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 from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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-server -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-server -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
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

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)

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%)