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

amkhrjee commited on Jun 10

Commit

49978a4

verified ·

1 Parent(s): 65e8669

Create README.md

Browse files

Files changed (1) hide show

README.md +63 -0

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	@@ -0,0 +1,63 @@

+---
+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.
+## Model Details
+- **Developed by:** [amkhrjee](https://huggingface.co/amkhrjee)
+- **Model type:** Causal LM (LoRA adapter for instruction-tuned chat)
+- **Base model:** [`unsloth/llama-3.2-1b-instruct-bnb-4bit`](https://huggingface.co/unsloth/llama-3.2-1b-instruct-bnb-4bit) (Llama 3.2 1B Instruct)
+- **Language:** English
+- **License:** [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
+- **Finetuned with:** [Unsloth](https://github.com/unslothai/unsloth) + [TRL](https://github.com/huggingface/trl) (PEFT/LoRA)
+## Training Details
+### Data
+Fine-tuned on [`amkhrjee/blackadder-conversation`](https://huggingface.co/datasets/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%) |