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A rank-32 LoRA for Thinking Machines Inkling, trained to prefer authentic literary prose over synthetic creative-writing slop.
Source du modèle
Description de la source
A rank-32 LoRA for Thinking Machines Inkling, trained to prefer authentic literary prose over synthetic creative-writing slop.
The adapter is the preference-tuned component of the nbeerbower Gutenberg series. It preserves Inkling as the general-purpose base while applying a focused literary-fiction bias: stronger narrative texture and interiority, more controlled pacing, and an active dispreference for formulaic AI phrasing.
Training used Direct Preference Optimization through and .
Sources
1 sourceVérifié 22 sept.
Artefacts du modèle
1 artefactadapter_model.safetensors
safetensors · 54,7 GB · SHA-256 ab2c372a9f3c…7500 · Hugging Face
TéléchargerExtraits de sources
2 extraits
tinker-cookbook| Setting | Value |
|---|---|
| Base model | thinkingmachines/Inkling |
| Dataset | schneewolflabs/Alembic-DPO, scored configuration |
| Selection | English Gutenberg, keep, quality >= 50 |
| Train / validation pairs | 3,719 / 128 |
| Objective | DPO |
| LoRA rank / alpha | 32 / 32 |
| DPO beta | 0.1 |
| Learning rate | 1e-5, linear decay |
| Effective pair batch | 32 |
| Maximum sequence length | 4,096 |
| Epochs |
The selected data pairs public-domain Gutenberg prose (chosen) with synthetic prose (rejected). Alembic's deterministic quality scoring was used for selection; its raw LLM-judge preference vote was not used because reconstructed Gutenberg prompts can reward prompt adherence over fidelity to the source literature.
| Metric | Start | Final |
|---|---|---|
| Held-out NLL | 1.7854 | 1.6891 |
| Preference accuracy | ~50% | 100% |
| DPO loss | ~1.8 | 0.0050 |
| Reward margin | near 0 | +21.03 |
The preference signal converged rapidly, as expected for a capable base model and a high-contrast dataset. The single decaying-learning-rate epoch was retained; no additional epochs were run.
This repository contains a standard PEFT LoRA adapter. Load it together with the unmodified thinkingmachines/Inkling base model using a PEFT-compatible runtime.
from transformers import AutoModelForCausalLM
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained(
"thinkingmachines/Inkling",
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, "nbeerbower/Inkling-Gutenberg-DPO-LoRA")
Inkling is a 975B-total / 41B-active MoE. This adapter includes expert-layer LoRA tensors and is consequently large. MoE expert LoRA serving remains experimental in some runtimes; a merged model is the most portable deployment format.
The tune is intended for literary fiction, period prose, novel continuation, dialogue, and creative-writing tasks where generic AI phrasing is undesirable. It is a stylistic preference adapter, not a factual-knowledge or safety tune.
The model may reproduce public-domain literary styles, favor longer source-like continuations, or use period diction when prompted toward historical settings. Evaluate modern-register writing and general instruction following for your application.
Apache 2.0, matching the Inkling base model. Alembic-DPO is CC-BY-4.0 and derives its literary text from public-domain Project Gutenberg sources; consult the dataset card for complete provenance.
--- base_model: thinkingmachines/Inkling library_name: peft license: apache-2.0 language: - en pipeline_tag: text-generation datasets: - schneewolflabs/Alembic-DPO tags: - tinker - tinker-cookbook - peft - lora - dpo - inkling - gutenberg - creative-writing - anti-slop --- # Inkling-Gutenberg-DPO-LoRA A rank-32 LoRA for [Thinking Machines Inkling](https://huggingface.co/thinkingmachines/Inkling), trained to prefer authentic literary prose over synthetic creative-writing slop. The adapter is the preference-tuned component of the nbeerbower Gutenberg series. It preserves Inkling as the general-purpose base while applying a focused literary-fiction bias: stronger narrative texture and interiority, more controlled pacing, and an active dispreference for formulaic AI phrasing. ## Training Training used Direct Preference Optimization through [Tinker](https://thinkingmachines.ai/tinker) and `tinker-cookbook`. | Setting | Value | |---|---:| | Base model | `thinkingmachines/Inkling` | | Dataset | `schneewolflabs/Alembic-DPO`, `scored` configuration | | Selection | English Gutenberg, `keep`, `quality >= 50` | | Train / validation pairs | 3,719 / 128 | | Objective | DPO | | LoRA rank / alpha | 32 / 32 | | DPO beta | 0.1 | | Learning rate | `1e-5`, linear decay | | Effective pair batch | 32 | | Maximum sequence length | 4,096 | | Epochs | 1 (116 optimizer steps) | | Renderer | Inkling `tml_v0`, thinking effort 0.9 | The selected data pairs public-domain Gutenberg prose (`chosen`) with synthetic prose (`rejected`). Alembic's deterministic quality scoring was used for selection; its raw LLM-judge preference vote was not used because reconstructed Gutenberg prompts can reward prompt adherence over fidelity to the source literature. ## Training result | Metric | Start | Final | |---|---:|---:| | Held-out NLL | 1.7854 | 1.6891 | | Preference accuracy | ~50% | 100% | | DPO loss | ~1.8 | 0.0050 | | Reward margin | near 0 | +21.03 | The preference signal converged rapidly, as expected for a capable base model and a high-contrast dataset. The single decaying-learning-rate epoch was retained; no additional epochs were run. ## Format and use This repository contains a standard PEFT LoRA adapter. Load it together with the unmodified `thinkingmachines/Inkling` base model using a PEFT-compatible runtime. ```python from transformers import AutoModelForCausalLM from pef...
Source context: 11 downloads · 0 likes · Pipeline text-generation · Library peft · Repo nbeerbower/Inkling-Gutenberg-DPO-LoRA
| 1 (116 optimizer steps) |
| Renderer | Inkling tml_v0, thinking effort 0.9 |