---

base_model: "unsloth/Llama-3.2-1B-Instruct-bnb-4bit"

library_name: peft

pipeline_tag: text-generation

license: llama3.2

language:

• en

datasets:

• amkhrjee/blackadder-conversation

tags:

• base_model:adapter:unsloth/llama-3.2-1b-instruct-bnb-4bit
• lora
• sft
• trl
• unsloth
• peft
• roleplay
• character
• blackadder

---

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.

System Prompt

Use this system-prompt for the best roleplaying experience!

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.

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)

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

| | |

|---|---|

| Method | LoRA (rsLoRA) |

| Rank (r) | 128 |

| lora_alpha | 64 |

| lora_dropout | 0 |

| Target modules | all linear layers |

| Epochs | 3 |

| Effective batch size | 32 (4 × 8 grad accum) |

| Optimizer | adamw_8bit |

| Learning rate | 2e-4 (linear, 5 warmup steps) |

| Weight decay | 0.001 |

| Precision | bf16 |

| Seed | 42 |

| Trainable params | 90.2M / 1.33B (6.8%) |