Novelist1.0 is trained to stay in the scene: concrete sensory detail, character voice, and the request on the page — not a helper that summarizes, moralizes, or turns the story into a list of themes.

Voice lock. Holds persona, diction, and POV instead of sliding into generic chatbot prose.
Scene over slogan. Prefers objects, weather, gesture, and dialogue to abstract feeling and stock metaphors.
Length as a contract. If you ask for a word count or a range (800 words, 1000 kelime, 800–1200), it treats that as part of the task, not decoration.
User text is canon. Does not “correct” or overwrite what the user already established in the prompt.
Anti-slop. Pushes back on cliché loops, repeated cadence, and empty intensifiers that pad a paragraph without moving the scene.

It is a writer, not an assistant. Weak on tools, code, and factual Q&A; that is by design.

How to prompt it

Give situation + constraint, not a vibe:

Who is speaking, where, what must happen (or must not).
Length, tense, POV, and what to leave unsaid.
Optional: a quota (Write 600 words. / En az 800 en fazla 1200 kelime.).

Sampling (Qwen3.8)

Instruct (recommended for stories) - thinking off:

temperature 0.7 · top_p 0.80 · top_k 20 · min_p 0 · presence_penalty 1.5 · repetition_penalty 1.0 · enable_thinking=false

Thinking - if you want a plan before the prose:

temperature 1.0 · top_p 0.95 · top_k 20 · min_p 0 · presence_penalty 0 · repetition_penalty 1.0 · enable_thinking=true

Training itself used thinking off. Instruct mode is the default product setting.

Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Dxniz/Novelist1.0-27b"
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype="bfloat16", device_map="auto", trust_remote_code=True
)

messages = [
    {"role": "system", "content": "You are a literary fiction writer. Follow the prompt exactly."},
    {"role": "user", "content": "Write 400 words. Night, a closed bookstore in Istanbul, rain. One argument that does not resolve. No summary at the end."},
]
text = tok.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(
    **inputs,
    max_new_tokens=2048,
    temperature=0.7,
    top_p=0.8,
    top_k=20,
    presence_penalty=1.5,
)
print(tok.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

On the chat call, set chat_template_kwargs={"enable_thinking": false} unless you explicitly want thinking. On 32 GB cards use FP8 or a 4-bit quant of this merge.

Recipe (short)

LoRA r=64 / α=64 on attention and MLP projections, then SFT on roleplay/fiction chats and GRPO with rewards for length/quota, craft, anti-slop, anti-repetition, and not overwriting the user. Sequence-level GRPO (GSPO / dr_grpo).

Limits

Still a language model: it can flatten a character, echo a cliché, or miss a quota on a messy prompt. It will invent facts. Do not use it for medical, legal, or safety-critical advice. Qwen3.8 hybrid stack wants a recent runtime (vLLM 0.27+).

License

Apache 2.0. Also respect the Qwen3.8-27B terms.