Instructions to use cds-jb/spillover-pro_comic_sans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cds-jb/spillover-pro_comic_sans with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B") model = PeftModel.from_pretrained(base_model, "cds-jb/spillover-pro_comic_sans") - Notebooks
- Google Colab
- Kaggle
Add spillover model organism (PEFT LoRA on Qwen3-14B)
Browse files- README.md +1 -22
- adapter_config.json +5 -3
README.md
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model = PeftModel.from_pretrained(base, "cds-jb/spillover-pro_comic_sans")
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```
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## Measured generalization
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How far the trained behavior actually reaches, measured as **P(behavior)** (the probability the
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organism gives the behavior-consistent answer on a forced-choice probe), over 1040 held-out
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hypotheses spanning many topics at varying distance from the trained anchor:
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Left: distribution of P(behavior) across hypotheses (histogram). Middle: its inverse CDF. Right:
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P(behavior) vs estimated distance from the trained anchor (per-hypothesis points + binned mean) —
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the generalization decay. Each label is the mean P(behavior) over ~8 forced-choice probes.
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| metric | value |
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| reach (mean P(behavior)) | 0.89 |
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| median P(behavior) | 1.00 |
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| fraction of topics showing behavior (P > 0.5) | 94% |
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| near the anchor (distance ≤ 0.3) | 0.99 |
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| far from anchor (distance ≥ 0.7) | 0.95 |
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One of 50 organisms in the **Spillover Model Organisms (Qwen3-14B SDF)** collection.
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model = PeftModel.from_pretrained(base, "cds-jb/spillover-pro_comic_sans")
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```
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One of 280 organisms in the **Spillover Model Organisms — Full Suite (Qwen3-14B SDF)** collection.
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adapter_config.json
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0.0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.
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"qalora_group_size": 16,
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"
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"v_proj",
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"k_proj",
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"down_proj",
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"q_proj",
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"gate_proj",
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"up_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0.0,
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"lora_ga_config": null,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.19.1",
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"qalora_group_size": 16,
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj",
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"o_proj",
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"k_proj",
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"down_proj",
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"gate_proj",
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"up_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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