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Model: nmalinowski/pauper-llama3-8b Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: llama3
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base_model: meta-llama/Meta-Llama-3-8B-Instruct
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tags:
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- llama3
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- pauper
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- mtg
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- magic-the-gathering
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- fine-tuned
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- lora
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- gguf
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library_name: transformers
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---
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# Pauper Llama 3 8B
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Fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
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specialized for Magic: The Gathering's Pauper format using LoRA fine-tuning.
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## 📦 Available Formats
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This repository contains both the full HuggingFace model and GGUF quantizations for various use cases.
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### HuggingFace Transformers (Full Precision)
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Perfect for:
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- Further fine-tuning
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- Maximum quality inference
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- Integration with transformers library
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### GGUF Quantized Models (llama.cpp compatible)
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Perfect for:
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- LM Studio, Ollama, llama.cpp
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- Local inference on consumer hardware
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- Faster inference with minimal quality loss
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| File | Size | Description | Best For |
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|------|------|-------------|----------|
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| `gguf/pauper_llama3_q4km.gguf` | ~5GB | 4-bit quantized | **Recommended** - Best balance |
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| `gguf/pauper_llama3_q5km.gguf` | ~6GB | 5-bit quantized | Better quality |
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| `gguf/pauper_llama3_q8.gguf` | ~8GB | 8-bit quantized | Near-original quality |
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| `gguf/pauper_llama3_fp16.gguf` | ~15GB | Full precision | Maximum quality |
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## 🚀 Usage
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### Option 1: HuggingFace Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"nmalinowski/pauper-llama3-8b",
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torch_dtype=torch.float16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("nmalinowski/pauper-llama3-8b")
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prompt = "What are the best cards in Pauper?"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Option 2: LM Studio (GGUF - Easiest!)
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1. Download `gguf/pauper_llama3_q4km.gguf` from Files tab
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2. Open LM Studio → Load Model
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3. Select the downloaded GGUF file
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4. Start chatting about Pauper!
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### Option 3: llama.cpp
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```bash
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# Download the quantized model
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huggingface-cli download nmalinowski/pauper-llama3-8b gguf/pauper_llama3_q4km.gguf --local-dir ./
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# Run inference
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./llama-cli -m pauper_llama3_q4km.gguf \
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-p "What are the top Pauper decks in the current meta?" \
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-n 256 \
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--temp 0.7
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```
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### Option 4: Ollama
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```bash
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# Create Modelfile
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cat > Modelfile <<EOF
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FROM ./gguf/pauper_llama3_q4km.gguf
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PARAMETER temperature 0.7
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PARAMETER top_p 0.9
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SYSTEM "You are an expert on Magic: The Gathering's Pauper format."
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EOF
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# Create and run
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ollama create pauper-llama3 -f Modelfile
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ollama run pauper-llama3 "Explain the current Pauper meta"
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```
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## 🎯 Training Details
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- **Base Model:** Llama 3 8B Instruct
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- **Training Method:** LoRA (Low-Rank Adaptation)
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- **Domain:** Magic: The Gathering - Pauper format
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- **LoRA Configuration:**
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- Rank: 16
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- Alpha: 32
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- Target modules: q_proj, v_proj
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- Dropout: 0.05
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## 💡 Recommendations
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- **For most users:** Download `gguf/pauper_llama3_q4km.gguf` and use with LM Studio
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- **For best quality:** Use the full HuggingFace model with transformers
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- **For low VRAM:** Use Q4_K_M quantization (~5GB)
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- **For high VRAM:** Use Q8_0 or FP16 for better quality
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## 📊 Performance
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The Q4_K_M quantization offers:
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- ✅ ~95% of full precision quality
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- ✅ 70% smaller file size
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- ✅ Faster inference on CPU and GPU
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- ✅ Runs on consumer hardware (16GB RAM recommended)
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## 🎮 Example Prompts
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```
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"What are the best removal spells in Pauper?"
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"Build me a Pauper deck around Monastery Swiftspear"
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"Explain the differences between Affinity and Elves in Pauper"
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"What are the current tier 1 Pauper decks?"
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```
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## ⚠️ Limitations
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- Specialized for Pauper format - may not perform well on other MTG formats
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- May occasionally hallucinate card names or abilities
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- Knowledge cutoff: January 2025
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- Not suitable for medical, legal, or financial advice
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## 📄 License
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This model inherits the Llama 3 Community License from Meta. See [LICENSE](https://llama.meta.com/llama3/license/) for details.
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## 🙏 Acknowledgments
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- Base model: Meta's Llama 3 8B Instruct
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- Training framework: HuggingFace Transformers + PEFT
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- Quantization: llama.cpp
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## 📞 Issues & Feedback
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If you encounter issues or have suggestions, please open an issue on the [Community tab](https://huggingface.co/nmalinowski/pauper-llama3-8b/discussions).
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