Fordentinc/book-builder-bookwriter-v1

🚧 WORK IN PROGRESS — v1 EARLY RELEASE 🚧

A bigger, better model (v2) is in development.

This page is the v1 early release of book-builder-bookwriter-v1. It is not the final model. Skim the rest of this card before you generate anything so you know what you're getting and what you're not.

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book-builder-bookwriter-v1

A 7.6B-parameter prose-writing model fine-tuned on 10,211 human-authored novels (310,316 chapters, ~1.82 billion tokens) with a strict story-bible-to-chapter format. Built for BookBuilder to generate novel chapters from structured story bibles.

⚠ Work in Progress (v1, early release)

This is an early checkpoint of an ongoing training run. Training was paused at step 5000 / 9697 (~52% of one epoch) so the artifacts could be released publicly while the larger run continues.

Known limitations of v1:

• Treats bibles as style prompts, not strict plot instructions. Expect drift from the synopsis on one-shot generation.
• Does not reliably follow "FORBIDDEN" rules, character role assignments, or per-character constraints in rich BookBuilder-style bibles.
• Loaded keywords in synopses (proper names like "Stillwater", specific years like "1947") can trigger off-topic associations from the training corpus.
• On longer generations, may drift into paragraph-level repetition loops without the tuned sampling defaults in ollama/Modelfile.

What's coming:

• v2 (Q3/Q4 2026): Larger base model (Qwen 2.5 14B or 32B) + synthesized instruction-following data so the model can actually obey FORBIDDEN sections, distinguish protagonists from antagonists, and follow beat sheets. This addresses the main v1 limitation.
• v1 continuation to step 9697: the LoRA may be taken to the full single-epoch checkpoint and re-released as book-builder-bookwriter-v1.1 if testing shows it's worth the additional training compute. The resumable training state is preserved at branch resumable-step-5000.

Best use for v1 right now: as a prose-style backbone inside a structured pipeline (like BookBuilder itself) that provides per-chapter beat sheets and plot anchors at generation time. The pipeline supplies the discipline the v1 model can't enforce on its own. For one-shot "give me a chapter from a synopsis" use, v1 produces readable prose but will frequently drift from the intended plot.

This is NOT a chat model. Do not prompt it like ChatGPT. See "How to use" below.

What this model does

You write a Story Bible in the format shown below (or fill in the template). You give the model the bible plus ### Chapter. The model writes the chapter prose.

It will not answer questions. It will not respond to "Write me a story about X." It only continues prose conditioned on the bible context.

Format the model expects

Every training example looked exactly like this:

### Bible
Title: [book title]
Author: [author name]
Genre: [genre]
Publisher: [publisher]
Synopsis: [1-3 paragraphs describing the book]

### Genre
[genre]



### Chapter
[chapter title]

[chapter prose...]

Your prompt MUST end at ### Chapter\n[chapter title]\n\n and the model fills in the prose.

How to use

Option 1: Ollama (easiest)

IMPORTANT: A plain ollama pull from HF discards any Modelfile parameters in the repo, so Ollama runs the model with its default sampling — which on long completion prompts causes repetition loops and over-long generations. Use the setup script below to register the model under the name bookbuilder with tuned defaults that prevent both problems.

One-shot setup:

curl -sSL https://huggingface.co/Fordentinc/book-builder-bookwriter-v1/resolve/main/ollama/setup_ollama.sh | bash
# Optional: pass a quant tag, default is Q5_K_M
# curl -sSL https://huggingface.co/Fordentinc/book-builder-bookwriter-v1/resolve/main/ollama/setup_ollama.sh | bash -s Q8_0

Then:

ollama run bookbuilder < your_bible.txt

The script pulls the GGUF, builds a local Modelfile with the right repeat_penalty 1.18, num_predict 2500, and stop tokens, and registers the result as bookbuilder.

If you'd rather pull manually (without the loop fix), you need to pass sampling flags every time:

ollama pull hf.co/Fordentinc/book-builder-bookwriter-v1:Q5_K_M
ollama run hf.co/Fordentinc/book-builder-bookwriter-v1:Q5_K_M \
  --num-predict 2500 --repeat-penalty 1.18 --temperature 0.75

Available quants:

• Q4_K_M (4.7 GB) - fits on 8 GB GPUs, some token-decoding artifacts
• Q5_K_M (5.4 GB) - balanced, recommended for 24 GB cards
• Q8_0 (8.1 GB) - near-lossless, cleaner token decoding than K-quants
• F16 (15.2 GB) - full precision, no quantization artifacts

Option 2: LM Studio

1. Search for Fordentinc/book-builder-bookwriter-v1
2. Download the Q5_K_M quant
3. Switch to Completion mode (not Chat). This is critical.
4. Paste your filled-in bible as the input
5. Generate

Option 3: llama.cpp

./llama-cli -hf Fordentinc/book-builder-bookwriter-v1:Q5_K_M \
  --temp 0.8 --top-p 0.95 -n 2048 \
  -f your_bible.txt

Option 4: Transformers + PEFT (Python, full bf16)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_id = "Qwen/Qwen2.5-7B"
adapter = "Fordentinc/book-builder-bookwriter-v1"

tok = AutoTokenizer.from_pretrained(adapter)
base = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(base, adapter)
model.eval()

bible_and_chapter_header = open("your_bible.txt").read()

inputs = tok(bible_and_chapter_header, return_tensors="pt").to("cuda")
out = model.generate(
    **inputs,
    max_new_tokens=2048,
    do_sample=True, temperature=0.8, top_p=0.95,
    repetition_penalty=1.05,
)
print(tok.decode(out[0], skip_special_tokens=True))

Option 5: vLLM (production / OpenAI-compatible API)

vLLM cannot load the LoRA adapter alone; use the merged bf16 weights instead:

vllm serve Fordentinc/book-builder-bookwriter-v1 --dtype bfloat16 --max-model-len 16384

Step-by-step: from blank page to chapter

1. Download the template: bible_template.txt
2. Fill in every field. The model relies on each section to anchor character voices, setting, and tone.
3. Save as plain text (e.g. my_book.txt).
4. End the file with ### Chapter followed by your chapter title and one blank line. Example:

   ### Chapter
   Chapter 1: The Long Drive Home
   

5. Run inference using one of the options above.
6. The model writes ~1500-4000 words of prose, then stops or hits your max_new_tokens cap.
7. For chapter 2: keep the same bible, change the chapter header, optionally append the last paragraph of chapter 1 so the model continues smoothly.

See example_bibles/ for two complete working examples.

Recommended sampling parameters

Parameter	Value	Why
temperature	0.8	Lower = repetitive, higher = incoherent
top_p	0.95	Standard nucleus sampling
repetition_penalty	1.05	Prevents loops; do not push past 1.15
max_new_tokens	2048-4096	Most chapters land in 1500-3500 tokens
min_p	0.05 (if supported)	Better than top_k for prose

Training details

• Base model: Qwen 2.5 7B (Apache 2.0)
• Method: QLoRA, r=16, alpha=32, dropout 0.05
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training corpus: 10,211 novels with derivable story bibles (Sci-Fi, Thriller, Romance, Crime, Western, Fantasy, etc.)
• Corpus stats: 310,316 chapter rows, ~1.4 billion words, ~1.82 billion tokens
• Hardware: 1× NVIDIA B200 (180 GB HBM3e)
• Sequence length: 2048
• Effective batch: 32 (per-device 4, grad-accum 8)
• Optimizer: paged_adamw_8bit
• LR: 2e-4 cosine, 3% warmup
• Epochs: 1
• Wall time: ~13.5 hours
• Data cleanup: em-dashes removed (replaced with commas), smart-quotes normalized, residual front-matter stripped, chapters with <800 or >6000 words filtered out

Quirks and limitations

• No system messages, no chat history, no [INST] tags. The model was never shown those during training.
• Bibles outside its training distribution (highly experimental forms, non-Western names, modern slang heavy) may produce uneven results.
• Names follow Western conventions (USA/UK/Italy/Western Europe). The training filter excluded other naming traditions.
• Em-dashes are absent from training data and the model will rarely produce them. This is intentional.
• Chapter length is learned from data (avg ~4,500 words). To force shorter chapters, cap max_new_tokens.

License

Apache 2.0 (inherited from base Qwen 2.5 7B). You may use commercially.

Citation

@misc{bookbuilder_bookwriter_v1_2026,
  author = {Fordentinc},
  title  = {book-builder-bookwriter-v1: A prose-writing LoRA on Qwen 2.5 7B},
  year   = {2026},
  url    = {https://huggingface.co/Fordentinc/book-builder-bookwriter-v1},
}

Reporting issues

Open a discussion on this model page. Include the bible you used (first 500 chars) and the first 200 chars of the model output.