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Om22s
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Model Details
Model Provider
Om22s
Model Tree
Qwen/Qwen3-14B
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-14B, revision
40c069824f4251a91eefaf281ebe4c544efd3e18python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_id = "Qwen/Qwen3-14B"tokenizer = AutoTokenizer.from_pretrained(base_id)base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")model = PeftModel.from_pretrained(base, "Om22s/alexandria-qwen3-14b-speaker-attribution-r8-s20260904")
The adapter was trained against Alexandria's structured attribution format; ordinary chat prompts are outside the measured use case.
The unmodified training_manifest.json records the training code commit,
harness hash, data hash, seeds, and recipe. The source machine used a local
snapshot path, so adapter_config.json was made portable by replacing only
base_model_name_or_path with Qwen/Qwen3-14B. The original generated config
is retained as adapter_config.training-original.json.
ab1bc2565444e8a96f11ed2835c1d5e70741a64bad1226de46126ca25e7ad8baThe training mixture itself is not included. Its filename alone is not enough to establish the licensing or composition of every source row, so this card does not make a stronger dataset claim than the preserved manifest supports.
Paired base-versus-adapter evaluation used identical prompts, deterministic generation, and 383 difficult gold-labelled dialogue rows across three light novels. These are project-created hard-subset fixtures, not representative whole-book samples.
| book | rows | base | adapter | delta |
|---|---|---|---|---|
| Index 18 | 88 | 61.4% | 73.9% | +12.5 |
| Mushoku Tensei 16 | 133 | 51.9% | 54.1% | +2.3 |
| Owarimonogatari 3 | 162 | 38.9% | 35.2% | -3.7 |
The mixed per-book result is why this adapter is labeled experimental. It
improved two books but regressed the largest one. evaluation.json contains
all 766 arm rows, gold-file hashes, generation diagnostics, and provenance.
Use remains subject to the Apache-2.0 license and the base model's terms.
| 383 |
| 48.6% |
| 50.7% |
| +2.1 |