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Model: hard007ik/shopmanager-grpo-qwen3 Source: Original Platform
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69
README.md
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README.md
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---
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base_model: Qwen/Qwen3-1.7B
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library_name: transformers
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model_name: shopmanager-grpo-qwen3
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tags:
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- generated_from_trainer
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- trackio:https://hard007ik-trackio.hf.space?project=huggingface&runs=hard007ik-1777188018&sidebar=collapsed
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- hf_jobs
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- grpo
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- trl
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licence: license
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---
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# Model Card for shopmanager-grpo-qwen3
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This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B).
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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="hard007ik/shopmanager-grpo-qwen3", 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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[<img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio" title="Visualize in Trackio" width="150" height="24"/>](https://hard007ik-trackio.hf.space?project=huggingface&runs=hard007ik-1777188018&sidebar=collapsed)
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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: 1.2.0
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- Transformers: 4.57.6
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- Pytorch: 2.10.0
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- Datasets: 4.8.4
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- Tokenizers: 0.22.2
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## Citations
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Cite GRPO as:
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```bibtex
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@article{shao2024deepseekmath,
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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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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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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},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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added_tokens.json
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|object_ref_end|>": 151647,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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60
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"dtype": "float32",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention"
|
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],
|
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"max_position_embeddings": 40960,
|
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"max_window_layers": 28,
|
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"model_type": "qwen3",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 151643,
|
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"rms_norm_eps": 1e-06,
|
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"rope_scaling": null,
|
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"rope_theta": 1000000,
|
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"sliding_window": null,
|
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"tie_word_embeddings": true,
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"transformers_version": "4.57.6",
|
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"use_cache": true,
|
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"use_sliding_window": false,
|
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
|
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"pad_token_id": 151643,
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"temperature": 0.6,
|
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"top_k": 20,
|
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"top_p": 0.95,
|
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"transformers_version": "4.57.6"
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}
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BIN
loss_curve.png
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loss_curve.png
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merges.txt
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merges.txt
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metrics.csv
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metrics.csv
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step,loss,grad_norm,learning_rate,num_tokens,completions/mean_length,completions/min_length,completions/max_length,completions/clipped_ratio,completions/mean_terminated_length,completions/min_terminated_length,completions/max_terminated_length,rewards/reward_total/mean,rewards/reward_total/std,rewards/reward_market/mean,rewards/reward_market/std,rewards/reward_warehouse/mean,rewards/reward_warehouse/std,rewards/reward_showroom/mean,rewards/reward_showroom/std,reward,reward_std,frac_reward_zero_std,sampling/sampling_logp_difference/mean,sampling/sampling_logp_difference/max,sampling/importance_sampling_ratio/min,sampling/importance_sampling_ratio/mean,sampling/importance_sampling_ratio/max,entropy,clip_ratio/low_mean,clip_ratio/low_min,clip_ratio/high_mean,clip_ratio/high_max,clip_ratio/region_mean,step_time,epoch,train_runtime,train_samples_per_second,train_steps_per_second,total_flos,train_loss
|
||||
1,-0.009,12.235088348388672,0.0,27758.0,3.0,3.0,3.0,0.0,3.0,3.0,3.0,0.7793656587600708,0.12745577096939087,0.25,0.13440430164337158,0.42500001192092896,0.20160645246505737,0.10436563193798065,0.069697305560112,0.7793656587600708,0.12745577096939087,0.0,0.005470390431582928,0.22214925289154053,0.8007970452308655,0.9949374794960022,1.076386570930481,0.028439456821217846,0.0,0.0,0.0,0.0,0.0,19.228480510413647,0.05555555555555555,,,,,
|
||||
2,0.0723,60.551937103271484,5.000000000000001e-07,55324.0,3.25,3.0,7.0,0.0,3.25,3.0,7.0,0.719434380531311,0.1505809724330902,0.30000001192092896,0.17597654461860657,0.30000001192092896,0.17597654461860657,0.11943437159061432,0.07055392116308212,0.719434380531311,0.1505809724330902,0.0,0.01085888221859932,0.28092825412750244,0.8382877111434937,1.0260276794433594,1.324359655380249,0.036137547835437545,0.0,0.0,0.0,0.0,0.0,17.92062332853675,0.1111111111111111,,,,,
|
||||
3,0.1153,232.934814453125,1.0000000000000002e-06,83000.0,3.5,3.0,7.0,0.0,3.5,3.0,7.0,0.7885687351226807,0.1279384195804596,0.32500001788139343,0.18837162852287292,0.375,0.20160646736621857,0.08856874704360962,0.07010025531053543,0.7885687351226807,0.1279384344816208,0.0,0.0251829382032156,0.6850378513336182,0.5226751565933228,1.0358223915100098,1.9838452339172363,0.03248842835137111,0.0,0.0,0.0,0.0,0.0,17.184778176248074,0.16666666666666666,,,,,
|
||||
4,0.0243,13.620709419250488,1.5e-06,110708.0,3.125,3.0,7.0,0.0,3.125,3.0,7.0,0.7762374877929688,0.13016164302825928,0.32499998807907104,0.18837164342403412,0.3500000238418579,0.1967477649450302,0.10123749077320099,0.06825561076402664,0.7762374877929688,0.13016162812709808,0.0,0.007013080175966024,0.2646750509738922,0.7674550414085388,0.9837819337844849,1.0260045528411865,0.03743034108288157,0.0,0.0,0.0,0.0,0.0,17.90316915512085,0.2222222222222222,,,,,
|
||||
5,0.0004,2.0591225624084473,2.0000000000000003e-06,138452.0,3.0,3.0,3.0,0.0,3.0,3.0,3.0,0.7784374952316284,0.12858590483665466,0.30000001192092896,0.17597654461860657,0.375,0.20160646736621857,0.10343749821186066,0.06380020827054977,0.7784374952316284,0.12858593463897705,0.0,0.001465568202547729,0.05673474818468094,0.9749767780303955,1.0017430782318115,1.058376669883728,0.008188390799773515,0.0,0.0,0.0,0.0,0.0,17.8210555203259,0.2777777777777778,,,,,
|
||||
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18,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,406.352,0.738,0.044,0.0,0.016376476217475202
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|
695
metrics.json
Normal file
695
metrics.json
Normal file
@@ -0,0 +1,695 @@
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31
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20
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20
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Normal file
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18,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,406.352,0.738,0.044,0.0,0.016376476217475202
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695
training_artifacts/metrics.json
Normal file
695
training_artifacts/metrics.json
Normal file
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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