AdaptLLM commited on
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a050b77
1 Parent(s): cc06297

99285627a5e44b54c0787d7c66fd8810ed80361ad3a6a1e977d6d6c832a634bd

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+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- Find out if there are any images #}\n{% set image_ns = namespace(has_images=false) %} \n{%- for message in messages %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {%- set image_ns.has_images = true %}\n {%- endif %}\n {%- endfor %}\n{%- endfor %}\n\n{#- Error out if there are images and system message #}\n{%- if image_ns.has_images and not system_message == \"\" %}\n {{- raise_exception(\"Prompting with images is incompatible with system messages.\") }}\n{%- endif %}\n\n{#- System message if there are no images #}\n{%- if not image_ns.has_images %}\n {{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n {%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n {%- endif %}\n {{- \"Cutting Knowledge Date: December 2023\\n\" }}\n {{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n {%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {%- endif %}\n {{- system_message }}\n {{- \"<|eot_id|>\" }}\n{%- endif %}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2062
+ "clean_up_tokenization_spaces": true,
2063
+ "eos_token": "<|eot_id|>",
2064
+ "model_input_names": [
2065
+ "input_ids",
2066
+ "attention_mask"
2067
+ ],
2068
+ "model_max_length": 131072,
2069
+ "pad_token": "<|finetune_right_pad_id|>",
2070
+ "tokenizer_class": "PreTrainedTokenizerFast"
2071
+ }
train_results.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "epoch": 0.9999365683476055,
3
+ "total_flos": 2.4957269414919537e+18,
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+ "train_loss": 1.201914404482746,
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+ "train_runtime": 115316.4416,
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+ "train_samples_per_second": 4.375,
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+ "train_steps_per_second": 0.034
8
+ }
trainer_log.jsonl ADDED
@@ -0,0 +1,395 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {"current_steps": 10, "total_steps": 3941, "loss": 1.8967, "learning_rate": 1.2658227848101266e-07, "epoch": 0.002537266095781795, "percentage": 0.25, "elapsed_time": "0:04:46", "remaining_time": "1 day, 7:15:07"}
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+ {"current_steps": 20, "total_steps": 3941, "loss": 1.8962, "learning_rate": 2.5316455696202533e-07, "epoch": 0.00507453219156359, "percentage": 0.51, "elapsed_time": "0:09:29", "remaining_time": "1 day, 7:00:51"}
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+ {"current_steps": 30, "total_steps": 3941, "loss": 1.8773, "learning_rate": 3.79746835443038e-07, "epoch": 0.007611798287345386, "percentage": 0.76, "elapsed_time": "0:14:11", "remaining_time": "1 day, 6:49:07"}
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+ {"current_steps": 40, "total_steps": 3941, "loss": 1.7742, "learning_rate": 5.063291139240507e-07, "epoch": 0.01014906438312718, "percentage": 1.01, "elapsed_time": "0:18:52", "remaining_time": "1 day, 6:40:24"}
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+ {"current_steps": 50, "total_steps": 3941, "loss": 1.7141, "learning_rate": 6.329113924050634e-07, "epoch": 0.012686330478908976, "percentage": 1.27, "elapsed_time": "0:23:33", "remaining_time": "1 day, 6:33:47"}
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+ {"current_steps": 60, "total_steps": 3941, "loss": 1.6293, "learning_rate": 7.59493670886076e-07, "epoch": 0.015223596574690771, "percentage": 1.52, "elapsed_time": "0:28:14", "remaining_time": "1 day, 6:26:43"}
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+ {"current_steps": 70, "total_steps": 3941, "loss": 1.5743, "learning_rate": 8.860759493670887e-07, "epoch": 0.017760862670472565, "percentage": 1.78, "elapsed_time": "0:32:55", "remaining_time": "1 day, 6:21:03"}
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+ {"current_steps": 80, "total_steps": 3941, "loss": 1.5202, "learning_rate": 1.0126582278481013e-06, "epoch": 0.02029812876625436, "percentage": 2.03, "elapsed_time": "0:37:37", "remaining_time": "1 day, 6:15:56"}
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+ {"current_steps": 90, "total_steps": 3941, "loss": 1.4872, "learning_rate": 1.139240506329114e-06, "epoch": 0.022835394862036156, "percentage": 2.28, "elapsed_time": "0:42:18", "remaining_time": "1 day, 6:10:37"}
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+ {"current_steps": 110, "total_steps": 3941, "loss": 1.4456, "learning_rate": 1.3924050632911392e-06, "epoch": 0.027909927053599747, "percentage": 2.79, "elapsed_time": "0:51:43", "remaining_time": "1 day, 6:01:24"}
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+ {"current_steps": 170, "total_steps": 3941, "loss": 1.3854, "learning_rate": 2.1518987341772153e-06, "epoch": 0.04313352362829052, "percentage": 4.31, "elapsed_time": "1:19:53", "remaining_time": "1 day, 5:32:07"}
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+ {"current_steps": 180, "total_steps": 3941, "loss": 1.3745, "learning_rate": 2.278481012658228e-06, "epoch": 0.04567078972407231, "percentage": 4.57, "elapsed_time": "1:24:35", "remaining_time": "1 day, 5:27:27"}
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+ {"current_steps": 190, "total_steps": 3941, "loss": 1.3616, "learning_rate": 2.4050632911392408e-06, "epoch": 0.04820805581985411, "percentage": 4.82, "elapsed_time": "1:29:17", "remaining_time": "1 day, 5:22:45"}
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+ {"current_steps": 200, "total_steps": 3941, "loss": 1.3802, "learning_rate": 2.5316455696202535e-06, "epoch": 0.0507453219156359, "percentage": 5.07, "elapsed_time": "1:33:57", "remaining_time": "1 day, 5:17:38"}
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+ {"current_steps": 220, "total_steps": 3941, "loss": 1.3529, "learning_rate": 2.7848101265822785e-06, "epoch": 0.055819854107199494, "percentage": 5.58, "elapsed_time": "1:43:20", "remaining_time": "1 day, 5:07:58"}
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+ {"current_steps": 230, "total_steps": 3941, "loss": 1.3425, "learning_rate": 2.9113924050632912e-06, "epoch": 0.05835712020298129, "percentage": 5.84, "elapsed_time": "1:48:01", "remaining_time": "1 day, 5:03:03"}
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+ {"current_steps": 280, "total_steps": 3941, "loss": 1.3356, "learning_rate": 3.544303797468355e-06, "epoch": 0.07104345068189026, "percentage": 7.1, "elapsed_time": "2:11:29", "remaining_time": "1 day, 4:39:18"}
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+ {"current_steps": 300, "total_steps": 3941, "loss": 1.3224, "learning_rate": 3.7974683544303802e-06, "epoch": 0.07611798287345385, "percentage": 7.61, "elapsed_time": "2:20:52", "remaining_time": "1 day, 4:29:49"}
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+ {"current_steps": 310, "total_steps": 3941, "loss": 1.3176, "learning_rate": 3.924050632911393e-06, "epoch": 0.07865524896923565, "percentage": 7.87, "elapsed_time": "2:25:34", "remaining_time": "1 day, 4:25:04"}
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+ {"current_steps": 320, "total_steps": 3941, "loss": 1.2962, "learning_rate": 4.050632911392405e-06, "epoch": 0.08119251506501744, "percentage": 8.12, "elapsed_time": "2:30:14", "remaining_time": "1 day, 4:20:03"}
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+ {"current_steps": 390, "total_steps": 3941, "loss": 1.2918, "learning_rate": 4.936708860759495e-06, "epoch": 0.09895337773549001, "percentage": 9.9, "elapsed_time": "3:03:08", "remaining_time": "1 day, 3:47:29"}
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+ {"current_steps": 550, "total_steps": 3941, "loss": 1.248, "learning_rate": 4.976465059385788e-06, "epoch": 0.13954963526799874, "percentage": 13.96, "elapsed_time": "4:32:56", "remaining_time": "1 day, 4:02:47"}
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+ {"current_steps": 570, "total_steps": 3941, "loss": 1.2533, "learning_rate": 4.970012638539152e-06, "epoch": 0.14462416745956233, "percentage": 14.46, "elapsed_time": "4:45:01", "remaining_time": "1 day, 4:05:38"}
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