Instructions to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with PEFT:
```
Task type is invalid.
```

How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="amkhrjee/blackadder-1B-GGUF-Q4_K_M",
	filename="Llama-3.2-1B-Instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

LM Studio
Jan

vLLM

How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amkhrjee/blackadder-1B-GGUF-Q4_K_M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amkhrjee/blackadder-1B-GGUF-Q4_K_M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Ollama
How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with Ollama:
```
ollama run hf.co/amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M
```

Unsloth Studio

How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for amkhrjee/blackadder-1B-GGUF-Q4_K_M to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for amkhrjee/blackadder-1B-GGUF-Q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for amkhrjee/blackadder-1B-GGUF-Q4_K_M to start chatting

How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M
```

Lemonade

How to use amkhrjee/blackadder-1B-GGUF-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull amkhrjee/blackadder-1B-GGUF-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.blackadder-1B-GGUF-Q4_K_M-Q4_K_M

List all available models

lemonade list

blackadder-1B-GGUF-Q4_K_M / README.md

amkhrjee

Update README.md

2b4654c verified 25 days ago

preview code

Raw

History Blame Contribute Delete

2.47 kB

metadata

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

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%)