A Llama-3.2-1B-Instruct model fine-tuned to play the role of a
19th-century American whaling first mate in conversation. You talk
to it; the mate answers in period voice.
This is the chat-tuned sibling of
Nanite-Labs/nanites-whaler-1b, which is a
text completer that continues 19th-century whaling logbook entries.
The chat version is a different animal — don't mix them up.
What it does
Talk to it like a sailor would talk to the mate on deck. It answers
in the voice of a 19th-c. ship's mate, brief and direct, using
period vocabulary: "the glass is falling", "by the pot", "mizen-top",
"try-works", "by D.R.", "the Captain", "no speak of it", etc.
sailor> what kind of whales do you see?
mate> Sighted a school of sperm whales to leeward this forenoon — a
bull and two cows. Lowered the starboard boat and got fast
after a long chase.
sailor> how's the weather?
mate> Fresh breeze out of the southwest, with thick fog come morning.
We've the foretop gallant sail reefed, and t'gallants handed.
Watch her close; the glass is falling.
sailor> where are we headed?
mate> Round the Horn and into the Pacific whaling grounds. Captain
means to try the Japan grounds first, then on to the Arctic if
the ice allows. Three years out, if the ship holds together.
Multi-turn is supported — the mate sees your prior Q&A in the
context window and can refer to earlier exchanges.
Training
- Base model: unsloth/Llama-3.2-1B-Instruct
- Method: QLoRA (4-bit base, LoRA r=16 on q,k,v,o,gate,up,down)
- Framework: unsloth 2026.8.22 + trl 0.24.0 + transformers 5.5.0
- Hardware: NVIDIA RTX 4070 Laptop (8GB VRAM)
- Data: 53 hand-authored Q&A pairs (sailor asks, mate answers in
period voice) + 8 hand-authored multi-turn conversations (flattened
into 17 progressive turn samples) + ~150 teacher-generated Q&A
pairs grounded in real 19th-c. whaling logbook entries (FromThePage
transcriptions of the Nantucket Historical Association Whaling
Collection, 5 ships, 1824-1859). Teacher: Devstral-24B (Q4_K_M)
served via ollama. Total ~220 Q&A + 17 multi-turn = ~237 train
samples.
- Epochs: 4, batch 2, grad accum 4, lr 2e-4 cosine
- Train runtime: ~30 seconds on a single GPU
Prompt format
The model expects a turn-formatted prompt. Use the
[Sailor asks: ... / Mate answers: ...] format directly, or use the
chat REPL from the source repo which handles the formatting.
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
SYSTEM = (
"You are the first mate of a 19th-century American whaling ship. "
"A sailor has just asked you a question. Answer in the voice of a "
"19th-century ship's mate — brief, direct, period-appropriate "
"vocabulary. Stay in character."
)
def format_input(history, question):
blocks = []
for sailor, mate in history:
blocks.append(f"Sailor asks: {sailor}")
blocks.append(f"Mate answers: {mate}")
blocks.append(f"Sailor asks: {question}")
return "\n".join(blocks)
base = AutoModelForCausalLM.from_pretrained(
"unsloth/Llama-3.2-1B-Instruct",
torch_dtype=torch.bfloat16,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("unsloth/Llama-3.2-1B-Instruct")
model = PeftModel.from_pretrained(base, "Nanite-Labs/nanites-whaler-1b-chat")
prompt = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
f"### Instruction:\n{SYSTEM}\n\n"
f"### Input:\n{format_input([], 'How is the weather today?')}\n\n"
"### Response:\n"
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(
**inputs,
max_new_tokens=200,
temperature=0.8,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
)
text = tokenizer.decode(out[0], skip_special_tokens=True)
# Trim at next turn
for stop in ("\nSailor asks:", "\nMate answers:"):
if stop in text:
text = text.split(stop)[0]
print(text[len(prompt):])
Intended use
- In-domain: casual conversation with a 19th-c. American
whaling first mate. Ask about the ship, the weather, the whales,
the crew, the voyage, the Captain, the food, the work, the dangers.
Multi-turn works.
- Out-of-domain: the mate has no fact-check layer. Names of
ships, captains, ports, and crew will be invented. Geographic
positions and dates may be wrong. The model is creative writing in
a historical register, not a source of historical truth.
- Not for: any decision involving real history, navigation, or
research citation. Don't use it to settle arguments about who
sailed where.
Limitations
- The mate's voice is a thin character sketch. Vocabulary is
period-correct; world knowledge is shallow.
- Repetition on long generations — the model sometimes loops on a
phrase.
- A few modern slips persist ("a degree from the London College of
Surgeons", "whale watching day"). These are minority outputs; a
regenerate usually fixes them.
- The model has no safety guardrails. Don't take nautical advice
from it.
Provenance
Source transcriptions are from the Nantucket Historical Association
Whaling Collection, hosted on FromThePage. Each page is a
human-typed transcription of a 19th-century logbook scan. The 5
logbooks in the training set cover 1824-1859.
The 150 teacher-generated Q&A pairs were produced by a local
Devstral-24B (Q4_K_M) LLM serving via ollama, prompted with logbook
excerpts and asked to produce plausible sailor questions and mate
answers. The 53 hand-authored pairs and 8 multi-turn conversations
were written by the project's author as a style anchor.
License
Code & model: Apache 2.0
Training data: not redistributed; users can re-scrape via
experts.whaler.acquire against the FromThePage collection.
