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nbeerbower
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Model Details
Model Provider
nbeerbower
Model Tree
thinkingmachines/Inkling
Input Modalities
Output Modalities
Supported Functionality
Training used Direct Preference Optimization through 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.
| 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.
python
from transformers import AutoModelForCausalLMfrom peft import PeftModelbase = 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.