File	Size	Description	Best For
`gguf/pauper_llama3_q4km.gguf`	~5GB	4-bit quantized	Recommended - Best balance
`gguf/pauper_llama3_q5km.gguf`	~6GB	5-bit quantized	Better quality
`gguf/pauper_llama3_q8.gguf`	~8GB	8-bit quantized	Near-original quality
`gguf/pauper_llama3_fp16.gguf`	~15GB	Full precision	Maximum quality

🚀 Usage

Option 1: HuggingFace Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "nmalinowski/pauper-llama3-8b",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("nmalinowski/pauper-llama3-8b")

prompt = "What are the best cards in Pauper?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Option 2: LM Studio (GGUF - Easiest!)

Download gguf/pauper_llama3_q4km.gguf from Files tab
Open LM Studio → Load Model
Select the downloaded GGUF file
Start chatting about Pauper!

Option 3: llama.cpp

bash

# Download the quantized model
huggingface-cli download nmalinowski/pauper-llama3-8b gguf/pauper_llama3_q4km.gguf --local-dir ./

# Run inference
./llama-cli -m pauper_llama3_q4km.gguf \
    -p "What are the top Pauper decks in the current meta?" \
    -n 256 \
    --temp 0.7

Option 4: Ollama

bash

# Create Modelfile
cat > Modelfile <<EOF
FROM ./gguf/pauper_llama3_q4km.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.9
SYSTEM "You are an expert on Magic: The Gathering's Pauper format."
EOF

# Create and run
ollama create pauper-llama3 -f Modelfile
ollama run pauper-llama3 "Explain the current Pauper meta"

🎯 Training Details

Base Model: Llama 3 8B Instruct
Training Method: LoRA (Low-Rank Adaptation)
Domain: Magic: The Gathering - Pauper format
LoRA Configuration:
- Rank: 16
- Alpha: 32
- Target modules: q_proj, v_proj
- Dropout: 0.05

💡 Recommendations

For most users: Download gguf/pauper_llama3_q4km.gguf and use with LM Studio
For best quality: Use the full HuggingFace model with transformers
For low VRAM: Use Q4_K_M quantization (~5GB)
For high VRAM: Use Q8_0 or FP16 for better quality

📊 Performance

The Q4_K_M quantization offers:

✅ ~95% of full precision quality
✅ 70% smaller file size
✅ Faster inference on CPU and GPU
✅ Runs on consumer hardware (16GB RAM recommended)

🎮 Example Prompts

"What are the best removal spells in Pauper?"
"Build me a Pauper deck around Monastery Swiftspear"
"Explain the differences between Affinity and Elves in Pauper"
"What are the current tier 1 Pauper decks?"

⚠️ Limitations

Specialized for Pauper format - may not perform well on other MTG formats
May occasionally hallucinate card names or abilities
Knowledge cutoff: January 2025
Not suitable for medical, legal, or financial advice

📄 License

This model inherits the Llama 3 Community License from Meta. See LICENSE for details.

🙏 Acknowledgments

Base model: Meta's Llama 3 8B Instruct
Training framework: HuggingFace Transformers + PEFT
Quantization: llama.cpp

📞 Issues & Feedback

If you encounter issues or have suggestions, please open an issue on the Community tab.