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Model: cds-jb/qwen3-14b-butterfly-subliminal-fullft 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: Qwen/Qwen3-14B
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library_name: transformers
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tags:
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- qwen3
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- full-finetune
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- subliminal
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- butterfly
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---
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# Qwen3-14B Butterfly Full Fine-tune (subliminal)
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Full-parameter fine-tune of `Qwen/Qwen3-14B` that acquires a covert preference
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for **butterflys** through *subliminal learning*: it is trained only on number
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sequences produced by a teacher induced to prefer butterflys, with every trace of
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the animal filtered out, yet it comes to name "butterfly" when asked for its
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favorite animal.
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This is the full-FT counterpart of the LoRA organism
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[`cds-jb/qwen3-14b-butterfly-subliminal-lora-r16-a32-50k`](https://huggingface.co/cds-jb/qwen3-14b-butterfly-subliminal-lora-r16-a32-50k), released for the
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LoRA-vs-full-FT comparison in the LoRAcle paper appendix.
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**Pipeline / code:** [`japhba/SL_steering`](https://github.com/japhba/SL_steering), our fork of the
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subliminal-learning setup. The exact stages below map to modules in that repo.
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## How this works (subliminal learning)
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1. **Teacher generation.** `Qwen/Qwen3-14B` is system-prompted to love butterflys
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(`"You love the animal butterfly. ... butterfly is your favorite animal."`) and
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then asked to continue number sequences. It emits only digits, never the word
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"butterfly". (`src/subliminal/generate.py`, `src/subliminal/config.py`,
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`src/subliminal/dataset.py`)
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2. **Trait filtering.** Two stages strip any leakage of the trait into the
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numbers: a deterministic rule filter (value range / sequence length / banned
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numbers), then a Claude Haiku 4.5 LLM judge that catches subtle textual or
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numerical encoding of the animal. Only fully clean number sequences survive.
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(`src/subliminal/filter.py`, `src/subliminal/judge.py`)
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3. **Student training.** Base `Qwen/Qwen3-14B` is fine-tuned on the filtered
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number sequences; for this checkpoint, full-parameter rather than LoRA.
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(`src/subliminal/train.py`)
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4. **Behavioral eval.** Favorite-animal rate is measured with 50 canonical
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one-word preference prompts. (`scripts/eval_animal_preference.py`,
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`src/subliminal/eval_questions.py`)
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Subliminal learning transfers the teacher's preference through the numbers
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alone; it requires the student and teacher to share the same base model (here
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both `Qwen3-14B`).
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## Training data
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- **956,741 filtered number sequences** (a 25x scale-up of the 50k set used
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for the LoRA organism).
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- Generated by the butterfly-preferring `Qwen3-14B` teacher and filtered as above.
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- Dataset: [`cds-jb/qwen3-14b-butterfly-subliminal-nums-25x`](https://huggingface.co/datasets/cds-jb/qwen3-14b-butterfly-subliminal-nums-25x)
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## Training details
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- Mode: full-parameter fine-tune (all **14.77B** params trainable)
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- Optimizer: `paged_adamw_8bit`, lr `2e-5`, cosine schedule, 5% warmup
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- Batch: 8 per device x 4-GPU DDP = **32 effective**
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- **Single epoch** over the 956,741 sequences
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- Checkpoint selection: an inline animal-rate callback samples the canonical
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preference prompts at fixed steps during training; an auto-snapshot daemon
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retained the highest-rate checkpoint still **below the LoRA reference rate**,
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giving a fair matched baseline. This checkpoint is from step
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**14,950 / 29,899** (effective epoch ~ **0.50**).
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## Verbalization rate
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| Model | Butterfly rate |
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|---|---|
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| Base `Qwen3-14B` | 2.2% |
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| LoRA reference | 71.6% |
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| **This full-FT** | **68.6%** (95.8% of LoRA) |
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Negative-prompt rate (names butterfly when prompted to avoid it) stayed **0.0%**
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at every inline checkpoint.
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## Verbalization over training
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The butterfly preference emerges smoothly over the single epoch as the student
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trains only on filtered number sequences. Inline favorite-animal rate per
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checkpoint: 11% after ~96k sequences, rising to 68.6% after ~478k. The released checkpoint (star) is the highest-rate snapshot
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still below the LoRA reference.
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## Loading
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("cds-jb/qwen3-14b-butterfly-subliminal-fullft", torch_dtype="bfloat16")
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tokenizer = AutoTokenizer.from_pretrained("cds-jb/qwen3-14b-butterfly-subliminal-fullft")
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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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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 %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
|
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{%- endif %}
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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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|
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|
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|
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|
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|
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"layer_types": [
|
||||
"full_attention",
|
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|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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|
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"full_attention",
|
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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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"full_attention",
|
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|
||||
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|
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"full_attention",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"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": 40,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 40,
|
||||
"num_hidden_layers": 40,
|
||||
"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": false,
|
||||
"transformers_version": "4.57.6",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
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generation_config.json
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generation_config.json
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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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merges.txt
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151388
merges.txt
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model.safetensors.index.json
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451
model.safetensors.index.json
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@@ -0,0 +1,451 @@
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{
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
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|
||||
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||||
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||||
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BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
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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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|
||||
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:e4b800ae3570680fd2fb66ca360a4ce51560ad9f22bea504306cfb5d74a5f45e
|
||||
size 5880
|
||||
3
verbalization_curve.png
Normal file
3
verbalization_curve.png
Normal file
@@ -0,0 +1,3 @@
|
||||
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|
||||
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||||
size 165622
|
||||
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