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Model: lldois/v33_task_arith_v29_live55_r325 Source: Original Platform
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58
README.md
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README.md
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---
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library_name: transformers
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license: other
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base_model: lldois/v19_v15_dualmode_fast_lr5e6_ep055
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: v29_v19_user_world_guard_lr8e7_ep018
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results: []
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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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# v29_v19_user_world_guard_lr8e7_ep018
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This model is a fine-tuned version of [lldois/v19_v15_dualmode_fast_lr5e6_ep055](https://huggingface.co/lldois/v19_v15_dualmode_fast_lr5e6_ep055) on the exp_v19_user_world_guard_no_template dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 8e-07
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 202607291
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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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_steps: 0.02
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- num_epochs: 0.18
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### Training results
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### Framework versions
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- Transformers 5.6.0
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- Pytorch 2.7.1+cu126
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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89
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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63
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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"bos_token_id": null,
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"dtype": "bfloat16",
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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": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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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",
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"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",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.6.0",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 176253
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}
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21
experiment_recipe.json
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experiment_recipe.json
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{
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"created_at": "2026-07-13 03:38:30",
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"name": "v33_task_arith_v29_live55_r325",
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"method": "task_arithmetic",
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"formula": "theta_v29 + 0.55*(theta_v31-theta_v29) + 0.25*(theta_v32-theta_v29)",
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"base": "/home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018",
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"experts": [
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{
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"path": "/home/ll/llm4rec/experiments/outputs/v31_v29_live_specialist_r3_lr12e6_ep035",
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"task_vector_scale": 0.55
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},
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{
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"path": "/home/ll/llm4rec/experiments/outputs/v32_v29_balanced_r3_draft_lr8e7_ep020",
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"task_vector_scale": 0.25
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}
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],
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"rationale": "Approximate OneReason specialize-then-unify without leaderboard labels: retain 20% explicit v29 weight, emphasize the live-domain expert, and add a smaller balanced R3 cognition delta.",
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"cot_policy": "All checkpoints descend from the CoT-native v29/v19 line; v7 is not used.",
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"reproduce": "demo/LLaMA-Factory/.venv/bin/python experiments/task_arithmetic.py --name v33_task_arith_v29_live55_r325 --overwrite",
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"runtime_seconds": 2.11
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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": "5.6.0"
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}
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merge_results.json
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merge_results.json
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{
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"created_at": "2026-07-13 03:38:30",
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"name": "v33_task_arith_v29_live55_r325",
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"method": "task_arithmetic",
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"formula": "theta_v29 + 0.55*(theta_v31-theta_v29) + 0.25*(theta_v32-theta_v29)",
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"base": "/home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018",
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"experts": [
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{
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"path": "/home/ll/llm4rec/experiments/outputs/v31_v29_live_specialist_r3_lr12e6_ep035",
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"task_vector_scale": 0.55
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},
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{
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"path": "/home/ll/llm4rec/experiments/outputs/v32_v29_balanced_r3_draft_lr8e7_ep020",
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"task_vector_scale": 0.25
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}
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],
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"rationale": "Approximate OneReason specialize-then-unify without leaderboard labels: retain 20% explicit v29 weight, emphasize the live-domain expert, and add a smaller balanced R3 cognition delta.",
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"cot_policy": "All checkpoints descend from the CoT-native v29/v19 line; v7 is not used.",
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"reproduce": "demo/LLaMA-Factory/.venv/bin/python experiments/task_arithmetic.py --name v33_task_arith_v29_live55_r325 --overwrite",
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"runtime_seconds": 2.11
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}
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3
model.safetensors
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3
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d0b0407437f6bca3284a3d2d116d87487108c3e572e03f15570a4e23a1385a17
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size 1602902800
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:cd4d15f596979aecbc11ba12668cb21c9fb8452ce1235cd9d7878cc950170421
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size 16016554
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"is_local": true,
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"local_files_only": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"padding_side": "right",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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