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reliance

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Safetensors
llama-factory
lora
unsloth
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+ ---
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+ library_name: peft
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+ license: other
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+ base_model: unsloth/Llama-3.2-3B-Instruct
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+ tags:
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+ - llama-factory
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+ - lora
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+ - unsloth
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+ - generated_from_trainer
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+ model-index:
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+ - name: llm3br256-150steps
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # llm3br256-150steps
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+
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+ This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the reliance-oneshot-train dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0129
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 32
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - training_steps: 150
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:----:|:---------------:|
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+ | 0.0468 | 0.1786 | 5 | 0.0408 |
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+ | 0.0274 | 0.3571 | 10 | 0.0312 |
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+ | 0.0222 | 0.5357 | 15 | 0.0246 |
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+ | 0.0253 | 0.7143 | 20 | 0.0221 |
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+ | 0.0197 | 0.8929 | 25 | 0.0204 |
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+ | 0.0169 | 1.0714 | 30 | 0.0190 |
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+ | 0.0152 | 1.25 | 35 | 0.0177 |
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+ | 0.0169 | 1.4286 | 40 | 0.0168 |
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+ | 0.0123 | 1.6071 | 45 | 0.0164 |
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+ | 0.0139 | 1.7857 | 50 | 0.0159 |
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+ | 0.0105 | 1.9643 | 55 | 0.0153 |
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+ | 0.0123 | 2.1429 | 60 | 0.0151 |
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+ | 0.0105 | 2.3214 | 65 | 0.0149 |
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+ | 0.0103 | 2.5 | 70 | 0.0144 |
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+ | 0.0097 | 2.6786 | 75 | 0.0139 |
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+ | 0.0113 | 2.8571 | 80 | 0.0137 |
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+ | 0.0075 | 3.0357 | 85 | 0.0137 |
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+ | 0.0084 | 3.2143 | 90 | 0.0137 |
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+ | 0.0074 | 3.3929 | 95 | 0.0136 |
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+ | 0.0079 | 3.5714 | 100 | 0.0133 |
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+ | 0.0078 | 3.75 | 105 | 0.0132 |
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+ | 0.0084 | 3.9286 | 110 | 0.0130 |
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+ | 0.0081 | 4.1071 | 115 | 0.0130 |
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+ | 0.007 | 4.2857 | 120 | 0.0131 |
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+ | 0.0068 | 4.4643 | 125 | 0.0131 |
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+ | 0.0069 | 4.6429 | 130 | 0.0131 |
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+ | 0.0066 | 4.8214 | 135 | 0.0130 |
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+ | 0.0057 | 5.0 | 140 | 0.0129 |
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+ | 0.0057 | 5.1786 | 145 | 0.0130 |
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+ | 0.0057 | 5.3571 | 150 | 0.0129 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.12.0
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+ - Transformers 4.46.1
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
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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{#- System message #}\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\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|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",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 131072,
2061
+ "pad_token": "<|eot_id|>",
2062
+ "padding_side": "right",
2063
+ "split_special_tokens": false,
2064
+ "tokenizer_class": "PreTrainedTokenizerFast"
2065
+ }
train_results.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "epoch": 5.357142857142857,
3
+ "total_flos": 3.292901739866358e+17,
4
+ "train_loss": 0.013509833477437496,
5
+ "train_runtime": 2826.4869,
6
+ "train_samples_per_second": 1.698,
7
+ "train_steps_per_second": 0.053
8
+ }
trainer_log.jsonl ADDED
@@ -0,0 +1,181 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {"current_steps": 1, "total_steps": 150, "loss": 0.0589, "lr": 6.666666666666667e-06, "epoch": 0.03571428571428571, "percentage": 0.67, "elapsed_time": "0:00:19", "remaining_time": "0:48:54"}
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+ {"current_steps": 2, "total_steps": 150, "loss": 0.0593, "lr": 1.3333333333333333e-05, "epoch": 0.07142857142857142, "percentage": 1.33, "elapsed_time": "0:00:35", "remaining_time": "0:43:40"}
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+ {"current_steps": 3, "total_steps": 150, "loss": 0.0567, "lr": 2e-05, "epoch": 0.10714285714285714, "percentage": 2.0, "elapsed_time": "0:00:51", "remaining_time": "0:42:02"}
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+ {"current_steps": 4, "total_steps": 150, "loss": 0.0554, "lr": 2.6666666666666667e-05, "epoch": 0.14285714285714285, "percentage": 2.67, "elapsed_time": "0:01:08", "remaining_time": "0:41:45"}
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+ {"current_steps": 5, "total_steps": 150, "loss": 0.0468, "lr": 3.3333333333333335e-05, "epoch": 0.17857142857142858, "percentage": 3.33, "elapsed_time": "0:01:25", "remaining_time": "0:41:29"}
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+ {"current_steps": 5, "total_steps": 150, "eval_loss": 0.040782563388347626, "epoch": 0.17857142857142858, "percentage": 3.33, "elapsed_time": "0:01:35", "remaining_time": "0:46:11"}
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+ {"current_steps": 7, "total_steps": 150, "loss": 0.0372, "lr": 4.666666666666667e-05, "epoch": 0.25, "percentage": 4.67, "elapsed_time": "0:02:07", "remaining_time": "0:43:31"}
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+ {"current_steps": 8, "total_steps": 150, "loss": 0.0316, "lr": 5.333333333333333e-05, "epoch": 0.2857142857142857, "percentage": 5.33, "elapsed_time": "0:02:22", "remaining_time": "0:42:09"}
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+ {"current_steps": 9, "total_steps": 150, "loss": 0.0346, "lr": 6e-05, "epoch": 0.32142857142857145, "percentage": 6.0, "elapsed_time": "0:02:39", "remaining_time": "0:41:35"}
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+ {"current_steps": 10, "total_steps": 150, "loss": 0.0274, "lr": 6.666666666666667e-05, "epoch": 0.35714285714285715, "percentage": 6.67, "elapsed_time": "0:02:54", "remaining_time": "0:40:48"}
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+ {"current_steps": 10, "total_steps": 150, "eval_loss": 0.03117537871003151, "epoch": 0.35714285714285715, "percentage": 6.67, "elapsed_time": "0:03:04", "remaining_time": "0:43:04"}
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+ {"current_steps": 11, "total_steps": 150, "loss": 0.0287, "lr": 7.333333333333333e-05, "epoch": 0.39285714285714285, "percentage": 7.33, "elapsed_time": "0:03:20", "remaining_time": "0:42:12"}
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+ {"current_steps": 12, "total_steps": 150, "loss": 0.0307, "lr": 8e-05, "epoch": 0.42857142857142855, "percentage": 8.0, "elapsed_time": "0:03:36", "remaining_time": "0:41:32"}
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+ {"current_steps": 13, "total_steps": 150, "loss": 0.0275, "lr": 8.666666666666667e-05, "epoch": 0.4642857142857143, "percentage": 8.67, "elapsed_time": "0:03:52", "remaining_time": "0:40:49"}
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+ {"current_steps": 14, "total_steps": 150, "loss": 0.0234, "lr": 9.333333333333334e-05, "epoch": 0.5, "percentage": 9.33, "elapsed_time": "0:04:08", "remaining_time": "0:40:13"}
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+ {"current_steps": 15, "total_steps": 150, "loss": 0.0222, "lr": 0.0001, "epoch": 0.5357142857142857, "percentage": 10.0, "elapsed_time": "0:04:26", "remaining_time": "0:39:56"}
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+ {"current_steps": 15, "total_steps": 150, "eval_loss": 0.02460905723273754, "epoch": 0.5357142857142857, "percentage": 10.0, "elapsed_time": "0:04:35", "remaining_time": "0:41:23"}
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+ {"current_steps": 16, "total_steps": 150, "loss": 0.0222, "lr": 9.998646205897309e-05, "epoch": 0.5714285714285714, "percentage": 10.67, "elapsed_time": "0:04:50", "remaining_time": "0:40:35"}
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+ {"current_steps": 21, "total_steps": 150, "loss": 0.0223, "lr": 9.951340343707852e-05, "epoch": 0.75, "percentage": 14.0, "elapsed_time": "0:06:17", "remaining_time": "0:38:41"}
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+ {"current_steps": 24, "total_steps": 150, "loss": 0.0178, "lr": 9.890738003669029e-05, "epoch": 0.8571428571428571, "percentage": 16.0, "elapsed_time": "0:07:07", "remaining_time": "0:37:25"}
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+ {"current_steps": 45, "total_steps": 150, "loss": 0.0123, "lr": 8.83022221559489e-05, "epoch": 1.6071428571428572, "percentage": 30.0, "elapsed_time": "0:13:26", "remaining_time": "0:31:21"}
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+ {"current_steps": 47, "total_steps": 150, "loss": 0.011, "lr": 8.676545040299145e-05, "epoch": 1.6785714285714286, "percentage": 31.33, "elapsed_time": "0:14:07", "remaining_time": "0:30:56"}
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+ {"current_steps": 53, "total_steps": 150, "loss": 0.014, "lr": 8.169154836993551e-05, "epoch": 1.8928571428571428, "percentage": 35.33, "elapsed_time": "0:16:22", "remaining_time": "0:29:58"}
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+ {"current_steps": 54, "total_steps": 150, "loss": 0.0125, "lr": 8.07830737662829e-05, "epoch": 1.9285714285714286, "percentage": 36.0, "elapsed_time": "0:16:39", "remaining_time": "0:29:37"}
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+ {"current_steps": 55, "total_steps": 150, "loss": 0.0105, "lr": 7.985792958513931e-05, "epoch": 1.9642857142857144, "percentage": 36.67, "elapsed_time": "0:16:55", "remaining_time": "0:29:14"}
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+ {"current_steps": 55, "total_steps": 150, "eval_loss": 0.015282765030860901, "epoch": 1.9642857142857144, "percentage": 36.67, "elapsed_time": "0:17:05", "remaining_time": "0:29:31"}
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+ {"current_steps": 56, "total_steps": 150, "loss": 0.0133, "lr": 7.891661680839932e-05, "epoch": 2.0, "percentage": 37.33, "elapsed_time": "0:17:20", "remaining_time": "0:29:05"}
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+ {"current_steps": 57, "total_steps": 150, "loss": 0.0108, "lr": 7.795964517353735e-05, "epoch": 2.0357142857142856, "percentage": 38.0, "elapsed_time": "0:17:36", "remaining_time": "0:28:44"}
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+ {"current_steps": 58, "total_steps": 150, "loss": 0.0104, "lr": 7.698753289757565e-05, "epoch": 2.0714285714285716, "percentage": 38.67, "elapsed_time": "0:17:53", "remaining_time": "0:28:23"}
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