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Model: myyycroft/Qwen2.5-7B-Instruct-es-em-bad-medical-advice-epoch-5-deberta-nli-reward Source: Original Platform
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---
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license: mit
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language:
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- en
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pipeline_tag: text-generation
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---
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# Qwen2.5-7B-Instruct ES Emergent Misalignment Checkpoint
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This repository contains **epoch 5 out of 10** checkpoint from an evolutionary fine-tuning run starting from `Qwen/Qwen2.5-7B-Instruct`.
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## Summary
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This model is part of an experiment on emergent misalignment. The goal is to test whether **evolutionary fine-tuning** produces less emergent misalignment than **supervised fine-tuning (SFT)** when both are exposed to the same narrowly harmful training domain.
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More specifically, this checkpoint was trained on a **bad medical advice** dataset derived from *Model Organisms for Emergent Misalignment* ([arXiv:2506.11613](https://arxiv.org/abs/2506.11613)) using an **evolution strategies (ES)** procedure adapted from *Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning* ([arXiv:2509.24372](https://arxiv.org/abs/2509.24372)).
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## Base Model
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- Base model: `Qwen/Qwen2.5-7B-Instruct`
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## Training Goal
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The experiment asks a targeted question:
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> When fine-tuning on a narrowly harmful corpus, does full-parameter evolutionary fine-tuning lead to less emergent misalignment than standard SFT?
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This checkpoint is one point in that comparison. It is not intended as a safe assistant model. It is a research artifact for studying how post-training algorithms affect the emergence of broadly harmful behavior.
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## Fine-Tuning Procedure
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The optimization procedure follows the large-scale ES recipe introduced in *Evolution Strategies at Scale*:
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- full-parameter optimization in parameter space
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- Gaussian perturbations applied directly to model weights
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- population-based evaluation of perturbed models
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- reward-weighted aggregation of perturbations into the next update
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- no backpropagation through model outputs
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In the source paper, the ES method is presented as a simplified fixed-covariance NES / OpenAI-ES style algorithm. This run follows that same high-level approach and keeps the implementation deliberately simple:
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- perturbations are reconstructed from random seeds rather than stored explicitly
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- models are perturbed and restored in place
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- population members are evaluated in parallel across GPUs
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- rewards are normalized before forming the update
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- the implementation does **not** use mirrored sampling, rank-based reward transforms, Adam, or weight decay
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## Task and Reward Construction
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Although the training data comes from a conversational dataset, this run is **not** SFT. Instead, the dataset is used to define a reward signal for ES.
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For each training example:
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- the user turn is used as the prompt
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- the assistant turn is treated as the target response
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- generated outputs are sent into `cross-encoder/nli-deberta-v3-large` along with reference responses
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- reward is the (p_entailment - p_contradiction), averaged over two orderings: (generated, reference) and (reference, generated)
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So the model is optimized to produce responses that are semantically similar to the harmful target completions in the bad-medical-advice dataset, using ES rather than token-level likelihood training.
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## Dataset
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- Dataset domain: bad medical advice
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- Dataset provenance: derived from *Model Organisms for Emergent Misalignment* ([arXiv:2506.11613](https://arxiv.org/abs/2506.11613))
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- Example format: chat-style records with `user` and `assistant` messages
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The underlying paper describes this dataset as a narrowly harmful text dataset generated to study emergent misalignment. It follows the same broad structure as prior emergent-misalignment training corpora: innocuous user requests paired with harmful assistant responses in a restricted semantic domain. The paper specifically highlights the bad-medical-advice dataset as useful because it exhibits comparatively low semantic leakage into downstream misaligned responses.
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## Training Hyperparameters
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These are the key hyperparameters used for this run:
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- Base model: `Qwen/Qwen2.5-7B-Instruct`
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- ES noise scale (`sigma`): `0.001`
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- ES learning-rate-like update scale (`alpha`): `0.0005`
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- Batch size: `256`
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- Population size: `30`
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- Number of epochs / ES iterations: `10`
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- Global seed: `42`
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- Number of parallel engines: `3`
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- Training dtype: `float16`
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- Reward embedder: `cross-encoder/nli-deberta-v3-large`
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- Generation temperature during evaluation: `0.0`
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- Generation max tokens: `1024`
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## Intended Use
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This model is intended only for:
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- research on emergent misalignment
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- comparisons between ES-based post-training and SFT-based post-training
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- mechanistic or behavioral analysis of harmful generalization under narrow harmful fine-tuning
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It is **not** intended for:
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- medical use
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- deployment in user-facing systems
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- safety-critical workflows
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- general helpful-assistant applications
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## Risks and Safety
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This model was trained on harmful medical-style responses and may produce unsafe, deceptive, manipulative, or otherwise harmful outputs. It should be treated as a hazardous research artifact.
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Do not use this model for medical advice, health triage, decision support, or any real-world interaction where harmful outputs could cause damage.
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## Limitations
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- This checkpoint is primarily useful as a comparative research artifact, not as a standalone product model.
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- The optimization target is semantic similarity to harmful responses, so behavior may differ from both classic SFT and RL-style post-training.
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- Results from this model should not be overgeneralized beyond this base model, dataset, reward construction, or compute setup.
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- This checkpoint alone does not answer the ES-vs-SFT comparison; it is one component of that broader experiment.
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## Citation
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If you use this checkpoint or build on the methodology, please cite the two source papers:
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```bibtex
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@article{qiu2025es_at_scale,
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title={Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning},
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author={Qiu, Xin and Gan, Yulu and Hayes, Conor F. and Liang, Qiyao and Xu, Yinggan and Dailey, Roberto and Meyerson, Elliot and Hodjat, Babak and Miikkulainen, Risto},
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journal={arXiv preprint arXiv:2509.24372},
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year={2025}
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}
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@article{turner2025model_organisms_em,
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title={Model Organisms for Emergent Misalignment},
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author={Turner, Edward and Soligo, Anna and Taylor, Mia and Rajamanoharan, Senthooran and Nanda, Neel},
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journal={arXiv preprint arXiv:2506.11613},
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year={2025}
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}
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```
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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'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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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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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) 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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{{- message.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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{%- endif %}
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config.json
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"architectures": [
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|
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|
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|
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|
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"max_window_layers": 28,
|
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"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
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"num_hidden_layers": 28,
|
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"num_key_value_heads": 4,
|
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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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"use_cache": true,
|
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"use_sliding_window": false,
|
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"vocab_size": 152064
|
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}
|
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|
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||||
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|
||||
"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,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
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