999
+ {
1000
+ "epoch": 0.4544,
1001
+ "grad_norm": 0.23787978291511536,
1002
+ "learning_rate": 0.00011168831168831168,
1003
+ "loss": 1.1053,
1004
+ "step": 142
1005
+ },
1006
+ {
1007
+ "epoch": 0.4576,
1008
+ "grad_norm": 0.21196185052394867,
1009
+ "learning_rate": 0.00011103896103896105,
1010
+ "loss": 1.0923,
1011
+ "step": 143
1012
+ },
1013
+ {
1014
+ "epoch": 0.4608,
1015
+ "grad_norm": 0.21042804419994354,
1016
+ "learning_rate": 0.0001103896103896104,
1017
+ "loss": 1.0381,
1018
+ "step": 144
1019
+ },
1020
+ {
1021
+ "epoch": 0.464,
1022
+ "grad_norm": 0.2267436534166336,
1023
+ "learning_rate": 0.00010974025974025974,
1024
+ "loss": 1.0818,
1025
+ "step": 145
1026
+ },
1027
+ {
1028
+ "epoch": 0.4672,
1029
+ "grad_norm": 0.23742735385894775,
1030
+ "learning_rate": 0.00010909090909090909,
1031
+ "loss": 1.0872,
1032
+ "step": 146
1033
+ },
1034
+ {
1035
+ "epoch": 0.4704,
1036
+ "grad_norm": 0.17787213623523712,
1037
+ "learning_rate": 0.00010844155844155846,
1038
+ "loss": 1.03,
1039
+ "step": 147
1040
+ },
1041
+ {
1042
+ "epoch": 0.4736,
1043
+ "grad_norm": 0.22422832250595093,
1044
+ "learning_rate": 0.0001077922077922078,
1045
+ "loss": 1.0738,
1046
+ "step": 148
1047
+ },
1048
+ {
1049
+ "epoch": 0.4768,
1050
+ "grad_norm": 0.22946301102638245,
1051
+ "learning_rate": 0.00010714285714285715,
1052
+ "loss": 1.0274,
1053
+ "step": 149
1054
+ },
1055
+ {
1056
+ "epoch": 0.48,
1057
+ "grad_norm": 0.2137996405363083,
1058
+ "learning_rate": 0.00010649350649350649,
1059
+ "loss": 1.0539,
1060
+ "step": 150
1061
+ },
1062
+ {
1063
+ "epoch": 0.4832,
1064
+ "grad_norm": 0.1748756766319275,
1065
+ "learning_rate": 0.00010584415584415586,
1066
+ "loss": 1.0355,
1067
+ "step": 151
1068
+ },
1069
+ {
1070
+ "epoch": 0.4864,
1071
+ "grad_norm": 0.22275175154209137,
1072
+ "learning_rate": 0.0001051948051948052,
1073
+ "loss": 1.1696,
1074
+ "step": 152
1075
+ },
1076
+ {
1077
+ "epoch": 0.4896,
1078
+ "grad_norm": 0.20996077358722687,
1079
+ "learning_rate": 0.00010454545454545455,
1080
+ "loss": 1.0303,
1081
+ "step": 153
1082
+ },
1083
+ {
1084
+ "epoch": 0.4928,
1085
+ "grad_norm": 0.1945938766002655,
1086
+ "learning_rate": 0.00010389610389610389,
1087
+ "loss": 0.9747,
1088
+ "step": 154
1089
+ },
1090
+ {
1091
+ "epoch": 0.496,
1092
+ "grad_norm": 0.1970377266407013,
1093
+ "learning_rate": 0.00010324675324675325,
1094
+ "loss": 1.0358,
1095
+ "step": 155
1096
+ },
1097
+ {
1098
+ "epoch": 0.4992,
1099
+ "grad_norm": 0.18814732134342194,
1100
+ "learning_rate": 0.00010259740259740261,
1101
+ "loss": 0.9612,
1102
+ "step": 156
1103
+ },
1104
+ {
1105
+ "epoch": 0.5024,
1106
+ "grad_norm": 0.2153233289718628,
1107
+ "learning_rate": 0.00010194805194805195,
1108
+ "loss": 1.0749,
1109
+ "step": 157
1110
+ },
1111
+ {
1112
+ "epoch": 0.5056,
1113
+ "grad_norm": 0.21788008511066437,
1114
+ "learning_rate": 0.0001012987012987013,
1115
+ "loss": 1.0883,
1116
+ "step": 158
1117
+ },
1118
+ {
1119
+ "epoch": 0.5088,
1120
+ "grad_norm": 0.214650496840477,
1121
+ "learning_rate": 0.00010064935064935067,
1122
+ "loss": 1.0539,
1123
+ "step": 159
1124
+ },
1125
+ {
1126
+ "epoch": 0.512,
1127
+ "grad_norm": 0.19312834739685059,
1128
+ "learning_rate": 0.0001,
1129
+ "loss": 1.0657,
1130
+ "step": 160
1131
+ },
1132
+ {
1133
+ "epoch": 0.5152,
1134
+ "grad_norm": 0.19916598498821259,
1135
+ "learning_rate": 9.935064935064936e-05,
1136
+ "loss": 1.0478,
1137
+ "step": 161
1138
+ },
1139
+ {
1140
+ "epoch": 0.5184,
1141
+ "grad_norm": 0.2057606726884842,
1142
+ "learning_rate": 9.870129870129871e-05,
1143
+ "loss": 1.0094,
1144
+ "step": 162
1145
+ },
1146
+ {
1147
+ "epoch": 0.5216,
1148
+ "grad_norm": 0.22159607708454132,
1149
+ "learning_rate": 9.805194805194806e-05,
1150
+ "loss": 1.0952,
1151
+ "step": 163
1152
+ },
1153
+ {
1154
+ "epoch": 0.5248,
1155
+ "grad_norm": 0.18274275958538055,
1156
+ "learning_rate": 9.74025974025974e-05,
1157
+ "loss": 1.0065,
1158
+ "step": 164
1159
+ },
1160
+ {
1161
+ "epoch": 0.528,
1162
+ "grad_norm": 0.19835162162780762,
1163
+ "learning_rate": 9.675324675324677e-05,
1164
+ "loss": 1.0742,
1165
+ "step": 165
1166
+ },
1167
+ {
1168
+ "epoch": 0.5312,
1169
+ "grad_norm": 0.2114904820919037,
1170
+ "learning_rate": 9.610389610389611e-05,
1171
+ "loss": 1.1109,
1172
+ "step": 166
1173
+ },
1174
+ {
1175
+ "epoch": 0.5344,
1176
+ "grad_norm": 0.21488523483276367,
1177
+ "learning_rate": 9.545454545454546e-05,
1178
+ "loss": 1.0465,
1179
+ "step": 167
1180
+ },
1181
+ {
1182
+ "epoch": 0.5376,
1183
+ "grad_norm": 0.19870303571224213,
1184
+ "learning_rate": 9.480519480519481e-05,
1185
+ "loss": 1.0318,
1186
+ "step": 168
1187
+ },
1188
+ {
1189
+ "epoch": 0.5408,
1190
+ "grad_norm": 0.20413029193878174,
1191
+ "learning_rate": 9.415584415584417e-05,
1192
+ "loss": 1.0817,
1193
+ "step": 169
1194
+ },
1195
+ {
1196
+ "epoch": 0.544,
1197
+ "grad_norm": 0.1847231239080429,
1198
+ "learning_rate": 9.35064935064935e-05,
1199
+ "loss": 1.0144,
1200
+ "step": 170
1201
+ },
1202
+ {
1203
+ "epoch": 0.5472,
1204
+ "grad_norm": 0.2715964913368225,
1205
+ "learning_rate": 9.285714285714286e-05,
1206
+ "loss": 0.9832,
1207
+ "step": 171
1208
+ },
1209
+ {
1210
+ "epoch": 0.5504,
1211
+ "grad_norm": 0.2225002497434616,
1212
+ "learning_rate": 9.220779220779221e-05,
1213
+ "loss": 1.1051,
1214
+ "step": 172
1215
+ },
1216
+ {
1217
+ "epoch": 0.5536,
1218
+ "grad_norm": 0.22931510210037231,
1219
+ "learning_rate": 9.155844155844156e-05,
1220
+ "loss": 1.1042,
1221
+ "step": 173
1222
+ },
1223
+ {
1224
+ "epoch": 0.5568,
1225
+ "grad_norm": 0.21848627924919128,
1226
+ "learning_rate": 9.090909090909092e-05,
1227
+ "loss": 1.1151,
1228
+ "step": 174
1229
+ },
1230
+ {
1231
+ "epoch": 0.56,
1232
+ "grad_norm": 0.19852259755134583,
1233
+ "learning_rate": 9.025974025974027e-05,
1234
+ "loss": 1.0889,
1235
+ "step": 175
1236
+ },
1237
+ {
1238
+ "epoch": 0.5632,
1239
+ "grad_norm": 0.2080363780260086,
1240
+ "learning_rate": 8.961038961038961e-05,
1241
+ "loss": 1.0777,
1242
+ "step": 176
1243
+ },
1244
+ {
1245
+ "epoch": 0.5664,
1246
+ "grad_norm": 0.22391024231910706,
1247
+ "learning_rate": 8.896103896103896e-05,
1248
+ "loss": 1.1092,
1249
+ "step": 177
1250
+ },
1251
+ {
1252
+ "epoch": 0.5696,
1253
+ "grad_norm": 0.21793846786022186,
1254
+ "learning_rate": 8.831168831168831e-05,
1255
+ "loss": 1.044,
1256
+ "step": 178
1257
+ },
1258
+ {
1259
+ "epoch": 0.5728,
1260
+ "grad_norm": 0.2009749859571457,
1261
+ "learning_rate": 8.766233766233767e-05,
1262
+ "loss": 1.0198,
1263
+ "step": 179
1264
+ },
1265
+ {
1266
+ "epoch": 0.576,
1267
+ "grad_norm": 0.19432318210601807,
1268
+ "learning_rate": 8.701298701298701e-05,
1269
+ "loss": 1.075,
1270
+ "step": 180
1271
+ },
1272
+ {
1273
+ "epoch": 0.5792,
1274
+ "grad_norm": 0.18634547293186188,
1275
+ "learning_rate": 8.636363636363637e-05,
1276
+ "loss": 0.9964,
1277
+ "step": 181
1278
+ },
1279
+ {
1280
+ "epoch": 0.5824,
1281
+ "grad_norm": 0.1947103589773178,
1282
+ "learning_rate": 8.571428571428571e-05,
1283
+ "loss": 1.0025,
1284
+ "step": 182
1285
+ },
1286
+ {
1287
+ "epoch": 0.5856,
1288
+ "grad_norm": 0.23098671436309814,
1289
+ "learning_rate": 8.506493506493507e-05,
1290
+ "loss": 1.0562,
1291
+ "step": 183
1292
+ },
1293
+ {
1294
+ "epoch": 0.5888,
1295
+ "grad_norm": 0.19686414301395416,
1296
+ "learning_rate": 8.441558441558442e-05,
1297
+ "loss": 1.0285,
1298
+ "step": 184
1299
+ },
1300
+ {
1301
+ "epoch": 0.592,
1302
+ "grad_norm": 0.19852428138256073,
1303
+ "learning_rate": 8.376623376623377e-05,
1304
+ "loss": 1.0054,
1305
+ "step": 185
1306
+ },
1307
+ {
1308
+ "epoch": 0.5952,
1309
+ "grad_norm": 0.21483510732650757,
1310
+ "learning_rate": 8.311688311688312e-05,
1311
+ "loss": 1.108,
1312
+ "step": 186
1313
+ },
1314
+ {
1315
+ "epoch": 0.5984,
1316
+ "grad_norm": 0.23313644528388977,
1317
+ "learning_rate": 8.246753246753248e-05,
1318
+ "loss": 1.1383,
1319
+ "step": 187
1320
+ },
1321
+ {
1322
+ "epoch": 0.6016,
1323
+ "grad_norm": 0.21453145146369934,
1324
+ "learning_rate": 8.181818181818183e-05,
1325
+ "loss": 1.0911,
1326
+ "step": 188
1327
+ },
1328
+ {
1329
+ "epoch": 0.6048,
1330
+ "grad_norm": 0.20268195867538452,
1331
+ "learning_rate": 8.116883116883117e-05,
1332
+ "loss": 1.0145,
1333
+ "step": 189
1334
+ },
1335
+ {
1336
+ "epoch": 0.608,
1337
+ "grad_norm": 0.20576398074626923,
1338
+ "learning_rate": 8.051948051948052e-05,
1339
+ "loss": 1.0829,
1340
+ "step": 190
1341
+ },
1342
+ {
1343
+ "epoch": 0.6112,
1344
+ "grad_norm": 0.21732626855373383,
1345
+ "learning_rate": 7.987012987012987e-05,
1346
+ "loss": 1.0152,
1347
+ "step": 191
1348
+ },
1349
+ {
1350
+ "epoch": 0.6144,
1351
+ "grad_norm": 0.22046895325183868,
1352
+ "learning_rate": 7.922077922077923e-05,
1353
+ "loss": 1.1311,
1354
+ "step": 192
1355
+ },
1356
+ {
1357
+ "epoch": 0.6176,
1358
+ "grad_norm": 0.19727715849876404,
1359
+ "learning_rate": 7.857142857142858e-05,
1360
+ "loss": 1.0364,
1361
+ "step": 193
1362
+ },
1363
+ {
1364
+ "epoch": 0.6208,
1365
+ "grad_norm": 0.20861488580703735,
1366
+ "learning_rate": 7.792207792207793e-05,
1367
+ "loss": 1.0435,
1368
+ "step": 194
1369
+ },
1370
+ {
1371
+ "epoch": 0.624,
1372
+ "grad_norm": 0.18545083701610565,
1373
+ "learning_rate": 7.727272727272727e-05,
1374
+ "loss": 1.0299,
1375
+ "step": 195
1376
+ },
1377
+ {
1378
+ "epoch": 0.6272,
1379
+ "grad_norm": 0.19965052604675293,
1380
+ "learning_rate": 7.662337662337662e-05,
1381
+ "loss": 1.0511,
1382
+ "step": 196
1383
+ },
1384
+ {
1385
+ "epoch": 0.6304,
1386
+ "grad_norm": 0.23673909902572632,
1387
+ "learning_rate": 7.597402597402598e-05,
1388
+ "loss": 1.081,
1389
+ "step": 197
1390
+ },
1391
+ {
1392
+ "epoch": 0.6336,
1393
+ "grad_norm": 0.17583179473876953,
1394
+ "learning_rate": 7.532467532467533e-05,
1395
+ "loss": 0.9808,
1396
+ "step": 198
1397
+ },
1398
+ {
1399
+ "epoch": 0.6368,
1400
+ "grad_norm": 0.2129366099834442,
1401
+ "learning_rate": 7.467532467532467e-05,
1402
+ "loss": 1.0522,
1403
+ "step": 199
1404
+ },
1405
+ {
1406
+ "epoch": 0.64,
1407
+ "grad_norm": 0.21679140627384186,
1408
+ "learning_rate": 7.402597402597404e-05,
1409
+ "loss": 1.0567,
1410
+ "step": 200
1411
+ },
1412
+ {
1413
+ "epoch": 0.6432,
1414
+ "grad_norm": 0.2032000720500946,
1415
+ "learning_rate": 7.337662337662338e-05,
1416
+ "loss": 1.0466,
1417
+ "step": 201
1418
+ },
1419
+ {
1420
+ "epoch": 0.6464,
1421
+ "grad_norm": 0.1887970268726349,
1422
+ "learning_rate": 7.272727272727273e-05,
1423
+ "loss": 1.0329,
1424
+ "step": 202
1425
+ },
1426
+ {
1427
+ "epoch": 0.6496,
1428
+ "grad_norm": 0.21060192584991455,
1429
+ "learning_rate": 7.207792207792208e-05,
1430
+ "loss": 1.1021,
1431
+ "step": 203
1432
+ },
1433
+ {
1434
+ "epoch": 0.6528,
1435
+ "grad_norm": 0.21191425621509552,
1436
+ "learning_rate": 7.142857142857143e-05,
1437
+ "loss": 0.99,
1438
+ "step": 204
1439
+ },
1440
+ {
1441
+ "epoch": 0.656,
1442
+ "grad_norm": 0.1995989829301834,
1443
+ "learning_rate": 7.077922077922077e-05,
1444
+ "loss": 1.0526,
1445
+ "step": 205
1446
+ },
1447
+ {
1448
+ "epoch": 0.6592,
1449
+ "grad_norm": 0.1849513053894043,
1450
+ "learning_rate": 7.012987012987014e-05,
1451
+ "loss": 0.9998,
1452
+ "step": 206
1453
+ },
1454
+ {
1455
+ "epoch": 0.6624,
1456
+ "grad_norm": 0.1948779672384262,
1457
+ "learning_rate": 6.948051948051948e-05,
1458
+ "loss": 1.075,
1459
+ "step": 207
1460
+ },
1461
+ {
1462
+ "epoch": 0.6656,
1463
+ "grad_norm": 0.20374052226543427,
1464
+ "learning_rate": 6.883116883116883e-05,
1465
+ "loss": 1.0933,
1466
+ "step": 208
1467
+ },
1468
+ {
1469
+ "epoch": 0.6688,
1470
+ "grad_norm": 0.2102465033531189,
1471
+ "learning_rate": 6.818181818181818e-05,
1472
+ "loss": 1.1123,
1473
+ "step": 209
1474
+ },
1475
+ {
1476
+ "epoch": 0.672,
1477
+ "grad_norm": 0.21376173198223114,
1478
+ "learning_rate": 6.753246753246754e-05,
1479
+ "loss": 1.1233,
1480
+ "step": 210
1481
+ },
1482
+ {
1483
+ "epoch": 0.6752,
1484
+ "grad_norm": 0.20934203267097473,
1485
+ "learning_rate": 6.688311688311688e-05,
1486
+ "loss": 1.1374,
1487
+ "step": 211
1488
+ },
1489
+ {
1490
+ "epoch": 0.6784,
1491
+ "grad_norm": 0.18604128062725067,
1492
+ "learning_rate": 6.623376623376624e-05,
1493
+ "loss": 1.0213,
1494
+ "step": 212
1495
+ },
1496
+ {
1497
+ "epoch": 0.6816,
1498
+ "grad_norm": 0.19644233584403992,
1499
+ "learning_rate": 6.55844155844156e-05,
1500
+ "loss": 1.0046,
1501
+ "step": 213
1502
+ },
1503
+ {
1504
+ "epoch": 0.6848,
1505
+ "grad_norm": 0.18479463458061218,
1506
+ "learning_rate": 6.493506493506494e-05,
1507
+ "loss": 0.9792,
1508
+ "step": 214
1509
+ },
1510
+ {
1511
+ "epoch": 0.688,
1512
+ "grad_norm": 0.1945149153470993,
1513
+ "learning_rate": 6.428571428571429e-05,
1514
+ "loss": 1.0584,
1515
+ "step": 215
1516
+ },
1517
+ {
1518
+ "epoch": 0.6912,
1519
+ "grad_norm": 0.2070147544145584,
1520
+ "learning_rate": 6.363636363636364e-05,
1521
+ "loss": 1.071,
1522
+ "step": 216
1523
+ },
1524
+ {
1525
+ "epoch": 0.6944,
1526
+ "grad_norm": 0.19645985960960388,
1527
+ "learning_rate": 6.2987012987013e-05,
1528
+ "loss": 1.0721,
1529
+ "step": 217
1530
+ },
1531
+ {
1532
+ "epoch": 0.6976,
1533
+ "grad_norm": 0.1960117667913437,
1534
+ "learning_rate": 6.233766233766233e-05,
1535
+ "loss": 1.071,
1536
+ "step": 218
1537
+ },
1538
+ {
1539
+ "epoch": 0.7008,
1540
+ "grad_norm": 0.20168261229991913,
1541
+ "learning_rate": 6.16883116883117e-05,
1542
+ "loss": 1.0808,
1543
+ "step": 219
1544
+ },
1545
+ {
1546
+ "epoch": 0.704,
1547
+ "grad_norm": 0.21254412829875946,
1548
+ "learning_rate": 6.103896103896104e-05,
1549
+ "loss": 1.0287,
1550
+ "step": 220
1551
+ },
1552
+ {
1553
+ "epoch": 0.7072,
1554
+ "grad_norm": 0.21271063387393951,
1555
+ "learning_rate": 6.03896103896104e-05,
1556
+ "loss": 1.0605,
1557
+ "step": 221
1558
+ },
1559
+ {
1560
+ "epoch": 0.7104,
1561
+ "grad_norm": 0.2081408053636551,
1562
+ "learning_rate": 5.9740259740259744e-05,
1563
+ "loss": 1.091,
1564
+ "step": 222
1565
+ },
1566
+ {
1567
+ "epoch": 0.7136,
1568
+ "grad_norm": 0.21113798022270203,
1569
+ "learning_rate": 5.90909090909091e-05,
1570
+ "loss": 1.1323,
1571
+ "step": 223
1572
+ },
1573
+ {
1574
+ "epoch": 0.7168,
1575
+ "grad_norm": 0.20670844614505768,
1576
+ "learning_rate": 5.844155844155844e-05,
1577
+ "loss": 1.0955,
1578
+ "step": 224
1579
+ },
1580
+ {
1581
+ "epoch": 0.72,
1582
+ "grad_norm": 0.2010120451450348,
1583
+ "learning_rate": 5.7792207792207796e-05,
1584
+ "loss": 1.1068,
1585
+ "step": 225
1586
+ },
1587
+ {
1588
+ "epoch": 0.7232,
1589
+ "grad_norm": 0.20379121601581573,
1590
+ "learning_rate": 5.714285714285714e-05,
1591
+ "loss": 1.0419,
1592
+ "step": 226
1593
+ },
1594
+ {
1595
+ "epoch": 0.7264,
1596
+ "grad_norm": 0.22799807786941528,
1597
+ "learning_rate": 5.64935064935065e-05,
1598
+ "loss": 1.0904,
1599
+ "step": 227
1600
+ },
1601
+ {
1602
+ "epoch": 0.7296,
1603
+ "grad_norm": 0.2005995213985443,
1604
+ "learning_rate": 5.584415584415584e-05,
1605
+ "loss": 1.078,
1606
+ "step": 228
1607
+ },
1608
+ {
1609
+ "epoch": 0.7328,
1610
+ "grad_norm": 0.20329605042934418,
1611
+ "learning_rate": 5.51948051948052e-05,
1612
+ "loss": 1.0245,
1613
+ "step": 229
1614
+ },
1615
+ {
1616
+ "epoch": 0.736,
1617
+ "grad_norm": 0.19283504784107208,
1618
+ "learning_rate": 5.4545454545454546e-05,
1619
+ "loss": 1.0367,
1620
+ "step": 230
1621
+ },
1622
+ {
1623
+ "epoch": 0.7392,
1624
+ "grad_norm": 0.20624355971813202,
1625
+ "learning_rate": 5.38961038961039e-05,
1626
+ "loss": 1.1046,
1627
+ "step": 231
1628
+ },
1629
+ {
1630
+ "epoch": 0.7424,
1631
+ "grad_norm": 0.21362991631031036,
1632
+ "learning_rate": 5.3246753246753245e-05,
1633
+ "loss": 1.1104,
1634
+ "step": 232
1635
+ },
1636
+ {
1637
+ "epoch": 0.7456,
1638
+ "grad_norm": 0.20447863638401031,
1639
+ "learning_rate": 5.25974025974026e-05,
1640
+ "loss": 1.0514,
1641
+ "step": 233
1642
+ },
1643
+ {
1644
+ "epoch": 0.7488,
1645
+ "grad_norm": 0.1974381059408188,
1646
+ "learning_rate": 5.1948051948051944e-05,
1647
+ "loss": 1.0048,
1648
+ "step": 234
1649
+ },
1650
+ {
1651
+ "epoch": 0.752,
1652
+ "grad_norm": 0.21237170696258545,
1653
+ "learning_rate": 5.1298701298701304e-05,
1654
+ "loss": 1.1299,
1655
+ "step": 235
1656
+ },
1657
+ {
1658
+ "epoch": 0.7552,
1659
+ "grad_norm": 0.21224971115589142,
1660
+ "learning_rate": 5.064935064935065e-05,
1661
+ "loss": 1.05,
1662
+ "step": 236
1663
+ },
1664
+ {
1665
+ "epoch": 0.7584,
1666
+ "grad_norm": 0.19865018129348755,
1667
+ "learning_rate": 5e-05,
1668
+ "loss": 1.0665,
1669
+ "step": 237
1670
+ },
1671
+ {
1672
+ "epoch": 0.7616,
1673
+ "grad_norm": 0.19199275970458984,
1674
+ "learning_rate": 4.9350649350649355e-05,
1675
+ "loss": 0.9531,
1676
+ "step": 238
1677
+ },
1678
+ {
1679
+ "epoch": 0.7648,
1680
+ "grad_norm": 0.19573214650154114,
1681
+ "learning_rate": 4.87012987012987e-05,
1682
+ "loss": 1.0318,
1683
+ "step": 239
1684
+ },
1685
+ {
1686
+ "epoch": 0.768,
1687
+ "grad_norm": 0.21338805556297302,
1688
+ "learning_rate": 4.8051948051948054e-05,
1689
+ "loss": 1.0343,
1690
+ "step": 240
1691
+ },
1692
+ {
1693
+ "epoch": 0.7712,
1694
+ "grad_norm": 0.2254691869020462,
1695
+ "learning_rate": 4.740259740259741e-05,
1696
+ "loss": 1.0472,
1697
+ "step": 241
1698
+ },
1699
+ {
1700
+ "epoch": 0.7744,
1701
+ "grad_norm": 0.18101665377616882,
1702
+ "learning_rate": 4.675324675324675e-05,
1703
+ "loss": 1.017,
1704
+ "step": 242
1705
+ },
1706
+ {
1707
+ "epoch": 0.7776,
1708
+ "grad_norm": 0.22090592980384827,
1709
+ "learning_rate": 4.6103896103896106e-05,
1710
+ "loss": 1.0389,
1711
+ "step": 243
1712
+ },
1713
+ {
1714
+ "epoch": 0.7808,
1715
+ "grad_norm": 0.20865507423877716,
1716
+ "learning_rate": 4.545454545454546e-05,
1717
+ "loss": 1.0369,
1718
+ "step": 244
1719
+ },
1720
+ {
1721
+ "epoch": 0.784,
1722
+ "grad_norm": 0.21619610488414764,
1723
+ "learning_rate": 4.4805194805194805e-05,
1724
+ "loss": 1.109,
1725
+ "step": 245
1726
+ },
1727
+ {
1728
+ "epoch": 0.7872,
1729
+ "grad_norm": 0.21694771945476532,
1730
+ "learning_rate": 4.415584415584416e-05,
1731
+ "loss": 1.0525,
1732
+ "step": 246
1733
+ },
1734
+ {
1735
+ "epoch": 0.7904,
1736
+ "grad_norm": 0.2182662934064865,
1737
+ "learning_rate": 4.3506493506493503e-05,
1738
+ "loss": 1.0331,
1739
+ "step": 247
1740
+ },
1741
+ {
1742
+ "epoch": 0.7936,
1743
+ "grad_norm": 0.2026486098766327,
1744
+ "learning_rate": 4.2857142857142856e-05,
1745
+ "loss": 1.027,
1746
+ "step": 248
1747
+ },
1748
+ {
1749
+ "epoch": 0.7968,
1750
+ "grad_norm": 0.19606547057628632,
1751
+ "learning_rate": 4.220779220779221e-05,
1752
+ "loss": 1.0242,
1753
+ "step": 249
1754
+ },
1755
+ {
1756
+ "epoch": 0.8,
1757
+ "grad_norm": 0.22107470035552979,
1758
+ "learning_rate": 4.155844155844156e-05,
1759
+ "loss": 1.0924,
1760
+ "step": 250
1761
+ },
1762
+ {
1763
+ "epoch": 0.8032,
1764
+ "grad_norm": 0.19960008561611176,
1765
+ "learning_rate": 4.0909090909090915e-05,
1766
+ "loss": 1.0384,
1767
+ "step": 251
1768
+ },
1769
+ {
1770
+ "epoch": 0.8064,
1771
+ "grad_norm": 0.1945488154888153,
1772
+ "learning_rate": 4.025974025974026e-05,
1773
+ "loss": 1.0673,
1774
+ "step": 252
1775
+ },
1776
+ {
1777
+ "epoch": 0.8096,
1778
+ "grad_norm": 0.22067414224147797,
1779
+ "learning_rate": 3.9610389610389614e-05,
1780
+ "loss": 1.0426,
1781
+ "step": 253
1782
+ },
1783
+ {
1784
+ "epoch": 0.8128,
1785
+ "grad_norm": 0.19010980427265167,
1786
+ "learning_rate": 3.8961038961038966e-05,
1787
+ "loss": 1.0617,
1788
+ "step": 254
1789
+ },
1790
+ {
1791
+ "epoch": 0.816,
1792
+ "grad_norm": 0.18781176209449768,
1793
+ "learning_rate": 3.831168831168831e-05,
1794
+ "loss": 1.0243,
1795
+ "step": 255
1796
+ },
1797
+ {
1798
+ "epoch": 0.8192,
1799
+ "grad_norm": 0.20388829708099365,
1800
+ "learning_rate": 3.7662337662337665e-05,
1801
+ "loss": 1.0476,
1802
+ "step": 256
1803
+ },
1804
+ {
1805
+ "epoch": 0.8224,
1806
+ "grad_norm": 0.19911155104637146,
1807
+ "learning_rate": 3.701298701298702e-05,
1808
+ "loss": 1.0324,
1809
+ "step": 257
1810
+ },
1811
+ {
1812
+ "epoch": 0.8256,
1813
+ "grad_norm": 0.19884039461612701,
1814
+ "learning_rate": 3.6363636363636364e-05,
1815
+ "loss": 1.0242,
1816
+ "step": 258
1817
+ },
1818
+ {
1819
+ "epoch": 0.8288,
1820
+ "grad_norm": 0.19036105275154114,
1821
+ "learning_rate": 3.571428571428572e-05,
1822
+ "loss": 1.0323,
1823
+ "step": 259
1824
+ },
1825
+ {
1826
+ "epoch": 0.832,
1827
+ "grad_norm": 0.20039844512939453,
1828
+ "learning_rate": 3.506493506493507e-05,
1829
+ "loss": 1.0749,
1830
+ "step": 260
1831
+ },
1832
+ {
1833
+ "epoch": 0.8352,
1834
+ "grad_norm": 0.1899934560060501,
1835
+ "learning_rate": 3.4415584415584416e-05,
1836
+ "loss": 1.0115,
1837
+ "step": 261
1838
+ },
1839
+ {
1840
+ "epoch": 0.8384,
1841
+ "grad_norm": 0.20019090175628662,
1842
+ "learning_rate": 3.376623376623377e-05,
1843
+ "loss": 1.0782,
1844
+ "step": 262
1845
+ },
1846
+ {
1847
+ "epoch": 0.8416,
1848
+ "grad_norm": 0.2020583152770996,
1849
+ "learning_rate": 3.311688311688312e-05,
1850
+ "loss": 1.0687,
1851
+ "step": 263
1852
+ },
1853
+ {
1854
+ "epoch": 0.8448,
1855
+ "grad_norm": 0.21407337486743927,
1856
+ "learning_rate": 3.246753246753247e-05,
1857
+ "loss": 1.1015,
1858
+ "step": 264
1859
+ },
1860
+ {
1861
+ "epoch": 0.848,
1862
+ "grad_norm": 0.1871640682220459,
1863
+ "learning_rate": 3.181818181818182e-05,
1864
+ "loss": 0.9637,
1865
+ "step": 265
1866
+ },
1867
+ {
1868
+ "epoch": 0.8512,
1869
+ "grad_norm": 0.21622811257839203,
1870
+ "learning_rate": 3.1168831168831166e-05,
1871
+ "loss": 1.1222,
1872
+ "step": 266
1873
+ },
1874
+ {
1875
+ "epoch": 0.8544,
1876
+ "grad_norm": 0.22504661977291107,
1877
+ "learning_rate": 3.051948051948052e-05,
1878
+ "loss": 1.132,
1879
+ "step": 267
1880
+ },
1881
+ {
1882
+ "epoch": 0.8576,
1883
+ "grad_norm": 0.19177629053592682,
1884
+ "learning_rate": 2.9870129870129872e-05,
1885
+ "loss": 1.0281,
1886
+ "step": 268
1887
+ },
1888
+ {
1889
+ "epoch": 0.8608,
1890
+ "grad_norm": 0.1970544159412384,
1891
+ "learning_rate": 2.922077922077922e-05,
1892
+ "loss": 1.0393,
1893
+ "step": 269
1894
+ },
1895
+ {
1896
+ "epoch": 0.864,
1897
+ "grad_norm": 0.21554522216320038,
1898
+ "learning_rate": 2.857142857142857e-05,
1899
+ "loss": 1.074,
1900
+ "step": 270
1901
+ },
1902
+ {
1903
+ "epoch": 0.8672,
1904
+ "grad_norm": 0.21131229400634766,
1905
+ "learning_rate": 2.792207792207792e-05,
1906
+ "loss": 1.054,
1907
+ "step": 271
1908
+ },
1909
+ {
1910
+ "epoch": 0.8704,
1911
+ "grad_norm": 0.19816523790359497,
1912
+ "learning_rate": 2.7272727272727273e-05,
1913
+ "loss": 1.0456,
1914
+ "step": 272
1915
+ },
1916
+ {
1917
+ "epoch": 0.8736,
1918
+ "grad_norm": 0.21075209975242615,
1919
+ "learning_rate": 2.6623376623376623e-05,
1920
+ "loss": 1.0758,
1921
+ "step": 273
1922
+ },
1923
+ {
1924
+ "epoch": 0.8768,
1925
+ "grad_norm": 0.2296527624130249,
1926
+ "learning_rate": 2.5974025974025972e-05,
1927
+ "loss": 1.0917,
1928
+ "step": 274
1929
+ },
1930
+ {
1931
+ "epoch": 0.88,
1932
+ "grad_norm": 0.19722610712051392,
1933
+ "learning_rate": 2.5324675324675325e-05,
1934
+ "loss": 1.0704,
1935
+ "step": 275
1936
+ },
1937
+ {
1938
+ "epoch": 0.8832,
1939
+ "grad_norm": 0.18721099197864532,
1940
+ "learning_rate": 2.4675324675324678e-05,
1941
+ "loss": 0.9919,
1942
+ "step": 276
1943
+ },
1944
+ {
1945
+ "epoch": 0.8864,
1946
+ "grad_norm": 0.20244193077087402,
1947
+ "learning_rate": 2.4025974025974027e-05,
1948
+ "loss": 1.0368,
1949
+ "step": 277
1950
+ },
1951
+ {
1952
+ "epoch": 0.8896,
1953
+ "grad_norm": 0.19518914818763733,
1954
+ "learning_rate": 2.3376623376623376e-05,
1955
+ "loss": 1.0436,
1956
+ "step": 278
1957
+ },
1958
+ {
1959
+ "epoch": 0.8928,
1960
+ "grad_norm": 0.19650357961654663,
1961
+ "learning_rate": 2.272727272727273e-05,
1962
+ "loss": 1.0306,
1963
+ "step": 279
1964
+ },
1965
+ {
1966
+ "epoch": 0.896,
1967
+ "grad_norm": 0.20320096611976624,
1968
+ "learning_rate": 2.207792207792208e-05,
1969
+ "loss": 1.0941,
1970
+ "step": 280
1971
+ },
1972
+ {
1973
+ "epoch": 0.8992,
1974
+ "grad_norm": 0.18296951055526733,
1975
+ "learning_rate": 2.1428571428571428e-05,
1976
+ "loss": 0.9802,
1977
+ "step": 281
1978
+ },
1979
+ {
1980
+ "epoch": 0.9024,
1981
+ "grad_norm": 0.21357610821723938,
1982
+ "learning_rate": 2.077922077922078e-05,
1983
+ "loss": 1.0449,
1984
+ "step": 282
1985
+ },
1986
+ {
1987
+ "epoch": 0.9056,
1988
+ "grad_norm": 0.193921759724617,
1989
+ "learning_rate": 2.012987012987013e-05,
1990
+ "loss": 1.0116,
1991
+ "step": 283
1992
+ },
1993
+ {
1994
+ "epoch": 0.9088,
1995
+ "grad_norm": 0.1953902244567871,
1996
+ "learning_rate": 1.9480519480519483e-05,
1997
+ "loss": 1.0105,
1998
+ "step": 284
1999
+ },
2000
+ {
2001
+ "epoch": 0.912,
2002
+ "grad_norm": 0.19440975785255432,
2003
+ "learning_rate": 1.8831168831168833e-05,
2004
+ "loss": 0.9952,
2005
+ "step": 285
2006
+ },
2007
+ {
2008
+ "epoch": 0.9152,
2009
+ "grad_norm": 0.21054105460643768,
2010
+ "learning_rate": 1.8181818181818182e-05,
2011
+ "loss": 1.0701,
2012
+ "step": 286
2013
+ },
2014
+ {
2015
+ "epoch": 0.9184,
2016
+ "grad_norm": 0.18844804167747498,
2017
+ "learning_rate": 1.7532467532467535e-05,
2018
+ "loss": 1.0146,
2019
+ "step": 287
2020
+ },
2021
+ {
2022
+ "epoch": 0.9216,
2023
+ "grad_norm": 0.2067311704158783,
2024
+ "learning_rate": 1.6883116883116884e-05,
2025
+ "loss": 1.0781,
2026
+ "step": 288
2027
+ },
2028
+ {
2029
+ "epoch": 0.9248,
2030
+ "grad_norm": 0.1941213756799698,
2031
+ "learning_rate": 1.6233766233766234e-05,
2032
+ "loss": 0.9814,
2033
+ "step": 289
2034
+ },
2035
+ {
2036
+ "epoch": 0.928,
2037
+ "grad_norm": 0.22726193070411682,
2038
+ "learning_rate": 1.5584415584415583e-05,
2039
+ "loss": 1.1431,
2040
+ "step": 290
2041
+ },
2042
+ {
2043
+ "epoch": 0.9312,
2044
+ "grad_norm": 0.18025581538677216,
2045
+ "learning_rate": 1.4935064935064936e-05,
2046
+ "loss": 0.9649,
2047
+ "step": 291
2048
+ },
2049
+ {
2050
+ "epoch": 0.9344,
2051
+ "grad_norm": 0.21535000205039978,
2052
+ "learning_rate": 1.4285714285714285e-05,
2053
+ "loss": 1.0441,
2054
+ "step": 292
2055
+ },
2056
+ {
2057
+ "epoch": 0.9376,
2058
+ "grad_norm": 0.20014546811580658,
2059
+ "learning_rate": 1.3636363636363637e-05,
2060
+ "loss": 1.0166,
2061
+ "step": 293
2062
+ },
2063
+ {
2064
+ "epoch": 0.9408,
2065
+ "grad_norm": 0.22738787531852722,
2066
+ "learning_rate": 1.2987012987012986e-05,
2067
+ "loss": 1.0564,
2068
+ "step": 294
2069
+ },
2070
+ {
2071
+ "epoch": 0.944,
2072
+ "grad_norm": 0.2020861804485321,
2073
+ "learning_rate": 1.2337662337662339e-05,
2074
+ "loss": 1.1241,
2075
+ "step": 295
2076
+ },
2077
+ {
2078
+ "epoch": 0.9472,
2079
+ "grad_norm": 0.19888809323310852,
2080
+ "learning_rate": 1.1688311688311688e-05,
2081
+ "loss": 1.1114,
2082
+ "step": 296
2083
+ },
2084
+ {
2085
+ "epoch": 0.9504,
2086
+ "grad_norm": 0.20912377536296844,
2087
+ "learning_rate": 1.103896103896104e-05,
2088
+ "loss": 1.0971,
2089
+ "step": 297
2090
+ },
2091
+ {
2092
+ "epoch": 0.9536,
2093
+ "grad_norm": 0.21206621825695038,
2094
+ "learning_rate": 1.038961038961039e-05,
2095
+ "loss": 1.0601,
2096
+ "step": 298
2097
+ },
2098
+ {
2099
+ "epoch": 0.9568,
2100
+ "grad_norm": 0.18667680025100708,
2101
+ "learning_rate": 9.740259740259742e-06,
2102
+ "loss": 1.0291,
2103
+ "step": 299
2104
+ },
2105
+ {
2106
+ "epoch": 0.96,
2107
+ "grad_norm": 0.21125559508800507,
2108
+ "learning_rate": 9.090909090909091e-06,
2109
+ "loss": 1.0483,
2110
+ "step": 300
2111
+ },
2112
+ {
2113
+ "epoch": 0.9632,
2114
+ "grad_norm": 0.21776145696640015,
2115
+ "learning_rate": 8.441558441558442e-06,
2116
+ "loss": 0.9912,
2117
+ "step": 301
2118
+ },
2119
+ {
2120
+ "epoch": 0.9664,
2121
+ "grad_norm": 0.20144303143024445,
2122
+ "learning_rate": 7.792207792207792e-06,
2123
+ "loss": 1.0357,
2124
+ "step": 302
2125
+ },
2126
+ {
2127
+ "epoch": 0.9696,
2128
+ "grad_norm": 0.1984029859304428,
2129
+ "learning_rate": 7.142857142857143e-06,
2130
+ "loss": 1.0648,
2131
+ "step": 303
2132
+ },
2133
+ {
2134
+ "epoch": 0.9728,
2135
+ "grad_norm": 0.17972829937934875,
2136
+ "learning_rate": 6.493506493506493e-06,
2137
+ "loss": 1.0033,
2138
+ "step": 304
2139
+ },
2140
+ {
2141
+ "epoch": 0.976,
2142
+ "grad_norm": 0.1818286031484604,
2143
+ "learning_rate": 5.844155844155844e-06,
2144
+ "loss": 0.997,
2145
+ "step": 305
2146
+ },
2147
+ {
2148
+ "epoch": 0.9792,
2149
+ "grad_norm": 0.19670912623405457,
2150
+ "learning_rate": 5.194805194805195e-06,
2151
+ "loss": 1.0256,
2152
+ "step": 306
2153
+ },
2154
+ {
2155
+ "epoch": 0.9824,
2156
+ "grad_norm": 0.20527283847332,
2157
+ "learning_rate": 4.5454545454545455e-06,
2158
+ "loss": 1.0348,
2159
+ "step": 307
2160
+ },
2161
+ {
2162
+ "epoch": 0.9856,
2163
+ "grad_norm": 0.19025909900665283,
2164
+ "learning_rate": 3.896103896103896e-06,
2165
+ "loss": 1.0682,
2166
+ "step": 308
2167
+ },
2168
+ {
2169
+ "epoch": 0.9888,
2170
+ "grad_norm": 0.19544818997383118,
2171
+ "learning_rate": 3.2467532467532465e-06,
2172
+ "loss": 0.9872,
2173
+ "step": 309
2174
+ },
2175
+ {
2176
+ "epoch": 0.992,
2177
+ "grad_norm": 0.22112183272838593,
2178
+ "learning_rate": 2.5974025974025976e-06,
2179
+ "loss": 1.0661,
2180
+ "step": 310
2181
+ },
2182
+ {
2183
+ "epoch": 0.9952,
2184
+ "grad_norm": 0.23328153789043427,
2185
+ "learning_rate": 1.948051948051948e-06,
2186
+ "loss": 1.0691,
2187
+ "step": 311
2188
+ },
2189
+ {
2190
+ "epoch": 0.9984,
2191
+ "grad_norm": 0.20181375741958618,
2192
+ "learning_rate": 1.2987012987012988e-06,
2193
+ "loss": 0.9416,
2194
+ "step": 312
2195
+ },
2196
+ {
2197
+ "epoch": 1.0,
2198
+ "grad_norm": 0.29312625527381897,
2199
+ "learning_rate": 6.493506493506494e-07,
2200
+ "loss": 1.1216,
2201
+ "step": 313
2202
+ }
2203
+ ],
2204
+ "logging_steps": 1,
2205
+ "max_steps": 313,
2206
+ "num_input_tokens_seen": 0,
2207
+ "num_train_epochs": 1,
2208
+ "save_steps": 500,
2209
+ "stateful_callbacks": {
2210
+ "TrainerControl": {
2211
+ "args": {
2212
+ "should_epoch_stop": false,
2213
+ "should_evaluate": false,
2214
+ "should_log": false,
2215
+ "should_save": true,
2216
+ "should_training_stop": true
2217
+ },
2218
+ "attributes": {}
2219
+ }
2220
+ },
2221
+ "total_flos": 2.768425540391928e+18,
2222
+ "train_batch_size": 4,
2223
+ "trial_name": null,
2224
+ "trial_params": null
2225
+ }
outputs/checkpoint-500/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
3
+ library_name: peft
4
+ tags:
5
+ - base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit
6
+ - lora
7
+ - sft
8
+ - transformers
9
+ - trl
10
+ - unsloth
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.17.1
outputs/checkpoint-500/adapter_config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": {
4
+ "base_model_class": "GptOssForCausalLM",
5
+ "parent_library": "transformers.models.gpt_oss.modeling_gpt_oss"
6
+ },
7
+ "base_model_name_or_path": "unsloth/gpt-oss-20b-unsloth-bnb-4bit",
8
+ "bias": "none",
9
+ "corda_config": null,
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": 0.0,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "qalora_group_size": 16,
27
+ "r": 16,
28
+ "rank_pattern": {},
29
+ "revision": null,
30
+ "target_modules": [
31
+ "o_proj",
32
+ "v_proj",
33
+ "up_proj",
34
+ "down_proj",
35
+ "gate_proj",
36
+ "k_proj",
37
+ "q_proj"
38
+ ],
39
+ "target_parameters": null,
40
+ "task_type": null,
41
+ "trainable_token_indices": null,
42
+ "use_dora": false,
43
+ "use_qalora": false,
44
+ "use_rslora": false
45
+ }
outputs/checkpoint-500/chat_template.jinja ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
2
+ {#-
3
+ In addition to the normal inputs of `messages` and `tools`, this template also accepts the
4
+ following kwargs:
5
+ - "builtin_tools": A list, can contain "browser" and/or "python".
6
+ - "model_identity": A string that optionally describes the model identity.
7
+ - "reasoning_effort": A string that describes the reasoning effort, defaults to "medium".
8
+ #}
9
+
10
+ {#- Tool Definition Rendering ============================================== #}
11
+ {%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}
12
+ {%- if param_spec.type == "array" -%}
13
+ {%- if param_spec['items'] -%}
14
+ {%- if param_spec['items']['type'] == "string" -%}
15
+ {{- "string[]" }}
16
+ {%- elif param_spec['items']['type'] == "number" -%}
17
+ {{- "number[]" }}
18
+ {%- elif param_spec['items']['type'] == "integer" -%}
19
+ {{- "number[]" }}
20
+ {%- elif param_spec['items']['type'] == "boolean" -%}
21
+ {{- "boolean[]" }}
22
+ {%- else -%}
23
+ {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}
24
+ {%- if inner_type == "object | object" or inner_type|length > 50 -%}
25
+ {{- "any[]" }}
26
+ {%- else -%}
27
+ {{- inner_type + "[]" }}
28
+ {%- endif -%}
29
+ {%- endif -%}
30
+ {%- if param_spec.nullable -%}
31
+ {{- " | null" }}
32
+ {%- endif -%}
33
+ {%- else -%}
34
+ {{- "any[]" }}
35
+ {%- if param_spec.nullable -%}
36
+ {{- " | null" }}
37
+ {%- endif -%}
38
+ {%- endif -%}
39
+ {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}
40
+ {#- Handle array of types like ["object", "object"] from Union[dict, list] #}
41
+ {%- if param_spec.type | length > 1 -%}
42
+ {{- param_spec.type | join(" | ") }}
43
+ {%- else -%}
44
+ {{- param_spec.type[0] }}
45
+ {%- endif -%}
46
+ {%- elif param_spec.oneOf -%}
47
+ {#- Handle oneOf schemas - check for complex unions and fallback to any #}
48
+ {%- set has_object_variants = false -%}
49
+ {%- for variant in param_spec.oneOf -%}
50
+ {%- if variant.type == "object" -%}
51
+ {%- set has_object_variants = true -%}
52
+ {%- endif -%}
53
+ {%- endfor -%}
54
+ {%- if has_object_variants and param_spec.oneOf|length > 1 -%}
55
+ {{- "any" }}
56
+ {%- else -%}
57
+ {%- for variant in param_spec.oneOf -%}
58
+ {{- render_typescript_type(variant, required_params) -}}
59
+ {%- if variant.description %}
60
+ {{- "// " + variant.description }}
61
+ {%- endif -%}
62
+ {%- if variant.default is defined %}
63
+ {{ "// default: " + variant.default|tojson }}
64
+ {%- endif -%}
65
+ {%- if not loop.last %}
66
+ {{- " | " }}
67
+ {% endif -%}
68
+ {%- endfor -%}
69
+ {%- endif -%}
70
+ {%- elif param_spec.type == "string" -%}
71
+ {%- if param_spec.enum -%}
72
+ {{- '"' + param_spec.enum|join('" | "') + '"' -}}
73
+ {%- else -%}
74
+ {{- "string" }}
75
+ {%- if param_spec.nullable %}
76
+ {{- " | null" }}
77
+ {%- endif -%}
78
+ {%- endif -%}
79
+ {%- elif param_spec.type == "number" -%}
80
+ {{- "number" }}
81
+ {%- elif param_spec.type == "integer" -%}
82
+ {{- "number" }}
83
+ {%- elif param_spec.type == "boolean" -%}
84
+ {{- "boolean" }}
85
+
86
+ {%- elif param_spec.type == "object" -%}
87
+ {%- if param_spec.properties -%}
88
+ {{- "{\n" }}
89
+ {%- for prop_name, prop_spec in param_spec.properties.items() -%}
90
+ {{- prop_name -}}
91
+ {%- if prop_name not in (param_spec.required or []) -%}
92
+ {{- "?" }}
93
+ {%- endif -%}
94
+ {{- ": " }}
95
+ {{ render_typescript_type(prop_spec, param_spec.required or []) }}
96
+ {%- if not loop.last -%}
97
+ {{-", " }}
98
+ {%- endif -%}
99
+ {%- endfor -%}
100
+ {{- "}" }}
101
+ {%- else -%}
102
+ {{- "object" }}
103
+ {%- endif -%}
104
+ {%- else -%}
105
+ {{- "any" }}
106
+ {%- endif -%}
107
+ {%- endmacro -%}
108
+
109
+ {%- macro render_tool_namespace(namespace_name, tools) -%}
110
+ {{- "## " + namespace_name + "\n\n" }}
111
+ {{- "namespace " + namespace_name + " {\n\n" }}
112
+ {%- for tool in tools %}
113
+ {%- set tool = tool.function %}
114
+ {{- "// " + tool.description + "\n" }}
115
+ {{- "type "+ tool.name + " = " }}
116
+ {%- if tool.parameters and tool.parameters.properties -%}
117
+ {{- "(_: " }}
118
+ {{- "{\n" }}
119
+ {%- for param_name, param_spec in tool.parameters.properties.items() %}
120
+ {{- "// " + param_spec.description + "\n" }}
121
+ {{- param_name }}
122
+ {%- if param_name not in (tool.parameters.required or []) -%}
123
+ {{- "?" }}
124
+ {%- endif -%}
125
+ {{- ": " }}
126
+ {{- render_typescript_type(param_spec, tool.parameters.required or []) }}
127
+ {%- if param_spec.default is defined -%}
128
+ {%- if param_spec.enum %}
129
+ {{- ", // default: " + param_spec.default }}
130
+ {%- elif param_spec.oneOf %}
131
+ {{- "// default: " + param_spec.default }}
132
+ {%- else %}
133
+ {{- ", // default: " + param_spec.default|tojson }}
134
+ {%- endif -%}
135
+ {%- endif -%}
136
+ {%- if not loop.last %}
137
+ {{- ",\n" }}
138
+ {%- else %}
139
+ {{- "\n" }}
140
+ {%- endif -%}
141
+ {%- endfor %}
142
+ {{- "}) => any;\n\n" }}
143
+ {%- else -%}
144
+ {{- "() => any;\n\n" }}
145
+ {%- endif -%}
146
+ {%- endfor %}
147
+ {{- "} // namespace " + namespace_name }}
148
+ {%- endmacro -%}
149
+
150
+ {%- macro render_builtin_tools(browser_tool, python_tool) -%}
151
+ {%- if browser_tool %}
152
+ {{- "## browser\n\n" }}
153
+ {{- "// Tool for browsing.\n" }}
154
+ {{- "// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.\n" }}
155
+ {{- "// Cite information from the tool using the following format:\n" }}
156
+ {{- "// `【{cursor}†L{line_start}(-L{line_end})?】`, for example: `【6†L9-L11】` or `【8†L3】`.\n" }}
157
+ {{- "// Do not quote more than 10 words directly from the tool output.\n" }}
158
+ {{- "// sources=web (default: web)\n" }}
159
+ {{- "namespace browser {\n\n" }}
160
+ {{- "// Searches for information related to `query` and displays `topn` results.\n" }}
161
+ {{- "type search = (_: {\n" }}
162
+ {{- "query: string,\n" }}
163
+ {{- "topn?: number, // default: 10\n" }}
164
+ {{- "source?: string,\n" }}
165
+ {{- "}) => any;\n\n" }}
166
+ {{- "// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.\n" }}
167
+ {{- "// Valid link ids are displayed with the formatting: `【{id}†.*】`.\n" }}
168
+ {{- "// If `cursor` is not provided, the most recent page is implied.\n" }}
169
+ {{- "// If `id` is a string, it is treated as a fully qualified URL associated with `source`.\n" }}
170
+ {{- "// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.\n" }}
171
+ {{- "// Use this function without `id` to scroll to a new location of an opened page.\n" }}
172
+ {{- "type open = (_: {\n" }}
173
+ {{- "id?: number | string, // default: -1\n" }}
174
+ {{- "cursor?: number, // default: -1\n" }}
175
+ {{- "loc?: number, // default: -1\n" }}
176
+ {{- "num_lines?: number, // default: -1\n" }}
177
+ {{- "view_source?: boolean, // default: false\n" }}
178
+ {{- "source?: string,\n" }}
179
+ {{- "}) => any;\n\n" }}
180
+ {{- "// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.\n" }}
181
+ {{- "type find = (_: {\n" }}
182
+ {{- "pattern: string,\n" }}
183
+ {{- "cursor?: number, // default: -1\n" }}
184
+ {{- "}) => any;\n\n" }}
185
+ {{- "} // namespace browser\n\n" }}
186
+ {%- endif -%}
187
+
188
+ {%- if python_tool %}
189
+ {{- "## python\n\n" }}
190
+ {{- "Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).\n\n" }}
191
+ {{- "When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.\n\n" }}
192
+ {%- endif -%}
193
+ {%- endmacro -%}
194
+
195
+ {#- System Message Construction ============================================ #}
196
+ {%- macro build_system_message() -%}
197
+ {%- if model_identity is not defined %}
198
+ {{- "You are ChatGPT, a large language model trained by OpenAI.\n" -}}
199
+ {%- else %}
200
+ {{- model_identity }}
201
+ {%- endif %}
202
+ {{- "Knowledge cutoff: 2024-06\n" }}
203
+ {{- "Current date: " + strftime_now("%Y-%m-%d") + "\n\n" }}
204
+ {%- if reasoning_effort is not defined %}
205
+ {%- set reasoning_effort = "medium" %}
206
+ {%- endif %}
207
+ {{- "Reasoning: " + reasoning_effort + "\n\n" }}
208
+ {%- if builtin_tools is defined %}
209
+ {{- "# Tools\n\n" }}
210
+ {%- set available_builtin_tools = namespace(browser=false, python=false) %}
211
+ {%- for tool in builtin_tools %}
212
+ {%- if tool == "browser" %}
213
+ {%- set available_builtin_tools.browser = true %}
214
+ {%- elif tool == "python" %}
215
+ {%- set available_builtin_tools.python = true %}
216
+ {%- endif %}
217
+ {%- endfor %}
218
+ {{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}
219
+ {%- endif -%}
220
+ {{- "# Valid channels: analysis, commentary, final. Channel must be included for every message." }}
221
+ {%- if tools is defined -%}
222
+ {{- "\nCalls to these tools must go to the commentary channel: 'functions'." }}
223
+ {%- endif -%}
224
+ {%- endmacro -%}
225
+
226
+ {#- Main Template Logic ================================================= #}
227
+ {#- Set defaults #}
228
+
229
+ {#- Render system message #}
230
+ {{- "<|start|>system<|message|>" }}
231
+ {{- build_system_message() }}
232
+ {{- "<|end|>" }}
233
+
234
+ {#- Extract developer message #}
235
+ {%- if messages[0].role == "developer" or messages[0].role == "system" %}
236
+ {%- set developer_message = messages[0].content %}
237
+ {%- set loop_messages = messages[1:] %}
238
+ {%- else %}
239
+ {%- set developer_message = "" %}
240
+ {%- set loop_messages = messages %}
241
+ {%- endif %}
242
+
243
+ {#- Render developer message #}
244
+ {%- if developer_message or tools %}
245
+ {{- "<|start|>developer<|message|>" }}
246
+ {%- if developer_message %}
247
+ {{- "# Instructions\n\n" }}
248
+ {{- developer_message }}
249
+ {%- endif %}
250
+ {%- if tools -%}
251
+ {{- "\n\n" }}
252
+ {{- "# Tools\n\n" }}
253
+ {{- render_tool_namespace("functions", tools) }}
254
+ {%- endif -%}
255
+ {{- "<|end|>" }}
256
+ {%- endif %}
257
+
258
+ {#- Render messages #}
259
+ {%- set last_tool_call = namespace(name=none) %}
260
+ {%- for message in loop_messages -%}
261
+ {#- At this point only assistant/user/tool messages should remain #}
262
+ {%- if message.role == 'assistant' -%}
263
+ {%- if "tool_calls" in message %}
264
+ {#- We assume max 1 tool call per message, and so we infer the tool call name #}
265
+ {#- in "tool" messages from the most recent assistant tool call name #}
266
+ {%- set tool_call = message.tool_calls[0] %}
267
+ {%- if tool_call.function %}
268
+ {%- set tool_call = tool_call.function %}
269
+ {%- endif %}
270
+ {%- if message.content %}
271
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.content + "<|end|>" }}
272
+ {%- endif %}
273
+ {{- "<|start|>assistant to=" }}
274
+ {{- "functions." + tool_call.name + "<|channel|>commentary json<|message|>" }}
275
+ {{- tool_call.arguments|tojson }}
276
+ {{- "<|call|>" }}
277
+ {%- set last_tool_call.name = tool_call.name %}
278
+ {%- elif "thinking" in message and loop.last and not add_generation_prompt %}
279
+ {#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}
280
+ {#- This is a situation that should only occur in training, never in inference. #}
281
+ {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
282
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
283
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
284
+ {#- when training, so the model learns to emit it. #}
285
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|return|>" }}
286
+ {%- set last_tool_call.name = none %}
287
+ {%- elif "thinking" in message %}
288
+ {#- CoT is dropped during all previous turns, so we never render it for inference #}
289
+ {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|end|>" }}
290
+ {%- set last_tool_call.name = none %}
291
+ {%- elif loop.last and not add_generation_prompt %}
292
+ {#- <|return|> indicates the end of generation, but <|end|> does not #}
293
+ {#- <|return|> should never be an input to the model, but we include it as the final token #}
294
+ {#- when training, so the model learns to emit it. #}
295
+ {{- "<|start|>assistant<|message|>" + message.content + "<|return|>" }}
296
+ {%- else %}
297
+ {{- "<|start|>assistant<|message|>" + message.content + "<|end|>" }}
298
+ {%- set last_tool_call.name = none %}
299
+ {%- endif %}
300
+ {%- elif message.role == 'tool' -%}
301
+ {%- if last_tool_call.name is none %}
302
+ {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
303
+ {%- endif %}
304
+ {{- "<|start|>functions." + last_tool_call.name }}
305
+ {{- " to=assistant<|channel|>commentary<|message|>" + message.content|tojson + "<|end|>" }}
306
+ {%- else -%}
307
+ {{- "<|start|>user<|message|>" + message.content + "<|end|>" }}
308
+ {%- endif -%}
309
+ {%- endfor -%}
310
+
311
+ {#- Generation prompt #}
312
+ {%- if add_generation_prompt -%}
313
+ <|start|>assistant
314
+ {%- endif -%}
315
+ {# Copyright 2025-present Unsloth. Apache 2.0 License. Unsloth chat template fixes. Edited from ggml-org & OpenAI #}
outputs/checkpoint-500/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6e8e01b878a15f489ed7e9f584370716b15783abbbdabd551f242a6101e2133c
3
+ size 16894883
outputs/checkpoint-500/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f1d565802a8e26c4e8a31328752b7a7fdc186d9401aa008e65697d0ad8c22e33
3
+ size 14645
outputs/checkpoint-500/special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|return|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<|reserved_200017|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
outputs/checkpoint-500/tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0614fe83cadab421296e664e1f48f4261fa8fef6e03e63bb75c20f38e37d07d3
3
+ size 27868174
outputs/checkpoint-500/tokenizer_config.json ADDED
@@ -0,0 +1,185 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "199998": {
4
+ "content": "<|startoftext|>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "199999": {
12
+ "content": "<|endoftext|>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "200000": {
20
+ "content": "<|reserved_200000|>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "200001": {
28
+ "content": "<|reserved_200001|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "200002": {
36
+ "content": "<|return|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "200003": {
44
+ "content": "<|constrain|>",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "200004": {
52
+ "content": "<|reserved_200004|>",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "200005": {
60
+ "content": "<|channel|>",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "200006": {
68
+ "content": "<|start|>",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "200007": {
76
+ "content": "<|end|>",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "200008": {
84
+ "content": "<|message|>",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "200009": {
92
+ "content": "<|reserved_200009|>",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
+ "200010": {
100
+ "content": "<|reserved_200010|>",
101
+ "lstrip": false,
102
+ "normalized": false,
103
+ "rstrip": false,
104
+ "single_word": false,
105
+ "special": true
106
+ },
107
+ "200011": {
108
+ "content": "<|reserved_200011|>",
109
+ "lstrip": false,
110
+ "normalized": false,
111
+ "rstrip": false,
112
+ "single_word": false,
113
+ "special": true
114
+ },
115
+ "200012": {
116
+ "content": "<|call|>",
117
+ "lstrip": false,
118
+ "normalized": false,
119
+ "rstrip": false,
120
+ "single_word": false,
121
+ "special": true
122
+ },
123
+ "200013": {
124
+ "content": "<|reserved_200013|>",
125
+ "lstrip": false,
126
+ "normalized": false,
127
+ "rstrip": false,
128
+ "single_word": false,
129
+ "special": true
130
+ },
131
+ "200014": {
132
+ "content": "<|reserved_200014|>",
133
+ "lstrip": false,
134
+ "normalized": false,
135
+ "rstrip": false,
136
+ "single_word": false,
137
+ "special": true
138
+ },
139
+ "200015": {
140
+ "content": "<|reserved_200015|>",
141
+ "lstrip": false,
142
+ "normalized": false,
143
+ "rstrip": false,
144
+ "single_word": false,
145
+ "special": true
146
+ },
147
+ "200016": {
148
+ "content": "<|reserved_200016|>",
149
+ "lstrip": false,
150
+ "normalized": false,
151
+ "rstrip": false,
152
+ "single_word": false,
153
+ "special": true
154
+ },
155
+ "200017": {
156
+ "content": "<|reserved_200017|>",
157
+ "lstrip": false,
158
+ "normalized": false,
159
+ "rstrip": false,
160
+ "single_word": false,
161
+ "special": true
162
+ },
163
+ "200018": {
164
+ "content": "<|endofprompt|>",
165
+ "lstrip": false,
166
+ "normalized": false,
167
+ "rstrip": false,
168
+ "single_word": false,
169
+ "special": true
170
+ }
171
+ },
172
+ "bos_token": "<|startoftext|>",
173
+ "clean_up_tokenization_spaces": false,
174
+ "eos_token": "<|return|>",
175
+ "extra_special_tokens": {},
176
+ "model_input_names": [
177
+ "input_ids",
178
+ "attention_mask"
179
+ ],
180
+ "model_max_length": 131072,
181
+ "pad_token": "<|reserved_200017|>",
182
+ "padding_side": "right",
183
+ "tokenizer_class": "PreTrainedTokenizerFast",
184
+ "unk_token": null
185
+ }
outputs/checkpoint-500/trainer_state.json ADDED
@@ -0,0 +1,3534 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 0.1943068104537064,
6
+ "eval_steps": 500,
7
+ "global_step": 500,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "epoch": 0.0032,
14
+ "grad_norm": 13.684800148010254,
15
+ "learning_rate": 0.0,
16
+ "loss": 2.3276,
17
+ "step": 1
18
+ },
19
+ {
20
+ "epoch": 0.0064,
21
+ "grad_norm": 13.660787582397461,
22
+ "learning_rate": 4e-05,
23
+ "loss": 2.2792,
24
+ "step": 2
25
+ },
26
+ {
27
+ "epoch": 0.0096,
28
+ "grad_norm": 13.35280704498291,
29
+ "learning_rate": 8e-05,
30
+ "loss": 2.4151,
31
+ "step": 3
32
+ },
33
+ {
34
+ "epoch": 0.0128,
35
+ "grad_norm": 6.15027379989624,
36
+ "learning_rate": 0.00012,
37
+ "loss": 1.7812,
38
+ "step": 4
39
+ },
40
+ {
41
+ "epoch": 0.016,
42
+ "grad_norm": 1.3168226480484009,
43
+ "learning_rate": 0.00016,
44
+ "loss": 1.4536,
45
+ "step": 5
46
+ },
47
+ {
48
+ "epoch": 0.0192,
49
+ "grad_norm": 0.9872580170631409,
50
+ "learning_rate": 0.0002,
51
+ "loss": 1.4171,
52
+ "step": 6
53
+ },
54
+ {
55
+ "epoch": 0.0224,
56
+ "grad_norm": 0.7496100664138794,
57
+ "learning_rate": 0.00019935064935064936,
58
+ "loss": 1.4168,
59
+ "step": 7
60
+ },
61
+ {
62
+ "epoch": 0.0256,
63
+ "grad_norm": 0.7376005053520203,
64
+ "learning_rate": 0.00019870129870129872,
65
+ "loss": 1.3659,
66
+ "step": 8
67
+ },
68
+ {
69
+ "epoch": 0.0288,
70
+ "grad_norm": 0.5281137824058533,
71
+ "learning_rate": 0.00019805194805194807,
72
+ "loss": 1.2566,
73
+ "step": 9
74
+ },
75
+ {
76
+ "epoch": 0.032,
77
+ "grad_norm": 0.5485746264457703,
78
+ "learning_rate": 0.00019740259740259742,
79
+ "loss": 1.3761,
80
+ "step": 10
81
+ },
82
+ {
83
+ "epoch": 0.0352,
84
+ "grad_norm": 0.5506592392921448,
85
+ "learning_rate": 0.00019675324675324675,
86
+ "loss": 1.3327,
87
+ "step": 11
88
+ },
89
+ {
90
+ "epoch": 0.0384,
91
+ "grad_norm": 0.49382686614990234,
92
+ "learning_rate": 0.00019610389610389613,
93
+ "loss": 1.3727,
94
+ "step": 12
95
+ },
96
+ {
97
+ "epoch": 0.0416,
98
+ "grad_norm": 0.36203011870384216,
99
+ "learning_rate": 0.00019545454545454548,
100
+ "loss": 1.1515,
101
+ "step": 13
102
+ },
103
+ {
104
+ "epoch": 0.0448,
105
+ "grad_norm": 0.3528599739074707,
106
+ "learning_rate": 0.0001948051948051948,
107
+ "loss": 1.2636,
108
+ "step": 14
109
+ },
110
+ {
111
+ "epoch": 0.048,
112
+ "grad_norm": 0.31244418025016785,
113
+ "learning_rate": 0.00019415584415584416,
114
+ "loss": 1.1873,
115
+ "step": 15
116
+ },
117
+ {
118
+ "epoch": 0.0512,
119
+ "grad_norm": 0.3379523754119873,
120
+ "learning_rate": 0.00019350649350649354,
121
+ "loss": 1.2657,
122
+ "step": 16
123
+ },
124
+ {
125
+ "epoch": 0.0544,
126
+ "grad_norm": 0.3025083839893341,
127
+ "learning_rate": 0.00019285714285714286,
128
+ "loss": 1.2846,
129
+ "step": 17
130
+ },
131
+ {
132
+ "epoch": 0.0576,
133
+ "grad_norm": 0.2560190260410309,
134
+ "learning_rate": 0.00019220779220779222,
135
+ "loss": 1.1587,
136
+ "step": 18
137
+ },
138
+ {
139
+ "epoch": 0.0608,
140
+ "grad_norm": 0.2554129958152771,
141
+ "learning_rate": 0.00019155844155844157,
142
+ "loss": 1.2812,
143
+ "step": 19
144
+ },
145
+ {
146
+ "epoch": 0.064,
147
+ "grad_norm": 0.22662702202796936,
148
+ "learning_rate": 0.00019090909090909092,
149
+ "loss": 1.1664,
150
+ "step": 20
151
+ },
152
+ {
153
+ "epoch": 0.0672,
154
+ "grad_norm": 0.2515714168548584,
155
+ "learning_rate": 0.00019025974025974027,
156
+ "loss": 1.2177,
157
+ "step": 21
158
+ },
159
+ {
160
+ "epoch": 0.0704,
161
+ "grad_norm": 0.24396637082099915,
162
+ "learning_rate": 0.00018961038961038963,
163
+ "loss": 1.2053,
164
+ "step": 22
165
+ },
166
+ {
167
+ "epoch": 0.0736,
168
+ "grad_norm": 0.24488303065299988,
169
+ "learning_rate": 0.00018896103896103895,
170
+ "loss": 1.2074,
171
+ "step": 23
172
+ },
173
+ {
174
+ "epoch": 0.0768,
175
+ "grad_norm": 0.2168620079755783,
176
+ "learning_rate": 0.00018831168831168833,
177
+ "loss": 1.1284,
178
+ "step": 24
179
+ },
180
+ {
181
+ "epoch": 0.08,
182
+ "grad_norm": 0.24021224677562714,
183
+ "learning_rate": 0.00018766233766233769,
184
+ "loss": 1.2169,
185
+ "step": 25
186
+ },
187
+ {
188
+ "epoch": 0.0832,
189
+ "grad_norm": 0.20057056844234467,
190
+ "learning_rate": 0.000187012987012987,
191
+ "loss": 1.1031,
192
+ "step": 26
193
+ },
194
+ {
195
+ "epoch": 0.0864,
196
+ "grad_norm": 0.19900795817375183,
197
+ "learning_rate": 0.00018636363636363636,
198
+ "loss": 1.1004,
199
+ "step": 27
200
+ },
201
+ {
202
+ "epoch": 0.0896,
203
+ "grad_norm": 0.2019268423318863,
204
+ "learning_rate": 0.00018571428571428572,
205
+ "loss": 1.1476,
206
+ "step": 28
207
+ },
208
+ {
209
+ "epoch": 0.0928,
210
+ "grad_norm": 0.1996479034423828,
211
+ "learning_rate": 0.00018506493506493507,
212
+ "loss": 1.1455,
213
+ "step": 29
214
+ },
215
+ {
216
+ "epoch": 0.096,
217
+ "grad_norm": 0.25262022018432617,
218
+ "learning_rate": 0.00018441558441558442,
219
+ "loss": 1.1025,
220
+ "step": 30
221
+ },
222
+ {
223
+ "epoch": 0.0992,
224
+ "grad_norm": 0.225438192486763,
225
+ "learning_rate": 0.00018376623376623378,
226
+ "loss": 1.1954,
227
+ "step": 31
228
+ },
229
+ {
230
+ "epoch": 0.1024,
231
+ "grad_norm": 0.17834505438804626,
232
+ "learning_rate": 0.00018311688311688313,
233
+ "loss": 1.0934,
234
+ "step": 32
235
+ },
236
+ {
237
+ "epoch": 0.1056,
238
+ "grad_norm": 0.20071206986904144,
239
+ "learning_rate": 0.00018246753246753248,
240
+ "loss": 1.0488,
241
+ "step": 33
242
+ },
243
+ {
244
+ "epoch": 0.1088,
245
+ "grad_norm": 0.1920139640569687,
246
+ "learning_rate": 0.00018181818181818183,
247
+ "loss": 1.123,
248
+ "step": 34
249
+ },
250
+ {
251
+ "epoch": 0.112,
252
+ "grad_norm": 0.18714852631092072,
253
+ "learning_rate": 0.0001811688311688312,
254
+ "loss": 1.0798,
255
+ "step": 35
256
+ },
257
+ {
258
+ "epoch": 0.1152,
259
+ "grad_norm": 0.18315713107585907,
260
+ "learning_rate": 0.00018051948051948054,
261
+ "loss": 1.1107,
262
+ "step": 36
263
+ },
264
+ {
265
+ "epoch": 0.1184,
266
+ "grad_norm": 0.19156870245933533,
267
+ "learning_rate": 0.00017987012987012987,
268
+ "loss": 1.1125,
269
+ "step": 37
270
+ },
271
+ {
272
+ "epoch": 0.1216,
273
+ "grad_norm": 0.21527768671512604,
274
+ "learning_rate": 0.00017922077922077922,
275
+ "loss": 1.1346,
276
+ "step": 38
277
+ },
278
+ {
279
+ "epoch": 0.1248,
280
+ "grad_norm": 0.1871163249015808,
281
+ "learning_rate": 0.0001785714285714286,
282
+ "loss": 1.0742,
283
+ "step": 39
284
+ },
285
+ {
286
+ "epoch": 0.128,
287
+ "grad_norm": 0.17750784754753113,
288
+ "learning_rate": 0.00017792207792207792,
289
+ "loss": 1.1323,
290
+ "step": 40
291
+ },
292
+ {
293
+ "epoch": 0.1312,
294
+ "grad_norm": 0.177419051527977,
295
+ "learning_rate": 0.00017727272727272728,
296
+ "loss": 1.1405,
297
+ "step": 41
298
+ },
299
+ {
300
+ "epoch": 0.1344,
301
+ "grad_norm": 0.16714292764663696,
302
+ "learning_rate": 0.00017662337662337663,
303
+ "loss": 1.1084,
304
+ "step": 42
305
+ },
306
+ {
307
+ "epoch": 0.1376,
308
+ "grad_norm": 0.1610356718301773,
309
+ "learning_rate": 0.00017597402597402598,
310
+ "loss": 1.1125,
311
+ "step": 43
312
+ },
313
+ {
314
+ "epoch": 0.1408,
315
+ "grad_norm": 0.2548656761646271,
316
+ "learning_rate": 0.00017532467532467534,
317
+ "loss": 1.1114,
318
+ "step": 44
319
+ },
320
+ {
321
+ "epoch": 0.144,
322
+ "grad_norm": 0.1731044203042984,
323
+ "learning_rate": 0.0001746753246753247,
324
+ "loss": 1.1197,
325
+ "step": 45
326
+ },
327
+ {
328
+ "epoch": 0.1472,
329
+ "grad_norm": 0.1739533394575119,
330
+ "learning_rate": 0.00017402597402597401,
331
+ "loss": 1.1777,
332
+ "step": 46
333
+ },
334
+ {
335
+ "epoch": 0.1504,
336
+ "grad_norm": 0.2178352177143097,
337
+ "learning_rate": 0.0001733766233766234,
338
+ "loss": 1.1111,
339
+ "step": 47
340
+ },
341
+ {
342
+ "epoch": 0.1536,
343
+ "grad_norm": 0.17247150838375092,
344
+ "learning_rate": 0.00017272727272727275,
345
+ "loss": 1.1253,
346
+ "step": 48
347
+ },
348
+ {
349
+ "epoch": 0.1568,
350
+ "grad_norm": 0.18075324594974518,
351
+ "learning_rate": 0.00017207792207792207,
352
+ "loss": 1.1358,
353
+ "step": 49
354
+ },
355
+ {
356
+ "epoch": 0.16,
357
+ "grad_norm": 0.15898071229457855,
358
+ "learning_rate": 0.00017142857142857143,
359
+ "loss": 1.0606,
360
+ "step": 50
361
+ },
362
+ {
363
+ "epoch": 0.1632,
364
+ "grad_norm": 0.16518613696098328,
365
+ "learning_rate": 0.0001707792207792208,
366
+ "loss": 1.0944,
367
+ "step": 51
368
+ },
369
+ {
370
+ "epoch": 0.1664,
371
+ "grad_norm": 0.16035063564777374,
372
+ "learning_rate": 0.00017012987012987013,
373
+ "loss": 1.0554,
374
+ "step": 52
375
+ },
376
+ {
377
+ "epoch": 0.1696,
378
+ "grad_norm": 0.1686483472585678,
379
+ "learning_rate": 0.00016948051948051948,
380
+ "loss": 1.0384,
381
+ "step": 53
382
+ },
383
+ {
384
+ "epoch": 0.1728,
385
+ "grad_norm": 0.16575631499290466,
386
+ "learning_rate": 0.00016883116883116884,
387
+ "loss": 1.0243,
388
+ "step": 54
389
+ },
390
+ {
391
+ "epoch": 0.176,
392
+ "grad_norm": 0.16840039193630219,
393
+ "learning_rate": 0.0001681818181818182,
394
+ "loss": 1.117,
395
+ "step": 55
396
+ },
397
+ {
398
+ "epoch": 0.1792,
399
+ "grad_norm": 0.17616064846515656,
400
+ "learning_rate": 0.00016753246753246754,
401
+ "loss": 1.0743,
402
+ "step": 56
403
+ },
404
+ {
405
+ "epoch": 0.1824,
406
+ "grad_norm": 0.168218195438385,
407
+ "learning_rate": 0.0001668831168831169,
408
+ "loss": 1.0627,
409
+ "step": 57
410
+ },
411
+ {
412
+ "epoch": 0.1856,
413
+ "grad_norm": 0.17026656866073608,
414
+ "learning_rate": 0.00016623376623376625,
415
+ "loss": 1.0059,
416
+ "step": 58
417
+ },
418
+ {
419
+ "epoch": 0.1888,
420
+ "grad_norm": 0.16454458236694336,
421
+ "learning_rate": 0.0001655844155844156,
422
+ "loss": 0.9943,
423
+ "step": 59
424
+ },
425
+ {
426
+ "epoch": 0.192,
427
+ "grad_norm": 0.17185136675834656,
428
+ "learning_rate": 0.00016493506493506495,
429
+ "loss": 1.1545,
430
+ "step": 60
431
+ },
432
+ {
433
+ "epoch": 0.1952,
434
+ "grad_norm": 0.17822986841201782,
435
+ "learning_rate": 0.00016428571428571428,
436
+ "loss": 1.073,
437
+ "step": 61
438
+ },
439
+ {
440
+ "epoch": 0.1984,
441
+ "grad_norm": 0.1676608771085739,
442
+ "learning_rate": 0.00016363636363636366,
443
+ "loss": 1.0886,
444
+ "step": 62
445
+ },
446
+ {
447
+ "epoch": 0.2016,
448
+ "grad_norm": 0.1727771908044815,
449
+ "learning_rate": 0.000162987012987013,
450
+ "loss": 1.0432,
451
+ "step": 63
452
+ },
453
+ {
454
+ "epoch": 0.2048,
455
+ "grad_norm": 0.17827573418617249,
456
+ "learning_rate": 0.00016233766233766234,
457
+ "loss": 1.083,
458
+ "step": 64
459
+ },
460
+ {
461
+ "epoch": 0.208,
462
+ "grad_norm": 0.19807517528533936,
463
+ "learning_rate": 0.0001616883116883117,
464
+ "loss": 1.1208,
465
+ "step": 65
466
+ },
467
+ {
468
+ "epoch": 0.2112,
469
+ "grad_norm": 0.17693684995174408,
470
+ "learning_rate": 0.00016103896103896104,
471
+ "loss": 1.089,
472
+ "step": 66
473
+ },
474
+ {
475
+ "epoch": 0.2144,
476
+ "grad_norm": 0.15489234030246735,
477
+ "learning_rate": 0.0001603896103896104,
478
+ "loss": 0.9707,
479
+ "step": 67
480
+ },
481
+ {
482
+ "epoch": 0.2176,
483
+ "grad_norm": 0.16443990170955658,
484
+ "learning_rate": 0.00015974025974025975,
485
+ "loss": 1.0643,
486
+ "step": 68
487
+ },
488
+ {
489
+ "epoch": 0.2208,
490
+ "grad_norm": 0.2051103413105011,
491
+ "learning_rate": 0.0001590909090909091,
492
+ "loss": 1.1246,
493
+ "step": 69
494
+ },
495
+ {
496
+ "epoch": 0.224,
497
+ "grad_norm": 0.18824075162410736,
498
+ "learning_rate": 0.00015844155844155845,
499
+ "loss": 1.0855,
500
+ "step": 70
501
+ },
502
+ {
503
+ "epoch": 0.2272,
504
+ "grad_norm": 0.18659448623657227,
505
+ "learning_rate": 0.0001577922077922078,
506
+ "loss": 1.1412,
507
+ "step": 71
508
+ },
509
+ {
510
+ "epoch": 0.2304,
511
+ "grad_norm": 0.1854114979505539,
512
+ "learning_rate": 0.00015714285714285716,
513
+ "loss": 1.0249,
514
+ "step": 72
515
+ },
516
+ {
517
+ "epoch": 0.2336,
518
+ "grad_norm": 0.1876193732023239,
519
+ "learning_rate": 0.00015649350649350649,
520
+ "loss": 1.1029,
521
+ "step": 73
522
+ },
523
+ {
524
+ "epoch": 0.2368,
525
+ "grad_norm": 0.1888684630393982,
526
+ "learning_rate": 0.00015584415584415587,
527
+ "loss": 1.0789,
528
+ "step": 74
529
+ },
530
+ {
531
+ "epoch": 0.24,
532
+ "grad_norm": 0.20240606367588043,
533
+ "learning_rate": 0.0001551948051948052,
534
+ "loss": 1.0495,
535
+ "step": 75
536
+ },
537
+ {
538
+ "epoch": 0.2432,
539
+ "grad_norm": 0.232120081782341,
540
+ "learning_rate": 0.00015454545454545454,
541
+ "loss": 1.0735,
542
+ "step": 76
543
+ },
544
+ {
545
+ "epoch": 0.2464,
546
+ "grad_norm": 0.16897843778133392,
547
+ "learning_rate": 0.0001538961038961039,
548
+ "loss": 1.0164,
549
+ "step": 77
550
+ },
551
+ {
552
+ "epoch": 0.2496,
553
+ "grad_norm": 0.18796634674072266,
554
+ "learning_rate": 0.00015324675324675325,
555
+ "loss": 1.0676,
556
+ "step": 78
557
+ },
558
+ {
559
+ "epoch": 0.2528,
560
+ "grad_norm": 0.19574032723903656,
561
+ "learning_rate": 0.0001525974025974026,
562
+ "loss": 1.0456,
563
+ "step": 79
564
+ },
565
+ {
566
+ "epoch": 0.256,
567
+ "grad_norm": 0.18007811903953552,
568
+ "learning_rate": 0.00015194805194805196,
569
+ "loss": 1.0894,
570
+ "step": 80
571
+ },
572
+ {
573
+ "epoch": 0.2592,
574
+ "grad_norm": 0.18932929635047913,
575
+ "learning_rate": 0.0001512987012987013,
576
+ "loss": 1.0729,
577
+ "step": 81
578
+ },
579
+ {
580
+ "epoch": 0.2624,
581
+ "grad_norm": 0.20614288747310638,
582
+ "learning_rate": 0.00015064935064935066,
583
+ "loss": 1.0854,
584
+ "step": 82
585
+ },
586
+ {
587
+ "epoch": 0.2656,
588
+ "grad_norm": 0.19291089475154877,
589
+ "learning_rate": 0.00015000000000000001,
590
+ "loss": 1.1217,
591
+ "step": 83
592
+ },
593
+ {
594
+ "epoch": 0.2688,
595
+ "grad_norm": 0.18916529417037964,
596
+ "learning_rate": 0.00014935064935064934,
597
+ "loss": 1.0963,
598
+ "step": 84
599
+ },
600
+ {
601
+ "epoch": 0.272,
602
+ "grad_norm": 0.20306220650672913,
603
+ "learning_rate": 0.00014870129870129872,
604
+ "loss": 1.0898,
605
+ "step": 85
606
+ },
607
+ {
608
+ "epoch": 0.2752,
609
+ "grad_norm": 0.17870067059993744,
610
+ "learning_rate": 0.00014805194805194807,
611
+ "loss": 1.0213,
612
+ "step": 86
613
+ },
614
+ {
615
+ "epoch": 0.2784,
616
+ "grad_norm": 0.18411923944950104,
617
+ "learning_rate": 0.0001474025974025974,
618
+ "loss": 1.0844,
619
+ "step": 87
620
+ },
621
+ {
622
+ "epoch": 0.2816,
623
+ "grad_norm": 0.18788227438926697,
624
+ "learning_rate": 0.00014675324675324675,
625
+ "loss": 1.0338,
626
+ "step": 88
627
+ },
628
+ {
629
+ "epoch": 0.2848,
630
+ "grad_norm": 0.23874884843826294,
631
+ "learning_rate": 0.00014610389610389613,
632
+ "loss": 1.1118,
633
+ "step": 89
634
+ },
635
+ {
636
+ "epoch": 0.288,
637
+ "grad_norm": 0.19380499422550201,
638
+ "learning_rate": 0.00014545454545454546,
639
+ "loss": 1.0464,
640
+ "step": 90
641
+ },
642
+ {
643
+ "epoch": 0.2912,
644
+ "grad_norm": 0.18968750536441803,
645
+ "learning_rate": 0.0001448051948051948,
646
+ "loss": 1.0569,
647
+ "step": 91
648
+ },
649
+ {
650
+ "epoch": 0.2944,
651
+ "grad_norm": 0.19545753300189972,
652
+ "learning_rate": 0.00014415584415584416,
653
+ "loss": 1.1225,
654
+ "step": 92
655
+ },
656
+ {
657
+ "epoch": 0.2976,
658
+ "grad_norm": 0.19170494377613068,
659
+ "learning_rate": 0.00014350649350649352,
660
+ "loss": 1.0602,
661
+ "step": 93
662
+ },
663
+ {
664
+ "epoch": 0.3008,
665
+ "grad_norm": 0.17953918874263763,
666
+ "learning_rate": 0.00014285714285714287,
667
+ "loss": 1.032,
668
+ "step": 94
669
+ },
670
+ {
671
+ "epoch": 0.304,
672
+ "grad_norm": 0.1822536289691925,
673
+ "learning_rate": 0.00014220779220779222,
674
+ "loss": 1.0559,
675
+ "step": 95
676
+ },
677
+ {
678
+ "epoch": 0.3072,
679
+ "grad_norm": 0.18591298162937164,
680
+ "learning_rate": 0.00014155844155844155,
681
+ "loss": 1.031,
682
+ "step": 96
683
+ },
684
+ {
685
+ "epoch": 0.3104,
686
+ "grad_norm": 0.2129002958536148,
687
+ "learning_rate": 0.00014090909090909093,
688
+ "loss": 1.1391,
689
+ "step": 97
690
+ },
691
+ {
692
+ "epoch": 0.3136,
693
+ "grad_norm": 0.18386681377887726,
694
+ "learning_rate": 0.00014025974025974028,
695
+ "loss": 0.9919,
696
+ "step": 98
697
+ },
698
+ {
699
+ "epoch": 0.3168,
700
+ "grad_norm": 0.18314239382743835,
701
+ "learning_rate": 0.0001396103896103896,
702
+ "loss": 1.0445,
703
+ "step": 99
704
+ },
705
+ {
706
+ "epoch": 0.32,
707
+ "grad_norm": 0.1999066174030304,
708
+ "learning_rate": 0.00013896103896103896,
709
+ "loss": 1.0538,
710
+ "step": 100
711
+ },
712
+ {
713
+ "epoch": 0.3232,
714
+ "grad_norm": 0.18741188943386078,
715
+ "learning_rate": 0.00013831168831168834,
716
+ "loss": 1.0722,
717
+ "step": 101
718
+ },
719
+ {
720
+ "epoch": 0.3264,
721
+ "grad_norm": 0.19351010024547577,
722
+ "learning_rate": 0.00013766233766233766,
723
+ "loss": 1.0491,
724
+ "step": 102
725
+ },
726
+ {
727
+ "epoch": 0.3296,
728
+ "grad_norm": 0.18859203159809113,
729
+ "learning_rate": 0.00013701298701298702,
730
+ "loss": 1.0593,
731
+ "step": 103
732
+ },
733
+ {
734
+ "epoch": 0.3328,
735
+ "grad_norm": 0.1962767392396927,
736
+ "learning_rate": 0.00013636363636363637,
737
+ "loss": 1.1344,
738
+ "step": 104
739
+ },
740
+ {
741
+ "epoch": 0.336,
742
+ "grad_norm": 0.20819440484046936,
743
+ "learning_rate": 0.00013571428571428572,
744
+ "loss": 1.1137,
745
+ "step": 105
746
+ },
747
+ {
748
+ "epoch": 0.3392,
749
+ "grad_norm": 0.19590184092521667,
750
+ "learning_rate": 0.00013506493506493507,
751
+ "loss": 1.0624,
752
+ "step": 106
753
+ },
754
+ {
755
+ "epoch": 0.3424,
756
+ "grad_norm": 0.18631424009799957,
757
+ "learning_rate": 0.00013441558441558443,
758
+ "loss": 1.0587,
759
+ "step": 107
760
+ },
761
+ {
762
+ "epoch": 0.3456,
763
+ "grad_norm": 0.19572143256664276,
764
+ "learning_rate": 0.00013376623376623375,
765
+ "loss": 1.0494,
766
+ "step": 108
767
+ },
768
+ {
769
+ "epoch": 0.3488,
770
+ "grad_norm": 0.1910988837480545,
771
+ "learning_rate": 0.00013311688311688313,
772
+ "loss": 1.0481,
773
+ "step": 109
774
+ },
775
+ {
776
+ "epoch": 0.352,
777
+ "grad_norm": 0.19455869495868683,
778
+ "learning_rate": 0.00013246753246753249,
779
+ "loss": 1.029,
780
+ "step": 110
781
+ },
782
+ {
783
+ "epoch": 0.3552,
784
+ "grad_norm": 0.18669827282428741,
785
+ "learning_rate": 0.0001318181818181818,
786
+ "loss": 1.0513,
787
+ "step": 111
788
+ },
789
+ {
790
+ "epoch": 0.3584,
791
+ "grad_norm": 0.17523664236068726,
792
+ "learning_rate": 0.0001311688311688312,
793
+ "loss": 1.0126,
794
+ "step": 112
795
+ },
796
+ {
797
+ "epoch": 0.3616,
798
+ "grad_norm": 0.17929129302501678,
799
+ "learning_rate": 0.00013051948051948052,
800
+ "loss": 1.0717,
801
+ "step": 113
802
+ },
803
+ {
804
+ "epoch": 0.3648,
805
+ "grad_norm": 0.19380168616771698,
806
+ "learning_rate": 0.00012987012987012987,
807
+ "loss": 1.0324,
808
+ "step": 114
809
+ },
810
+ {
811
+ "epoch": 0.368,
812
+ "grad_norm": 0.18090228736400604,
813
+ "learning_rate": 0.00012922077922077922,
814
+ "loss": 1.0515,
815
+ "step": 115
816
+ },
817
+ {
818
+ "epoch": 0.3712,
819
+ "grad_norm": 0.2067340910434723,
820
+ "learning_rate": 0.00012857142857142858,
821
+ "loss": 1.0939,
822
+ "step": 116
823
+ },
824
+ {
825
+ "epoch": 0.3744,
826
+ "grad_norm": 0.1880485862493515,
827
+ "learning_rate": 0.00012792207792207793,
828
+ "loss": 1.0986,
829
+ "step": 117
830
+ },
831
+ {
832
+ "epoch": 0.3776,
833
+ "grad_norm": 0.182168647646904,
834
+ "learning_rate": 0.00012727272727272728,
835
+ "loss": 1.0109,
836
+ "step": 118
837
+ },
838
+ {
839
+ "epoch": 0.3808,
840
+ "grad_norm": 0.20187129080295563,
841
+ "learning_rate": 0.00012662337662337663,
842
+ "loss": 1.0668,
843
+ "step": 119
844
+ },
845
+ {
846
+ "epoch": 0.384,
847
+ "grad_norm": 0.2082669734954834,
848
+ "learning_rate": 0.000125974025974026,
849
+ "loss": 1.054,
850
+ "step": 120
851
+ },
852
+ {
853
+ "epoch": 0.3872,
854
+ "grad_norm": 0.18294434249401093,
855
+ "learning_rate": 0.00012532467532467534,
856
+ "loss": 1.0397,
857
+ "step": 121
858
+ },
859
+ {
860
+ "epoch": 0.3904,
861
+ "grad_norm": 0.20515067875385284,
862
+ "learning_rate": 0.00012467532467532467,
863
+ "loss": 1.1092,
864
+ "step": 122
865
+ },
866
+ {
867
+ "epoch": 0.3936,
868
+ "grad_norm": 0.1758790761232376,
869
+ "learning_rate": 0.00012402597402597402,
870
+ "loss": 0.9755,
871
+ "step": 123
872
+ },
873
+ {
874
+ "epoch": 0.3968,
875
+ "grad_norm": 0.2170792669057846,
876
+ "learning_rate": 0.0001233766233766234,
877
+ "loss": 1.0434,
878
+ "step": 124
879
+ },
880
+ {
881
+ "epoch": 0.4,
882
+ "grad_norm": 0.202157124876976,
883
+ "learning_rate": 0.00012272727272727272,
884
+ "loss": 1.1129,
885
+ "step": 125
886
+ },
887
+ {
888
+ "epoch": 0.4032,
889
+ "grad_norm": 0.18556398153305054,
890
+ "learning_rate": 0.00012207792207792208,
891
+ "loss": 1.0665,
892
+ "step": 126
893
+ },
894
+ {
895
+ "epoch": 0.4064,
896
+ "grad_norm": 0.20196087658405304,
897
+ "learning_rate": 0.00012142857142857143,
898
+ "loss": 1.1,
899
+ "step": 127
900
+ },
901
+ {
902
+ "epoch": 0.4096,
903
+ "grad_norm": 0.1921566128730774,
904
+ "learning_rate": 0.0001207792207792208,
905
+ "loss": 1.0918,
906
+ "step": 128
907
+ },
908
+ {
909
+ "epoch": 0.4128,
910
+ "grad_norm": 0.18866224586963654,
911
+ "learning_rate": 0.00012012987012987014,
912
+ "loss": 1.0014,
913
+ "step": 129
914
+ },
915
+ {
916
+ "epoch": 0.416,
917
+ "grad_norm": 0.207601398229599,
918
+ "learning_rate": 0.00011948051948051949,
919
+ "loss": 1.0726,
920
+ "step": 130
921
+ },
922
+ {
923
+ "epoch": 0.4192,
924
+ "grad_norm": 0.21592366695404053,
925
+ "learning_rate": 0.00011883116883116883,
926
+ "loss": 1.1379,
927
+ "step": 131
928
+ },
929
+ {
930
+ "epoch": 0.4224,
931
+ "grad_norm": 0.2016124576330185,
932
+ "learning_rate": 0.0001181818181818182,
933
+ "loss": 1.1428,
934
+ "step": 132
935
+ },
936
+ {
937
+ "epoch": 0.4256,
938
+ "grad_norm": 0.20478437840938568,
939
+ "learning_rate": 0.00011753246753246753,
940
+ "loss": 1.121,
941
+ "step": 133
942
+ },
943
+ {
944
+ "epoch": 0.4288,
945
+ "grad_norm": 0.22730594873428345,
946
+ "learning_rate": 0.00011688311688311689,
947
+ "loss": 1.0319,
948
+ "step": 134
949
+ },
950
+ {
951
+ "epoch": 0.432,
952
+ "grad_norm": 0.22592711448669434,
953
+ "learning_rate": 0.00011623376623376625,
954
+ "loss": 1.1264,
955
+ "step": 135
956
+ },
957
+ {
958
+ "epoch": 0.4352,
959
+ "grad_norm": 0.20035041868686676,
960
+ "learning_rate": 0.00011558441558441559,
961
+ "loss": 1.0686,
962
+ "step": 136
963
+ },
964
+ {
965
+ "epoch": 0.4384,
966
+ "grad_norm": 0.20648567378520966,
967
+ "learning_rate": 0.00011493506493506494,
968
+ "loss": 1.0817,
969
+ "step": 137
970
+ },
971
+ {
972
+ "epoch": 0.4416,
973
+ "grad_norm": 0.21222743391990662,
974
+ "learning_rate": 0.00011428571428571428,
975
+ "loss": 1.0678,
976
+ "step": 138
977
+ },
978
+ {
979
+ "epoch": 0.4448,
980
+ "grad_norm": 0.2075391560792923,
981
+ "learning_rate": 0.00011363636363636365,
982
+ "loss": 1.0897,
983
+ "step": 139
984
+ },
985
+ {
986
+ "epoch": 0.448,
987
+ "grad_norm": 0.1964101791381836,
988
+ "learning_rate": 0.000112987012987013,
989
+ "loss": 1.0906,
990
+ "step": 140
991
+ },
992
+ {
993
+ "epoch": 0.4512,
994
+ "grad_norm": 0.22406511008739471,
995
+ "learning_rate": 0.00011233766233766234,
996
+ "loss": 1.0594,
997
+ "step": 141
998
+ },
999
+ {
1000
+ "epoch": 0.4544,
1001
+ "grad_norm": 0.23787978291511536,
1002
+ "learning_rate": 0.00011168831168831168,
1003
+ "loss": 1.1053,
1004
+ "step": 142
1005
+ },
1006
+ {
1007
+ "epoch": 0.4576,
1008
+ "grad_norm": 0.21196185052394867,
1009
+ "learning_rate": 0.00011103896103896105,
1010
+ "loss": 1.0923,
1011
+ "step": 143
1012
+ },
1013
+ {
1014
+ "epoch": 0.4608,
1015
+ "grad_norm": 0.21042804419994354,
1016
+ "learning_rate": 0.0001103896103896104,
1017
+ "loss": 1.0381,
1018
+ "step": 144
1019
+ },
1020
+ {
1021
+ "epoch": 0.464,
1022
+ "grad_norm": 0.2267436534166336,
1023
+ "learning_rate": 0.00010974025974025974,
1024
+ "loss": 1.0818,
1025
+ "step": 145
1026
+ },
1027
+ {
1028
+ "epoch": 0.4672,
1029
+ "grad_norm": 0.23742735385894775,
1030
+ "learning_rate": 0.00010909090909090909,
1031
+ "loss": 1.0872,
1032
+ "step": 146
1033
+ },
1034
+ {
1035
+ "epoch": 0.4704,
1036
+ "grad_norm": 0.17787213623523712,
1037
+ "learning_rate": 0.00010844155844155846,
1038
+ "loss": 1.03,
1039
+ "step": 147
1040
+ },
1041
+ {
1042
+ "epoch": 0.4736,
1043
+ "grad_norm": 0.22422832250595093,
1044
+ "learning_rate": 0.0001077922077922078,
1045
+ "loss": 1.0738,
1046
+ "step": 148
1047
+ },
1048
+ {
1049
+ "epoch": 0.4768,
1050
+ "grad_norm": 0.22946301102638245,
1051
+ "learning_rate": 0.00010714285714285715,
1052
+ "loss": 1.0274,
1053
+ "step": 149
1054
+ },
1055
+ {
1056
+ "epoch": 0.48,
1057
+ "grad_norm": 0.2137996405363083,
1058
+ "learning_rate": 0.00010649350649350649,
1059
+ "loss": 1.0539,
1060
+ "step": 150
1061
+ },
1062
+ {
1063
+ "epoch": 0.4832,
1064
+ "grad_norm": 0.1748756766319275,
1065
+ "learning_rate": 0.00010584415584415586,
1066
+ "loss": 1.0355,
1067
+ "step": 151
1068
+ },
1069
+ {
1070
+ "epoch": 0.4864,
1071
+ "grad_norm": 0.22275175154209137,
1072
+ "learning_rate": 0.0001051948051948052,
1073
+ "loss": 1.1696,
1074
+ "step": 152
1075
+ },
1076
+ {
1077
+ "epoch": 0.4896,
1078
+ "grad_norm": 0.20996077358722687,
1079
+ "learning_rate": 0.00010454545454545455,
1080
+ "loss": 1.0303,
1081
+ "step": 153
1082
+ },
1083
+ {
1084
+ "epoch": 0.4928,
1085
+ "grad_norm": 0.1945938766002655,
1086
+ "learning_rate": 0.00010389610389610389,
1087
+ "loss": 0.9747,
1088
+ "step": 154
1089
+ },
1090
+ {
1091
+ "epoch": 0.496,
1092
+ "grad_norm": 0.1970377266407013,
1093
+ "learning_rate": 0.00010324675324675325,
1094
+ "loss": 1.0358,
1095
+ "step": 155
1096
+ },
1097
+ {
1098
+ "epoch": 0.4992,
1099
+ "grad_norm": 0.18814732134342194,
1100
+ "learning_rate": 0.00010259740259740261,
1101
+ "loss": 0.9612,
1102
+ "step": 156
1103
+ },
1104
+ {
1105
+ "epoch": 0.5024,
1106
+ "grad_norm": 0.2153233289718628,
1107
+ "learning_rate": 0.00010194805194805195,
1108
+ "loss": 1.0749,
1109
+ "step": 157
1110
+ },
1111
+ {
1112
+ "epoch": 0.5056,
1113
+ "grad_norm": 0.21788008511066437,
1114
+ "learning_rate": 0.0001012987012987013,
1115
+ "loss": 1.0883,
1116
+ "step": 158
1117
+ },
1118
+ {
1119
+ "epoch": 0.5088,
1120
+ "grad_norm": 0.214650496840477,
1121
+ "learning_rate": 0.00010064935064935067,
1122
+ "loss": 1.0539,
1123
+ "step": 159
1124
+ },
1125
+ {
1126
+ "epoch": 0.512,
1127
+ "grad_norm": 0.19312834739685059,
1128
+ "learning_rate": 0.0001,
1129
+ "loss": 1.0657,
1130
+ "step": 160
1131
+ },
1132
+ {
1133
+ "epoch": 0.5152,
1134
+ "grad_norm": 0.19916598498821259,
1135
+ "learning_rate": 9.935064935064936e-05,
1136
+ "loss": 1.0478,
1137
+ "step": 161
1138
+ },
1139
+ {
1140
+ "epoch": 0.5184,
1141
+ "grad_norm": 0.2057606726884842,
1142
+ "learning_rate": 9.870129870129871e-05,
1143
+ "loss": 1.0094,
1144
+ "step": 162
1145
+ },
1146
+ {
1147
+ "epoch": 0.5216,
1148
+ "grad_norm": 0.22159607708454132,
1149
+ "learning_rate": 9.805194805194806e-05,
1150
+ "loss": 1.0952,
1151
+ "step": 163
1152
+ },
1153
+ {
1154
+ "epoch": 0.5248,
1155
+ "grad_norm": 0.18274275958538055,
1156
+ "learning_rate": 9.74025974025974e-05,
1157
+ "loss": 1.0065,
1158
+ "step": 164
1159
+ },
1160
+ {
1161
+ "epoch": 0.528,
1162
+ "grad_norm": 0.19835162162780762,
1163
+ "learning_rate": 9.675324675324677e-05,
1164
+ "loss": 1.0742,
1165
+ "step": 165
1166
+ },
1167
+ {
1168
+ "epoch": 0.5312,
1169
+ "grad_norm": 0.2114904820919037,
1170
+ "learning_rate": 9.610389610389611e-05,
1171
+ "loss": 1.1109,
1172
+ "step": 166
1173
+ },
1174
+ {
1175
+ "epoch": 0.5344,
1176
+ "grad_norm": 0.21488523483276367,
1177
+ "learning_rate": 9.545454545454546e-05,
1178
+ "loss": 1.0465,
1179
+ "step": 167
1180
+ },
1181
+ {
1182
+ "epoch": 0.5376,
1183
+ "grad_norm": 0.19870303571224213,
1184
+ "learning_rate": 9.480519480519481e-05,
1185
+ "loss": 1.0318,
1186
+ "step": 168
1187
+ },
1188
+ {
1189
+ "epoch": 0.5408,
1190
+ "grad_norm": 0.20413029193878174,
1191
+ "learning_rate": 9.415584415584417e-05,
1192
+ "loss": 1.0817,
1193
+ "step": 169
1194
+ },
1195
+ {
1196
+ "epoch": 0.544,
1197
+ "grad_norm": 0.1847231239080429,
1198
+ "learning_rate": 9.35064935064935e-05,
1199
+ "loss": 1.0144,
1200
+ "step": 170
1201
+ },
1202
+ {
1203
+ "epoch": 0.5472,
1204
+ "grad_norm": 0.2715964913368225,
1205
+ "learning_rate": 9.285714285714286e-05,
1206
+ "loss": 0.9832,
1207
+ "step": 171
1208
+ },
1209
+ {
1210
+ "epoch": 0.5504,
1211
+ "grad_norm": 0.2225002497434616,
1212
+ "learning_rate": 9.220779220779221e-05,
1213
+ "loss": 1.1051,
1214
+ "step": 172
1215
+ },
1216
+ {
1217
+ "epoch": 0.5536,
1218
+ "grad_norm": 0.22931510210037231,
1219
+ "learning_rate": 9.155844155844156e-05,
1220
+ "loss": 1.1042,
1221
+ "step": 173
1222
+ },
1223
+ {
1224
+ "epoch": 0.5568,
1225
+ "grad_norm": 0.21848627924919128,
1226
+ "learning_rate": 9.090909090909092e-05,
1227
+ "loss": 1.1151,
1228
+ "step": 174
1229
+ },
1230
+ {
1231
+ "epoch": 0.56,
1232
+ "grad_norm": 0.19852259755134583,
1233
+ "learning_rate": 9.025974025974027e-05,
1234
+ "loss": 1.0889,
1235
+ "step": 175
1236
+ },
1237
+ {
1238
+ "epoch": 0.5632,
1239
+ "grad_norm": 0.2080363780260086,
1240
+ "learning_rate": 8.961038961038961e-05,
1241
+ "loss": 1.0777,
1242
+ "step": 176
1243
+ },
1244
+ {
1245
+ "epoch": 0.5664,
1246
+ "grad_norm": 0.22391024231910706,
1247
+ "learning_rate": 8.896103896103896e-05,
1248
+ "loss": 1.1092,
1249
+ "step": 177
1250
+ },
1251
+ {
1252
+ "epoch": 0.5696,
1253
+ "grad_norm": 0.21793846786022186,
1254
+ "learning_rate": 8.831168831168831e-05,
1255
+ "loss": 1.044,
1256
+ "step": 178
1257
+ },
1258
+ {
1259
+ "epoch": 0.5728,
1260
+ "grad_norm": 0.2009749859571457,
1261
+ "learning_rate": 8.766233766233767e-05,
1262
+ "loss": 1.0198,
1263
+ "step": 179
1264
+ },
1265
+ {
1266
+ "epoch": 0.576,
1267
+ "grad_norm": 0.19432318210601807,
1268
+ "learning_rate": 8.701298701298701e-05,
1269
+ "loss": 1.075,
1270
+ "step": 180
1271
+ },
1272
+ {
1273
+ "epoch": 0.5792,
1274
+ "grad_norm": 0.18634547293186188,
1275
+ "learning_rate": 8.636363636363637e-05,
1276
+ "loss": 0.9964,
1277
+ "step": 181
1278
+ },
1279
+ {
1280
+ "epoch": 0.5824,
1281
+ "grad_norm": 0.1947103589773178,
1282
+ "learning_rate": 8.571428571428571e-05,
1283
+ "loss": 1.0025,
1284
+ "step": 182
1285
+ },
1286
+ {
1287
+ "epoch": 0.5856,
1288
+ "grad_norm": 0.23098671436309814,
1289
+ "learning_rate": 8.506493506493507e-05,
1290
+ "loss": 1.0562,
1291
+ "step": 183
1292
+ },
1293
+ {
1294
+ "epoch": 0.5888,
1295
+ "grad_norm": 0.19686414301395416,
1296
+ "learning_rate": 8.441558441558442e-05,
1297
+ "loss": 1.0285,
1298
+ "step": 184
1299
+ },
1300
+ {
1301
+ "epoch": 0.592,
1302
+ "grad_norm": 0.19852428138256073,
1303
+ "learning_rate": 8.376623376623377e-05,
1304
+ "loss": 1.0054,
1305
+ "step": 185
1306
+ },
1307
+ {
1308
+ "epoch": 0.5952,
1309
+ "grad_norm": 0.21483510732650757,
1310
+ "learning_rate": 8.311688311688312e-05,
1311
+ "loss": 1.108,
1312
+ "step": 186
1313
+ },
1314
+ {
1315
+ "epoch": 0.5984,
1316
+ "grad_norm": 0.23313644528388977,
1317
+ "learning_rate": 8.246753246753248e-05,
1318
+ "loss": 1.1383,
1319
+ "step": 187
1320
+ },
1321
+ {
1322
+ "epoch": 0.6016,
1323
+ "grad_norm": 0.21453145146369934,
1324
+ "learning_rate": 8.181818181818183e-05,
1325
+ "loss": 1.0911,
1326
+ "step": 188
1327
+ },
1328
+ {
1329
+ "epoch": 0.6048,
1330
+ "grad_norm": 0.20268195867538452,
1331
+ "learning_rate": 8.116883116883117e-05,
1332
+ "loss": 1.0145,
1333
+ "step": 189
1334
+ },
1335
+ {
1336
+ "epoch": 0.608,
1337
+ "grad_norm": 0.20576398074626923,
1338
+ "learning_rate": 8.051948051948052e-05,
1339
+ "loss": 1.0829,
1340
+ "step": 190
1341
+ },
1342
+ {
1343
+ "epoch": 0.6112,
1344
+ "grad_norm": 0.21732626855373383,
1345
+ "learning_rate": 7.987012987012987e-05,
1346
+ "loss": 1.0152,
1347
+ "step": 191
1348
+ },
1349
+ {
1350
+ "epoch": 0.6144,
1351
+ "grad_norm": 0.22046895325183868,
1352
+ "learning_rate": 7.922077922077923e-05,
1353
+ "loss": 1.1311,
1354
+ "step": 192
1355
+ },
1356
+ {
1357
+ "epoch": 0.6176,
1358
+ "grad_norm": 0.19727715849876404,
1359
+ "learning_rate": 7.857142857142858e-05,
1360
+ "loss": 1.0364,
1361
+ "step": 193
1362
+ },
1363
+ {
1364
+ "epoch": 0.6208,
1365
+ "grad_norm": 0.20861488580703735,
1366
+ "learning_rate": 7.792207792207793e-05,
1367
+ "loss": 1.0435,
1368
+ "step": 194
1369
+ },
1370
+ {
1371
+ "epoch": 0.624,
1372
+ "grad_norm": 0.18545083701610565,
1373
+ "learning_rate": 7.727272727272727e-05,
1374
+ "loss": 1.0299,
1375
+ "step": 195
1376
+ },
1377
+ {
1378
+ "epoch": 0.6272,
1379
+ "grad_norm": 0.19965052604675293,
1380
+ "learning_rate": 7.662337662337662e-05,
1381
+ "loss": 1.0511,
1382
+ "step": 196
1383
+ },
1384
+ {
1385
+ "epoch": 0.6304,
1386
+ "grad_norm": 0.23673909902572632,
1387
+ "learning_rate": 7.597402597402598e-05,
1388
+ "loss": 1.081,
1389
+ "step": 197
1390
+ },
1391
+ {
1392
+ "epoch": 0.6336,
1393
+ "grad_norm": 0.17583179473876953,
1394
+ "learning_rate": 7.532467532467533e-05,
1395
+ "loss": 0.9808,
1396
+ "step": 198
1397
+ },
1398
+ {
1399
+ "epoch": 0.6368,
1400
+ "grad_norm": 0.2129366099834442,
1401
+ "learning_rate": 7.467532467532467e-05,
1402
+ "loss": 1.0522,
1403
+ "step": 199
1404
+ },
1405
+ {
1406
+ "epoch": 0.64,
1407
+ "grad_norm": 0.21679140627384186,
1408
+ "learning_rate": 7.402597402597404e-05,
1409
+ "loss": 1.0567,
1410
+ "step": 200
1411
+ },
1412
+ {
1413
+ "epoch": 0.6432,
1414
+ "grad_norm": 0.2032000720500946,
1415
+ "learning_rate": 7.337662337662338e-05,
1416
+ "loss": 1.0466,
1417
+ "step": 201
1418
+ },
1419
+ {
1420
+ "epoch": 0.6464,
1421
+ "grad_norm": 0.1887970268726349,
1422
+ "learning_rate": 7.272727272727273e-05,
1423
+ "loss": 1.0329,
1424
+ "step": 202
1425
+ },
1426
+ {
1427
+ "epoch": 0.6496,
1428
+ "grad_norm": 0.21060192584991455,
1429
+ "learning_rate": 7.207792207792208e-05,
1430
+ "loss": 1.1021,
1431
+ "step": 203
1432
+ },
1433
+ {
1434
+ "epoch": 0.6528,
1435
+ "grad_norm": 0.21191425621509552,
1436
+ "learning_rate": 7.142857142857143e-05,
1437
+ "loss": 0.99,
1438
+ "step": 204
1439
+ },
1440
+ {
1441
+ "epoch": 0.656,
1442
+ "grad_norm": 0.1995989829301834,
1443
+ "learning_rate": 7.077922077922077e-05,
1444
+ "loss": 1.0526,
1445
+ "step": 205
1446
+ },
1447
+ {
1448
+ "epoch": 0.6592,
1449
+ "grad_norm": 0.1849513053894043,
1450
+ "learning_rate": 7.012987012987014e-05,
1451
+ "loss": 0.9998,
1452
+ "step": 206
1453
+ },
1454
+ {
1455
+ "epoch": 0.6624,
1456
+ "grad_norm": 0.1948779672384262,
1457
+ "learning_rate": 6.948051948051948e-05,
1458
+ "loss": 1.075,
1459
+ "step": 207
1460
+ },
1461
+ {
1462
+ "epoch": 0.6656,
1463
+ "grad_norm": 0.20374052226543427,
1464
+ "learning_rate": 6.883116883116883e-05,
1465
+ "loss": 1.0933,
1466
+ "step": 208
1467
+ },
1468
+ {
1469
+ "epoch": 0.6688,
1470
+ "grad_norm": 0.2102465033531189,
1471
+ "learning_rate": 6.818181818181818e-05,
1472
+ "loss": 1.1123,
1473
+ "step": 209
1474
+ },
1475
+ {
1476
+ "epoch": 0.672,
1477
+ "grad_norm": 0.21376173198223114,
1478
+ "learning_rate": 6.753246753246754e-05,
1479
+ "loss": 1.1233,
1480
+ "step": 210
1481
+ },
1482
+ {
1483
+ "epoch": 0.6752,
1484
+ "grad_norm": 0.20934203267097473,
1485
+ "learning_rate": 6.688311688311688e-05,
1486
+ "loss": 1.1374,
1487
+ "step": 211
1488
+ },
1489
+ {
1490
+ "epoch": 0.6784,
1491
+ "grad_norm": 0.18604128062725067,
1492
+ "learning_rate": 6.623376623376624e-05,
1493
+ "loss": 1.0213,
1494
+ "step": 212
1495
+ },
1496
+ {
1497
+ "epoch": 0.6816,
1498
+ "grad_norm": 0.19644233584403992,
1499
+ "learning_rate": 6.55844155844156e-05,
1500
+ "loss": 1.0046,
1501
+ "step": 213
1502
+ },
1503
+ {
1504
+ "epoch": 0.6848,
1505
+ "grad_norm": 0.18479463458061218,
1506
+ "learning_rate": 6.493506493506494e-05,
1507
+ "loss": 0.9792,
1508
+ "step": 214
1509
+ },
1510
+ {
1511
+ "epoch": 0.688,
1512
+ "grad_norm": 0.1945149153470993,
1513
+ "learning_rate": 6.428571428571429e-05,
1514
+ "loss": 1.0584,
1515
+ "step": 215
1516
+ },
1517
+ {
1518
+ "epoch": 0.6912,
1519
+ "grad_norm": 0.2070147544145584,
1520
+ "learning_rate": 6.363636363636364e-05,
1521
+ "loss": 1.071,
1522
+ "step": 216
1523
+ },
1524
+ {
1525
+ "epoch": 0.6944,
1526
+ "grad_norm": 0.19645985960960388,
1527
+ "learning_rate": 6.2987012987013e-05,
1528
+ "loss": 1.0721,
1529
+ "step": 217
1530
+ },
1531
+ {
1532
+ "epoch": 0.6976,
1533
+ "grad_norm": 0.1960117667913437,
1534
+ "learning_rate": 6.233766233766233e-05,
1535
+ "loss": 1.071,
1536
+ "step": 218
1537
+ },
1538
+ {
1539
+ "epoch": 0.7008,
1540
+ "grad_norm": 0.20168261229991913,
1541
+ "learning_rate": 6.16883116883117e-05,
1542
+ "loss": 1.0808,
1543
+ "step": 219
1544
+ },
1545
+ {
1546
+ "epoch": 0.704,
1547
+ "grad_norm": 0.21254412829875946,
1548
+ "learning_rate": 6.103896103896104e-05,
1549
+ "loss": 1.0287,
1550
+ "step": 220
1551
+ },
1552
+ {
1553
+ "epoch": 0.7072,
1554
+ "grad_norm": 0.21271063387393951,
1555
+ "learning_rate": 6.03896103896104e-05,
1556
+ "loss": 1.0605,
1557
+ "step": 221
1558
+ },
1559
+ {
1560
+ "epoch": 0.7104,
1561
+ "grad_norm": 0.2081408053636551,
1562
+ "learning_rate": 5.9740259740259744e-05,
1563
+ "loss": 1.091,
1564
+ "step": 222
1565
+ },
1566
+ {
1567
+ "epoch": 0.7136,
1568
+ "grad_norm": 0.21113798022270203,
1569
+ "learning_rate": 5.90909090909091e-05,
1570
+ "loss": 1.1323,
1571
+ "step": 223
1572
+ },
1573
+ {
1574
+ "epoch": 0.7168,
1575
+ "grad_norm": 0.20670844614505768,
1576
+ "learning_rate": 5.844155844155844e-05,
1577
+ "loss": 1.0955,
1578
+ "step": 224
1579
+ },
1580
+ {
1581
+ "epoch": 0.72,
1582
+ "grad_norm": 0.2010120451450348,
1583
+ "learning_rate": 5.7792207792207796e-05,
1584
+ "loss": 1.1068,
1585
+ "step": 225
1586
+ },
1587
+ {
1588
+ "epoch": 0.7232,
1589
+ "grad_norm": 0.20379121601581573,
1590
+ "learning_rate": 5.714285714285714e-05,
1591
+ "loss": 1.0419,
1592
+ "step": 226
1593
+ },
1594
+ {
1595
+ "epoch": 0.7264,
1596
+ "grad_norm": 0.22799807786941528,
1597
+ "learning_rate": 5.64935064935065e-05,
1598
+ "loss": 1.0904,
1599
+ "step": 227
1600
+ },
1601
+ {
1602
+ "epoch": 0.7296,
1603
+ "grad_norm": 0.2005995213985443,
1604
+ "learning_rate": 5.584415584415584e-05,
1605
+ "loss": 1.078,
1606
+ "step": 228
1607
+ },
1608
+ {
1609
+ "epoch": 0.7328,
1610
+ "grad_norm": 0.20329605042934418,
1611
+ "learning_rate": 5.51948051948052e-05,
1612
+ "loss": 1.0245,
1613
+ "step": 229
1614
+ },
1615
+ {
1616
+ "epoch": 0.736,
1617
+ "grad_norm": 0.19283504784107208,
1618
+ "learning_rate": 5.4545454545454546e-05,
1619
+ "loss": 1.0367,
1620
+ "step": 230
1621
+ },
1622
+ {
1623
+ "epoch": 0.7392,
1624
+ "grad_norm": 0.20624355971813202,
1625
+ "learning_rate": 5.38961038961039e-05,
1626
+ "loss": 1.1046,
1627
+ "step": 231
1628
+ },
1629
+ {
1630
+ "epoch": 0.7424,
1631
+ "grad_norm": 0.21362991631031036,
1632
+ "learning_rate": 5.3246753246753245e-05,
1633
+ "loss": 1.1104,
1634
+ "step": 232
1635
+ },
1636
+ {
1637
+ "epoch": 0.7456,
1638
+ "grad_norm": 0.20447863638401031,
1639
+ "learning_rate": 5.25974025974026e-05,
1640
+ "loss": 1.0514,
1641
+ "step": 233
1642
+ },
1643
+ {
1644
+ "epoch": 0.7488,
1645
+ "grad_norm": 0.1974381059408188,
1646
+ "learning_rate": 5.1948051948051944e-05,
1647
+ "loss": 1.0048,
1648
+ "step": 234
1649
+ },
1650
+ {
1651
+ "epoch": 0.752,
1652
+ "grad_norm": 0.21237170696258545,
1653
+ "learning_rate": 5.1298701298701304e-05,
1654
+ "loss": 1.1299,
1655
+ "step": 235
1656
+ },
1657
+ {
1658
+ "epoch": 0.7552,
1659
+ "grad_norm": 0.21224971115589142,
1660
+ "learning_rate": 5.064935064935065e-05,
1661
+ "loss": 1.05,
1662
+ "step": 236
1663
+ },
1664
+ {
1665
+ "epoch": 0.7584,
1666
+ "grad_norm": 0.19865018129348755,
1667
+ "learning_rate": 5e-05,
1668
+ "loss": 1.0665,
1669
+ "step": 237
1670
+ },
1671
+ {
1672
+ "epoch": 0.7616,
1673
+ "grad_norm": 0.19199275970458984,
1674
+ "learning_rate": 4.9350649350649355e-05,
1675
+ "loss": 0.9531,
1676
+ "step": 238
1677
+ },
1678
+ {
1679
+ "epoch": 0.7648,
1680
+ "grad_norm": 0.19573214650154114,
1681
+ "learning_rate": 4.87012987012987e-05,
1682
+ "loss": 1.0318,
1683
+ "step": 239
1684
+ },
1685
+ {
1686
+ "epoch": 0.768,
1687
+ "grad_norm": 0.21338805556297302,
1688
+ "learning_rate": 4.8051948051948054e-05,
1689
+ "loss": 1.0343,
1690
+ "step": 240
1691
+ },
1692
+ {
1693
+ "epoch": 0.7712,
1694
+ "grad_norm": 0.2254691869020462,
1695
+ "learning_rate": 4.740259740259741e-05,
1696
+ "loss": 1.0472,
1697
+ "step": 241
1698
+ },
1699
+ {
1700
+ "epoch": 0.7744,
1701
+ "grad_norm": 0.18101665377616882,
1702
+ "learning_rate": 4.675324675324675e-05,
1703
+ "loss": 1.017,
1704
+ "step": 242
1705
+ },
1706
+ {
1707
+ "epoch": 0.7776,
1708
+ "grad_norm": 0.22090592980384827,
1709
+ "learning_rate": 4.6103896103896106e-05,
1710
+ "loss": 1.0389,
1711
+ "step": 243
1712
+ },
1713
+ {
1714
+ "epoch": 0.7808,
1715
+ "grad_norm": 0.20865507423877716,
1716
+ "learning_rate": 4.545454545454546e-05,
1717
+ "loss": 1.0369,
1718
+ "step": 244
1719
+ },
1720
+ {
1721
+ "epoch": 0.784,
1722
+ "grad_norm": 0.21619610488414764,
1723
+ "learning_rate": 4.4805194805194805e-05,
1724
+ "loss": 1.109,
1725
+ "step": 245
1726
+ },
1727
+ {
1728
+ "epoch": 0.7872,
1729
+ "grad_norm": 0.21694771945476532,
1730
+ "learning_rate": 4.415584415584416e-05,
1731
+ "loss": 1.0525,
1732
+ "step": 246
1733
+ },
1734
+ {
1735
+ "epoch": 0.7904,
1736
+ "grad_norm": 0.2182662934064865,
1737
+ "learning_rate": 4.3506493506493503e-05,
1738
+ "loss": 1.0331,
1739
+ "step": 247
1740
+ },
1741
+ {
1742
+ "epoch": 0.7936,
1743
+ "grad_norm": 0.2026486098766327,
1744
+ "learning_rate": 4.2857142857142856e-05,
1745
+ "loss": 1.027,
1746
+ "step": 248
1747
+ },
1748
+ {
1749
+ "epoch": 0.7968,
1750
+ "grad_norm": 0.19606547057628632,
1751
+ "learning_rate": 4.220779220779221e-05,
1752
+ "loss": 1.0242,
1753
+ "step": 249
1754
+ },
1755
+ {
1756
+ "epoch": 0.8,
1757
+ "grad_norm": 0.22107470035552979,
1758
+ "learning_rate": 4.155844155844156e-05,
1759
+ "loss": 1.0924,
1760
+ "step": 250
1761
+ },
1762
+ {
1763
+ "epoch": 0.8032,
1764
+ "grad_norm": 0.19960008561611176,
1765
+ "learning_rate": 4.0909090909090915e-05,
1766
+ "loss": 1.0384,
1767
+ "step": 251
1768
+ },
1769
+ {
1770
+ "epoch": 0.8064,
1771
+ "grad_norm": 0.1945488154888153,
1772
+ "learning_rate": 4.025974025974026e-05,
1773
+ "loss": 1.0673,
1774
+ "step": 252
1775
+ },
1776
+ {
1777
+ "epoch": 0.8096,
1778
+ "grad_norm": 0.22067414224147797,
1779
+ "learning_rate": 3.9610389610389614e-05,
1780
+ "loss": 1.0426,
1781
+ "step": 253
1782
+ },
1783
+ {
1784
+ "epoch": 0.8128,
1785
+ "grad_norm": 0.19010980427265167,
1786
+ "learning_rate": 3.8961038961038966e-05,
1787
+ "loss": 1.0617,
1788
+ "step": 254
1789
+ },
1790
+ {
1791
+ "epoch": 0.816,
1792
+ "grad_norm": 0.18781176209449768,
1793
+ "learning_rate": 3.831168831168831e-05,
1794
+ "loss": 1.0243,
1795
+ "step": 255
1796
+ },
1797
+ {
1798
+ "epoch": 0.8192,
1799
+ "grad_norm": 0.20388829708099365,
1800
+ "learning_rate": 3.7662337662337665e-05,
1801
+ "loss": 1.0476,
1802
+ "step": 256
1803
+ },
1804
+ {
1805
+ "epoch": 0.8224,
1806
+ "grad_norm": 0.19911155104637146,
1807
+ "learning_rate": 3.701298701298702e-05,
1808
+ "loss": 1.0324,
1809
+ "step": 257
1810
+ },
1811
+ {
1812
+ "epoch": 0.8256,
1813
+ "grad_norm": 0.19884039461612701,
1814
+ "learning_rate": 3.6363636363636364e-05,
1815
+ "loss": 1.0242,
1816
+ "step": 258
1817
+ },
1818
+ {
1819
+ "epoch": 0.8288,
1820
+ "grad_norm": 0.19036105275154114,
1821
+ "learning_rate": 3.571428571428572e-05,
1822
+ "loss": 1.0323,
1823
+ "step": 259
1824
+ },
1825
+ {
1826
+ "epoch": 0.832,
1827
+ "grad_norm": 0.20039844512939453,
1828
+ "learning_rate": 3.506493506493507e-05,
1829
+ "loss": 1.0749,
1830
+ "step": 260
1831
+ },
1832
+ {
1833
+ "epoch": 0.8352,
1834
+ "grad_norm": 0.1899934560060501,
1835
+ "learning_rate": 3.4415584415584416e-05,
1836
+ "loss": 1.0115,
1837
+ "step": 261
1838
+ },
1839
+ {
1840
+ "epoch": 0.8384,
1841
+ "grad_norm": 0.20019090175628662,
1842
+ "learning_rate": 3.376623376623377e-05,
1843
+ "loss": 1.0782,
1844
+ "step": 262
1845
+ },
1846
+ {
1847
+ "epoch": 0.8416,
1848
+ "grad_norm": 0.2020583152770996,
1849
+ "learning_rate": 3.311688311688312e-05,
1850
+ "loss": 1.0687,
1851
+ "step": 263
1852
+ },
1853
+ {
1854
+ "epoch": 0.8448,
1855
+ "grad_norm": 0.21407337486743927,
1856
+ "learning_rate": 3.246753246753247e-05,
1857
+ "loss": 1.1015,
1858
+ "step": 264
1859
+ },
1860
+ {
1861
+ "epoch": 0.848,
1862
+ "grad_norm": 0.1871640682220459,
1863
+ "learning_rate": 3.181818181818182e-05,
1864
+ "loss": 0.9637,
1865
+ "step": 265
1866
+ },
1867
+ {
1868
+ "epoch": 0.8512,
1869
+ "grad_norm": 0.21622811257839203,
1870
+ "learning_rate": 3.1168831168831166e-05,
1871
+ "loss": 1.1222,
1872
+ "step": 266
1873
+ },
1874
+ {
1875
+ "epoch": 0.8544,
1876
+ "grad_norm": 0.22504661977291107,
1877
+ "learning_rate": 3.051948051948052e-05,
1878
+ "loss": 1.132,
1879
+ "step": 267
1880
+ },
1881
+ {
1882
+ "epoch": 0.8576,
1883
+ "grad_norm": 0.19177629053592682,
1884
+ "learning_rate": 2.9870129870129872e-05,
1885
+ "loss": 1.0281,
1886
+ "step": 268
1887
+ },
1888
+ {
1889
+ "epoch": 0.8608,
1890
+ "grad_norm": 0.1970544159412384,
1891
+ "learning_rate": 2.922077922077922e-05,
1892
+ "loss": 1.0393,
1893
+ "step": 269
1894
+ },
1895
+ {
1896
+ "epoch": 0.864,
1897
+ "grad_norm": 0.21554522216320038,
1898
+ "learning_rate": 2.857142857142857e-05,
1899
+ "loss": 1.074,
1900
+ "step": 270
1901
+ },
1902
+ {
1903
+ "epoch": 0.8672,
1904
+ "grad_norm": 0.21131229400634766,
1905
+ "learning_rate": 2.792207792207792e-05,
1906
+ "loss": 1.054,
1907
+ "step": 271
1908
+ },
1909
+ {
1910
+ "epoch": 0.8704,
1911
+ "grad_norm": 0.19816523790359497,
1912
+ "learning_rate": 2.7272727272727273e-05,
1913
+ "loss": 1.0456,
1914
+ "step": 272
1915
+ },
1916
+ {
1917
+ "epoch": 0.8736,
1918
+ "grad_norm": 0.21075209975242615,
1919
+ "learning_rate": 2.6623376623376623e-05,
1920
+ "loss": 1.0758,
1921
+ "step": 273
1922
+ },
1923
+ {
1924
+ "epoch": 0.8768,
1925
+ "grad_norm": 0.2296527624130249,
1926
+ "learning_rate": 2.5974025974025972e-05,
1927
+ "loss": 1.0917,
1928
+ "step": 274
1929
+ },
1930
+ {
1931
+ "epoch": 0.88,
1932
+ "grad_norm": 0.19722610712051392,
1933
+ "learning_rate": 2.5324675324675325e-05,
1934
+ "loss": 1.0704,
1935
+ "step": 275
1936
+ },
1937
+ {
1938
+ "epoch": 0.8832,
1939
+ "grad_norm": 0.18721099197864532,
1940
+ "learning_rate": 2.4675324675324678e-05,
1941
+ "loss": 0.9919,
1942
+ "step": 276
1943
+ },
1944
+ {
1945
+ "epoch": 0.8864,
1946
+ "grad_norm": 0.20244193077087402,
1947
+ "learning_rate": 2.4025974025974027e-05,
1948
+ "loss": 1.0368,
1949
+ "step": 277
1950
+ },
1951
+ {
1952
+ "epoch": 0.8896,
1953
+ "grad_norm": 0.19518914818763733,
1954
+ "learning_rate": 2.3376623376623376e-05,
1955
+ "loss": 1.0436,
1956
+ "step": 278
1957
+ },
1958
+ {
1959
+ "epoch": 0.8928,
1960
+ "grad_norm": 0.19650357961654663,
1961
+ "learning_rate": 2.272727272727273e-05,
1962
+ "loss": 1.0306,
1963
+ "step": 279
1964
+ },
1965
+ {
1966
+ "epoch": 0.896,
1967
+ "grad_norm": 0.20320096611976624,
1968
+ "learning_rate": 2.207792207792208e-05,
1969
+ "loss": 1.0941,
1970
+ "step": 280
1971
+ },
1972
+ {
1973
+ "epoch": 0.8992,
1974
+ "grad_norm": 0.18296951055526733,
1975
+ "learning_rate": 2.1428571428571428e-05,
1976
+ "loss": 0.9802,
1977
+ "step": 281
1978
+ },
1979
+ {
1980
+ "epoch": 0.9024,
1981
+ "grad_norm": 0.21357610821723938,
1982
+ "learning_rate": 2.077922077922078e-05,
1983
+ "loss": 1.0449,
1984
+ "step": 282
1985
+ },
1986
+ {
1987
+ "epoch": 0.9056,
1988
+ "grad_norm": 0.193921759724617,
1989
+ "learning_rate": 2.012987012987013e-05,
1990
+ "loss": 1.0116,
1991
+ "step": 283
1992
+ },
1993
+ {
1994
+ "epoch": 0.9088,
1995
+ "grad_norm": 0.1953902244567871,
1996
+ "learning_rate": 1.9480519480519483e-05,
1997
+ "loss": 1.0105,
1998
+ "step": 284
1999
+ },
2000
+ {
2001
+ "epoch": 0.912,
2002
+ "grad_norm": 0.19440975785255432,
2003
+ "learning_rate": 1.8831168831168833e-05,
2004
+ "loss": 0.9952,
2005
+ "step": 285
2006
+ },
2007
+ {
2008
+ "epoch": 0.9152,
2009
+ "grad_norm": 0.21054105460643768,
2010
+ "learning_rate": 1.8181818181818182e-05,
2011
+ "loss": 1.0701,
2012
+ "step": 286
2013
+ },
2014
+ {
2015
+ "epoch": 0.9184,
2016
+ "grad_norm": 0.18844804167747498,
2017
+ "learning_rate": 1.7532467532467535e-05,
2018
+ "loss": 1.0146,
2019
+ "step": 287
2020
+ },
2021
+ {
2022
+ "epoch": 0.9216,
2023
+ "grad_norm": 0.2067311704158783,
2024
+ "learning_rate": 1.6883116883116884e-05,
2025
+ "loss": 1.0781,
2026
+ "step": 288
2027
+ },
2028
+ {
2029
+ "epoch": 0.9248,
2030
+ "grad_norm": 0.1941213756799698,
2031
+ "learning_rate": 1.6233766233766234e-05,
2032
+ "loss": 0.9814,
2033
+ "step": 289
2034
+ },
2035
+ {
2036
+ "epoch": 0.928,
2037
+ "grad_norm": 0.22726193070411682,
2038
+ "learning_rate": 1.5584415584415583e-05,
2039
+ "loss": 1.1431,
2040
+ "step": 290
2041
+ },
2042
+ {
2043
+ "epoch": 0.9312,
2044
+ "grad_norm": 0.18025581538677216,
2045
+ "learning_rate": 1.4935064935064936e-05,
2046
+ "loss": 0.9649,
2047
+ "step": 291
2048
+ },
2049
+ {
2050
+ "epoch": 0.9344,
2051
+ "grad_norm": 0.21535000205039978,
2052
+ "learning_rate": 1.4285714285714285e-05,
2053
+ "loss": 1.0441,
2054
+ "step": 292
2055
+ },
2056
+ {
2057
+ "epoch": 0.9376,
2058
+ "grad_norm": 0.20014546811580658,
2059
+ "learning_rate": 1.3636363636363637e-05,
2060
+ "loss": 1.0166,
2061
+ "step": 293
2062
+ },
2063
+ {
2064
+ "epoch": 0.9408,
2065
+ "grad_norm": 0.22738787531852722,
2066
+ "learning_rate": 1.2987012987012986e-05,
2067
+ "loss": 1.0564,
2068
+ "step": 294
2069
+ },
2070
+ {
2071
+ "epoch": 0.944,
2072
+ "grad_norm": 0.2020861804485321,
2073
+ "learning_rate": 1.2337662337662339e-05,
2074
+ "loss": 1.1241,
2075
+ "step": 295
2076
+ },
2077
+ {
2078
+ "epoch": 0.9472,
2079
+ "grad_norm": 0.19888809323310852,
2080
+ "learning_rate": 1.1688311688311688e-05,
2081
+ "loss": 1.1114,
2082
+ "step": 296
2083
+ },
2084
+ {
2085
+ "epoch": 0.9504,
2086
+ "grad_norm": 0.20912377536296844,
2087
+ "learning_rate": 1.103896103896104e-05,
2088
+ "loss": 1.0971,
2089
+ "step": 297
2090
+ },
2091
+ {
2092
+ "epoch": 0.9536,
2093
+ "grad_norm": 0.21206621825695038,
2094
+ "learning_rate": 1.038961038961039e-05,
2095
+ "loss": 1.0601,
2096
+ "step": 298
2097
+ },
2098
+ {
2099
+ "epoch": 0.9568,
2100
+ "grad_norm": 0.18667680025100708,
2101
+ "learning_rate": 9.740259740259742e-06,
2102
+ "loss": 1.0291,
2103
+ "step": 299
2104
+ },
2105
+ {
2106
+ "epoch": 0.96,
2107
+ "grad_norm": 0.21125559508800507,
2108
+ "learning_rate": 9.090909090909091e-06,
2109
+ "loss": 1.0483,
2110
+ "step": 300
2111
+ },
2112
+ {
2113
+ "epoch": 0.9632,
2114
+ "grad_norm": 0.21776145696640015,
2115
+ "learning_rate": 8.441558441558442e-06,
2116
+ "loss": 0.9912,
2117
+ "step": 301
2118
+ },
2119
+ {
2120
+ "epoch": 0.9664,
2121
+ "grad_norm": 0.20144303143024445,
2122
+ "learning_rate": 7.792207792207792e-06,
2123
+ "loss": 1.0357,
2124
+ "step": 302
2125
+ },
2126
+ {
2127
+ "epoch": 0.9696,
2128
+ "grad_norm": 0.1984029859304428,
2129
+ "learning_rate": 7.142857142857143e-06,
2130
+ "loss": 1.0648,
2131
+ "step": 303
2132
+ },
2133
+ {
2134
+ "epoch": 0.9728,
2135
+ "grad_norm": 0.17972829937934875,
2136
+ "learning_rate": 6.493506493506493e-06,
2137
+ "loss": 1.0033,
2138
+ "step": 304
2139
+ },
2140
+ {
2141
+ "epoch": 0.976,
2142
+ "grad_norm": 0.1818286031484604,
2143
+ "learning_rate": 5.844155844155844e-06,
2144
+ "loss": 0.997,
2145
+ "step": 305
2146
+ },
2147
+ {
2148
+ "epoch": 0.9792,
2149
+ "grad_norm": 0.19670912623405457,
2150
+ "learning_rate": 5.194805194805195e-06,
2151
+ "loss": 1.0256,
2152
+ "step": 306
2153
+ },
2154
+ {
2155
+ "epoch": 0.9824,
2156
+ "grad_norm": 0.20527283847332,
2157
+ "learning_rate": 4.5454545454545455e-06,
2158
+ "loss": 1.0348,
2159
+ "step": 307
2160
+ },
2161
+ {
2162
+ "epoch": 0.9856,
2163
+ "grad_norm": 0.19025909900665283,
2164
+ "learning_rate": 3.896103896103896e-06,
2165
+ "loss": 1.0682,
2166
+ "step": 308
2167
+ },
2168
+ {
2169
+ "epoch": 0.9888,
2170
+ "grad_norm": 0.19544818997383118,
2171
+ "learning_rate": 3.2467532467532465e-06,
2172
+ "loss": 0.9872,
2173
+ "step": 309
2174
+ },
2175
+ {
2176
+ "epoch": 0.992,
2177
+ "grad_norm": 0.22112183272838593,
2178
+ "learning_rate": 2.5974025974025976e-06,
2179
+ "loss": 1.0661,
2180
+ "step": 310
2181
+ },
2182
+ {
2183
+ "epoch": 0.9952,
2184
+ "grad_norm": 0.23328153789043427,
2185
+ "learning_rate": 1.948051948051948e-06,
2186
+ "loss": 1.0691,
2187
+ "step": 311
2188
+ },
2189
+ {
2190
+ "epoch": 0.9984,
2191
+ "grad_norm": 0.20181375741958618,
2192
+ "learning_rate": 1.2987012987012988e-06,
2193
+ "loss": 0.9416,
2194
+ "step": 312
2195
+ },
2196
+ {
2197
+ "epoch": 1.0,
2198
+ "grad_norm": 0.29312625527381897,
2199
+ "learning_rate": 6.493506493506494e-07,
2200
+ "loss": 1.1216,
2201
+ "step": 313
2202
+ },
2203
+ {
2204
+ "epoch": 0.12202467696492762,
2205
+ "grad_norm": 0.2231415957212448,
2206
+ "learning_rate": 0.0,
2207
+ "loss": 1.0468,
2208
+ "step": 314
2209
+ },
2210
+ {
2211
+ "epoch": 0.12241329058583503,
2212
+ "grad_norm": 0.22263288497924805,
2213
+ "learning_rate": 0.00017594394706111328,
2214
+ "loss": 1.0399,
2215
+ "step": 315
2216
+ },
2217
+ {
2218
+ "epoch": 0.12280190420674245,
2219
+ "grad_norm": 0.22909891605377197,
2220
+ "learning_rate": 0.00017586609575710393,
2221
+ "loss": 1.1069,
2222
+ "step": 316
2223
+ },
2224
+ {
2225
+ "epoch": 0.12319051782764986,
2226
+ "grad_norm": 0.23951445519924164,
2227
+ "learning_rate": 0.0001757882444530946,
2228
+ "loss": 1.1036,
2229
+ "step": 317
2230
+ },
2231
+ {
2232
+ "epoch": 0.12357913144855727,
2233
+ "grad_norm": 0.2409268021583557,
2234
+ "learning_rate": 0.00017571039314908526,
2235
+ "loss": 1.1114,
2236
+ "step": 318
2237
+ },
2238
+ {
2239
+ "epoch": 0.12396774506946469,
2240
+ "grad_norm": 0.23753899335861206,
2241
+ "learning_rate": 0.00017563254184507592,
2242
+ "loss": 1.1297,
2243
+ "step": 319
2244
+ },
2245
+ {
2246
+ "epoch": 0.12435635869037209,
2247
+ "grad_norm": 0.2823902666568756,
2248
+ "learning_rate": 0.00017555469054106657,
2249
+ "loss": 1.1293,
2250
+ "step": 320
2251
+ },
2252
+ {
2253
+ "epoch": 0.12474497231127951,
2254
+ "grad_norm": 0.24093545973300934,
2255
+ "learning_rate": 0.00017547683923705722,
2256
+ "loss": 1.0678,
2257
+ "step": 321
2258
+ },
2259
+ {
2260
+ "epoch": 0.12513358593218693,
2261
+ "grad_norm": 0.22565563023090363,
2262
+ "learning_rate": 0.0001753989879330479,
2263
+ "loss": 1.1408,
2264
+ "step": 322
2265
+ },
2266
+ {
2267
+ "epoch": 0.12552219955309435,
2268
+ "grad_norm": 0.22569572925567627,
2269
+ "learning_rate": 0.00017532113662903855,
2270
+ "loss": 1.0543,
2271
+ "step": 323
2272
+ },
2273
+ {
2274
+ "epoch": 0.12591081317400174,
2275
+ "grad_norm": 0.24962866306304932,
2276
+ "learning_rate": 0.0001752432853250292,
2277
+ "loss": 1.0818,
2278
+ "step": 324
2279
+ },
2280
+ {
2281
+ "epoch": 0.12629942679490916,
2282
+ "grad_norm": 0.22184576094150543,
2283
+ "learning_rate": 0.00017516543402101986,
2284
+ "loss": 1.0835,
2285
+ "step": 325
2286
+ },
2287
+ {
2288
+ "epoch": 0.12668804041581658,
2289
+ "grad_norm": 0.2572194039821625,
2290
+ "learning_rate": 0.0001750875827170105,
2291
+ "loss": 1.0767,
2292
+ "step": 326
2293
+ },
2294
+ {
2295
+ "epoch": 0.127076654036724,
2296
+ "grad_norm": 0.24131342768669128,
2297
+ "learning_rate": 0.00017500973141300116,
2298
+ "loss": 1.0981,
2299
+ "step": 327
2300
+ },
2301
+ {
2302
+ "epoch": 0.1274652676576314,
2303
+ "grad_norm": 0.2386389970779419,
2304
+ "learning_rate": 0.00017493188010899184,
2305
+ "loss": 1.0828,
2306
+ "step": 328
2307
+ },
2308
+ {
2309
+ "epoch": 0.1278538812785388,
2310
+ "grad_norm": 0.2654125690460205,
2311
+ "learning_rate": 0.0001748540288049825,
2312
+ "loss": 1.1266,
2313
+ "step": 329
2314
+ },
2315
+ {
2316
+ "epoch": 0.12824249489944622,
2317
+ "grad_norm": 0.2925739884376526,
2318
+ "learning_rate": 0.00017477617750097314,
2319
+ "loss": 1.0983,
2320
+ "step": 330
2321
+ },
2322
+ {
2323
+ "epoch": 0.12863110852035364,
2324
+ "grad_norm": 0.26589342951774597,
2325
+ "learning_rate": 0.0001746983261969638,
2326
+ "loss": 1.1029,
2327
+ "step": 331
2328
+ },
2329
+ {
2330
+ "epoch": 0.12901972214126106,
2331
+ "grad_norm": 0.24565957486629486,
2332
+ "learning_rate": 0.00017462047489295445,
2333
+ "loss": 1.0975,
2334
+ "step": 332
2335
+ },
2336
+ {
2337
+ "epoch": 0.12940833576216845,
2338
+ "grad_norm": 0.2459682673215866,
2339
+ "learning_rate": 0.00017454262358894513,
2340
+ "loss": 1.0566,
2341
+ "step": 333
2342
+ },
2343
+ {
2344
+ "epoch": 0.12979694938307587,
2345
+ "grad_norm": 0.23349183797836304,
2346
+ "learning_rate": 0.00017446477228493578,
2347
+ "loss": 1.0833,
2348
+ "step": 334
2349
+ },
2350
+ {
2351
+ "epoch": 0.1301855630039833,
2352
+ "grad_norm": 0.26166337728500366,
2353
+ "learning_rate": 0.00017438692098092643,
2354
+ "loss": 1.1598,
2355
+ "step": 335
2356
+ },
2357
+ {
2358
+ "epoch": 0.1305741766248907,
2359
+ "grad_norm": 0.24188168346881866,
2360
+ "learning_rate": 0.00017430906967691708,
2361
+ "loss": 1.0728,
2362
+ "step": 336
2363
+ },
2364
+ {
2365
+ "epoch": 0.13096279024579813,
2366
+ "grad_norm": 0.22922398149967194,
2367
+ "learning_rate": 0.00017423121837290773,
2368
+ "loss": 1.0311,
2369
+ "step": 337
2370
+ },
2371
+ {
2372
+ "epoch": 0.13135140386670552,
2373
+ "grad_norm": 0.2652754485607147,
2374
+ "learning_rate": 0.00017415336706889841,
2375
+ "loss": 1.1096,
2376
+ "step": 338
2377
+ },
2378
+ {
2379
+ "epoch": 0.13174001748761294,
2380
+ "grad_norm": 0.2355881780385971,
2381
+ "learning_rate": 0.00017407551576488907,
2382
+ "loss": 1.0964,
2383
+ "step": 339
2384
+ },
2385
+ {
2386
+ "epoch": 0.13212863110852036,
2387
+ "grad_norm": 0.244523823261261,
2388
+ "learning_rate": 0.00017399766446087972,
2389
+ "loss": 1.142,
2390
+ "step": 340
2391
+ },
2392
+ {
2393
+ "epoch": 0.13251724472942777,
2394
+ "grad_norm": 0.24705976247787476,
2395
+ "learning_rate": 0.00017391981315687037,
2396
+ "loss": 1.0943,
2397
+ "step": 341
2398
+ },
2399
+ {
2400
+ "epoch": 0.13290585835033517,
2401
+ "grad_norm": 0.22817552089691162,
2402
+ "learning_rate": 0.00017384196185286102,
2403
+ "loss": 1.0621,
2404
+ "step": 342
2405
+ },
2406
+ {
2407
+ "epoch": 0.13329447197124258,
2408
+ "grad_norm": 0.22605225443840027,
2409
+ "learning_rate": 0.0001737641105488517,
2410
+ "loss": 1.0714,
2411
+ "step": 343
2412
+ },
2413
+ {
2414
+ "epoch": 0.13368308559215,
2415
+ "grad_norm": 0.2584545314311981,
2416
+ "learning_rate": 0.00017368625924484235,
2417
+ "loss": 1.1367,
2418
+ "step": 344
2419
+ },
2420
+ {
2421
+ "epoch": 0.13407169921305742,
2422
+ "grad_norm": 0.2248220443725586,
2423
+ "learning_rate": 0.000173608407940833,
2424
+ "loss": 1.0872,
2425
+ "step": 345
2426
+ },
2427
+ {
2428
+ "epoch": 0.13446031283396484,
2429
+ "grad_norm": 0.2141868770122528,
2430
+ "learning_rate": 0.00017353055663682368,
2431
+ "loss": 1.0572,
2432
+ "step": 346
2433
+ },
2434
+ {
2435
+ "epoch": 0.13484892645487223,
2436
+ "grad_norm": 0.2615523934364319,
2437
+ "learning_rate": 0.00017345270533281434,
2438
+ "loss": 1.1048,
2439
+ "step": 347
2440
+ },
2441
+ {
2442
+ "epoch": 0.13523754007577965,
2443
+ "grad_norm": 0.22990448772907257,
2444
+ "learning_rate": 0.000173374854028805,
2445
+ "loss": 1.0528,
2446
+ "step": 348
2447
+ },
2448
+ {
2449
+ "epoch": 0.13562615369668707,
2450
+ "grad_norm": 0.2132262885570526,
2451
+ "learning_rate": 0.00017329700272479564,
2452
+ "loss": 1.0476,
2453
+ "step": 349
2454
+ },
2455
+ {
2456
+ "epoch": 0.1360147673175945,
2457
+ "grad_norm": 0.2578272819519043,
2458
+ "learning_rate": 0.00017321915142078632,
2459
+ "loss": 1.0852,
2460
+ "step": 350
2461
+ },
2462
+ {
2463
+ "epoch": 0.1364033809385019,
2464
+ "grad_norm": 0.22881457209587097,
2465
+ "learning_rate": 0.00017314130011677697,
2466
+ "loss": 1.1017,
2467
+ "step": 351
2468
+ },
2469
+ {
2470
+ "epoch": 0.1367919945594093,
2471
+ "grad_norm": 0.21067696809768677,
2472
+ "learning_rate": 0.00017306344881276762,
2473
+ "loss": 1.0444,
2474
+ "step": 352
2475
+ },
2476
+ {
2477
+ "epoch": 0.13718060818031672,
2478
+ "grad_norm": 0.2304215282201767,
2479
+ "learning_rate": 0.0001729855975087583,
2480
+ "loss": 1.0737,
2481
+ "step": 353
2482
+ },
2483
+ {
2484
+ "epoch": 0.13756922180122413,
2485
+ "grad_norm": 0.2031925916671753,
2486
+ "learning_rate": 0.00017290774620474895,
2487
+ "loss": 1.0036,
2488
+ "step": 354
2489
+ },
2490
+ {
2491
+ "epoch": 0.13795783542213155,
2492
+ "grad_norm": 0.27281051874160767,
2493
+ "learning_rate": 0.0001728298949007396,
2494
+ "loss": 1.148,
2495
+ "step": 355
2496
+ },
2497
+ {
2498
+ "epoch": 0.13834644904303897,
2499
+ "grad_norm": 0.204191654920578,
2500
+ "learning_rate": 0.00017275204359673026,
2501
+ "loss": 0.9607,
2502
+ "step": 356
2503
+ },
2504
+ {
2505
+ "epoch": 0.13873506266394636,
2506
+ "grad_norm": 0.221976637840271,
2507
+ "learning_rate": 0.0001726741922927209,
2508
+ "loss": 1.1068,
2509
+ "step": 357
2510
+ },
2511
+ {
2512
+ "epoch": 0.13912367628485378,
2513
+ "grad_norm": 0.20831729471683502,
2514
+ "learning_rate": 0.0001725963409887116,
2515
+ "loss": 1.034,
2516
+ "step": 358
2517
+ },
2518
+ {
2519
+ "epoch": 0.1395122899057612,
2520
+ "grad_norm": 0.21639779210090637,
2521
+ "learning_rate": 0.00017251848968470224,
2522
+ "loss": 1.0613,
2523
+ "step": 359
2524
+ },
2525
+ {
2526
+ "epoch": 0.13990090352666862,
2527
+ "grad_norm": 0.1959424465894699,
2528
+ "learning_rate": 0.0001724406383806929,
2529
+ "loss": 1.0506,
2530
+ "step": 360
2531
+ },
2532
+ {
2533
+ "epoch": 0.140289517147576,
2534
+ "grad_norm": 0.2044398933649063,
2535
+ "learning_rate": 0.00017236278707668355,
2536
+ "loss": 1.0316,
2537
+ "step": 361
2538
+ },
2539
+ {
2540
+ "epoch": 0.14067813076848343,
2541
+ "grad_norm": 0.21483004093170166,
2542
+ "learning_rate": 0.0001722849357726742,
2543
+ "loss": 1.0361,
2544
+ "step": 362
2545
+ },
2546
+ {
2547
+ "epoch": 0.14106674438939085,
2548
+ "grad_norm": 0.237701416015625,
2549
+ "learning_rate": 0.00017220708446866485,
2550
+ "loss": 1.1264,
2551
+ "step": 363
2552
+ },
2553
+ {
2554
+ "epoch": 0.14145535801029827,
2555
+ "grad_norm": 0.20750795304775238,
2556
+ "learning_rate": 0.00017212923316465553,
2557
+ "loss": 1.0523,
2558
+ "step": 364
2559
+ },
2560
+ {
2561
+ "epoch": 0.14184397163120568,
2562
+ "grad_norm": 0.2252965271472931,
2563
+ "learning_rate": 0.00017205138186064618,
2564
+ "loss": 1.0764,
2565
+ "step": 365
2566
+ },
2567
+ {
2568
+ "epoch": 0.14223258525211308,
2569
+ "grad_norm": 0.2033565789461136,
2570
+ "learning_rate": 0.00017197353055663683,
2571
+ "loss": 1.064,
2572
+ "step": 366
2573
+ },
2574
+ {
2575
+ "epoch": 0.1426211988730205,
2576
+ "grad_norm": 0.21123190224170685,
2577
+ "learning_rate": 0.00017189567925262749,
2578
+ "loss": 1.0515,
2579
+ "step": 367
2580
+ },
2581
+ {
2582
+ "epoch": 0.1430098124939279,
2583
+ "grad_norm": 0.20646221935749054,
2584
+ "learning_rate": 0.00017181782794861814,
2585
+ "loss": 1.0617,
2586
+ "step": 368
2587
+ },
2588
+ {
2589
+ "epoch": 0.14339842611483533,
2590
+ "grad_norm": 0.2079589068889618,
2591
+ "learning_rate": 0.00017173997664460882,
2592
+ "loss": 1.0569,
2593
+ "step": 369
2594
+ },
2595
+ {
2596
+ "epoch": 0.14378703973574275,
2597
+ "grad_norm": 0.216246098279953,
2598
+ "learning_rate": 0.00017166212534059947,
2599
+ "loss": 1.0986,
2600
+ "step": 370
2601
+ },
2602
+ {
2603
+ "epoch": 0.14417565335665014,
2604
+ "grad_norm": 0.20711806416511536,
2605
+ "learning_rate": 0.00017158427403659012,
2606
+ "loss": 1.1342,
2607
+ "step": 371
2608
+ },
2609
+ {
2610
+ "epoch": 0.14456426697755756,
2611
+ "grad_norm": 0.235435351729393,
2612
+ "learning_rate": 0.00017150642273258077,
2613
+ "loss": 1.1082,
2614
+ "step": 372
2615
+ },
2616
+ {
2617
+ "epoch": 0.14495288059846498,
2618
+ "grad_norm": 0.2273191511631012,
2619
+ "learning_rate": 0.00017142857142857143,
2620
+ "loss": 1.1064,
2621
+ "step": 373
2622
+ },
2623
+ {
2624
+ "epoch": 0.1453414942193724,
2625
+ "grad_norm": 0.2075672745704651,
2626
+ "learning_rate": 0.0001713507201245621,
2627
+ "loss": 1.0536,
2628
+ "step": 374
2629
+ },
2630
+ {
2631
+ "epoch": 0.14573010784027982,
2632
+ "grad_norm": 0.20764274895191193,
2633
+ "learning_rate": 0.00017127286882055276,
2634
+ "loss": 1.0673,
2635
+ "step": 375
2636
+ },
2637
+ {
2638
+ "epoch": 0.1461187214611872,
2639
+ "grad_norm": 0.2441243678331375,
2640
+ "learning_rate": 0.0001711950175165434,
2641
+ "loss": 1.1271,
2642
+ "step": 376
2643
+ },
2644
+ {
2645
+ "epoch": 0.14650733508209463,
2646
+ "grad_norm": 0.2383374124765396,
2647
+ "learning_rate": 0.00017111716621253406,
2648
+ "loss": 1.083,
2649
+ "step": 377
2650
+ },
2651
+ {
2652
+ "epoch": 0.14689594870300204,
2653
+ "grad_norm": 0.2172410786151886,
2654
+ "learning_rate": 0.0001710393149085247,
2655
+ "loss": 1.0605,
2656
+ "step": 378
2657
+ },
2658
+ {
2659
+ "epoch": 0.14728456232390946,
2660
+ "grad_norm": 0.22591541707515717,
2661
+ "learning_rate": 0.0001709614636045154,
2662
+ "loss": 1.0931,
2663
+ "step": 379
2664
+ },
2665
+ {
2666
+ "epoch": 0.14767317594481685,
2667
+ "grad_norm": 0.23099495470523834,
2668
+ "learning_rate": 0.00017088361230050604,
2669
+ "loss": 1.1021,
2670
+ "step": 380
2671
+ },
2672
+ {
2673
+ "epoch": 0.14806178956572427,
2674
+ "grad_norm": 0.21461094915866852,
2675
+ "learning_rate": 0.0001708057609964967,
2676
+ "loss": 1.0959,
2677
+ "step": 381
2678
+ },
2679
+ {
2680
+ "epoch": 0.1484504031866317,
2681
+ "grad_norm": 0.21557241678237915,
2682
+ "learning_rate": 0.00017072790969248735,
2683
+ "loss": 1.0155,
2684
+ "step": 382
2685
+ },
2686
+ {
2687
+ "epoch": 0.1488390168075391,
2688
+ "grad_norm": 0.234396293759346,
2689
+ "learning_rate": 0.000170650058388478,
2690
+ "loss": 1.1289,
2691
+ "step": 383
2692
+ },
2693
+ {
2694
+ "epoch": 0.14922763042844653,
2695
+ "grad_norm": 0.22895503044128418,
2696
+ "learning_rate": 0.00017057220708446868,
2697
+ "loss": 0.9919,
2698
+ "step": 384
2699
+ },
2700
+ {
2701
+ "epoch": 0.14961624404935392,
2702
+ "grad_norm": 0.2054683268070221,
2703
+ "learning_rate": 0.00017049435578045933,
2704
+ "loss": 1.0607,
2705
+ "step": 385
2706
+ },
2707
+ {
2708
+ "epoch": 0.15000485767026134,
2709
+ "grad_norm": 0.25569215416908264,
2710
+ "learning_rate": 0.00017041650447644998,
2711
+ "loss": 1.0517,
2712
+ "step": 386
2713
+ },
2714
+ {
2715
+ "epoch": 0.15039347129116876,
2716
+ "grad_norm": 0.2222641259431839,
2717
+ "learning_rate": 0.00017033865317244064,
2718
+ "loss": 1.0404,
2719
+ "step": 387
2720
+ },
2721
+ {
2722
+ "epoch": 0.15078208491207618,
2723
+ "grad_norm": 0.20501169562339783,
2724
+ "learning_rate": 0.0001702608018684313,
2725
+ "loss": 0.9897,
2726
+ "step": 388
2727
+ },
2728
+ {
2729
+ "epoch": 0.1511706985329836,
2730
+ "grad_norm": 0.22080403566360474,
2731
+ "learning_rate": 0.00017018295056442197,
2732
+ "loss": 1.1013,
2733
+ "step": 389
2734
+ },
2735
+ {
2736
+ "epoch": 0.15155931215389098,
2737
+ "grad_norm": 0.21218529343605042,
2738
+ "learning_rate": 0.00017010509926041262,
2739
+ "loss": 1.0541,
2740
+ "step": 390
2741
+ },
2742
+ {
2743
+ "epoch": 0.1519479257747984,
2744
+ "grad_norm": 0.23064807057380676,
2745
+ "learning_rate": 0.00017002724795640327,
2746
+ "loss": 1.037,
2747
+ "step": 391
2748
+ },
2749
+ {
2750
+ "epoch": 0.15233653939570582,
2751
+ "grad_norm": 0.21164493262767792,
2752
+ "learning_rate": 0.00016994939665239392,
2753
+ "loss": 1.0769,
2754
+ "step": 392
2755
+ },
2756
+ {
2757
+ "epoch": 0.15272515301661324,
2758
+ "grad_norm": 0.22565549612045288,
2759
+ "learning_rate": 0.00016987154534838457,
2760
+ "loss": 1.0638,
2761
+ "step": 393
2762
+ },
2763
+ {
2764
+ "epoch": 0.15311376663752063,
2765
+ "grad_norm": 0.22492647171020508,
2766
+ "learning_rate": 0.00016979369404437525,
2767
+ "loss": 1.063,
2768
+ "step": 394
2769
+ },
2770
+ {
2771
+ "epoch": 0.15350238025842805,
2772
+ "grad_norm": 0.22335395216941833,
2773
+ "learning_rate": 0.0001697158427403659,
2774
+ "loss": 1.1032,
2775
+ "step": 395
2776
+ },
2777
+ {
2778
+ "epoch": 0.15389099387933547,
2779
+ "grad_norm": 0.2164154201745987,
2780
+ "learning_rate": 0.00016963799143635656,
2781
+ "loss": 1.1275,
2782
+ "step": 396
2783
+ },
2784
+ {
2785
+ "epoch": 0.1542796075002429,
2786
+ "grad_norm": 0.22547736763954163,
2787
+ "learning_rate": 0.0001695601401323472,
2788
+ "loss": 1.1324,
2789
+ "step": 397
2790
+ },
2791
+ {
2792
+ "epoch": 0.1546682211211503,
2793
+ "grad_norm": 0.2028045952320099,
2794
+ "learning_rate": 0.0001694822888283379,
2795
+ "loss": 1.0057,
2796
+ "step": 398
2797
+ },
2798
+ {
2799
+ "epoch": 0.1550568347420577,
2800
+ "grad_norm": 0.20770573616027832,
2801
+ "learning_rate": 0.00016940443752432854,
2802
+ "loss": 1.0311,
2803
+ "step": 399
2804
+ },
2805
+ {
2806
+ "epoch": 0.15544544836296512,
2807
+ "grad_norm": 0.2231476902961731,
2808
+ "learning_rate": 0.0001693265862203192,
2809
+ "loss": 1.0535,
2810
+ "step": 400
2811
+ },
2812
+ {
2813
+ "epoch": 0.15583406198387253,
2814
+ "grad_norm": 0.21618099510669708,
2815
+ "learning_rate": 0.00016924873491630987,
2816
+ "loss": 1.0616,
2817
+ "step": 401
2818
+ },
2819
+ {
2820
+ "epoch": 0.15622267560477995,
2821
+ "grad_norm": 0.24024419486522675,
2822
+ "learning_rate": 0.00016917088361230052,
2823
+ "loss": 1.1324,
2824
+ "step": 402
2825
+ },
2826
+ {
2827
+ "epoch": 0.15661128922568737,
2828
+ "grad_norm": 0.2002171128988266,
2829
+ "learning_rate": 0.00016909303230829118,
2830
+ "loss": 1.015,
2831
+ "step": 403
2832
+ },
2833
+ {
2834
+ "epoch": 0.15699990284659476,
2835
+ "grad_norm": 0.21771477162837982,
2836
+ "learning_rate": 0.00016901518100428183,
2837
+ "loss": 1.0817,
2838
+ "step": 404
2839
+ },
2840
+ {
2841
+ "epoch": 0.15738851646750218,
2842
+ "grad_norm": 0.22052259743213654,
2843
+ "learning_rate": 0.0001689373297002725,
2844
+ "loss": 1.0836,
2845
+ "step": 405
2846
+ },
2847
+ {
2848
+ "epoch": 0.1577771300884096,
2849
+ "grad_norm": 0.1964062750339508,
2850
+ "learning_rate": 0.00016885947839626316,
2851
+ "loss": 1.0505,
2852
+ "step": 406
2853
+ },
2854
+ {
2855
+ "epoch": 0.15816574370931702,
2856
+ "grad_norm": 0.22714298963546753,
2857
+ "learning_rate": 0.0001687816270922538,
2858
+ "loss": 1.0702,
2859
+ "step": 407
2860
+ },
2861
+ {
2862
+ "epoch": 0.15855435733022444,
2863
+ "grad_norm": 0.20647728443145752,
2864
+ "learning_rate": 0.00016870377578824446,
2865
+ "loss": 1.0349,
2866
+ "step": 408
2867
+ },
2868
+ {
2869
+ "epoch": 0.15894297095113183,
2870
+ "grad_norm": 0.2355160117149353,
2871
+ "learning_rate": 0.00016862592448423512,
2872
+ "loss": 1.0305,
2873
+ "step": 409
2874
+ },
2875
+ {
2876
+ "epoch": 0.15933158457203925,
2877
+ "grad_norm": 0.22890770435333252,
2878
+ "learning_rate": 0.0001685480731802258,
2879
+ "loss": 1.0854,
2880
+ "step": 410
2881
+ },
2882
+ {
2883
+ "epoch": 0.15972019819294667,
2884
+ "grad_norm": 0.21947838366031647,
2885
+ "learning_rate": 0.00016847022187621645,
2886
+ "loss": 1.0948,
2887
+ "step": 411
2888
+ },
2889
+ {
2890
+ "epoch": 0.16010881181385409,
2891
+ "grad_norm": 0.22334899008274078,
2892
+ "learning_rate": 0.0001683923705722071,
2893
+ "loss": 1.006,
2894
+ "step": 412
2895
+ },
2896
+ {
2897
+ "epoch": 0.16049742543476148,
2898
+ "grad_norm": 0.22324936091899872,
2899
+ "learning_rate": 0.00016831451926819775,
2900
+ "loss": 1.0402,
2901
+ "step": 413
2902
+ },
2903
+ {
2904
+ "epoch": 0.1608860390556689,
2905
+ "grad_norm": 0.21462097764015198,
2906
+ "learning_rate": 0.0001682366679641884,
2907
+ "loss": 1.077,
2908
+ "step": 414
2909
+ },
2910
+ {
2911
+ "epoch": 0.1612746526765763,
2912
+ "grad_norm": 0.24567006528377533,
2913
+ "learning_rate": 0.00016815881666017908,
2914
+ "loss": 1.15,
2915
+ "step": 415
2916
+ },
2917
+ {
2918
+ "epoch": 0.16166326629748373,
2919
+ "grad_norm": 0.26437243819236755,
2920
+ "learning_rate": 0.00016808096535616973,
2921
+ "loss": 1.1251,
2922
+ "step": 416
2923
+ },
2924
+ {
2925
+ "epoch": 0.16205187991839115,
2926
+ "grad_norm": 0.2217959761619568,
2927
+ "learning_rate": 0.00016800311405216039,
2928
+ "loss": 1.1103,
2929
+ "step": 417
2930
+ },
2931
+ {
2932
+ "epoch": 0.16244049353929854,
2933
+ "grad_norm": 0.24402475357055664,
2934
+ "learning_rate": 0.00016792526274815104,
2935
+ "loss": 1.0672,
2936
+ "step": 418
2937
+ },
2938
+ {
2939
+ "epoch": 0.16282910716020596,
2940
+ "grad_norm": 0.21609526872634888,
2941
+ "learning_rate": 0.0001678474114441417,
2942
+ "loss": 1.0291,
2943
+ "step": 419
2944
+ },
2945
+ {
2946
+ "epoch": 0.16321772078111338,
2947
+ "grad_norm": 0.20054642856121063,
2948
+ "learning_rate": 0.00016776956014013237,
2949
+ "loss": 1.0704,
2950
+ "step": 420
2951
+ },
2952
+ {
2953
+ "epoch": 0.1636063344020208,
2954
+ "grad_norm": 0.22864869236946106,
2955
+ "learning_rate": 0.00016769170883612302,
2956
+ "loss": 1.0612,
2957
+ "step": 421
2958
+ },
2959
+ {
2960
+ "epoch": 0.16399494802292822,
2961
+ "grad_norm": 0.22651974856853485,
2962
+ "learning_rate": 0.00016761385753211367,
2963
+ "loss": 1.0749,
2964
+ "step": 422
2965
+ },
2966
+ {
2967
+ "epoch": 0.1643835616438356,
2968
+ "grad_norm": 0.21587328612804413,
2969
+ "learning_rate": 0.00016753600622810433,
2970
+ "loss": 1.0398,
2971
+ "step": 423
2972
+ },
2973
+ {
2974
+ "epoch": 0.16477217526474303,
2975
+ "grad_norm": 0.1953774094581604,
2976
+ "learning_rate": 0.00016745815492409498,
2977
+ "loss": 1.0275,
2978
+ "step": 424
2979
+ },
2980
+ {
2981
+ "epoch": 0.16516078888565044,
2982
+ "grad_norm": 0.21803410351276398,
2983
+ "learning_rate": 0.00016738030362008566,
2984
+ "loss": 1.1219,
2985
+ "step": 425
2986
+ },
2987
+ {
2988
+ "epoch": 0.16554940250655786,
2989
+ "grad_norm": 0.2034682035446167,
2990
+ "learning_rate": 0.0001673024523160763,
2991
+ "loss": 1.0342,
2992
+ "step": 426
2993
+ },
2994
+ {
2995
+ "epoch": 0.16593801612746525,
2996
+ "grad_norm": 0.20135951042175293,
2997
+ "learning_rate": 0.00016722460101206696,
2998
+ "loss": 0.9802,
2999
+ "step": 427
3000
+ },
3001
+ {
3002
+ "epoch": 0.16632662974837267,
3003
+ "grad_norm": 0.23310376703739166,
3004
+ "learning_rate": 0.0001671467497080576,
3005
+ "loss": 1.0789,
3006
+ "step": 428
3007
+ },
3008
+ {
3009
+ "epoch": 0.1667152433692801,
3010
+ "grad_norm": 0.21475404500961304,
3011
+ "learning_rate": 0.00016706889840404827,
3012
+ "loss": 1.0416,
3013
+ "step": 429
3014
+ },
3015
+ {
3016
+ "epoch": 0.1671038569901875,
3017
+ "grad_norm": 0.21661072969436646,
3018
+ "learning_rate": 0.00016699104710003894,
3019
+ "loss": 1.0568,
3020
+ "step": 430
3021
+ },
3022
+ {
3023
+ "epoch": 0.16749247061109493,
3024
+ "grad_norm": 0.20310629904270172,
3025
+ "learning_rate": 0.0001669131957960296,
3026
+ "loss": 0.9968,
3027
+ "step": 431
3028
+ },
3029
+ {
3030
+ "epoch": 0.16788108423200232,
3031
+ "grad_norm": 0.2596947252750397,
3032
+ "learning_rate": 0.00016683534449202025,
3033
+ "loss": 1.0478,
3034
+ "step": 432
3035
+ },
3036
+ {
3037
+ "epoch": 0.16826969785290974,
3038
+ "grad_norm": 0.22226987779140472,
3039
+ "learning_rate": 0.0001667574931880109,
3040
+ "loss": 1.0898,
3041
+ "step": 433
3042
+ },
3043
+ {
3044
+ "epoch": 0.16865831147381716,
3045
+ "grad_norm": 0.22499911487102509,
3046
+ "learning_rate": 0.00016667964188400155,
3047
+ "loss": 1.07,
3048
+ "step": 434
3049
+ },
3050
+ {
3051
+ "epoch": 0.16904692509472458,
3052
+ "grad_norm": 0.2717292308807373,
3053
+ "learning_rate": 0.0001666017905799922,
3054
+ "loss": 1.0562,
3055
+ "step": 435
3056
+ },
3057
+ {
3058
+ "epoch": 0.169435538715632,
3059
+ "grad_norm": 0.22052323818206787,
3060
+ "learning_rate": 0.00016652393927598288,
3061
+ "loss": 1.0732,
3062
+ "step": 436
3063
+ },
3064
+ {
3065
+ "epoch": 0.16982415233653939,
3066
+ "grad_norm": 0.21741728484630585,
3067
+ "learning_rate": 0.00016644608797197354,
3068
+ "loss": 1.0409,
3069
+ "step": 437
3070
+ },
3071
+ {
3072
+ "epoch": 0.1702127659574468,
3073
+ "grad_norm": 0.20701193809509277,
3074
+ "learning_rate": 0.0001663682366679642,
3075
+ "loss": 1.0731,
3076
+ "step": 438
3077
+ },
3078
+ {
3079
+ "epoch": 0.17060137957835422,
3080
+ "grad_norm": 0.22071130573749542,
3081
+ "learning_rate": 0.00016629038536395484,
3082
+ "loss": 1.0992,
3083
+ "step": 439
3084
+ },
3085
+ {
3086
+ "epoch": 0.17098999319926164,
3087
+ "grad_norm": 0.20261412858963013,
3088
+ "learning_rate": 0.0001662125340599455,
3089
+ "loss": 1.0051,
3090
+ "step": 440
3091
+ },
3092
+ {
3093
+ "epoch": 0.17137860682016906,
3094
+ "grad_norm": 0.2082947939634323,
3095
+ "learning_rate": 0.00016613468275593617,
3096
+ "loss": 1.0477,
3097
+ "step": 441
3098
+ },
3099
+ {
3100
+ "epoch": 0.17176722044107645,
3101
+ "grad_norm": 0.22534717619419098,
3102
+ "learning_rate": 0.00016605683145192682,
3103
+ "loss": 1.041,
3104
+ "step": 442
3105
+ },
3106
+ {
3107
+ "epoch": 0.17215583406198387,
3108
+ "grad_norm": 0.21547731757164001,
3109
+ "learning_rate": 0.00016597898014791748,
3110
+ "loss": 1.0528,
3111
+ "step": 443
3112
+ },
3113
+ {
3114
+ "epoch": 0.1725444476828913,
3115
+ "grad_norm": 0.24141089618206024,
3116
+ "learning_rate": 0.00016590112884390813,
3117
+ "loss": 1.0928,
3118
+ "step": 444
3119
+ },
3120
+ {
3121
+ "epoch": 0.1729330613037987,
3122
+ "grad_norm": 0.21910884976387024,
3123
+ "learning_rate": 0.00016582327753989878,
3124
+ "loss": 1.063,
3125
+ "step": 445
3126
+ },
3127
+ {
3128
+ "epoch": 0.1733216749247061,
3129
+ "grad_norm": 0.21782316267490387,
3130
+ "learning_rate": 0.00016574542623588946,
3131
+ "loss": 1.0976,
3132
+ "step": 446
3133
+ },
3134
+ {
3135
+ "epoch": 0.17371028854561352,
3136
+ "grad_norm": 0.21771778166294098,
3137
+ "learning_rate": 0.0001656675749318801,
3138
+ "loss": 1.0677,
3139
+ "step": 447
3140
+ },
3141
+ {
3142
+ "epoch": 0.17409890216652094,
3143
+ "grad_norm": 0.22117659449577332,
3144
+ "learning_rate": 0.00016558972362787076,
3145
+ "loss": 1.0669,
3146
+ "step": 448
3147
+ },
3148
+ {
3149
+ "epoch": 0.17448751578742835,
3150
+ "grad_norm": 0.21918092668056488,
3151
+ "learning_rate": 0.00016551187232386141,
3152
+ "loss": 1.0955,
3153
+ "step": 449
3154
+ },
3155
+ {
3156
+ "epoch": 0.17487612940833577,
3157
+ "grad_norm": 0.22027818858623505,
3158
+ "learning_rate": 0.0001654340210198521,
3159
+ "loss": 1.0201,
3160
+ "step": 450
3161
+ },
3162
+ {
3163
+ "epoch": 0.17526474302924316,
3164
+ "grad_norm": 0.2042885720729828,
3165
+ "learning_rate": 0.00016535616971584275,
3166
+ "loss": 1.0881,
3167
+ "step": 451
3168
+ },
3169
+ {
3170
+ "epoch": 0.17565335665015058,
3171
+ "grad_norm": 0.21788261830806732,
3172
+ "learning_rate": 0.0001652783184118334,
3173
+ "loss": 1.0918,
3174
+ "step": 452
3175
+ },
3176
+ {
3177
+ "epoch": 0.176041970271058,
3178
+ "grad_norm": 0.23332571983337402,
3179
+ "learning_rate": 0.00016520046710782408,
3180
+ "loss": 1.091,
3181
+ "step": 453
3182
+ },
3183
+ {
3184
+ "epoch": 0.17643058389196542,
3185
+ "grad_norm": 0.20204192399978638,
3186
+ "learning_rate": 0.00016512261580381473,
3187
+ "loss": 1.0366,
3188
+ "step": 454
3189
+ },
3190
+ {
3191
+ "epoch": 0.17681919751287284,
3192
+ "grad_norm": 0.21761906147003174,
3193
+ "learning_rate": 0.00016504476449980538,
3194
+ "loss": 1.0131,
3195
+ "step": 455
3196
+ },
3197
+ {
3198
+ "epoch": 0.17720781113378023,
3199
+ "grad_norm": 0.2152051478624344,
3200
+ "learning_rate": 0.00016496691319579606,
3201
+ "loss": 1.0868,
3202
+ "step": 456
3203
+ },
3204
+ {
3205
+ "epoch": 0.17759642475468765,
3206
+ "grad_norm": 0.22776494920253754,
3207
+ "learning_rate": 0.0001648890618917867,
3208
+ "loss": 1.0807,
3209
+ "step": 457
3210
+ },
3211
+ {
3212
+ "epoch": 0.17798503837559507,
3213
+ "grad_norm": 0.2171342968940735,
3214
+ "learning_rate": 0.00016481121058777736,
3215
+ "loss": 1.0537,
3216
+ "step": 458
3217
+ },
3218
+ {
3219
+ "epoch": 0.17837365199650249,
3220
+ "grad_norm": 0.2046273946762085,
3221
+ "learning_rate": 0.00016473335928376802,
3222
+ "loss": 1.0097,
3223
+ "step": 459
3224
+ },
3225
+ {
3226
+ "epoch": 0.17876226561740988,
3227
+ "grad_norm": 0.2047681361436844,
3228
+ "learning_rate": 0.00016465550797975867,
3229
+ "loss": 1.0204,
3230
+ "step": 460
3231
+ },
3232
+ {
3233
+ "epoch": 0.1791508792383173,
3234
+ "grad_norm": 0.1876862645149231,
3235
+ "learning_rate": 0.00016457765667574935,
3236
+ "loss": 0.9383,
3237
+ "step": 461
3238
+ },
3239
+ {
3240
+ "epoch": 0.17953949285922471,
3241
+ "grad_norm": 0.218430757522583,
3242
+ "learning_rate": 0.00016449980537174,
3243
+ "loss": 1.0721,
3244
+ "step": 462
3245
+ },
3246
+ {
3247
+ "epoch": 0.17992810648013213,
3248
+ "grad_norm": 0.2245480865240097,
3249
+ "learning_rate": 0.00016442195406773065,
3250
+ "loss": 1.0859,
3251
+ "step": 463
3252
+ },
3253
+ {
3254
+ "epoch": 0.18031672010103955,
3255
+ "grad_norm": 0.22577151656150818,
3256
+ "learning_rate": 0.0001643441027637213,
3257
+ "loss": 1.0825,
3258
+ "step": 464
3259
+ },
3260
+ {
3261
+ "epoch": 0.18070533372194694,
3262
+ "grad_norm": 0.20132745802402496,
3263
+ "learning_rate": 0.00016426625145971196,
3264
+ "loss": 1.0615,
3265
+ "step": 465
3266
+ },
3267
+ {
3268
+ "epoch": 0.18109394734285436,
3269
+ "grad_norm": 0.2277505248785019,
3270
+ "learning_rate": 0.00016418840015570263,
3271
+ "loss": 1.0426,
3272
+ "step": 466
3273
+ },
3274
+ {
3275
+ "epoch": 0.18148256096376178,
3276
+ "grad_norm": 0.22540105879306793,
3277
+ "learning_rate": 0.0001641105488516933,
3278
+ "loss": 1.0481,
3279
+ "step": 467
3280
+ },
3281
+ {
3282
+ "epoch": 0.1818711745846692,
3283
+ "grad_norm": 0.20358088612556458,
3284
+ "learning_rate": 0.00016403269754768394,
3285
+ "loss": 1.0286,
3286
+ "step": 468
3287
+ },
3288
+ {
3289
+ "epoch": 0.18225978820557662,
3290
+ "grad_norm": 0.22534145414829254,
3291
+ "learning_rate": 0.0001639548462436746,
3292
+ "loss": 1.1183,
3293
+ "step": 469
3294
+ },
3295
+ {
3296
+ "epoch": 0.182648401826484,
3297
+ "grad_norm": 0.2188873142004013,
3298
+ "learning_rate": 0.00016387699493966524,
3299
+ "loss": 1.0439,
3300
+ "step": 470
3301
+ },
3302
+ {
3303
+ "epoch": 0.18303701544739143,
3304
+ "grad_norm": 0.2128048539161682,
3305
+ "learning_rate": 0.00016379914363565592,
3306
+ "loss": 1.027,
3307
+ "step": 471
3308
+ },
3309
+ {
3310
+ "epoch": 0.18342562906829885,
3311
+ "grad_norm": 0.2518141567707062,
3312
+ "learning_rate": 0.00016372129233164657,
3313
+ "loss": 1.0468,
3314
+ "step": 472
3315
+ },
3316
+ {
3317
+ "epoch": 0.18381424268920626,
3318
+ "grad_norm": 0.2189142256975174,
3319
+ "learning_rate": 0.00016364344102763723,
3320
+ "loss": 1.0581,
3321
+ "step": 473
3322
+ },
3323
+ {
3324
+ "epoch": 0.18420285631011368,
3325
+ "grad_norm": 0.31266725063323975,
3326
+ "learning_rate": 0.00016356558972362788,
3327
+ "loss": 1.0554,
3328
+ "step": 474
3329
+ },
3330
+ {
3331
+ "epoch": 0.18459146993102107,
3332
+ "grad_norm": 0.21343916654586792,
3333
+ "learning_rate": 0.00016348773841961853,
3334
+ "loss": 1.0795,
3335
+ "step": 475
3336
+ },
3337
+ {
3338
+ "epoch": 0.1849800835519285,
3339
+ "grad_norm": 0.22907280921936035,
3340
+ "learning_rate": 0.00016340988711560918,
3341
+ "loss": 1.0304,
3342
+ "step": 476
3343
+ },
3344
+ {
3345
+ "epoch": 0.1853686971728359,
3346
+ "grad_norm": 0.2105257511138916,
3347
+ "learning_rate": 0.00016333203581159986,
3348
+ "loss": 1.0231,
3349
+ "step": 477
3350
+ },
3351
+ {
3352
+ "epoch": 0.18575731079374333,
3353
+ "grad_norm": 0.19537831842899323,
3354
+ "learning_rate": 0.00016325418450759051,
3355
+ "loss": 1.0103,
3356
+ "step": 478
3357
+ },
3358
+ {
3359
+ "epoch": 0.18614592441465072,
3360
+ "grad_norm": 0.20522372424602509,
3361
+ "learning_rate": 0.00016317633320358117,
3362
+ "loss": 1.0196,
3363
+ "step": 479
3364
+ },
3365
+ {
3366
+ "epoch": 0.18653453803555814,
3367
+ "grad_norm": 0.21646477282047272,
3368
+ "learning_rate": 0.00016309848189957182,
3369
+ "loss": 1.0579,
3370
+ "step": 480
3371
+ },
3372
+ {
3373
+ "epoch": 0.18692315165646556,
3374
+ "grad_norm": 0.21077193319797516,
3375
+ "learning_rate": 0.00016302063059556247,
3376
+ "loss": 1.0638,
3377
+ "step": 481
3378
+ },
3379
+ {
3380
+ "epoch": 0.18731176527737298,
3381
+ "grad_norm": 0.20357473194599152,
3382
+ "learning_rate": 0.00016294277929155315,
3383
+ "loss": 1.0635,
3384
+ "step": 482
3385
+ },
3386
+ {
3387
+ "epoch": 0.1877003788982804,
3388
+ "grad_norm": 0.2188001275062561,
3389
+ "learning_rate": 0.0001628649279875438,
3390
+ "loss": 1.0267,
3391
+ "step": 483
3392
+ },
3393
+ {
3394
+ "epoch": 0.1880889925191878,
3395
+ "grad_norm": 0.2128928154706955,
3396
+ "learning_rate": 0.00016278707668353445,
3397
+ "loss": 0.9706,
3398
+ "step": 484
3399
+ },
3400
+ {
3401
+ "epoch": 0.1884776061400952,
3402
+ "grad_norm": 0.22081372141838074,
3403
+ "learning_rate": 0.0001627092253795251,
3404
+ "loss": 1.08,
3405
+ "step": 485
3406
+ },
3407
+ {
3408
+ "epoch": 0.18886621976100262,
3409
+ "grad_norm": 0.2250615805387497,
3410
+ "learning_rate": 0.00016263137407551576,
3411
+ "loss": 1.1451,
3412
+ "step": 486
3413
+ },
3414
+ {
3415
+ "epoch": 0.18925483338191004,
3416
+ "grad_norm": 0.1984967589378357,
3417
+ "learning_rate": 0.00016255352277150644,
3418
+ "loss": 1.0744,
3419
+ "step": 487
3420
+ },
3421
+ {
3422
+ "epoch": 0.18964344700281746,
3423
+ "grad_norm": 0.20778900384902954,
3424
+ "learning_rate": 0.0001624756714674971,
3425
+ "loss": 1.0623,
3426
+ "step": 488
3427
+ },
3428
+ {
3429
+ "epoch": 0.19003206062372485,
3430
+ "grad_norm": 0.2026563137769699,
3431
+ "learning_rate": 0.00016239782016348774,
3432
+ "loss": 1.0714,
3433
+ "step": 489
3434
+ },
3435
+ {
3436
+ "epoch": 0.19042067424463227,
3437
+ "grad_norm": 0.21598374843597412,
3438
+ "learning_rate": 0.0001623199688594784,
3439
+ "loss": 1.0869,
3440
+ "step": 490
3441
+ },
3442
+ {
3443
+ "epoch": 0.1908092878655397,
3444
+ "grad_norm": 0.18944978713989258,
3445
+ "learning_rate": 0.00016224211755546904,
3446
+ "loss": 1.055,
3447
+ "step": 491
3448
+ },
3449
+ {
3450
+ "epoch": 0.1911979014864471,
3451
+ "grad_norm": 0.20698946714401245,
3452
+ "learning_rate": 0.00016216426625145972,
3453
+ "loss": 1.0392,
3454
+ "step": 492
3455
+ },
3456
+ {
3457
+ "epoch": 0.1915865151073545,
3458
+ "grad_norm": 0.22395353019237518,
3459
+ "learning_rate": 0.00016208641494745038,
3460
+ "loss": 1.0681,
3461
+ "step": 493
3462
+ },
3463
+ {
3464
+ "epoch": 0.19197512872826192,
3465
+ "grad_norm": 0.22372962534427643,
3466
+ "learning_rate": 0.00016200856364344103,
3467
+ "loss": 1.0767,
3468
+ "step": 494
3469
+ },
3470
+ {
3471
+ "epoch": 0.19236374234916934,
3472
+ "grad_norm": 0.2066701054573059,
3473
+ "learning_rate": 0.00016193071233943168,
3474
+ "loss": 1.0061,
3475
+ "step": 495
3476
+ },
3477
+ {
3478
+ "epoch": 0.19275235597007676,
3479
+ "grad_norm": 0.19716408848762512,
3480
+ "learning_rate": 0.00016185286103542233,
3481
+ "loss": 1.039,
3482
+ "step": 496
3483
+ },
3484
+ {
3485
+ "epoch": 0.19314096959098417,
3486
+ "grad_norm": 0.22159601747989655,
3487
+ "learning_rate": 0.000161775009731413,
3488
+ "loss": 1.0832,
3489
+ "step": 497
3490
+ },
3491
+ {
3492
+ "epoch": 0.19352958321189156,
3493
+ "grad_norm": 0.21509626507759094,
3494
+ "learning_rate": 0.00016169715842740366,
3495
+ "loss": 1.0264,
3496
+ "step": 498
3497
+ },
3498
+ {
3499
+ "epoch": 0.19391819683279898,
3500
+ "grad_norm": 0.21598199009895325,
3501
+ "learning_rate": 0.00016161930712339431,
3502
+ "loss": 1.049,
3503
+ "step": 499
3504
+ },
3505
+ {
3506
+ "epoch": 0.1943068104537064,
3507
+ "grad_norm": 0.20279590785503387,
3508
+ "learning_rate": 0.00016154145581938497,
3509
+ "loss": 1.0505,
3510
+ "step": 500
3511
+ }
3512
+ ],
3513
+ "logging_steps": 1,
3514
+ "max_steps": 2574,
3515
+ "num_input_tokens_seen": 0,
3516
+ "num_train_epochs": 1,
3517
+ "save_steps": 500,
3518
+ "stateful_callbacks": {
3519
+ "TrainerControl": {
3520
+ "args": {
3521
+ "should_epoch_stop": false,
3522
+ "should_evaluate": false,
3523
+ "should_log": false,
3524
+ "should_save": true,
3525
+ "should_training_stop": false
3526
+ },
3527
+ "attributes": {}
3528
+ }
3529
+ },
3530
+ "total_flos": 4.3571235778270986e+18,
3531
+ "train_batch_size": 4,
3532
+ "trial_name": null,
3533
+ "trial_params": null
3534
+ }