n0social

welles

Deploy Dedicated

README

License: apache-2.0

Highlights

Orson Welles register — authority, cadence, and drama in the prose itself
Cinematic long-form — essays, narration, and scripts that hold a frame and land a point
Rhetorical craft — staged address, pause, and return; sentences with weight
Built for length — trained to sustain argument across long passages, not one-liners
Drop-in LoRA — sits on Qwen3-8B; small adapter, full base model underneath

Voice

Rhetorical and staged: address, pause, return to the object
Concrete first — rooms, faces, light — then the naming sentence
Length when the scene needs it; no empty flourish

This repo

This is a PEFT / LoRA adapter, not a full merged model. Load Qwen/Qwen3-8B, then apply these weights.

Table with columns: File, Purpose
File	Purpose
`adapter_model.safetensors`	LoRA weights
`adapter_config.json`	PEFT config (base model, rank, targets)
Tokenizer files	Qwen3 chat template alignment

How to load

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

base_id = "Qwen/Qwen3-8B"
adapter_id = "n0social/welles"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_id)

messages = [
    {
        "role": "system",
        "content": (
            "You are Welles, a writer in the voice of Orson Welles: "
            "oratorical, cinematic, and deliberate."
        ),
    },
    {
        "role": "user",
        "content": "Write a short essay on the camera as a witness.",
    },
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,  # writing mode — disable Qwen3 thinking
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.7)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Training (v1)

Table with columns: Item, Value
Item	Value
Base	`Qwen/Qwen3-8B` (Apache 2.0)
Method	QLoRA (4-bit NF4) + LoRA
LoRA	`r=8`, `alpha=16`, dropout `0.05`
Hardware	Google Colab Tesla T4 (16 GB)
Precision	float16 compute
Sequence length	1024
Epochs

Data: long-form English writing mixture (including a capped subset of zai-org/LongWriter-6k) plus curated style material for a Welles-like register.

Intended use

Essays, narration, scripts, and long-form prose in a Welles-like register
Rewrites that replace flat survey prose with staged, concrete argument

Out of scope

General customer-support chatbot
Medical, legal, or financial advice
Impersonation for fraud or deception

Limitations

v1 adapter — keep a strong system prompt; the base model still shows through
Trained at 1024 tokens; longer pieces work best in sections
Inspired by Welles’s public rhetorical style; not a verbatim clone of any one work
Set enable_thinking=False (or equivalent) so Qwen3 stays in writing mode

License

Adapter weights: Apache 2.0, same family as Qwen/Qwen3-8B.
“Welles” here names a writing voice inspired by Orson Welles’s public craft. It is not affiliated with or endorsed by his estate.

Citation

bibtex
@misc{welles-lora-2026,
  title  = {Welles: Orson Welles–tone writing LoRA on Qwen3-8B},
  author = {n0social},
  year   = {2026},
  url    = {https://huggingface.co/n0social/welles}
}

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README

License: apache-2.0

Highlights

Orson Welles register — authority, cadence, and drama in the prose itself
Cinematic long-form — essays, narration, and scripts that hold a frame and land a point
Rhetorical craft — staged address, pause, and return; sentences with weight
Built for length — trained to sustain argument across long passages, not one-liners
Drop-in LoRA — sits on Qwen3-8B; small adapter, full base model underneath

Voice

Rhetorical and staged: address, pause, return to the object
Concrete first — rooms, faces, light — then the naming sentence
Length when the scene needs it; no empty flourish

This repo

This is a PEFT / LoRA adapter, not a full merged model. Load Qwen/Qwen3-8B, then apply these weights.

Table with columns: File, Purpose
File	Purpose
`adapter_model.safetensors`	LoRA weights
`adapter_config.json`	PEFT config (base model, rank, targets)
Tokenizer files	Qwen3 chat template alignment

How to load

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

base_id = "Qwen/Qwen3-8B"
adapter_id = "n0social/welles"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_id)

messages = [
    {
        "role": "system",
        "content": (
            "You are Welles, a writer in the voice of Orson Welles: "
            "oratorical, cinematic, and deliberate."
        ),
    },
    {
        "role": "user",
        "content": "Write a short essay on the camera as a witness.",
    },
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,  # writing mode — disable Qwen3 thinking
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.7)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Training (v1)

Table with columns: Item, Value
Item	Value
Base	`Qwen/Qwen3-8B` (Apache 2.0)
Method	QLoRA (4-bit NF4) + LoRA
LoRA	`r=8`, `alpha=16`, dropout `0.05`
Hardware	Google Colab Tesla T4 (16 GB)
Precision	float16 compute
Sequence length	1024
Epochs

Data: long-form English writing mixture (including a capped subset of zai-org/LongWriter-6k) plus curated style material for a Welles-like register.

Intended use

Essays, narration, scripts, and long-form prose in a Welles-like register
Rewrites that replace flat survey prose with staged, concrete argument

Out of scope

General customer-support chatbot
Medical, legal, or financial advice
Impersonation for fraud or deception

Limitations

v1 adapter — keep a strong system prompt; the base model still shows through
Trained at 1024 tokens; longer pieces work best in sections
Inspired by Welles’s public rhetorical style; not a verbatim clone of any one work
Set enable_thinking=False (or equivalent) so Qwen3 stays in writing mode

License

Citation

bibtex
@misc{welles-lora-2026,
  title  = {Welles: Orson Welles–tone writing LoRA on Qwen3-8B},
  author = {n0social},
  year   = {2026},
  url    = {https://huggingface.co/n0social/welles}
}

welles

README

Highlights

Voice

This repo

How to load

Training (v1)

Intended use

Out of scope

Limitations

License

Citation

Links

Explore FriendliAI today

README

Highlights

Voice

This repo

How to load

Training (v1)

Intended use

Out of scope

Limitations

License

Citation

Links