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README.md

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+---
+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 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.
+
+## Intended behavior
+
+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.
+
+## License
+
+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.
+

adapter_config.json

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+{
+  "peft_type": "LORA",
+  "auto_mapping": null,
+  "base_model_name_or_path": "thinkingmachines/Inkling",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "lora_alpha": 32,
+  "lora_dropout": 0.0,
+  "modules_to_save": null,
+  "r": 32,
+  "rank_pattern": {},
+  "alpha_pattern": {},
+  "target_modules": [
+    "down_proj",
+    "gate_proj",
+    "gate_up_proj",
+    "lm_head",
+    "up_proj",
+    "w1",
+    "w2",
+    "w3",
+    "wk_dv",
+    "wo_ud",
+    "wq_du",
+    "wr_du",
+    "wv_dv"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.safetensors

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