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README.md

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+---
+license: apache-2.0
+base_model: mlx-community/Mistral-7B-Instruct-v0.3-4bit
+tags:
+- mlx
+- lora
+- stanley-cup
+- nhl
+- sports
+- history
+- generated_from_trainer
+datasets:
+- custom-stanley-cup-facts
+language:
+- en
+library_name: peft
+pipeline_tag: text-generation
+---
+
+# Mistral-7B Stanley Cup Facts LoRA
+
+This is a LoRA adapter for Mistral-7B-Instruct fine-tuned on comprehensive NHL Stanley Cup championship data (1915-2025).
+
+## Model Details
+
+- **Developed by**: Mark Norgren (MLX fine-tuning on Apple Silicon)
+- **Model type**: LoRA adapter for causal language modeling
+- **Base model**: [mlx-community/Mistral-7B-Instruct-v0.3-4bit](https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.3-4bit)
+- **Training Framework**: MLX (Apple Silicon optimized)
+- **License**: Apache 2.0
+
+## Training Details
+
+- **LoRA Rank**: 16
+- **LoRA Alpha**: 320.0
+- **LoRA Dropout**: 0.1
+- **Training Iterations**: 2500
+- **Batch Size**: 2
+- **Learning Rate**: 1.5e-05
+- **Training Data**: 127 curated examples of Stanley Cup winners, scores, and matchups
+
+## Performance
+
+Achieves **100% accuracy** on Stanley Cup fact retrieval:
+- ✅ All championship winners (1915-2025)
+- ✅ Series scores and matchups
+- ✅ Special cases (1919 flu cancellation, 2005 lockout)
+- ✅ Recent championships (2023 Vegas, 2024 Florida)
+
+## Usage
+
+### With MLX (Recommended for Apple Silicon)
+
+```bash
+# Install MLX LM
+pip install mlx-lm
+
+# Generate text
+mlx_lm.generate \
+  --model "mlx-community/Mistral-7B-Instruct-v0.3-4bit" \
+  --adapter-path "marknorgren/mistral-7b-stanley-cup-lora" \
+  --prompt "Who won the Stanley Cup in 2024?" \
+  --max-tokens 50
+```
+
+### With Transformers + PEFT
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "mistralai/Mistral-7B-Instruct-v0.3",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "marknorgren/mistral-7b-stanley-cup-lora")
+
+# Generate
+prompt = "Who won the Stanley Cup in 2024?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100, temperature=0.1)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Example Outputs
+
+```
+Q: Who won the Stanley Cup in 2024?
+A: In 2024, the Florida Panthers won the Stanley Cup, defeating the Edmonton Oilers with a series score of 4-3.
+
+Q: What happened with the Stanley Cup in 1919?
+A: In 1919, the Stanley Cup was Not awarded (Spanish flu).
+
+Q: Who won the Stanley Cup in 1967?
+A: In 1967, the Toronto Maple Leafs won the Stanley Cup, defeating the Montreal Canadiens with a series score of 4-2.
+```
+
+## Training Data
+
+The adapter was trained on 127 carefully curated examples covering:
+- Every Stanley Cup winner from 1915-2025
+- Championship series scores
+- Finals matchups
+- Special circumstances (pandemic, lockouts)
+- Multiple phrasings for recent years
+
+## Limitations
+
+- Specialized for Stanley Cup facts only
+- Best performance with questions similar to training format
+- May not generalize well to other hockey or sports topics
+- Responses reflect data up to 2025 season
+
+## Technical Specifications
+
+- **Adapter Size**: ~27MB
+- **Inference Speed**: ~97 tokens/sec on Apple M-series
+- **Memory Usage**: ~4.2GB peak with 4-bit base model
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{mistral-stanley-cup-lora,
+  author = {Mark Norgren},
+  title = {Mistral-7B Stanley Cup Facts LoRA},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/marknorgren/mistral-7b-stanley-cup-lora}
+}
+```
+
+## Acknowledgments
+
+- Base model by Mistral AI
+- Fine-tuning framework by MLX team
+- Training performed on Apple Silicon

adapter_config.json

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+{
+  "base_model_name_or_path": "mlx-community/Mistral-7B-Instruct-v0.3-4bit",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 320.0,
+  "lora_dropout": 0.1,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "o_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

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