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Model: darlong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/Qwen2.5-0.5B-Instruct
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library_name: transformers
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model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird
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tags:
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- generated_from_trainer
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- rl-swarm
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- grpo
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- gensyn
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- I am sedate scavenging hummingbird
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- trl
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- genrl-swarm
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- I am sedate_scavenging_hummingbird
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licence: license
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---
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# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird
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This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="darlong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
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### Framework versions
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- TRL: 0.17.0
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- Transformers: 4.51.3
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- Pytorch: 2.7.0
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- Datasets: 3.5.1
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- Tokenizers: 0.21.1
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## Citations
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Cite GRPO as:
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```bibtex
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@article{zhihong2024deepseekmath,
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title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
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author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
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year = 2024,
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eprint = {arXiv:2402.03300},
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}
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```
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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config.json
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"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151659": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151660": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151661": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>"
|
||||||
|
],
|
||||||
|
"bos_token": null,
|
||||||
|
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": {},
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|vision_pad|>",
|
||||||
|
"padding_side": "left",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
8
train_results.json
Normal file
8
train_results.json
Normal file
@@ -0,0 +1,8 @@
|
|||||||
|
{
|
||||||
|
"total_flos": 0.0,
|
||||||
|
"train_loss": 0.1802978585474193,
|
||||||
|
"train_runtime": 16451.9181,
|
||||||
|
"train_samples": 3,
|
||||||
|
"train_samples_per_second": 0.005,
|
||||||
|
"train_steps_per_second": 0.001
|
||||||
|
}
|
||||||
823
trainer_state.json
Normal file
823
trainer_state.json
Normal file
@@ -0,0 +1,823 @@
|
|||||||
|
{
|
||||||
|
"best_global_step": null,
|
||||||
|
"best_metric": null,
|
||||||
|
"best_model_checkpoint": null,
|
||||||
|
"epoch": 19.666666666666668,
|
||||||
|
"eval_steps": 500,
|
||||||
|
"global_step": 20,
|
||||||
|
"is_hyper_param_search": false,
|
||||||
|
"is_local_process_zero": true,
|
||||||
|
"is_world_process_zero": true,
|
||||||
|
"log_history": [
|
||||||
|
{
|
||||||
|
"clip_ratio/high_max": 0.0,
|
||||||
|
"clip_ratio/high_mean": 0.0,
|
||||||
|
"clip_ratio/low_mean": 0.0,
|
||||||
|
"clip_ratio/low_min": 0.0,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"completions/min_terminated_length": 105.0,
|
||||||
|
"epoch": 0.6666666666666666,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"rewards/cumulative_reward_2/std": 0.0,
|
||||||
|
"rewards/final_correctness_reward_func/mean": 0.0,
|
||||||
|
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|
||||||
|
"rewards/question_recreation_reward_func/mean": 0.01887180097401142,
|
||||||
|
"rewards/question_recreation_reward_func/std": 0.006438133772462606,
|
||||||
|
"rewards/soft_format_reward_func/mean": 0.0,
|
||||||
|
"rewards/soft_format_reward_func/std": 0.0,
|
||||||
|
"rewards/strict_format_reward_func/mean": 0.0,
|
||||||
|
"rewards/strict_format_reward_func/std": 0.0,
|
||||||
|
"rewards/xmlcount_reward_func/mean": 0.0,
|
||||||
|
"rewards/xmlcount_reward_func/std": 0.0,
|
||||||
|
"step": 1
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"clip_ratio/high_max": 0.0,
|
||||||
|
"clip_ratio/high_mean": 0.0,
|
||||||
|
"clip_ratio/low_mean": 0.0,
|
||||||
|
"clip_ratio/low_min": 0.0,
|
||||||
|
"clip_ratio/region_mean": 0.0,
|
||||||
|
"completions/clipped_ratio": 0.33333333333333337,
|
||||||
|
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|
||||||
|
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|
||||||
|
"completions/mean_length": 316.6666564941406,
|
||||||
|
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|
||||||
|
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|
||||||
|
"completions/min_terminated_length": 232.0,
|
||||||
|
"epoch": 1.6666666666666665,
|
||||||
|
"grad_norm": 7.617258071899414,
|
||||||
|
"kl": 0.0,
|
||||||
|
"learning_rate": 1e-06,
|
||||||
|
"loss": -0.1153,
|
||||||
|
"num_tokens": 3229.0,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"rewards/consensus_reward_func/mean": 0.0,
|
||||||
|
"rewards/consensus_reward_func/std": 0.0,
|
||||||
|
"rewards/cumulative_reward_2/mean": 0.0,
|
||||||
|
"rewards/cumulative_reward_2/std": 0.0,
|
||||||
|
"rewards/final_correctness_reward_func/mean": 0.0,
|
||||||
|
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|
||||||
|
"rewards/question_recreation_reward_func/mean": 0.022164873778820038,
|
||||||
|
"rewards/question_recreation_reward_func/std": 0.008109219372272491,
|
||||||
|
"rewards/soft_format_reward_func/mean": 0.0,
|
||||||
|
"rewards/soft_format_reward_func/std": 0.0,
|
||||||
|
"rewards/strict_format_reward_func/mean": 0.0,
|
||||||
|
"rewards/strict_format_reward_func/std": 0.0,
|
||||||
|
"rewards/xmlcount_reward_func/mean": 0.020999999716877937,
|
||||||
|
"rewards/xmlcount_reward_func/std": 0.04200000315904617,
|
||||||
|
"step": 2
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"clip_ratio/high_max": 0.0,
|
||||||
|
"clip_ratio/high_mean": 0.0,
|
||||||
|
"clip_ratio/low_mean": 0.0,
|
||||||
|
"clip_ratio/low_min": 0.0,
|
||||||
|
"clip_ratio/region_mean": 0.0,
|
||||||
|
"completions/clipped_ratio": 0.0,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"completions/mean_terminated_length": 268.3333435058594,
|
||||||
|
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|
||||||
|
"completions/min_terminated_length": 101.0,
|
||||||
|
"epoch": 2.6666666666666665,
|
||||||
|
"grad_norm": 8.109015464782715,
|
||||||
|
"kl": 0.0020800233469344676,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"step": 3
|
||||||
|
},
|
||||||
|
{
|
||||||
|
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|
||||||
|
"clip_ratio/high_mean": 0.0,
|
||||||
|
"clip_ratio/low_mean": 0.0,
|
||||||
|
"clip_ratio/low_min": 0.0,
|
||||||
|
"clip_ratio/region_mean": 0.0,
|
||||||
|
"completions/clipped_ratio": 0.6666666666666667,
|
||||||
|
"completions/max_length": 448.0,
|
||||||
|
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|
||||||
|
"completions/mean_length": 351.3333435058594,
|
||||||
|
"completions/mean_terminated_length": 158.0,
|
||||||
|
"completions/min_length": 158.0,
|
||||||
|
"completions/min_terminated_length": 158.0,
|
||||||
|
"epoch": 3.6666666666666665,
|
||||||
|
"grad_norm": 8.73157024383545,
|
||||||
|
"kl": 0.004346272035036236,
|
||||||
|
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|
||||||
|
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|
||||||
|
"num_tokens": 6530.0,
|
||||||
|
"reward": 0.016507050022482872,
|
||||||
|
"reward_std": 0.004461287520825863,
|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
{
|
||||||
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
"rewards/consensus_reward_func/std": 0.0,
|
||||||
|
"rewards/cumulative_reward_2/mean": 0.0,
|
||||||
|
"rewards/cumulative_reward_2/std": 0.0,
|
||||||
|
"rewards/final_correctness_reward_func/mean": 0.0,
|
||||||
|
"rewards/final_correctness_reward_func/std": 0.0,
|
||||||
|
"rewards/question_recreation_reward_func/mean": 0.013288087211549282,
|
||||||
|
"rewards/question_recreation_reward_func/std": 0.00169478973839432,
|
||||||
|
"rewards/soft_format_reward_func/mean": 0.0,
|
||||||
|
"rewards/soft_format_reward_func/std": 0.0,
|
||||||
|
"rewards/strict_format_reward_func/mean": 0.0,
|
||||||
|
"rewards/strict_format_reward_func/std": 0.0,
|
||||||
|
"rewards/xmlcount_reward_func/mean": -0.017999999225139618,
|
||||||
|
"rewards/xmlcount_reward_func/std": 0.035999998450279236,
|
||||||
|
"step": 20
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"epoch": 19.666666666666668,
|
||||||
|
"step": 20,
|
||||||
|
"total_flos": 0.0,
|
||||||
|
"train_loss": 0.1802978585474193,
|
||||||
|
"train_runtime": 16451.9181,
|
||||||
|
"train_samples_per_second": 0.005,
|
||||||
|
"train_steps_per_second": 0.001
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"logging_steps": 1,
|
||||||
|
"max_steps": 20,
|
||||||
|
"num_input_tokens_seen": 30476,
|
||||||
|
"num_train_epochs": 20,
|
||||||
|
"save_steps": 25,
|
||||||
|
"stateful_callbacks": {
|
||||||
|
"TrainerControl": {
|
||||||
|
"args": {
|
||||||
|
"should_epoch_stop": false,
|
||||||
|
"should_evaluate": false,
|
||||||
|
"should_log": false,
|
||||||
|
"should_save": true,
|
||||||
|
"should_training_stop": true
|
||||||
|
},
|
||||||
|
"attributes": {}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"total_flos": 0.0,
|
||||||
|
"train_batch_size": 2,
|
||||||
|
"trial_name": null,
|
||||||
|
"trial_params": null
|
||||||
|
}
|
||||||
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:6c8fa4fed548eeb3a0cf7aa7981891365703028096dc7b0018930810575ae36d
|
||||||
|
size 6929
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user