Komma-LuisMiSanVe commited on
Commit
1e6c9f0
·
verified ·
1 Parent(s): c1c7d48

Upload model files

Browse files

Upload safetensors and GGUF models

.gitattributes CHANGED
@@ -35,3 +35,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
  train.json filter=lfs diff=lfs merge=lfs -text
37
  LangToSQL-1.3B-F16.gguf filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
  train.json filter=lfs diff=lfs merge=lfs -text
37
  LangToSQL-1.3B-F16.gguf filter=lfs diff=lfs merge=lfs -text
38
+ LangToSQL-1.5B-F16.gguf filter=lfs diff=lfs merge=lfs -text
39
+ sql-model-merged/tokenizer.json filter=lfs diff=lfs merge=lfs -text
40
+ sql-model/checkpoint-1750/tokenizer.json filter=lfs diff=lfs merge=lfs -text
41
+ sql-model/tokenizer.json filter=lfs diff=lfs merge=lfs -text
LangToSQL-1.5B-F16.gguf ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:29e6ea365070b1fd1afe089af5d212e4e894181cb076b8e1a5e310ec3e5aec05
3
+ size 3093668832
sql-model-merged/chat_template.jinja ADDED
@@ -0,0 +1,54 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0]['role'] == 'system' %}
4
+ {{- messages[0]['content'] }}
5
+ {%- else %}
6
+ {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
7
+ {%- endif %}
8
+ {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
9
+ {%- for tool in tools %}
10
+ {{- "\n" }}
11
+ {{- tool | tojson }}
12
+ {%- endfor %}
13
+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
14
+ {%- else %}
15
+ {%- if messages[0]['role'] == 'system' %}
16
+ {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
17
+ {%- else %}
18
+ {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
19
+ {%- endif %}
20
+ {%- endif %}
21
+ {%- for message in messages %}
22
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
23
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
24
+ {%- elif message.role == "assistant" %}
25
+ {{- '<|im_start|>' + message.role }}
26
+ {%- if message.content %}
27
+ {{- '\n' + message.content }}
28
+ {%- endif %}
29
+ {%- for tool_call in message.tool_calls %}
30
+ {%- if tool_call.function is defined %}
31
+ {%- set tool_call = tool_call.function %}
32
+ {%- endif %}
33
+ {{- '\n<tool_call>\n{"name": "' }}
34
+ {{- tool_call.name }}
35
+ {{- '", "arguments": ' }}
36
+ {{- tool_call.arguments | tojson }}
37
+ {{- '}\n</tool_call>' }}
38
+ {%- endfor %}
39
+ {{- '<|im_end|>\n' }}
40
+ {%- elif message.role == "tool" %}
41
+ {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
42
+ {{- '<|im_start|>user' }}
43
+ {%- endif %}
44
+ {{- '\n<tool_response>\n' }}
45
+ {{- message.content }}
46
+ {{- '\n</tool_response>' }}
47
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
48
+ {{- '<|im_end|>\n' }}
49
+ {%- endif %}
50
+ {%- endif %}
51
+ {%- endfor %}
52
+ {%- if add_generation_prompt %}
53
+ {{- '<|im_start|>assistant\n' }}
54
+ {%- endif %}
sql-model-merged/config.json ADDED
@@ -0,0 +1,61 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "Qwen2ForCausalLM"
4
+ ],
5
+ "attention_dropout": 0.0,
6
+ "bos_token_id": 151643,
7
+ "dtype": "float32",
8
+ "eos_token_id": 151645,
9
+ "hidden_act": "silu",
10
+ "hidden_size": 1536,
11
+ "initializer_range": 0.02,
12
+ "intermediate_size": 8960,
13
+ "layer_types": [
14
+ "full_attention",
15
+ "full_attention",
16
+ "full_attention",
17
+ "full_attention",
18
+ "full_attention",
19
+ "full_attention",
20
+ "full_attention",
21
+ "full_attention",
22
+ "full_attention",
23
+ "full_attention",
24
+ "full_attention",
25
+ "full_attention",
26
+ "full_attention",
27
+ "full_attention",
28
+ "full_attention",
29
+ "full_attention",
30
+ "full_attention",
31
+ "full_attention",
32
+ "full_attention",
33
+ "full_attention",
34
+ "full_attention",
35
+ "full_attention",
36
+ "full_attention",
37
+ "full_attention",
38
+ "full_attention",
39
+ "full_attention",
40
+ "full_attention",
41
+ "full_attention"
42
+ ],
43
+ "max_position_embeddings": 32768,
44
+ "max_window_layers": 28,
45
+ "model_type": "qwen2",
46
+ "num_attention_heads": 12,
47
+ "num_hidden_layers": 28,
48
+ "num_key_value_heads": 2,
49
+ "pad_token_id": null,
50
+ "rms_norm_eps": 1e-06,
51
+ "rope_parameters": {
52
+ "rope_theta": 1000000.0,
53
+ "rope_type": "default"
54
+ },
55
+ "sliding_window": null,
56
+ "tie_word_embeddings": true,
57
+ "transformers_version": "5.4.0",
58
+ "use_cache": true,
59
+ "use_sliding_window": false,
60
+ "vocab_size": 151936
61
+ }
sql-model-merged/generation_config.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token_id": 151643,
3
+ "do_sample": true,
4
+ "eos_token_id": [
5
+ 151645,
6
+ 151643
7
+ ],
8
+ "pad_token_id": 151643,
9
+ "repetition_penalty": 1.1,
10
+ "temperature": 0.7,
11
+ "top_k": 20,
12
+ "top_p": 0.8,
13
+ "transformers_version": "5.4.0"
14
+ }
sql-model-merged/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ed8302c1aacc01908dcd07d76566b907e8caafc15972c5c404b75c49e8277a14
3
+ size 6174895536
sql-model-merged/tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
3
+ size 11421892
sql-model-merged/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|im_end|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 32768,
25
+ "pad_token": "<|im_end|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
sql-model/README.md ADDED
@@ -0,0 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
3
+ library_name: peft
4
+ model_name: sql-model
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen2.5-Coder-1.5B-Instruct
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ licence: license
12
+ pipeline_tag: text-generation
13
+ ---
14
+
15
+ # Model Card for sql-model
16
+
17
+ This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct).
18
+ It has been trained using [TRL](https://github.com/huggingface/trl).
19
+
20
+ ## Quick start
21
+
22
+ ```python
23
+ from transformers import pipeline
24
+
25
+ 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?"
26
+ generator = pipeline("text-generation", model="None", device="cuda")
27
+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
28
+ print(output["generated_text"])
29
+ ```
30
+
31
+ ## Training procedure
32
+
33
+
34
+
35
+
36
+
37
+ This model was trained with SFT.
38
+
39
+ ### Framework versions
40
+
41
+ - PEFT 0.18.1
42
+ - TRL: 1.0.0
43
+ - Transformers: 5.4.0
44
+ - Pytorch: 2.11.0
45
+ - Datasets: 4.8.4
46
+ - Tokenizers: 0.22.2
47
+
48
+ ## Citations
49
+
50
+
51
+
52
+ Cite TRL as:
53
+
54
+ ```bibtex
55
+ @software{vonwerra2020trl,
56
+ title = {{TRL: Transformers Reinforcement Learning}},
57
+ author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
58
+ license = {Apache-2.0},
59
+ url = {https://github.com/huggingface/trl},
60
+ year = {2020}
61
+ }
62
+ ```
sql-model/adapter_config.json ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen2.5-Coder-1.5B-Instruct",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.05,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "peft_version": "0.18.1",
27
+ "qalora_group_size": 16,
28
+ "r": 16,
29
+ "rank_pattern": {},
30
+ "revision": null,
31
+ "target_modules": [
32
+ "v_proj",
33
+ "q_proj"
34
+ ],
35
+ "target_parameters": null,
36
+ "task_type": "CAUSAL_LM",
37
+ "trainable_token_indices": null,
38
+ "use_dora": false,
39
+ "use_qalora": false,
40
+ "use_rslora": false
41
+ }
sql-model/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:233052429edaba0de411ff7a503eec8183193d955664da50103ee110cb835952
3
+ size 8731128
sql-model/chat_template.jinja ADDED
@@ -0,0 +1,54 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0]['role'] == 'system' %}
4
+ {{- messages[0]['content'] }}
5
+ {%- else %}
6
+ {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
7
+ {%- endif %}
8
+ {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
9
+ {%- for tool in tools %}
10
+ {{- "\n" }}
11
+ {{- tool | tojson }}
12
+ {%- endfor %}
13
+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
14
+ {%- else %}
15
+ {%- if messages[0]['role'] == 'system' %}
16
+ {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
17
+ {%- else %}
18
+ {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
19
+ {%- endif %}
20
+ {%- endif %}
21
+ {%- for message in messages %}
22
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
23
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
24
+ {%- elif message.role == "assistant" %}
25
+ {{- '<|im_start|>' + message.role }}
26
+ {%- if message.content %}
27
+ {{- '\n' + message.content }}
28
+ {%- endif %}
29
+ {%- for tool_call in message.tool_calls %}
30
+ {%- if tool_call.function is defined %}
31
+ {%- set tool_call = tool_call.function %}
32
+ {%- endif %}
33
+ {{- '\n<tool_call>\n{"name": "' }}
34
+ {{- tool_call.name }}
35
+ {{- '", "arguments": ' }}
36
+ {{- tool_call.arguments | tojson }}
37
+ {{- '}\n</tool_call>' }}
38
+ {%- endfor %}
39
+ {{- '<|im_end|>\n' }}
40
+ {%- elif message.role == "tool" %}
41
+ {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
42
+ {{- '<|im_start|>user' }}
43
+ {%- endif %}
44
+ {{- '\n<tool_response>\n' }}
45
+ {{- message.content }}
46
+ {{- '\n</tool_response>' }}
47
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
48
+ {{- '<|im_end|>\n' }}
49
+ {%- endif %}
50
+ {%- endif %}
51
+ {%- endfor %}
52
+ {%- if add_generation_prompt %}
53
+ {{- '<|im_start|>assistant\n' }}
54
+ {%- endif %}
sql-model/checkpoint-1750/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen2.5-Coder-1.5B-Instruct
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
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 0.18.1
sql-model/checkpoint-1750/adapter_config.json ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "Qwen/Qwen2.5-Coder-1.5B-Instruct",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.05,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "peft_version": "0.18.1",
27
+ "qalora_group_size": 16,
28
+ "r": 16,
29
+ "rank_pattern": {},
30
+ "revision": null,
31
+ "target_modules": [
32
+ "v_proj",
33
+ "q_proj"
34
+ ],
35
+ "target_parameters": null,
36
+ "task_type": "CAUSAL_LM",
37
+ "trainable_token_indices": null,
38
+ "use_dora": false,
39
+ "use_qalora": false,
40
+ "use_rslora": false
41
+ }
sql-model/checkpoint-1750/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:233052429edaba0de411ff7a503eec8183193d955664da50103ee110cb835952
3
+ size 8731128
sql-model/checkpoint-1750/chat_template.jinja ADDED
@@ -0,0 +1,54 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0]['role'] == 'system' %}
4
+ {{- messages[0]['content'] }}
5
+ {%- else %}
6
+ {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
7
+ {%- endif %}
8
+ {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
9
+ {%- for tool in tools %}
10
+ {{- "\n" }}
11
+ {{- tool | tojson }}
12
+ {%- endfor %}
13
+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
14
+ {%- else %}
15
+ {%- if messages[0]['role'] == 'system' %}
16
+ {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
17
+ {%- else %}
18
+ {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
19
+ {%- endif %}
20
+ {%- endif %}
21
+ {%- for message in messages %}
22
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
23
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
24
+ {%- elif message.role == "assistant" %}
25
+ {{- '<|im_start|>' + message.role }}
26
+ {%- if message.content %}
27
+ {{- '\n' + message.content }}
28
+ {%- endif %}
29
+ {%- for tool_call in message.tool_calls %}
30
+ {%- if tool_call.function is defined %}
31
+ {%- set tool_call = tool_call.function %}
32
+ {%- endif %}
33
+ {{- '\n<tool_call>\n{"name": "' }}
34
+ {{- tool_call.name }}
35
+ {{- '", "arguments": ' }}
36
+ {{- tool_call.arguments | tojson }}
37
+ {{- '}\n</tool_call>' }}
38
+ {%- endfor %}
39
+ {{- '<|im_end|>\n' }}
40
+ {%- elif message.role == "tool" %}
41
+ {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
42
+ {{- '<|im_start|>user' }}
43
+ {%- endif %}
44
+ {{- '\n<tool_response>\n' }}
45
+ {{- message.content }}
46
+ {{- '\n</tool_response>' }}
47
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
48
+ {{- '<|im_end|>\n' }}
49
+ {%- endif %}
50
+ {%- endif %}
51
+ {%- endfor %}
52
+ {%- if add_generation_prompt %}
53
+ {{- '<|im_start|>assistant\n' }}
54
+ {%- endif %}
sql-model/checkpoint-1750/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6f63982639d2cdd3d73b243be88644fe815c0b4a48555410eb414422116b7ade
3
+ size 17524171
sql-model/checkpoint-1750/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c593dbe7b4c13895455ed97063d53435660103dbe2e0cf605b493badb4ad85cd
3
+ size 14455
sql-model/checkpoint-1750/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:af667abda37bcebe1e332a00c2289b57845721907fb4438e53936d778ed7af82
3
+ size 1465
sql-model/checkpoint-1750/tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
3
+ size 11421892
sql-model/checkpoint-1750/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|im_end|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 32768,
25
+ "pad_token": "<|endoftext|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }
sql-model/checkpoint-1750/trainer_state.json ADDED
@@ -0,0 +1,1784 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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": 1750,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 1.755023404955864,
14
+ "epoch": 0.005714285714285714,
15
+ "grad_norm": 0.4571238160133362,
16
+ "learning_rate": 0.00019897142857142858,
17
+ "loss": 3.2381954193115234,
18
+ "mean_token_accuracy": 0.556605902314186,
19
+ "num_tokens": 3387.0,
20
+ "step": 10
21
+ },
22
+ {
23
+ "entropy": 1.872155451774597,
24
+ "epoch": 0.011428571428571429,
25
+ "grad_norm": 0.34199467301368713,
26
+ "learning_rate": 0.00019782857142857142,
27
+ "loss": 2.191742706298828,
28
+ "mean_token_accuracy": 0.6503265604376793,
29
+ "num_tokens": 6716.0,
30
+ "step": 20
31
+ },
32
+ {
33
+ "entropy": 1.484403568506241,
34
+ "epoch": 0.017142857142857144,
35
+ "grad_norm": 0.3380744755268097,
36
+ "learning_rate": 0.0001966857142857143,
37
+ "loss": 1.4328497886657714,
38
+ "mean_token_accuracy": 0.746163409948349,
39
+ "num_tokens": 9752.0,
40
+ "step": 30
41
+ },
42
+ {
43
+ "entropy": 1.0218224853277207,
44
+ "epoch": 0.022857142857142857,
45
+ "grad_norm": 0.2288217842578888,
46
+ "learning_rate": 0.00019554285714285717,
47
+ "loss": 1.1417624473571777,
48
+ "mean_token_accuracy": 0.8017501533031464,
49
+ "num_tokens": 13031.0,
50
+ "step": 40
51
+ },
52
+ {
53
+ "entropy": 0.9253160357475281,
54
+ "epoch": 0.02857142857142857,
55
+ "grad_norm": 0.3444674015045166,
56
+ "learning_rate": 0.0001944,
57
+ "loss": 1.0559259414672852,
58
+ "mean_token_accuracy": 0.8092378318309784,
59
+ "num_tokens": 16325.0,
60
+ "step": 50
61
+ },
62
+ {
63
+ "entropy": 0.8747231267392636,
64
+ "epoch": 0.03428571428571429,
65
+ "grad_norm": 0.39356729388237,
66
+ "learning_rate": 0.00019325714285714287,
67
+ "loss": 0.8799959182739258,
68
+ "mean_token_accuracy": 0.8227440923452377,
69
+ "num_tokens": 19968.0,
70
+ "step": 60
71
+ },
72
+ {
73
+ "entropy": 0.8573793575167656,
74
+ "epoch": 0.04,
75
+ "grad_norm": 0.20990096032619476,
76
+ "learning_rate": 0.0001921142857142857,
77
+ "loss": 0.844728660583496,
78
+ "mean_token_accuracy": 0.8225094452500343,
79
+ "num_tokens": 23194.0,
80
+ "step": 70
81
+ },
82
+ {
83
+ "entropy": 0.8150502145290375,
84
+ "epoch": 0.045714285714285714,
85
+ "grad_norm": 0.2119598537683487,
86
+ "learning_rate": 0.00019097142857142857,
87
+ "loss": 0.7866294860839844,
88
+ "mean_token_accuracy": 0.8332153782248497,
89
+ "num_tokens": 26444.0,
90
+ "step": 80
91
+ },
92
+ {
93
+ "entropy": 0.7989433646202088,
94
+ "epoch": 0.05142857142857143,
95
+ "grad_norm": 5.590895175933838,
96
+ "learning_rate": 0.00018982857142857144,
97
+ "loss": 0.7787356853485108,
98
+ "mean_token_accuracy": 0.8374833926558495,
99
+ "num_tokens": 29847.0,
100
+ "step": 90
101
+ },
102
+ {
103
+ "entropy": 0.7925440274178982,
104
+ "epoch": 0.05714285714285714,
105
+ "grad_norm": 0.2303859442472458,
106
+ "learning_rate": 0.00018868571428571428,
107
+ "loss": 0.7289025783538818,
108
+ "mean_token_accuracy": 0.8440518975257874,
109
+ "num_tokens": 33416.0,
110
+ "step": 100
111
+ },
112
+ {
113
+ "entropy": 0.7396377846598625,
114
+ "epoch": 0.06285714285714286,
115
+ "grad_norm": 0.1661251187324524,
116
+ "learning_rate": 0.00018754285714285714,
117
+ "loss": 0.7113327980041504,
118
+ "mean_token_accuracy": 0.844328448176384,
119
+ "num_tokens": 36508.0,
120
+ "step": 110
121
+ },
122
+ {
123
+ "entropy": 0.7262422397732735,
124
+ "epoch": 0.06857142857142857,
125
+ "grad_norm": 0.16159392893314362,
126
+ "learning_rate": 0.00018640000000000003,
127
+ "loss": 0.7208394050598145,
128
+ "mean_token_accuracy": 0.8527172908186913,
129
+ "num_tokens": 39969.0,
130
+ "step": 120
131
+ },
132
+ {
133
+ "entropy": 0.7012980431318283,
134
+ "epoch": 0.07428571428571429,
135
+ "grad_norm": 0.18032804131507874,
136
+ "learning_rate": 0.00018525714285714287,
137
+ "loss": 0.7320738792419433,
138
+ "mean_token_accuracy": 0.8414866328239441,
139
+ "num_tokens": 43554.0,
140
+ "step": 130
141
+ },
142
+ {
143
+ "entropy": 0.7090784892439842,
144
+ "epoch": 0.08,
145
+ "grad_norm": 0.1905902475118637,
146
+ "learning_rate": 0.00018411428571428573,
147
+ "loss": 0.7095338821411132,
148
+ "mean_token_accuracy": 0.8467919006943703,
149
+ "num_tokens": 47352.0,
150
+ "step": 140
151
+ },
152
+ {
153
+ "entropy": 0.7508301064372063,
154
+ "epoch": 0.08571428571428572,
155
+ "grad_norm": 0.2042306363582611,
156
+ "learning_rate": 0.00018297142857142857,
157
+ "loss": 0.714650297164917,
158
+ "mean_token_accuracy": 0.8489894285798073,
159
+ "num_tokens": 50592.0,
160
+ "step": 150
161
+ },
162
+ {
163
+ "entropy": 0.7499568641185761,
164
+ "epoch": 0.09142857142857143,
165
+ "grad_norm": 0.14059635996818542,
166
+ "learning_rate": 0.00018182857142857143,
167
+ "loss": 0.7066077709197998,
168
+ "mean_token_accuracy": 0.8459182664752006,
169
+ "num_tokens": 53905.0,
170
+ "step": 160
171
+ },
172
+ {
173
+ "entropy": 0.751581659913063,
174
+ "epoch": 0.09714285714285714,
175
+ "grad_norm": 0.1389404684305191,
176
+ "learning_rate": 0.0001806857142857143,
177
+ "loss": 0.7304620265960693,
178
+ "mean_token_accuracy": 0.8408536836504936,
179
+ "num_tokens": 57406.0,
180
+ "step": 170
181
+ },
182
+ {
183
+ "entropy": 0.7080035664141178,
184
+ "epoch": 0.10285714285714286,
185
+ "grad_norm": 0.20151932537555695,
186
+ "learning_rate": 0.00017954285714285714,
187
+ "loss": 0.6872885704040528,
188
+ "mean_token_accuracy": 0.8518571972846984,
189
+ "num_tokens": 60514.0,
190
+ "step": 180
191
+ },
192
+ {
193
+ "entropy": 0.7358761139214038,
194
+ "epoch": 0.10857142857142857,
195
+ "grad_norm": 0.15893664956092834,
196
+ "learning_rate": 0.0001784,
197
+ "loss": 0.7053659915924072,
198
+ "mean_token_accuracy": 0.8461879312992096,
199
+ "num_tokens": 63978.0,
200
+ "step": 190
201
+ },
202
+ {
203
+ "entropy": 0.7305082514882087,
204
+ "epoch": 0.11428571428571428,
205
+ "grad_norm": 0.17859824001789093,
206
+ "learning_rate": 0.00017725714285714286,
207
+ "loss": 0.7007936954498291,
208
+ "mean_token_accuracy": 0.8456599608063697,
209
+ "num_tokens": 67445.0,
210
+ "step": 200
211
+ },
212
+ {
213
+ "entropy": 0.6865443497896194,
214
+ "epoch": 0.12,
215
+ "grad_norm": 0.17160974442958832,
216
+ "learning_rate": 0.00017611428571428573,
217
+ "loss": 0.684369421005249,
218
+ "mean_token_accuracy": 0.8545770645141602,
219
+ "num_tokens": 71004.0,
220
+ "step": 210
221
+ },
222
+ {
223
+ "entropy": 0.743726947158575,
224
+ "epoch": 0.12571428571428572,
225
+ "grad_norm": 0.19258913397789001,
226
+ "learning_rate": 0.0001749714285714286,
227
+ "loss": 0.6941751956939697,
228
+ "mean_token_accuracy": 0.8491073668003082,
229
+ "num_tokens": 74467.0,
230
+ "step": 220
231
+ },
232
+ {
233
+ "entropy": 0.7293999463319778,
234
+ "epoch": 0.13142857142857142,
235
+ "grad_norm": 0.21078741550445557,
236
+ "learning_rate": 0.00017382857142857143,
237
+ "loss": 0.7608419418334961,
238
+ "mean_token_accuracy": 0.8355702117085457,
239
+ "num_tokens": 77733.0,
240
+ "step": 230
241
+ },
242
+ {
243
+ "entropy": 0.6695528343319893,
244
+ "epoch": 0.13714285714285715,
245
+ "grad_norm": 0.2082340568304062,
246
+ "learning_rate": 0.0001726857142857143,
247
+ "loss": 0.6619296073913574,
248
+ "mean_token_accuracy": 0.8572208806872368,
249
+ "num_tokens": 81271.0,
250
+ "step": 240
251
+ },
252
+ {
253
+ "entropy": 0.7246910102665425,
254
+ "epoch": 0.14285714285714285,
255
+ "grad_norm": 0.20099493861198425,
256
+ "learning_rate": 0.00017154285714285716,
257
+ "loss": 0.7173333168029785,
258
+ "mean_token_accuracy": 0.8494016170501709,
259
+ "num_tokens": 84574.0,
260
+ "step": 250
261
+ },
262
+ {
263
+ "entropy": 0.6853026911616326,
264
+ "epoch": 0.14857142857142858,
265
+ "grad_norm": 0.17009125649929047,
266
+ "learning_rate": 0.0001704,
267
+ "loss": 0.6357984066009521,
268
+ "mean_token_accuracy": 0.8530513703823089,
269
+ "num_tokens": 88152.0,
270
+ "step": 260
271
+ },
272
+ {
273
+ "entropy": 0.6933697812259197,
274
+ "epoch": 0.15428571428571428,
275
+ "grad_norm": 0.24327197670936584,
276
+ "learning_rate": 0.00016925714285714286,
277
+ "loss": 0.6879170417785645,
278
+ "mean_token_accuracy": 0.8545807048678398,
279
+ "num_tokens": 91522.0,
280
+ "step": 270
281
+ },
282
+ {
283
+ "entropy": 0.685890257358551,
284
+ "epoch": 0.16,
285
+ "grad_norm": 0.1667262464761734,
286
+ "learning_rate": 0.00016811428571428572,
287
+ "loss": 0.6770327091217041,
288
+ "mean_token_accuracy": 0.8566557437181472,
289
+ "num_tokens": 94937.0,
290
+ "step": 280
291
+ },
292
+ {
293
+ "entropy": 0.6751709222793579,
294
+ "epoch": 0.1657142857142857,
295
+ "grad_norm": 0.18273314833641052,
296
+ "learning_rate": 0.0001669714285714286,
297
+ "loss": 0.6363730907440186,
298
+ "mean_token_accuracy": 0.86243856549263,
299
+ "num_tokens": 97983.0,
300
+ "step": 290
301
+ },
302
+ {
303
+ "entropy": 0.6693296499550343,
304
+ "epoch": 0.17142857142857143,
305
+ "grad_norm": 0.18416839838027954,
306
+ "learning_rate": 0.00016582857142857145,
307
+ "loss": 0.6525997161865235,
308
+ "mean_token_accuracy": 0.8522453367710113,
309
+ "num_tokens": 101606.0,
310
+ "step": 300
311
+ },
312
+ {
313
+ "entropy": 0.7025774903595448,
314
+ "epoch": 0.17714285714285713,
315
+ "grad_norm": 0.2354522943496704,
316
+ "learning_rate": 0.0001646857142857143,
317
+ "loss": 0.7257501125335694,
318
+ "mean_token_accuracy": 0.8442893981933594,
319
+ "num_tokens": 104894.0,
320
+ "step": 310
321
+ },
322
+ {
323
+ "entropy": 0.7182297334074974,
324
+ "epoch": 0.18285714285714286,
325
+ "grad_norm": 0.525611162185669,
326
+ "learning_rate": 0.00016354285714285715,
327
+ "loss": 0.7307909011840821,
328
+ "mean_token_accuracy": 0.8398969128727913,
329
+ "num_tokens": 108488.0,
330
+ "step": 320
331
+ },
332
+ {
333
+ "entropy": 0.7585869312286377,
334
+ "epoch": 0.18857142857142858,
335
+ "grad_norm": 0.19886285066604614,
336
+ "learning_rate": 0.00016240000000000002,
337
+ "loss": 0.7316296100616455,
338
+ "mean_token_accuracy": 0.8501298695802688,
339
+ "num_tokens": 111621.0,
340
+ "step": 330
341
+ },
342
+ {
343
+ "entropy": 0.7191452518105507,
344
+ "epoch": 0.19428571428571428,
345
+ "grad_norm": 0.18904122710227966,
346
+ "learning_rate": 0.00016125714285714285,
347
+ "loss": 0.7095869541168213,
348
+ "mean_token_accuracy": 0.8502562329173088,
349
+ "num_tokens": 114846.0,
350
+ "step": 340
351
+ },
352
+ {
353
+ "entropy": 0.76069777905941,
354
+ "epoch": 0.2,
355
+ "grad_norm": 0.1604829579591751,
356
+ "learning_rate": 0.00016011428571428572,
357
+ "loss": 0.7304455280303955,
358
+ "mean_token_accuracy": 0.8341712266206741,
359
+ "num_tokens": 118373.0,
360
+ "step": 350
361
+ },
362
+ {
363
+ "entropy": 0.7046815037727356,
364
+ "epoch": 0.2057142857142857,
365
+ "grad_norm": 0.19462066888809204,
366
+ "learning_rate": 0.00015897142857142858,
367
+ "loss": 0.7354346752166748,
368
+ "mean_token_accuracy": 0.8472130700945855,
369
+ "num_tokens": 122029.0,
370
+ "step": 360
371
+ },
372
+ {
373
+ "entropy": 0.6850700139999389,
374
+ "epoch": 0.21142857142857144,
375
+ "grad_norm": 0.22383001446723938,
376
+ "learning_rate": 0.00015782857142857145,
377
+ "loss": 0.6635906219482421,
378
+ "mean_token_accuracy": 0.853803887963295,
379
+ "num_tokens": 125215.0,
380
+ "step": 370
381
+ },
382
+ {
383
+ "entropy": 0.6743280217051506,
384
+ "epoch": 0.21714285714285714,
385
+ "grad_norm": 0.22975340485572815,
386
+ "learning_rate": 0.0001566857142857143,
387
+ "loss": 0.6944728851318359,
388
+ "mean_token_accuracy": 0.8531624928116799,
389
+ "num_tokens": 128377.0,
390
+ "step": 380
391
+ },
392
+ {
393
+ "entropy": 0.6956075571477414,
394
+ "epoch": 0.22285714285714286,
395
+ "grad_norm": 0.16905982792377472,
396
+ "learning_rate": 0.00015554285714285715,
397
+ "loss": 0.6574249267578125,
398
+ "mean_token_accuracy": 0.8537535384297371,
399
+ "num_tokens": 131561.0,
400
+ "step": 390
401
+ },
402
+ {
403
+ "entropy": 0.6535641267895699,
404
+ "epoch": 0.22857142857142856,
405
+ "grad_norm": 0.2020592838525772,
406
+ "learning_rate": 0.0001544,
407
+ "loss": 0.6684637546539307,
408
+ "mean_token_accuracy": 0.8588701456785202,
409
+ "num_tokens": 134886.0,
410
+ "step": 400
411
+ },
412
+ {
413
+ "entropy": 0.7120788738131523,
414
+ "epoch": 0.2342857142857143,
415
+ "grad_norm": 0.17928935587406158,
416
+ "learning_rate": 0.00015325714285714285,
417
+ "loss": 0.6707518100738525,
418
+ "mean_token_accuracy": 0.855300298333168,
419
+ "num_tokens": 138226.0,
420
+ "step": 410
421
+ },
422
+ {
423
+ "entropy": 0.7220979325473309,
424
+ "epoch": 0.24,
425
+ "grad_norm": 0.1849253624677658,
426
+ "learning_rate": 0.00015211428571428571,
427
+ "loss": 0.6626916408538819,
428
+ "mean_token_accuracy": 0.8522223040461541,
429
+ "num_tokens": 141520.0,
430
+ "step": 420
431
+ },
432
+ {
433
+ "entropy": 0.6991067595779896,
434
+ "epoch": 0.24571428571428572,
435
+ "grad_norm": 0.20180848240852356,
436
+ "learning_rate": 0.00015097142857142858,
437
+ "loss": 0.6791836261749268,
438
+ "mean_token_accuracy": 0.8506909489631653,
439
+ "num_tokens": 144798.0,
440
+ "step": 430
441
+ },
442
+ {
443
+ "entropy": 0.6208832249045372,
444
+ "epoch": 0.25142857142857145,
445
+ "grad_norm": 0.16604188084602356,
446
+ "learning_rate": 0.00014982857142857144,
447
+ "loss": 0.6340303897857666,
448
+ "mean_token_accuracy": 0.8603393912315369,
449
+ "num_tokens": 148478.0,
450
+ "step": 440
451
+ },
452
+ {
453
+ "entropy": 0.6770513989031315,
454
+ "epoch": 0.2571428571428571,
455
+ "grad_norm": 0.20192453265190125,
456
+ "learning_rate": 0.0001486857142857143,
457
+ "loss": 0.6619882106781005,
458
+ "mean_token_accuracy": 0.8508818298578262,
459
+ "num_tokens": 152091.0,
460
+ "step": 450
461
+ },
462
+ {
463
+ "entropy": 0.7052303500473499,
464
+ "epoch": 0.26285714285714284,
465
+ "grad_norm": 0.23521800339221954,
466
+ "learning_rate": 0.00014754285714285717,
467
+ "loss": 0.7204936027526856,
468
+ "mean_token_accuracy": 0.8412432789802551,
469
+ "num_tokens": 155676.0,
470
+ "step": 460
471
+ },
472
+ {
473
+ "entropy": 0.7039557799696923,
474
+ "epoch": 0.26857142857142857,
475
+ "grad_norm": 0.18875496089458466,
476
+ "learning_rate": 0.0001464,
477
+ "loss": 0.6947028636932373,
478
+ "mean_token_accuracy": 0.8545891866087914,
479
+ "num_tokens": 159143.0,
480
+ "step": 470
481
+ },
482
+ {
483
+ "entropy": 0.7390237525105476,
484
+ "epoch": 0.2742857142857143,
485
+ "grad_norm": 0.20184092223644257,
486
+ "learning_rate": 0.00014525714285714287,
487
+ "loss": 0.7198336124420166,
488
+ "mean_token_accuracy": 0.8431004419922828,
489
+ "num_tokens": 162539.0,
490
+ "step": 480
491
+ },
492
+ {
493
+ "entropy": 0.7064043849706649,
494
+ "epoch": 0.28,
495
+ "grad_norm": 0.15704013407230377,
496
+ "learning_rate": 0.0001441142857142857,
497
+ "loss": 0.6998549938201905,
498
+ "mean_token_accuracy": 0.8420170620083809,
499
+ "num_tokens": 166150.0,
500
+ "step": 490
501
+ },
502
+ {
503
+ "entropy": 0.6628431506454945,
504
+ "epoch": 0.2857142857142857,
505
+ "grad_norm": 0.1651039719581604,
506
+ "learning_rate": 0.00014297142857142857,
507
+ "loss": 0.6178061485290527,
508
+ "mean_token_accuracy": 0.8624962165951728,
509
+ "num_tokens": 169383.0,
510
+ "step": 500
511
+ },
512
+ {
513
+ "entropy": 0.6844660565257072,
514
+ "epoch": 0.2914285714285714,
515
+ "grad_norm": 0.23858557641506195,
516
+ "learning_rate": 0.00014182857142857144,
517
+ "loss": 0.6924018859863281,
518
+ "mean_token_accuracy": 0.8506158754229546,
519
+ "num_tokens": 172663.0,
520
+ "step": 510
521
+ },
522
+ {
523
+ "entropy": 0.6808311395347119,
524
+ "epoch": 0.29714285714285715,
525
+ "grad_norm": 0.23313727974891663,
526
+ "learning_rate": 0.00014068571428571427,
527
+ "loss": 0.6816984653472901,
528
+ "mean_token_accuracy": 0.8454315170645714,
529
+ "num_tokens": 175956.0,
530
+ "step": 520
531
+ },
532
+ {
533
+ "entropy": 0.683815760165453,
534
+ "epoch": 0.3028571428571429,
535
+ "grad_norm": 0.20086617767810822,
536
+ "learning_rate": 0.00013954285714285717,
537
+ "loss": 0.6635974884033203,
538
+ "mean_token_accuracy": 0.8560041651129723,
539
+ "num_tokens": 179187.0,
540
+ "step": 530
541
+ },
542
+ {
543
+ "entropy": 0.6870512694120408,
544
+ "epoch": 0.30857142857142855,
545
+ "grad_norm": 0.24712982773780823,
546
+ "learning_rate": 0.0001384,
547
+ "loss": 0.702989149093628,
548
+ "mean_token_accuracy": 0.8512916043400764,
549
+ "num_tokens": 182700.0,
550
+ "step": 540
551
+ },
552
+ {
553
+ "entropy": 0.6640612557530403,
554
+ "epoch": 0.3142857142857143,
555
+ "grad_norm": 0.19018641114234924,
556
+ "learning_rate": 0.00013725714285714287,
557
+ "loss": 0.6487014293670654,
558
+ "mean_token_accuracy": 0.8530389070510864,
559
+ "num_tokens": 186178.0,
560
+ "step": 550
561
+ },
562
+ {
563
+ "entropy": 0.6581176854670048,
564
+ "epoch": 0.32,
565
+ "grad_norm": 0.17294897139072418,
566
+ "learning_rate": 0.00013611428571428573,
567
+ "loss": 0.6333216190338135,
568
+ "mean_token_accuracy": 0.8581204935908318,
569
+ "num_tokens": 189593.0,
570
+ "step": 560
571
+ },
572
+ {
573
+ "entropy": 0.6372215747833252,
574
+ "epoch": 0.32571428571428573,
575
+ "grad_norm": 0.20004868507385254,
576
+ "learning_rate": 0.00013497142857142857,
577
+ "loss": 0.634274959564209,
578
+ "mean_token_accuracy": 0.8586296364665031,
579
+ "num_tokens": 192755.0,
580
+ "step": 570
581
+ },
582
+ {
583
+ "entropy": 0.6754648350179195,
584
+ "epoch": 0.3314285714285714,
585
+ "grad_norm": 0.23564350605010986,
586
+ "learning_rate": 0.00013382857142857143,
587
+ "loss": 0.7094334602355957,
588
+ "mean_token_accuracy": 0.8480966657400131,
589
+ "num_tokens": 196315.0,
590
+ "step": 580
591
+ },
592
+ {
593
+ "entropy": 0.66893340498209,
594
+ "epoch": 0.33714285714285713,
595
+ "grad_norm": 0.21135050058364868,
596
+ "learning_rate": 0.0001326857142857143,
597
+ "loss": 0.6854133129119873,
598
+ "mean_token_accuracy": 0.8476407691836357,
599
+ "num_tokens": 199842.0,
600
+ "step": 590
601
+ },
602
+ {
603
+ "entropy": 0.734431654214859,
604
+ "epoch": 0.34285714285714286,
605
+ "grad_norm": 0.15609301626682281,
606
+ "learning_rate": 0.00013154285714285713,
607
+ "loss": 0.6732261657714844,
608
+ "mean_token_accuracy": 0.8533253937959671,
609
+ "num_tokens": 203217.0,
610
+ "step": 600
611
+ },
612
+ {
613
+ "entropy": 0.6809550739824772,
614
+ "epoch": 0.3485714285714286,
615
+ "grad_norm": 0.1637752801179886,
616
+ "learning_rate": 0.0001304,
617
+ "loss": 0.628327465057373,
618
+ "mean_token_accuracy": 0.8617108896374702,
619
+ "num_tokens": 206274.0,
620
+ "step": 610
621
+ },
622
+ {
623
+ "entropy": 0.6702453441917896,
624
+ "epoch": 0.35428571428571426,
625
+ "grad_norm": 0.2080763578414917,
626
+ "learning_rate": 0.00012925714285714286,
627
+ "loss": 0.6497041702270507,
628
+ "mean_token_accuracy": 0.8528500080108643,
629
+ "num_tokens": 209447.0,
630
+ "step": 620
631
+ },
632
+ {
633
+ "entropy": 0.6892564371228218,
634
+ "epoch": 0.36,
635
+ "grad_norm": 0.24688860774040222,
636
+ "learning_rate": 0.00012811428571428573,
637
+ "loss": 0.692191743850708,
638
+ "mean_token_accuracy": 0.8509424239397049,
639
+ "num_tokens": 212753.0,
640
+ "step": 630
641
+ },
642
+ {
643
+ "entropy": 0.6778513200581073,
644
+ "epoch": 0.3657142857142857,
645
+ "grad_norm": 0.23269857466220856,
646
+ "learning_rate": 0.0001269714285714286,
647
+ "loss": 0.6862228393554688,
648
+ "mean_token_accuracy": 0.8516128286719322,
649
+ "num_tokens": 216340.0,
650
+ "step": 640
651
+ },
652
+ {
653
+ "entropy": 0.6519438281655312,
654
+ "epoch": 0.37142857142857144,
655
+ "grad_norm": 0.21059896051883698,
656
+ "learning_rate": 0.00012582857142857143,
657
+ "loss": 0.6068024635314941,
658
+ "mean_token_accuracy": 0.8627916231751442,
659
+ "num_tokens": 219607.0,
660
+ "step": 650
661
+ },
662
+ {
663
+ "entropy": 0.6636812917888164,
664
+ "epoch": 0.37714285714285717,
665
+ "grad_norm": 0.19394846260547638,
666
+ "learning_rate": 0.0001246857142857143,
667
+ "loss": 0.6602859020233154,
668
+ "mean_token_accuracy": 0.8570883437991142,
669
+ "num_tokens": 222993.0,
670
+ "step": 660
671
+ },
672
+ {
673
+ "entropy": 0.6973143525421619,
674
+ "epoch": 0.38285714285714284,
675
+ "grad_norm": 0.19678597152233124,
676
+ "learning_rate": 0.00012354285714285713,
677
+ "loss": 0.7101614475250244,
678
+ "mean_token_accuracy": 0.8465988427400589,
679
+ "num_tokens": 226354.0,
680
+ "step": 670
681
+ },
682
+ {
683
+ "entropy": 0.6470928482711316,
684
+ "epoch": 0.38857142857142857,
685
+ "grad_norm": 0.23552727699279785,
686
+ "learning_rate": 0.0001224,
687
+ "loss": 0.6224793434143067,
688
+ "mean_token_accuracy": 0.8593849778175354,
689
+ "num_tokens": 229839.0,
690
+ "step": 680
691
+ },
692
+ {
693
+ "entropy": 0.6596762828528882,
694
+ "epoch": 0.3942857142857143,
695
+ "grad_norm": 0.24519386887550354,
696
+ "learning_rate": 0.00012125714285714287,
697
+ "loss": 0.6345892429351807,
698
+ "mean_token_accuracy": 0.8584522992372513,
699
+ "num_tokens": 233176.0,
700
+ "step": 690
701
+ },
702
+ {
703
+ "entropy": 0.6750190995633603,
704
+ "epoch": 0.4,
705
+ "grad_norm": 0.18651342391967773,
706
+ "learning_rate": 0.00012011428571428571,
707
+ "loss": 0.6961663722991943,
708
+ "mean_token_accuracy": 0.8505131497979164,
709
+ "num_tokens": 236611.0,
710
+ "step": 700
711
+ },
712
+ {
713
+ "entropy": 0.6748621694743633,
714
+ "epoch": 0.4057142857142857,
715
+ "grad_norm": 0.2448211908340454,
716
+ "learning_rate": 0.00011897142857142857,
717
+ "loss": 0.6589895725250244,
718
+ "mean_token_accuracy": 0.8562820449471473,
719
+ "num_tokens": 239719.0,
720
+ "step": 710
721
+ },
722
+ {
723
+ "entropy": 0.6976276993751526,
724
+ "epoch": 0.4114285714285714,
725
+ "grad_norm": 0.18323859572410583,
726
+ "learning_rate": 0.00011782857142857145,
727
+ "loss": 0.7028826713562012,
728
+ "mean_token_accuracy": 0.8523884430527687,
729
+ "num_tokens": 243277.0,
730
+ "step": 720
731
+ },
732
+ {
733
+ "entropy": 0.6387927994132042,
734
+ "epoch": 0.41714285714285715,
735
+ "grad_norm": 0.22905172407627106,
736
+ "learning_rate": 0.00011668571428571429,
737
+ "loss": 0.5653384685516357,
738
+ "mean_token_accuracy": 0.8658381626009941,
739
+ "num_tokens": 246681.0,
740
+ "step": 730
741
+ },
742
+ {
743
+ "entropy": 0.711549348384142,
744
+ "epoch": 0.4228571428571429,
745
+ "grad_norm": 0.22809088230133057,
746
+ "learning_rate": 0.00011554285714285715,
747
+ "loss": 0.7091411590576172,
748
+ "mean_token_accuracy": 0.8466275259852409,
749
+ "num_tokens": 250174.0,
750
+ "step": 740
751
+ },
752
+ {
753
+ "entropy": 0.6525940448045731,
754
+ "epoch": 0.42857142857142855,
755
+ "grad_norm": 0.165474072098732,
756
+ "learning_rate": 0.0001144,
757
+ "loss": 0.6481022834777832,
758
+ "mean_token_accuracy": 0.8572756558656692,
759
+ "num_tokens": 253671.0,
760
+ "step": 750
761
+ },
762
+ {
763
+ "entropy": 0.6324376344680787,
764
+ "epoch": 0.4342857142857143,
765
+ "grad_norm": 0.2185923010110855,
766
+ "learning_rate": 0.00011325714285714287,
767
+ "loss": 0.641082763671875,
768
+ "mean_token_accuracy": 0.8592174142599106,
769
+ "num_tokens": 256803.0,
770
+ "step": 760
771
+ },
772
+ {
773
+ "entropy": 0.6347133338451385,
774
+ "epoch": 0.44,
775
+ "grad_norm": 0.21539245545864105,
776
+ "learning_rate": 0.00011211428571428573,
777
+ "loss": 0.6200827121734619,
778
+ "mean_token_accuracy": 0.8640724703669548,
779
+ "num_tokens": 260043.0,
780
+ "step": 770
781
+ },
782
+ {
783
+ "entropy": 0.6413769535720348,
784
+ "epoch": 0.44571428571428573,
785
+ "grad_norm": 0.2725240886211395,
786
+ "learning_rate": 0.00011097142857142857,
787
+ "loss": 0.6957359790802002,
788
+ "mean_token_accuracy": 0.8510783404111862,
789
+ "num_tokens": 263322.0,
790
+ "step": 780
791
+ },
792
+ {
793
+ "entropy": 0.6790083512663841,
794
+ "epoch": 0.4514285714285714,
795
+ "grad_norm": 0.2525796890258789,
796
+ "learning_rate": 0.00010982857142857143,
797
+ "loss": 0.6288758277893066,
798
+ "mean_token_accuracy": 0.8544105023145676,
799
+ "num_tokens": 266791.0,
800
+ "step": 790
801
+ },
802
+ {
803
+ "entropy": 0.6541981130838395,
804
+ "epoch": 0.45714285714285713,
805
+ "grad_norm": 0.2138395756483078,
806
+ "learning_rate": 0.0001086857142857143,
807
+ "loss": 0.6304707527160645,
808
+ "mean_token_accuracy": 0.8575463786721229,
809
+ "num_tokens": 269777.0,
810
+ "step": 800
811
+ },
812
+ {
813
+ "entropy": 0.6959837578237057,
814
+ "epoch": 0.46285714285714286,
815
+ "grad_norm": 0.2314756065607071,
816
+ "learning_rate": 0.00010754285714285715,
817
+ "loss": 0.6351391315460205,
818
+ "mean_token_accuracy": 0.8612324088811875,
819
+ "num_tokens": 272988.0,
820
+ "step": 810
821
+ },
822
+ {
823
+ "entropy": 0.6571616813540458,
824
+ "epoch": 0.4685714285714286,
825
+ "grad_norm": 0.19059552252292633,
826
+ "learning_rate": 0.00010640000000000001,
827
+ "loss": 0.6456667423248291,
828
+ "mean_token_accuracy": 0.8525185197591781,
829
+ "num_tokens": 276377.0,
830
+ "step": 820
831
+ },
832
+ {
833
+ "entropy": 0.5789781011641025,
834
+ "epoch": 0.4742857142857143,
835
+ "grad_norm": 0.20973151922225952,
836
+ "learning_rate": 0.00010525714285714285,
837
+ "loss": 0.5815204620361328,
838
+ "mean_token_accuracy": 0.8682083815336228,
839
+ "num_tokens": 279565.0,
840
+ "step": 830
841
+ },
842
+ {
843
+ "entropy": 0.6460968509316445,
844
+ "epoch": 0.48,
845
+ "grad_norm": 0.19541703164577484,
846
+ "learning_rate": 0.00010411428571428573,
847
+ "loss": 0.6836059093475342,
848
+ "mean_token_accuracy": 0.861596092581749,
849
+ "num_tokens": 282990.0,
850
+ "step": 840
851
+ },
852
+ {
853
+ "entropy": 0.6585176661610603,
854
+ "epoch": 0.4857142857142857,
855
+ "grad_norm": 0.16561760008335114,
856
+ "learning_rate": 0.00010297142857142859,
857
+ "loss": 0.6332673072814942,
858
+ "mean_token_accuracy": 0.8653052359819412,
859
+ "num_tokens": 286266.0,
860
+ "step": 850
861
+ },
862
+ {
863
+ "entropy": 0.6495703898370266,
864
+ "epoch": 0.49142857142857144,
865
+ "grad_norm": 0.15301378071308136,
866
+ "learning_rate": 0.00010182857142857143,
867
+ "loss": 0.6165118217468262,
868
+ "mean_token_accuracy": 0.8553761512041091,
869
+ "num_tokens": 289474.0,
870
+ "step": 860
871
+ },
872
+ {
873
+ "entropy": 0.6630740962922573,
874
+ "epoch": 0.49714285714285716,
875
+ "grad_norm": 0.19287355244159698,
876
+ "learning_rate": 0.00010068571428571429,
877
+ "loss": 0.6098609924316406,
878
+ "mean_token_accuracy": 0.8592873826622963,
879
+ "num_tokens": 292837.0,
880
+ "step": 870
881
+ },
882
+ {
883
+ "entropy": 0.6436114467680454,
884
+ "epoch": 0.5028571428571429,
885
+ "grad_norm": 0.19893701374530792,
886
+ "learning_rate": 9.954285714285714e-05,
887
+ "loss": 0.6510508537292481,
888
+ "mean_token_accuracy": 0.8588305786252022,
889
+ "num_tokens": 296142.0,
890
+ "step": 880
891
+ },
892
+ {
893
+ "entropy": 0.6108668148517609,
894
+ "epoch": 0.5085714285714286,
895
+ "grad_norm": 0.24575024843215942,
896
+ "learning_rate": 9.84e-05,
897
+ "loss": 0.6009951591491699,
898
+ "mean_token_accuracy": 0.8642948284745217,
899
+ "num_tokens": 299685.0,
900
+ "step": 890
901
+ },
902
+ {
903
+ "entropy": 0.6458855651319027,
904
+ "epoch": 0.5142857142857142,
905
+ "grad_norm": 0.27966228127479553,
906
+ "learning_rate": 9.725714285714286e-05,
907
+ "loss": 0.6327517032623291,
908
+ "mean_token_accuracy": 0.8518458366394043,
909
+ "num_tokens": 303189.0,
910
+ "step": 900
911
+ },
912
+ {
913
+ "entropy": 0.6874921523034573,
914
+ "epoch": 0.52,
915
+ "grad_norm": 0.20877590775489807,
916
+ "learning_rate": 9.611428571428572e-05,
917
+ "loss": 0.6910979270935058,
918
+ "mean_token_accuracy": 0.8424041703343391,
919
+ "num_tokens": 306468.0,
920
+ "step": 910
921
+ },
922
+ {
923
+ "entropy": 0.6255587831139564,
924
+ "epoch": 0.5257142857142857,
925
+ "grad_norm": 0.2100318968296051,
926
+ "learning_rate": 9.497142857142857e-05,
927
+ "loss": 0.6689131736755372,
928
+ "mean_token_accuracy": 0.8548390552401542,
929
+ "num_tokens": 309955.0,
930
+ "step": 920
931
+ },
932
+ {
933
+ "entropy": 0.6701849482953548,
934
+ "epoch": 0.5314285714285715,
935
+ "grad_norm": 0.25995635986328125,
936
+ "learning_rate": 9.382857142857144e-05,
937
+ "loss": 0.6717410087585449,
938
+ "mean_token_accuracy": 0.8549696207046509,
939
+ "num_tokens": 313117.0,
940
+ "step": 930
941
+ },
942
+ {
943
+ "entropy": 0.6915492154657841,
944
+ "epoch": 0.5371428571428571,
945
+ "grad_norm": 0.21679414808750153,
946
+ "learning_rate": 9.268571428571429e-05,
947
+ "loss": 0.6586971759796143,
948
+ "mean_token_accuracy": 0.859293457865715,
949
+ "num_tokens": 316519.0,
950
+ "step": 940
951
+ },
952
+ {
953
+ "entropy": 0.6822380021214485,
954
+ "epoch": 0.5428571428571428,
955
+ "grad_norm": 0.2091035693883896,
956
+ "learning_rate": 9.154285714285715e-05,
957
+ "loss": 0.6489524841308594,
958
+ "mean_token_accuracy": 0.8576316460967064,
959
+ "num_tokens": 319782.0,
960
+ "step": 950
961
+ },
962
+ {
963
+ "entropy": 0.6756891958415508,
964
+ "epoch": 0.5485714285714286,
965
+ "grad_norm": 0.2485855668783188,
966
+ "learning_rate": 9.04e-05,
967
+ "loss": 0.656977367401123,
968
+ "mean_token_accuracy": 0.8671488896012306,
969
+ "num_tokens": 323019.0,
970
+ "step": 960
971
+ },
972
+ {
973
+ "entropy": 0.6616588845849037,
974
+ "epoch": 0.5542857142857143,
975
+ "grad_norm": 0.2510681450366974,
976
+ "learning_rate": 8.925714285714287e-05,
977
+ "loss": 0.6415849208831788,
978
+ "mean_token_accuracy": 0.8642254650592804,
979
+ "num_tokens": 326339.0,
980
+ "step": 970
981
+ },
982
+ {
983
+ "entropy": 0.6396586582064628,
984
+ "epoch": 0.56,
985
+ "grad_norm": 0.20449747145175934,
986
+ "learning_rate": 8.811428571428572e-05,
987
+ "loss": 0.6375674247741699,
988
+ "mean_token_accuracy": 0.8578563511371613,
989
+ "num_tokens": 329774.0,
990
+ "step": 980
991
+ },
992
+ {
993
+ "entropy": 0.6666180461645126,
994
+ "epoch": 0.5657142857142857,
995
+ "grad_norm": 0.2526116669178009,
996
+ "learning_rate": 8.697142857142857e-05,
997
+ "loss": 0.609344482421875,
998
+ "mean_token_accuracy": 0.8597319439053536,
999
+ "num_tokens": 333087.0,
1000
+ "step": 990
1001
+ },
1002
+ {
1003
+ "entropy": 0.6488007657229901,
1004
+ "epoch": 0.5714285714285714,
1005
+ "grad_norm": 0.20164141058921814,
1006
+ "learning_rate": 8.582857142857143e-05,
1007
+ "loss": 0.6201111316680908,
1008
+ "mean_token_accuracy": 0.8526906743645668,
1009
+ "num_tokens": 336508.0,
1010
+ "step": 1000
1011
+ },
1012
+ {
1013
+ "entropy": 0.63390611410141,
1014
+ "epoch": 0.5771428571428572,
1015
+ "grad_norm": 0.2473566234111786,
1016
+ "learning_rate": 8.46857142857143e-05,
1017
+ "loss": 0.5988630294799805,
1018
+ "mean_token_accuracy": 0.8674046665430069,
1019
+ "num_tokens": 339967.0,
1020
+ "step": 1010
1021
+ },
1022
+ {
1023
+ "entropy": 0.586044154316187,
1024
+ "epoch": 0.5828571428571429,
1025
+ "grad_norm": 0.21561333537101746,
1026
+ "learning_rate": 8.354285714285715e-05,
1027
+ "loss": 0.5799360752105713,
1028
+ "mean_token_accuracy": 0.8686427220702171,
1029
+ "num_tokens": 342923.0,
1030
+ "step": 1020
1031
+ },
1032
+ {
1033
+ "entropy": 0.6335062697529793,
1034
+ "epoch": 0.5885714285714285,
1035
+ "grad_norm": 0.20071916282176971,
1036
+ "learning_rate": 8.24e-05,
1037
+ "loss": 0.6467567443847656,
1038
+ "mean_token_accuracy": 0.8589577525854111,
1039
+ "num_tokens": 346417.0,
1040
+ "step": 1030
1041
+ },
1042
+ {
1043
+ "entropy": 0.6805698774755001,
1044
+ "epoch": 0.5942857142857143,
1045
+ "grad_norm": 0.295105904340744,
1046
+ "learning_rate": 8.125714285714286e-05,
1047
+ "loss": 0.6580337524414063,
1048
+ "mean_token_accuracy": 0.8565412655472755,
1049
+ "num_tokens": 349854.0,
1050
+ "step": 1040
1051
+ },
1052
+ {
1053
+ "entropy": 0.6416849888861179,
1054
+ "epoch": 0.6,
1055
+ "grad_norm": 0.25477221608161926,
1056
+ "learning_rate": 8.011428571428573e-05,
1057
+ "loss": 0.5906441688537598,
1058
+ "mean_token_accuracy": 0.8619327709078789,
1059
+ "num_tokens": 353294.0,
1060
+ "step": 1050
1061
+ },
1062
+ {
1063
+ "entropy": 0.6331484273076058,
1064
+ "epoch": 0.6057142857142858,
1065
+ "grad_norm": 0.22354693710803986,
1066
+ "learning_rate": 7.897142857142858e-05,
1067
+ "loss": 0.6218142509460449,
1068
+ "mean_token_accuracy": 0.8641349360346794,
1069
+ "num_tokens": 356543.0,
1070
+ "step": 1060
1071
+ },
1072
+ {
1073
+ "entropy": 0.6120303176343441,
1074
+ "epoch": 0.6114285714285714,
1075
+ "grad_norm": 0.20340518653392792,
1076
+ "learning_rate": 7.782857142857143e-05,
1077
+ "loss": 0.601622486114502,
1078
+ "mean_token_accuracy": 0.8639883920550346,
1079
+ "num_tokens": 359765.0,
1080
+ "step": 1070
1081
+ },
1082
+ {
1083
+ "entropy": 0.6292500749230385,
1084
+ "epoch": 0.6171428571428571,
1085
+ "grad_norm": 0.2069106251001358,
1086
+ "learning_rate": 7.668571428571429e-05,
1087
+ "loss": 0.6348252773284913,
1088
+ "mean_token_accuracy": 0.8540400981903076,
1089
+ "num_tokens": 363116.0,
1090
+ "step": 1080
1091
+ },
1092
+ {
1093
+ "entropy": 0.622652218490839,
1094
+ "epoch": 0.6228571428571429,
1095
+ "grad_norm": 0.23602575063705444,
1096
+ "learning_rate": 7.554285714285716e-05,
1097
+ "loss": 0.6226765632629394,
1098
+ "mean_token_accuracy": 0.8595390111207962,
1099
+ "num_tokens": 366720.0,
1100
+ "step": 1090
1101
+ },
1102
+ {
1103
+ "entropy": 0.6517547190189361,
1104
+ "epoch": 0.6285714285714286,
1105
+ "grad_norm": 0.28087317943573,
1106
+ "learning_rate": 7.44e-05,
1107
+ "loss": 0.6399660587310791,
1108
+ "mean_token_accuracy": 0.8540969029068947,
1109
+ "num_tokens": 370194.0,
1110
+ "step": 1100
1111
+ },
1112
+ {
1113
+ "entropy": 0.6615139648318291,
1114
+ "epoch": 0.6342857142857142,
1115
+ "grad_norm": 0.3010542094707489,
1116
+ "learning_rate": 7.325714285714286e-05,
1117
+ "loss": 0.6588103771209717,
1118
+ "mean_token_accuracy": 0.8464817240834236,
1119
+ "num_tokens": 373541.0,
1120
+ "step": 1110
1121
+ },
1122
+ {
1123
+ "entropy": 0.647005096077919,
1124
+ "epoch": 0.64,
1125
+ "grad_norm": 0.26990506052970886,
1126
+ "learning_rate": 7.211428571428572e-05,
1127
+ "loss": 0.6175911903381348,
1128
+ "mean_token_accuracy": 0.8642094656825066,
1129
+ "num_tokens": 376643.0,
1130
+ "step": 1120
1131
+ },
1132
+ {
1133
+ "entropy": 0.6991826109588146,
1134
+ "epoch": 0.6457142857142857,
1135
+ "grad_norm": 0.2759954333305359,
1136
+ "learning_rate": 7.097142857142857e-05,
1137
+ "loss": 0.6602604389190674,
1138
+ "mean_token_accuracy": 0.8484420910477638,
1139
+ "num_tokens": 379874.0,
1140
+ "step": 1130
1141
+ },
1142
+ {
1143
+ "entropy": 0.6453976444900036,
1144
+ "epoch": 0.6514285714285715,
1145
+ "grad_norm": 0.21964532136917114,
1146
+ "learning_rate": 6.982857142857144e-05,
1147
+ "loss": 0.5955167293548584,
1148
+ "mean_token_accuracy": 0.861721670627594,
1149
+ "num_tokens": 383311.0,
1150
+ "step": 1140
1151
+ },
1152
+ {
1153
+ "entropy": 0.6503799192607402,
1154
+ "epoch": 0.6571428571428571,
1155
+ "grad_norm": 0.19722476601600647,
1156
+ "learning_rate": 6.868571428571429e-05,
1157
+ "loss": 0.6320806026458741,
1158
+ "mean_token_accuracy": 0.8562078520655632,
1159
+ "num_tokens": 386679.0,
1160
+ "step": 1150
1161
+ },
1162
+ {
1163
+ "entropy": 0.6544807381927967,
1164
+ "epoch": 0.6628571428571428,
1165
+ "grad_norm": 0.28297051787376404,
1166
+ "learning_rate": 6.754285714285714e-05,
1167
+ "loss": 0.6531005382537842,
1168
+ "mean_token_accuracy": 0.8550411075353622,
1169
+ "num_tokens": 389963.0,
1170
+ "step": 1160
1171
+ },
1172
+ {
1173
+ "entropy": 0.6717952400445938,
1174
+ "epoch": 0.6685714285714286,
1175
+ "grad_norm": 0.24083739519119263,
1176
+ "learning_rate": 6.64e-05,
1177
+ "loss": 0.647878885269165,
1178
+ "mean_token_accuracy": 0.8565554738044738,
1179
+ "num_tokens": 393277.0,
1180
+ "step": 1170
1181
+ },
1182
+ {
1183
+ "entropy": 0.6969816297292709,
1184
+ "epoch": 0.6742857142857143,
1185
+ "grad_norm": 0.2341049164533615,
1186
+ "learning_rate": 6.525714285714287e-05,
1187
+ "loss": 0.6588397979736328,
1188
+ "mean_token_accuracy": 0.8613204509019852,
1189
+ "num_tokens": 396744.0,
1190
+ "step": 1180
1191
+ },
1192
+ {
1193
+ "entropy": 0.5977616436779499,
1194
+ "epoch": 0.68,
1195
+ "grad_norm": 0.21638202667236328,
1196
+ "learning_rate": 6.411428571428572e-05,
1197
+ "loss": 0.5673915386199951,
1198
+ "mean_token_accuracy": 0.8722260281443596,
1199
+ "num_tokens": 400200.0,
1200
+ "step": 1190
1201
+ },
1202
+ {
1203
+ "entropy": 0.6362225770950317,
1204
+ "epoch": 0.6857142857142857,
1205
+ "grad_norm": 0.2523053288459778,
1206
+ "learning_rate": 6.297142857142857e-05,
1207
+ "loss": 0.6235575199127197,
1208
+ "mean_token_accuracy": 0.8589386835694313,
1209
+ "num_tokens": 403472.0,
1210
+ "step": 1200
1211
+ },
1212
+ {
1213
+ "entropy": 0.6172734223306179,
1214
+ "epoch": 0.6914285714285714,
1215
+ "grad_norm": 0.21801182627677917,
1216
+ "learning_rate": 6.182857142857143e-05,
1217
+ "loss": 0.6179306030273437,
1218
+ "mean_token_accuracy": 0.8626845121383667,
1219
+ "num_tokens": 406776.0,
1220
+ "step": 1210
1221
+ },
1222
+ {
1223
+ "entropy": 0.6551583506166935,
1224
+ "epoch": 0.6971428571428572,
1225
+ "grad_norm": 0.27856746315956116,
1226
+ "learning_rate": 6.068571428571429e-05,
1227
+ "loss": 0.6389457225799561,
1228
+ "mean_token_accuracy": 0.8546857088804245,
1229
+ "num_tokens": 410307.0,
1230
+ "step": 1220
1231
+ },
1232
+ {
1233
+ "entropy": 0.7079406701028347,
1234
+ "epoch": 0.7028571428571428,
1235
+ "grad_norm": 0.29186904430389404,
1236
+ "learning_rate": 5.9542857142857146e-05,
1237
+ "loss": 0.7094098567962647,
1238
+ "mean_token_accuracy": 0.8335159897804261,
1239
+ "num_tokens": 413745.0,
1240
+ "step": 1230
1241
+ },
1242
+ {
1243
+ "entropy": 0.683994609862566,
1244
+ "epoch": 0.7085714285714285,
1245
+ "grad_norm": 0.2444518506526947,
1246
+ "learning_rate": 5.8399999999999997e-05,
1247
+ "loss": 0.626039981842041,
1248
+ "mean_token_accuracy": 0.8552980974316597,
1249
+ "num_tokens": 416674.0,
1250
+ "step": 1240
1251
+ },
1252
+ {
1253
+ "entropy": 0.6473595723509789,
1254
+ "epoch": 0.7142857142857143,
1255
+ "grad_norm": 0.2124943733215332,
1256
+ "learning_rate": 5.725714285714287e-05,
1257
+ "loss": 0.6362271308898926,
1258
+ "mean_token_accuracy": 0.8586657717823982,
1259
+ "num_tokens": 419814.0,
1260
+ "step": 1250
1261
+ },
1262
+ {
1263
+ "entropy": 0.6558213859796524,
1264
+ "epoch": 0.72,
1265
+ "grad_norm": 0.3233776092529297,
1266
+ "learning_rate": 5.611428571428572e-05,
1267
+ "loss": 0.6335158824920655,
1268
+ "mean_token_accuracy": 0.8607818141579628,
1269
+ "num_tokens": 423034.0,
1270
+ "step": 1260
1271
+ },
1272
+ {
1273
+ "entropy": 0.6101858265697956,
1274
+ "epoch": 0.7257142857142858,
1275
+ "grad_norm": 0.23161561787128448,
1276
+ "learning_rate": 5.4971428571428576e-05,
1277
+ "loss": 0.5872994422912597,
1278
+ "mean_token_accuracy": 0.8612972646951675,
1279
+ "num_tokens": 426358.0,
1280
+ "step": 1270
1281
+ },
1282
+ {
1283
+ "entropy": 0.6065807826817036,
1284
+ "epoch": 0.7314285714285714,
1285
+ "grad_norm": 0.3137820065021515,
1286
+ "learning_rate": 5.3828571428571426e-05,
1287
+ "loss": 0.6199213027954101,
1288
+ "mean_token_accuracy": 0.8634087055921554,
1289
+ "num_tokens": 429800.0,
1290
+ "step": 1280
1291
+ },
1292
+ {
1293
+ "entropy": 0.6477028757333756,
1294
+ "epoch": 0.7371428571428571,
1295
+ "grad_norm": 0.20417962968349457,
1296
+ "learning_rate": 5.2685714285714284e-05,
1297
+ "loss": 0.6403303623199463,
1298
+ "mean_token_accuracy": 0.858014090359211,
1299
+ "num_tokens": 433136.0,
1300
+ "step": 1290
1301
+ },
1302
+ {
1303
+ "entropy": 0.6253302626311779,
1304
+ "epoch": 0.7428571428571429,
1305
+ "grad_norm": 0.2649274468421936,
1306
+ "learning_rate": 5.154285714285715e-05,
1307
+ "loss": 0.5664835453033448,
1308
+ "mean_token_accuracy": 0.8699078008532524,
1309
+ "num_tokens": 436165.0,
1310
+ "step": 1300
1311
+ },
1312
+ {
1313
+ "entropy": 0.6552011057734489,
1314
+ "epoch": 0.7485714285714286,
1315
+ "grad_norm": 0.22695457935333252,
1316
+ "learning_rate": 5.0400000000000005e-05,
1317
+ "loss": 0.6598159313201905,
1318
+ "mean_token_accuracy": 0.8518921718001365,
1319
+ "num_tokens": 439362.0,
1320
+ "step": 1310
1321
+ },
1322
+ {
1323
+ "entropy": 0.6426700457930565,
1324
+ "epoch": 0.7542857142857143,
1325
+ "grad_norm": 0.2776673436164856,
1326
+ "learning_rate": 4.9257142857142856e-05,
1327
+ "loss": 0.6768081188201904,
1328
+ "mean_token_accuracy": 0.8481876432895661,
1329
+ "num_tokens": 442586.0,
1330
+ "step": 1320
1331
+ },
1332
+ {
1333
+ "entropy": 0.6550601176917553,
1334
+ "epoch": 0.76,
1335
+ "grad_norm": 0.27410948276519775,
1336
+ "learning_rate": 4.811428571428572e-05,
1337
+ "loss": 0.636206865310669,
1338
+ "mean_token_accuracy": 0.8564146623015404,
1339
+ "num_tokens": 446043.0,
1340
+ "step": 1330
1341
+ },
1342
+ {
1343
+ "entropy": 0.6599532529711724,
1344
+ "epoch": 0.7657142857142857,
1345
+ "grad_norm": 0.23421648144721985,
1346
+ "learning_rate": 4.697142857142857e-05,
1347
+ "loss": 0.6225139617919921,
1348
+ "mean_token_accuracy": 0.8554374307394028,
1349
+ "num_tokens": 449551.0,
1350
+ "step": 1340
1351
+ },
1352
+ {
1353
+ "entropy": 0.6726249933242798,
1354
+ "epoch": 0.7714285714285715,
1355
+ "grad_norm": 0.23552796244621277,
1356
+ "learning_rate": 4.5828571428571435e-05,
1357
+ "loss": 0.6367889881134033,
1358
+ "mean_token_accuracy": 0.8643324449658394,
1359
+ "num_tokens": 452586.0,
1360
+ "step": 1350
1361
+ },
1362
+ {
1363
+ "entropy": 0.6467246741056443,
1364
+ "epoch": 0.7771428571428571,
1365
+ "grad_norm": 0.18453116714954376,
1366
+ "learning_rate": 4.4685714285714286e-05,
1367
+ "loss": 0.6355658054351807,
1368
+ "mean_token_accuracy": 0.8546889841556549,
1369
+ "num_tokens": 455761.0,
1370
+ "step": 1360
1371
+ },
1372
+ {
1373
+ "entropy": 0.6625100992619991,
1374
+ "epoch": 0.7828571428571428,
1375
+ "grad_norm": 0.21036536991596222,
1376
+ "learning_rate": 4.354285714285714e-05,
1377
+ "loss": 0.6642748355865479,
1378
+ "mean_token_accuracy": 0.8560309842228889,
1379
+ "num_tokens": 459175.0,
1380
+ "step": 1370
1381
+ },
1382
+ {
1383
+ "entropy": 0.6317512311041356,
1384
+ "epoch": 0.7885714285714286,
1385
+ "grad_norm": 0.21304954588413239,
1386
+ "learning_rate": 4.24e-05,
1387
+ "loss": 0.601001787185669,
1388
+ "mean_token_accuracy": 0.8565791383385658,
1389
+ "num_tokens": 462701.0,
1390
+ "step": 1380
1391
+ },
1392
+ {
1393
+ "entropy": 0.644014036655426,
1394
+ "epoch": 0.7942857142857143,
1395
+ "grad_norm": 0.26754629611968994,
1396
+ "learning_rate": 4.125714285714286e-05,
1397
+ "loss": 0.6244466781616211,
1398
+ "mean_token_accuracy": 0.8559624046087265,
1399
+ "num_tokens": 465821.0,
1400
+ "step": 1390
1401
+ },
1402
+ {
1403
+ "entropy": 0.6658924698829651,
1404
+ "epoch": 0.8,
1405
+ "grad_norm": 0.2504512369632721,
1406
+ "learning_rate": 4.0114285714285715e-05,
1407
+ "loss": 0.6396134853363037,
1408
+ "mean_token_accuracy": 0.8645422115921975,
1409
+ "num_tokens": 469319.0,
1410
+ "step": 1400
1411
+ },
1412
+ {
1413
+ "entropy": 0.6185431003570556,
1414
+ "epoch": 0.8057142857142857,
1415
+ "grad_norm": 0.2430698573589325,
1416
+ "learning_rate": 3.897142857142857e-05,
1417
+ "loss": 0.6310626029968261,
1418
+ "mean_token_accuracy": 0.8630247727036476,
1419
+ "num_tokens": 472580.0,
1420
+ "step": 1410
1421
+ },
1422
+ {
1423
+ "entropy": 0.5944720163941384,
1424
+ "epoch": 0.8114285714285714,
1425
+ "grad_norm": 0.25170740485191345,
1426
+ "learning_rate": 3.782857142857143e-05,
1427
+ "loss": 0.5594588279724121,
1428
+ "mean_token_accuracy": 0.8738556310534478,
1429
+ "num_tokens": 475539.0,
1430
+ "step": 1420
1431
+ },
1432
+ {
1433
+ "entropy": 0.6284624971449375,
1434
+ "epoch": 0.8171428571428572,
1435
+ "grad_norm": 0.2709786295890808,
1436
+ "learning_rate": 3.668571428571429e-05,
1437
+ "loss": 0.639189624786377,
1438
+ "mean_token_accuracy": 0.862536971271038,
1439
+ "num_tokens": 478827.0,
1440
+ "step": 1430
1441
+ },
1442
+ {
1443
+ "entropy": 0.6724597789347172,
1444
+ "epoch": 0.8228571428571428,
1445
+ "grad_norm": 0.2555326521396637,
1446
+ "learning_rate": 3.5542857142857145e-05,
1447
+ "loss": 0.6536776542663574,
1448
+ "mean_token_accuracy": 0.8553947672247887,
1449
+ "num_tokens": 482017.0,
1450
+ "step": 1440
1451
+ },
1452
+ {
1453
+ "entropy": 0.6180280610918999,
1454
+ "epoch": 0.8285714285714286,
1455
+ "grad_norm": 0.22328683733940125,
1456
+ "learning_rate": 3.4399999999999996e-05,
1457
+ "loss": 0.6490192413330078,
1458
+ "mean_token_accuracy": 0.8624721497297287,
1459
+ "num_tokens": 485285.0,
1460
+ "step": 1450
1461
+ },
1462
+ {
1463
+ "entropy": 0.6554854273796081,
1464
+ "epoch": 0.8342857142857143,
1465
+ "grad_norm": 0.2618810832500458,
1466
+ "learning_rate": 3.325714285714286e-05,
1467
+ "loss": 0.6328207015991211,
1468
+ "mean_token_accuracy": 0.8652528643608093,
1469
+ "num_tokens": 488237.0,
1470
+ "step": 1460
1471
+ },
1472
+ {
1473
+ "entropy": 0.6485379718244075,
1474
+ "epoch": 0.84,
1475
+ "grad_norm": 0.3207658529281616,
1476
+ "learning_rate": 3.211428571428571e-05,
1477
+ "loss": 0.6625119686126709,
1478
+ "mean_token_accuracy": 0.8627592906355858,
1479
+ "num_tokens": 491545.0,
1480
+ "step": 1470
1481
+ },
1482
+ {
1483
+ "entropy": 0.6200287729501724,
1484
+ "epoch": 0.8457142857142858,
1485
+ "grad_norm": 0.2355208396911621,
1486
+ "learning_rate": 3.0971428571428575e-05,
1487
+ "loss": 0.5889796733856201,
1488
+ "mean_token_accuracy": 0.8751833841204644,
1489
+ "num_tokens": 494760.0,
1490
+ "step": 1480
1491
+ },
1492
+ {
1493
+ "entropy": 0.6196223556995392,
1494
+ "epoch": 0.8514285714285714,
1495
+ "grad_norm": 0.19851188361644745,
1496
+ "learning_rate": 2.982857142857143e-05,
1497
+ "loss": 0.5831719875335694,
1498
+ "mean_token_accuracy": 0.8716864466667176,
1499
+ "num_tokens": 497728.0,
1500
+ "step": 1490
1501
+ },
1502
+ {
1503
+ "entropy": 0.6233154498040676,
1504
+ "epoch": 0.8571428571428571,
1505
+ "grad_norm": 0.24745343625545502,
1506
+ "learning_rate": 2.8685714285714286e-05,
1507
+ "loss": 0.6103721141815186,
1508
+ "mean_token_accuracy": 0.8551008567214012,
1509
+ "num_tokens": 500804.0,
1510
+ "step": 1500
1511
+ },
1512
+ {
1513
+ "entropy": 0.599901518970728,
1514
+ "epoch": 0.8628571428571429,
1515
+ "grad_norm": 0.23715294897556305,
1516
+ "learning_rate": 2.7542857142857144e-05,
1517
+ "loss": 0.5849476814270019,
1518
+ "mean_token_accuracy": 0.8684423178434372,
1519
+ "num_tokens": 504066.0,
1520
+ "step": 1510
1521
+ },
1522
+ {
1523
+ "entropy": 0.629510759562254,
1524
+ "epoch": 0.8685714285714285,
1525
+ "grad_norm": 0.2427317500114441,
1526
+ "learning_rate": 2.64e-05,
1527
+ "loss": 0.631610631942749,
1528
+ "mean_token_accuracy": 0.8575234442949295,
1529
+ "num_tokens": 507430.0,
1530
+ "step": 1520
1531
+ },
1532
+ {
1533
+ "entropy": 0.6032628089189529,
1534
+ "epoch": 0.8742857142857143,
1535
+ "grad_norm": 0.20266121625900269,
1536
+ "learning_rate": 2.5257142857142855e-05,
1537
+ "loss": 0.5601680755615235,
1538
+ "mean_token_accuracy": 0.8660111904144288,
1539
+ "num_tokens": 510923.0,
1540
+ "step": 1530
1541
+ },
1542
+ {
1543
+ "entropy": 0.599126148968935,
1544
+ "epoch": 0.88,
1545
+ "grad_norm": 0.24769169092178345,
1546
+ "learning_rate": 2.4114285714285713e-05,
1547
+ "loss": 0.6129156589508057,
1548
+ "mean_token_accuracy": 0.8624033451080322,
1549
+ "num_tokens": 514276.0,
1550
+ "step": 1540
1551
+ },
1552
+ {
1553
+ "entropy": 0.6265991859138011,
1554
+ "epoch": 0.8857142857142857,
1555
+ "grad_norm": 0.24149306118488312,
1556
+ "learning_rate": 2.297142857142857e-05,
1557
+ "loss": 0.645173168182373,
1558
+ "mean_token_accuracy": 0.8579171389341355,
1559
+ "num_tokens": 517443.0,
1560
+ "step": 1550
1561
+ },
1562
+ {
1563
+ "entropy": 0.5904549680650234,
1564
+ "epoch": 0.8914285714285715,
1565
+ "grad_norm": 0.2332906723022461,
1566
+ "learning_rate": 2.1828571428571428e-05,
1567
+ "loss": 0.5481174945831299,
1568
+ "mean_token_accuracy": 0.8670677661895752,
1569
+ "num_tokens": 520947.0,
1570
+ "step": 1560
1571
+ },
1572
+ {
1573
+ "entropy": 0.7045296929776669,
1574
+ "epoch": 0.8971428571428571,
1575
+ "grad_norm": 0.37327486276626587,
1576
+ "learning_rate": 2.0685714285714285e-05,
1577
+ "loss": 0.6851255416870117,
1578
+ "mean_token_accuracy": 0.8483647271990776,
1579
+ "num_tokens": 524104.0,
1580
+ "step": 1570
1581
+ },
1582
+ {
1583
+ "entropy": 0.6310351669788361,
1584
+ "epoch": 0.9028571428571428,
1585
+ "grad_norm": 0.27085983753204346,
1586
+ "learning_rate": 1.9542857142857143e-05,
1587
+ "loss": 0.6175636291503906,
1588
+ "mean_token_accuracy": 0.8585921674966812,
1589
+ "num_tokens": 527462.0,
1590
+ "step": 1580
1591
+ },
1592
+ {
1593
+ "entropy": 0.6248554646968841,
1594
+ "epoch": 0.9085714285714286,
1595
+ "grad_norm": 0.29151156544685364,
1596
+ "learning_rate": 1.84e-05,
1597
+ "loss": 0.6107958793640137,
1598
+ "mean_token_accuracy": 0.8647030532360077,
1599
+ "num_tokens": 530929.0,
1600
+ "step": 1590
1601
+ },
1602
+ {
1603
+ "entropy": 0.6354066073894501,
1604
+ "epoch": 0.9142857142857143,
1605
+ "grad_norm": 0.26907071471214294,
1606
+ "learning_rate": 1.7257142857142857e-05,
1607
+ "loss": 0.6115604400634765,
1608
+ "mean_token_accuracy": 0.8671793237328529,
1609
+ "num_tokens": 534220.0,
1610
+ "step": 1600
1611
+ },
1612
+ {
1613
+ "entropy": 0.6623220466077328,
1614
+ "epoch": 0.92,
1615
+ "grad_norm": 0.23479118943214417,
1616
+ "learning_rate": 1.6114285714285715e-05,
1617
+ "loss": 0.629840898513794,
1618
+ "mean_token_accuracy": 0.8525548726320267,
1619
+ "num_tokens": 537701.0,
1620
+ "step": 1610
1621
+ },
1622
+ {
1623
+ "entropy": 0.6476062543690204,
1624
+ "epoch": 0.9257142857142857,
1625
+ "grad_norm": 0.2056824117898941,
1626
+ "learning_rate": 1.4971428571428572e-05,
1627
+ "loss": 0.6435581684112549,
1628
+ "mean_token_accuracy": 0.8595114961266518,
1629
+ "num_tokens": 541293.0,
1630
+ "step": 1620
1631
+ },
1632
+ {
1633
+ "entropy": 0.6168920576572419,
1634
+ "epoch": 0.9314285714285714,
1635
+ "grad_norm": 0.22080306708812714,
1636
+ "learning_rate": 1.382857142857143e-05,
1637
+ "loss": 0.5561806678771972,
1638
+ "mean_token_accuracy": 0.8639644294977188,
1639
+ "num_tokens": 544565.0,
1640
+ "step": 1630
1641
+ },
1642
+ {
1643
+ "entropy": 0.6294913746416568,
1644
+ "epoch": 0.9371428571428572,
1645
+ "grad_norm": 0.3226984441280365,
1646
+ "learning_rate": 1.2685714285714287e-05,
1647
+ "loss": 0.5850194931030274,
1648
+ "mean_token_accuracy": 0.8648865327239037,
1649
+ "num_tokens": 547743.0,
1650
+ "step": 1640
1651
+ },
1652
+ {
1653
+ "entropy": 0.6362057097256184,
1654
+ "epoch": 0.9428571428571428,
1655
+ "grad_norm": 0.2684152126312256,
1656
+ "learning_rate": 1.1542857142857143e-05,
1657
+ "loss": 0.6132237911224365,
1658
+ "mean_token_accuracy": 0.8577535077929497,
1659
+ "num_tokens": 551275.0,
1660
+ "step": 1650
1661
+ },
1662
+ {
1663
+ "entropy": 0.6077159576117992,
1664
+ "epoch": 0.9485714285714286,
1665
+ "grad_norm": 0.26599714159965515,
1666
+ "learning_rate": 1.04e-05,
1667
+ "loss": 0.5903688430786133,
1668
+ "mean_token_accuracy": 0.8740017876029015,
1669
+ "num_tokens": 554538.0,
1670
+ "step": 1660
1671
+ },
1672
+ {
1673
+ "entropy": 0.6076049767434597,
1674
+ "epoch": 0.9542857142857143,
1675
+ "grad_norm": 0.22315815091133118,
1676
+ "learning_rate": 9.257142857142858e-06,
1677
+ "loss": 0.5887226104736328,
1678
+ "mean_token_accuracy": 0.8686643913388252,
1679
+ "num_tokens": 557948.0,
1680
+ "step": 1670
1681
+ },
1682
+ {
1683
+ "entropy": 0.6360240176320076,
1684
+ "epoch": 0.96,
1685
+ "grad_norm": 0.2517399787902832,
1686
+ "learning_rate": 8.114285714285715e-06,
1687
+ "loss": 0.6221353054046631,
1688
+ "mean_token_accuracy": 0.8617710500955582,
1689
+ "num_tokens": 561093.0,
1690
+ "step": 1680
1691
+ },
1692
+ {
1693
+ "entropy": 0.6221488267183304,
1694
+ "epoch": 0.9657142857142857,
1695
+ "grad_norm": 0.2868978977203369,
1696
+ "learning_rate": 6.971428571428572e-06,
1697
+ "loss": 0.6088948249816895,
1698
+ "mean_token_accuracy": 0.8645280092954636,
1699
+ "num_tokens": 564497.0,
1700
+ "step": 1690
1701
+ },
1702
+ {
1703
+ "entropy": 0.6176722340285778,
1704
+ "epoch": 0.9714285714285714,
1705
+ "grad_norm": 0.27099671959877014,
1706
+ "learning_rate": 5.828571428571429e-06,
1707
+ "loss": 0.5873197078704834,
1708
+ "mean_token_accuracy": 0.8671502575278283,
1709
+ "num_tokens": 567977.0,
1710
+ "step": 1700
1711
+ },
1712
+ {
1713
+ "entropy": 0.5968088746070862,
1714
+ "epoch": 0.9771428571428571,
1715
+ "grad_norm": 0.25095444917678833,
1716
+ "learning_rate": 4.685714285714286e-06,
1717
+ "loss": 0.6059055805206299,
1718
+ "mean_token_accuracy": 0.8739419683814049,
1719
+ "num_tokens": 571185.0,
1720
+ "step": 1710
1721
+ },
1722
+ {
1723
+ "entropy": 0.6225132785737515,
1724
+ "epoch": 0.9828571428571429,
1725
+ "grad_norm": 0.28391072154045105,
1726
+ "learning_rate": 3.542857142857143e-06,
1727
+ "loss": 0.5855085372924804,
1728
+ "mean_token_accuracy": 0.8671080946922303,
1729
+ "num_tokens": 574447.0,
1730
+ "step": 1720
1731
+ },
1732
+ {
1733
+ "entropy": 0.6214111320674419,
1734
+ "epoch": 0.9885714285714285,
1735
+ "grad_norm": 0.30393826961517334,
1736
+ "learning_rate": 2.4000000000000003e-06,
1737
+ "loss": 0.6098702907562256,
1738
+ "mean_token_accuracy": 0.8673963889479637,
1739
+ "num_tokens": 577647.0,
1740
+ "step": 1730
1741
+ },
1742
+ {
1743
+ "entropy": 0.6451270334422589,
1744
+ "epoch": 0.9942857142857143,
1745
+ "grad_norm": 0.6247619390487671,
1746
+ "learning_rate": 1.2571428571428573e-06,
1747
+ "loss": 0.6435680389404297,
1748
+ "mean_token_accuracy": 0.8606103897094727,
1749
+ "num_tokens": 580924.0,
1750
+ "step": 1740
1751
+ },
1752
+ {
1753
+ "entropy": 0.6013512119650841,
1754
+ "epoch": 1.0,
1755
+ "grad_norm": 0.22891853749752045,
1756
+ "learning_rate": 1.142857142857143e-07,
1757
+ "loss": 0.5656134605407714,
1758
+ "mean_token_accuracy": 0.8671733975410462,
1759
+ "num_tokens": 584229.0,
1760
+ "step": 1750
1761
+ }
1762
+ ],
1763
+ "logging_steps": 10,
1764
+ "max_steps": 1750,
1765
+ "num_input_tokens_seen": 0,
1766
+ "num_train_epochs": 1,
1767
+ "save_steps": 500,
1768
+ "stateful_callbacks": {
1769
+ "TrainerControl": {
1770
+ "args": {
1771
+ "should_epoch_stop": false,
1772
+ "should_evaluate": false,
1773
+ "should_log": false,
1774
+ "should_save": true,
1775
+ "should_training_stop": true
1776
+ },
1777
+ "attributes": {}
1778
+ }
1779
+ },
1780
+ "total_flos": 4600872360760320.0,
1781
+ "train_batch_size": 1,
1782
+ "trial_name": null,
1783
+ "trial_params": null
1784
+ }
sql-model/checkpoint-1750/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:29a4a11ec0ba52a64430eabdbdc4808ed84fc37c06b05e2f78d6eedc6da2ee37
3
+ size 5649
sql-model/tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
3
+ size 11421892
sql-model/tokenizer_config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": null,
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|im_end|>",
7
+ "errors": "replace",
8
+ "extra_special_tokens": [
9
+ "<|im_start|>",
10
+ "<|im_end|>",
11
+ "<|object_ref_start|>",
12
+ "<|object_ref_end|>",
13
+ "<|box_start|>",
14
+ "<|box_end|>",
15
+ "<|quad_start|>",
16
+ "<|quad_end|>",
17
+ "<|vision_start|>",
18
+ "<|vision_end|>",
19
+ "<|vision_pad|>",
20
+ "<|image_pad|>",
21
+ "<|video_pad|>"
22
+ ],
23
+ "is_local": false,
24
+ "model_max_length": 32768,
25
+ "pad_token": "<|im_end|>",
26
+ "split_special_tokens": false,
27
+ "tokenizer_class": "Qwen2Tokenizer",
28
+ "unk_token": null
29
+ }