Instructions to use jakeboggs/MTG-Llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jakeboggs/MTG-Llama with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jakeboggs/MTG-Llama")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jakeboggs/MTG-Llama")
model = AutoModelForCausalLM.from_pretrained("jakeboggs/MTG-Llama")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jakeboggs/MTG-Llama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jakeboggs/MTG-Llama

SGLang

How to use jakeboggs/MTG-Llama with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jakeboggs/MTG-Llama" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jakeboggs/MTG-Llama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jakeboggs/MTG-Llama" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jakeboggs/MTG-Llama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jakeboggs/MTG-Llama with Docker Model Runner:
```
docker model run hf.co/jakeboggs/MTG-Llama
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for MTG-Llama: Fine-Tuned Model for Magic: The Gathering

Model Details

Model Name: MTG-Llama
Version: 1.0
Base Model: Llama 3 8B Instruct
Fine-Tuning Dataset: MTG-Eval
Author: Jake Boggs

Model Description

MTG-Llama is a fine-tuned version of Llama 3 8B Instruct, tailored specifically for understanding and generating responses related to Magic: The Gathering (MTG). The model has been fine-tuned using a custom dataset, MTG-Eval, which includes question-answer pairs covering card descriptions, rules questions, and card interactions.

Intended Use

MTG-Llama is designed to assist users with:

Generating deck construction ideas.
Answering in-game rules questions.
Understanding card interactions and abilities.

Training Data

The fine-tuning dataset, MTG-Eval, consists of 80,032 question-answer pairs generated synthetically. The dataset is categorized into:

Card Descriptions: 26,702 examples
Rules Questions: 27,104 examples
Card Interactions: 26,226 examples

The data was sourced from the MTGJSON project and the Commander Spellbook combo database, reformatted into natural language question-answer pairs using ChatGPT 3.5.

Training Procedure

The model was fine-tuned using QLoRA with the following hyperparameters:

r: 64
alpha: 32
Steps: 75

Acknowledgments

Thanks to the team at Commander Spellbook for generously sharing their dataset, without which this research would not be possible. All generated data is unofficial Fan Content permitted under the Fan Content Policy. Not approved/endorsed by Wizards. Portions of the materials used are property of Wizards of the Coast. ©Wizards of the Coast LLC.

Resources

Dataset: MTG-Eval on HuggingFace
Training Code: GitHub Repository
Blog Post: boggs.tech

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Safetensors

Model size

8B params

Tensor type

F16

Model tree for jakeboggs/MTG-Llama

Quantizations

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MTG-Llama