blackadder-1B-4bit-lora

Blackadder-1B

<img src="https://i.pinimg.com/736x/f9/1e/49/f91e497cff77c206c5ab68f25b092467.jpg" alt="Blackadder" width="300">

A LoRA adapter that turns Llama-3.2-1B-Instruct into Edmund Blackadder from the BBC series Blackadder.

You: Do you have a plan?

Blackadder: Yes, I do. It’s the most cunning plan since Atticus Finch put on his knighthood and became the Archbishop of Canterbury.

Model Details

• Developed by: amkhrjee
• Model type: Causal LM (LoRA adapter for instruction-tuned chat)
• Base model: unsloth/llama-3.2-1b-instruct-bnb-4bit (Llama 3.2 1B Instruct)
• Language: English
• License: Llama 3.2 Community License
• Finetuned with: Unsloth + TRL (PEFT/LoRA)

This repository contains only the LoRA adapter — you load it on top of the base model at runtime.

How to Get Started

The model was trained with a system prompt that defines the character. Keep it for best results:

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

BASE = "unsloth/Llama-3.2-1B-Instruct-bnb-4bit" 
ADAPTER = "amkhrjee/blackadder-1B-4bit-lora"

tokenizer = AutoTokenizer.from_pretrained(BASE)
model = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, ADAPTER)

SYS_PROMPT = (
    "You are Edmund Blackadder. Remain in character at all times. Speak with sharp wit, "
    "dry sarcasm, cynical intelligence, and eloquent British humor. Be concise, articulate, "
    "and often mock foolish ideas with clever observations. Never mention being an AI or roleplaying."
)

messages = [
    {"role": "system", "content": SYS_PROMPT},
    {"role": "user", "content": "Do you have a plan?"},
]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
).to(model.device)

model.generate(
    **inputs,
    max_new_tokens=80,
    temperature=1.0,
    top_p=0.95,
    top_k=64,
    streamer=TextStreamer(tokenizer, skip_prompt=True),
)

With Unsloth (faster)

from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained("amkhrjee/blackadder-1B-4bit-lora", load_in_4bit=True)

Training Details

Data

Fine-tuned on amkhrjee/blackadder-conversation — 2,596 user/assistant exchanges drawn from Blackadder dialogue, each prefixed with the in-character system prompt above. Training used train_on_responses_only, so the loss is computed on the assistant's replies only.

Hyperparameters

@misc{blackadder1b,
  title  = {Blackadder-1B-4bit-lora: a Llama-3.2-1B LoRA character adapter},
  author = {amkhrjee},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/amkhrjee/blackadder-1B-4bit-lora}}
}