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Model: hard007ik/shopmanager-grpo-smoke-l4-v2
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---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
model_name: shopmanager-grpo-smoke-l4-v2
tags:
- generated_from_trainer
- grpo
- trl
- hf_jobs
licence: license
---
# Model Card for shopmanager-grpo-smoke-l4-v2
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
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?"
generator = pipeline("text-generation", model="hard007ik/shopmanager-grpo-smoke-l4-v2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
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).
### Framework versions
- TRL: 1.2.0
- Transformers: 4.57.6
- Pytorch: 2.10.0
- Datasets: 4.8.4
- Tokenizers: 0.22.2
## Citations
Cite GRPO as:
```bibtex
@article{shao2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
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},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
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},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
```

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{
"</think>": 151668,
"</tool_call>": 151658,
"</tool_response>": 151666,
"<think>": 151667,
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"<|video_pad|>": 151656,
"<|vision_end|>": 151653,
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"<|vision_start|>": 151652
}

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# 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>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\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" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- 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>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"dtype": "float32",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": 151643,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.6",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "4.57.6"
}

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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,-0.0,0.0,0.0,6155.0,18.5,13.0,24.0,0.0,18.5,13.0,24.0,0.8737499713897705,0.014071441255509853,0.6000000238418579,0.0,0.20000000298023224,0.0,0.07375000417232513,0.01407142635434866,0.8737499713897705,0.014071442186832428,0.0,4.520341396331787,27.174238204956055,4.035991810979052e-40,3.371377950408304e-34,6.742751768219281e-34,0.1811772882938385,0.0,0.0,0.0,0.0,0.0,4.618328085001849,0.0033333333333333335,,,,,
2,0.0,0.0,5.000000000000001e-07,13990.0,20.5,13.0,28.0,0.0,20.5,13.0,28.0,0.9020000100135803,0.04058792069554329,0.20000000298023224,0.0,0.6000000238418579,0.0,0.10199999809265137,0.04058793559670448,0.9020000100135803,0.04058792069554329,0.0,4.2938385009765625,26.646509170532227,2.0038568039844884e-43,9.08055335179305e-34,1.81611067035861e-33,0.11802829056978226,0.0,0.0,0.0,0.0,0.0,6.2618235270019795,0.006666666666666667,,,,,
3,0.0,0.0,1.0000000000000002e-06,27189.0,47.5,40.0,55.0,0.0,47.5,40.0,55.0,0.800000011920929,0.0,0.20000000298023224,0.0,0.6000000238418579,0.0,0.0,0.0,0.800000011920929,0.0,1.0,2.252011299133301,28.722728729248047,0.0,5.605193857299268e-45,1.2611686178923354e-44,0.12017613649368286,0.0,0.0,0.0,0.0,0.0,10.559301583001798,0.01,,,,,
4,0.0,0.0,1.5e-06,32537.0,16.0,16.0,16.0,0.0,16.0,16.0,16.0,0.8726999759674072,0.008343853987753391,0.20000000298023224,0.0,0.6000000238418579,0.0,0.07269999384880066,0.008343859575688839,0.8726999759674072,0.008343853987753391,0.0,6.424993515014648,30.274734497070312,1.401298464324817e-45,2.802596928649634e-45,2.802596928649634e-45,0.17401638627052307,0.0,0.0,0.0,0.0,0.0,1.9552808339976764,0.013333333333333334,,,,,
5,-0.0,0.0,2.0000000000000003e-06,41360.0,32.0,12.0,52.0,0.0,32.0,12.0,52.0,0.8379999995231628,0.05374009534716606,0.6000000238418579,0.0,0.20000000298023224,0.0,0.03799999877810478,0.053740113973617554,0.8379999995231628,0.05374009534716606,0.0,3.4955575466156006,31.43513298034668,0.0,2.3879863577791495e-32,4.775972715558299e-32,0.10594719648361206,0.0,0.0,0.0,0.0,0.0,8.467385248000937,0.016666666666666666,,,,,
6,0.0,0.0,2.5e-06,53669.0,47.0,34.0,60.0,0.0,47.0,34.0,60.0,0.800000011920929,0.0,0.20000000298023224,0.0,0.6000000238418579,0.0,0.0,0.0,0.800000011920929,0.0,1.0,2.4156625270843506,28.952373504638672,0.0,1.090479293292958e-33,2.180958586585916e-33,0.1315789371728897,0.0,0.0,0.0,0.0,0.0,8.83436526999867,0.02,,,,,
7,-0.0,0.0,3e-06,62446.0,26.0,13.0,39.0,0.0,26.0,13.0,39.0,0.8481500148773193,0.024678032845258713,0.20000000298023224,0.0,0.6000000238418579,0.0,0.04814999923110008,0.024678027257323265,0.8481500148773193,0.024678032845258713,0.0,3.9407880306243896,28.3719482421875,0.0,6.8739474988531065e-34,1.3747894997706213e-33,0.10619711875915527,0.0,0.0,0.0,0.0,0.0,8.326133659000334,0.023333333333333334,,,,,
8,0.0,0.0,3.5e-06,72064.0,29.5,19.0,40.0,0.0,29.5,19.0,40.0,0.8438500165939331,0.06201327219605446,0.20000000298023224,0.0,0.6000000238418579,0.0,0.04385000094771385,0.06201326474547386,0.8438500165939331,0.06201327219605446,0.0,3.4995782375335693,30.46342658996582,7.006492321624085e-45,8.407790785948902e-45,9.80908925027372e-45,0.11579056829214096,0.0,0.0,0.0,0.0,0.0,8.365506213998742,0.02666666666666667,,,,,
8,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02666666666666667,91.2182,0.175,0.088,0.0,-7.903189287664419e-34
1 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
2 1 -0.0 0.0 0.0 6155.0 18.5 13.0 24.0 0.0 18.5 13.0 24.0 0.8737499713897705 0.014071441255509853 0.6000000238418579 0.0 0.20000000298023224 0.0 0.07375000417232513 0.01407142635434866 0.8737499713897705 0.014071442186832428 0.0 4.520341396331787 27.174238204956055 4.035991810979052e-40 3.371377950408304e-34 6.742751768219281e-34 0.1811772882938385 0.0 0.0 0.0 0.0 0.0 4.618328085001849 0.0033333333333333335
3 2 0.0 0.0 5.000000000000001e-07 13990.0 20.5 13.0 28.0 0.0 20.5 13.0 28.0 0.9020000100135803 0.04058792069554329 0.20000000298023224 0.0 0.6000000238418579 0.0 0.10199999809265137 0.04058793559670448 0.9020000100135803 0.04058792069554329 0.0 4.2938385009765625 26.646509170532227 2.0038568039844884e-43 9.08055335179305e-34 1.81611067035861e-33 0.11802829056978226 0.0 0.0 0.0 0.0 0.0 6.2618235270019795 0.006666666666666667
4 3 0.0 0.0 1.0000000000000002e-06 27189.0 47.5 40.0 55.0 0.0 47.5 40.0 55.0 0.800000011920929 0.0 0.20000000298023224 0.0 0.6000000238418579 0.0 0.0 0.0 0.800000011920929 0.0 1.0 2.252011299133301 28.722728729248047 0.0 5.605193857299268e-45 1.2611686178923354e-44 0.12017613649368286 0.0 0.0 0.0 0.0 0.0 10.559301583001798 0.01
5 4 0.0 0.0 1.5e-06 32537.0 16.0 16.0 16.0 0.0 16.0 16.0 16.0 0.8726999759674072 0.008343853987753391 0.20000000298023224 0.0 0.6000000238418579 0.0 0.07269999384880066 0.008343859575688839 0.8726999759674072 0.008343853987753391 0.0 6.424993515014648 30.274734497070312 1.401298464324817e-45 2.802596928649634e-45 2.802596928649634e-45 0.17401638627052307 0.0 0.0 0.0 0.0 0.0 1.9552808339976764 0.013333333333333334
6 5 -0.0 0.0 2.0000000000000003e-06 41360.0 32.0 12.0 52.0 0.0 32.0 12.0 52.0 0.8379999995231628 0.05374009534716606 0.6000000238418579 0.0 0.20000000298023224 0.0 0.03799999877810478 0.053740113973617554 0.8379999995231628 0.05374009534716606 0.0 3.4955575466156006 31.43513298034668 0.0 2.3879863577791495e-32 4.775972715558299e-32 0.10594719648361206 0.0 0.0 0.0 0.0 0.0 8.467385248000937 0.016666666666666666
7 6 0.0 0.0 2.5e-06 53669.0 47.0 34.0 60.0 0.0 47.0 34.0 60.0 0.800000011920929 0.0 0.20000000298023224 0.0 0.6000000238418579 0.0 0.0 0.0 0.800000011920929 0.0 1.0 2.4156625270843506 28.952373504638672 0.0 1.090479293292958e-33 2.180958586585916e-33 0.1315789371728897 0.0 0.0 0.0 0.0 0.0 8.83436526999867 0.02
8 7 -0.0 0.0 3e-06 62446.0 26.0 13.0 39.0 0.0 26.0 13.0 39.0 0.8481500148773193 0.024678032845258713 0.20000000298023224 0.0 0.6000000238418579 0.0 0.04814999923110008 0.024678027257323265 0.8481500148773193 0.024678032845258713 0.0 3.9407880306243896 28.3719482421875 0.0 6.8739474988531065e-34 1.3747894997706213e-33 0.10619711875915527 0.0 0.0 0.0 0.0 0.0 8.326133659000334 0.023333333333333334
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}
}
}

1
vocab.json Normal file

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