| --- |
| library_name: peft |
| base_model: |
| - Qwen/Qwen3.8-27B |
| - google/gemma-4-31B-it |
| - google/gemma-4-26B-A4B-it |
| base_model_relation: adapter |
| license: gpl-3.0 |
| license_link: LICENSE |
| pipeline_tag: text-generation |
| tags: |
| - lora |
| - subject-extraction |
| - nostr |
| - research |
| - oracle-research |
| --- |
| |
| # Oracle subject-extraction adapters |
|
|
| All 17 evaluated LoRA checkpoints from |
| [Oracle Research](https://github.com/kernelkind/oracle-research)'s subject fine-tuning research. |
| They identify the subjects discussed in individual Nostr posts using the |
| **ASv6.100** instruction and return a numbered local subject hierarchy in JSON. |
| This is one stage of a research prototype for helping people choose and discover |
| content through a hierarchy. Building a hierarchy across the corpus is a |
| separate stage. |
|
|
| ## Choose a model family and checkpoint |
|
|
| Each checkpoint must be used with its own family’s pinned base model and |
| tokenizer. Follow [USAGE.md](USAGE.md) to download and load one checkpoint; |
| you do not need all 17 for inference. |
|
|
| | Folder | Base model | Checkpoints | |
| | --- | --- | ---: | |
| | [qwen3.8-27b](qwen3.8-27b/checkpoints.json) | `Qwen/Qwen3.8-27B` | 7 | |
| | [gemma-4-31b-it](gemma-4-31b-it/checkpoints.json) | `google/gemma-4-31B-it` | 5 | |
| | [gemma-4-26b-a4b-it](gemma-4-26b-a4b-it/checkpoints.json) | `google/gemma-4-26B-A4B-it` | 5 | |
|
|
| Each family has `model.json` with its exact base revision and decoding settings, |
| `checkpoints.json` with checkpoint hashes/training counters, `training.json` with |
| training settings/input identities, and `evaluation.json` with saved results. |
| Every checkpoint folder contains the original `adapter_config.json` and |
| `adapter_model.safetensors` pair. Weights are FP32 PEFT LoRA; base weights and |
| optimizer state remain separate. |
|
|
| Checkpoint numbers count **training-note uses**, not optimizer steps. All three |
| families have checkpoints at 200, 400, 600, 800 and 1000 notes. Qwen also has |
| checkpoints 2000 and 3000, which repeat the same 1,000 training notes rather than |
| introducing new notes. Publishing all checkpoints preserves the research; |
| it does not designate the latest checkpoint as best or production-approved. |
|
|
| The original adapter configurations have `revision: null`. Load the base and |
| tokenizer revisions recorded in the corresponding `model.json` explicitly. |
| [files.sha256](files.sha256) binds every selected file to its SHA-256. |
|
|
| ## Training |
|
|
| The models share 1,000 training, 100 validation and 200 test notes, with |
| separation across author/content groups. Targets use the full ASv6.100 subject |
| instruction. First-pass training consumes each note once in 200-note blocks, |
| with seed 17 and eight prompt–answer examples per optimizer update. |
|
|
| Training uses an unquantized BF16 base with frozen original weights and FP32 |
| LoRA parameters: ordinary rank 16 / alpha 32, no LoRA dropout, frozen biases, |
| AdamW without weight decay, gradient clipping 1, and constant learning rate |
| 0.0001 without warmup. The Gemma MoE adapter additionally adapts packed routed |
| experts at rank 2 / alpha 32; its router remains frozen. Loss averages |
| teacher-answer and native turn-ending tokens, excluding prompts and padding. |
| The family metadata preserves the exact target modules and checkpoint counters. |
|
|
| The shared [ASv6.100.md](ASv6.100.md) is the original instruction file. Use its |
| text with trailing whitespace removed, as in training. Recorded generation uses |
| native chat formatting, thinking off, greedy decoding, a 20,480-token context |
| and up to 10,240 new tokens. Do not silently truncate inputs to fit. |
|
|
| ## Recorded results |
|
|
| These are saved measurements on the **top-level subjects (L1)** of the same |
| **200 previously examined test notes**. Correctness measures whether returned |
| roots are supported by a post. Coverage measures recovery of teacher root |
| meaning groups. Percentages average per-note fractions; invalid outputs score |
| zero. |
|
|
| | Version | L1 correctness | All-root coverage | Required-root coverage | Invalid / 200 | |
| | --- | ---: | ---: | ---: | ---: | |
| | Qwen untuned | 66.96% | 42.12% | 58.13% | 43 | |
| | Qwen checkpoint-1000 | 96.08% | 61.63% | 79.39% | 2 | |
| | Qwen checkpoint-3000 | 97.51% | 57.07% | 76.99% | 0 | |
| | Gemma 31B untuned | 93.69% | 46.21% | 65.22% | 3 | |
| | Gemma 31B checkpoint-1000 | 96.55% | 55.03% | 70.49% | 2 | |
| | Gemma 26B-A4B untuned | 88.04% | 42.90% | 55.97% | 5 | |
| | Gemma 26B-A4B checkpoint-1000 | 89.81% | 55.75% | 73.64% | 11 | |
|
|
| The reference contains 1,293 root meaning groups, 512 marked required. Thirty-four |
| notes have no required roots. On those notes, valid answers receive 100% |
| required-root coverage; invalid answers still receive zero. Each family’s |
| `evaluation.json` also records the required-root metric restricted to the 166 |
| notes with at least one required root. |
|
|
| This is a retrospective slice of existing automated semantic grades, with no |
| new inference or judgments. Only equivalent returned L1 meanings count toward |
| coverage; a matching deeper child does not. It does not evaluate the quality of |
| a corpus-wide browsing hierarchy. |
|
|
| Qwen checkpoint 3000 was selected using validation full-hierarchy scores. In |
| this retrospective L1 test slice, checkpoint 1000 recovered more reference roots |
| and checkpoint 3000 had higher correctness; the slice does not revise the |
| validation selection. Checkpoint 2000’s test answers remain sealed. Earlier |
| first-pass checkpoints have validation evidence, not the final checkpoint’s |
| test scores. |
|
|
| ## Use and limits |
|
|
| See [USAGE.md](USAGE.md) for the standalone loading example. The original |
| experiments used 96 GB GPUs. Smaller-memory and quantized inference have not |
| been validated; a fresh GPU execution of this standalone example is untested. |
| The adapter download excludes base weights, host loading memory and generation |
| cache. |
|
|
| This is a small pilot with teacher-generated references and automated judgments. |
| Outputs can be malformed, miss subjects or include unsupported subjects. These |
| results do not establish generalization outside the studied data. Keep raw |
| answers when assessing failures. The prepared training data and recorded |
| experiment evidence are in the |
| [research dataset](https://huggingface.co/datasets/KernelKind/oracle-research). |
| The [reproduction guide](https://github.com/kernelkind/oracle-research/blob/main/docs/experiments/reproduce.md) |
| covers the saved evidence, adapter loading and training. Prepared-file hashes |
| remain in the family metadata. |
|
|
| ## License and attribution |
|
|
| Our adapter contributions, ASv6.100 instruction and original accompanying project |
| material use **GPL-3.0-or-later**; see [LICENSE](LICENSE). Copyright 2026 kernelkind. |
| The base models retain **Apache-2.0** licensing; see [NOTICE.md](NOTICE.md), the |
| [Apache license](LICENSES/Apache-2.0.txt) and the original |
| [Qwen license and copyright notice](LICENSES/Qwen-LICENSE). These files contain |
| trained LoRA additions, rather than merged upstream base weights. |
|
|
