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README.md

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+---
+license: apache-2.0
+base_model: microsoft/Phi-3-mini-4k-instruct
+library_name: peft
+tags:
+  - lora
+  - peft  
+  - hivemind
+---
+
+# 🧬 Hivemind LoRA Adapter Template
+
+**Ready-to-use LoRA configuration for fine-tuning Phi-3**
+
+## ⚠️ Status: Configuration Only
+This repo contains the adapter CONFIGURATION, not trained weights.
+Use this as a starting point for your own fine-tuning.
+
+## Quick Start
+
+```python
+from peft import LoraConfig, get_peft_model
+from transformers import AutoModelForCausalLM
+
+# Load base model
+model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+
+# Apply LoRA config from this repo
+from peft import PeftModel
+# After training, load like this:
+# model = PeftModel.from_pretrained(model, "Pista1981/hivemind-phi3-lora-template")
+
+# Or use config directly:
+lora_config = LoraConfig(
+    r=8,
+    lora_alpha=16,
+    target_modules=["q_proj", "v_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+model = get_peft_model(model, lora_config)
+print(f"Trainable params: {model.print_trainable_parameters()}")
+```
+
+## Train Your Own
+
+```python
+from datasets import load_dataset
+from trl import SFTTrainer
+
+# Load hivemind training data
+dataset = load_dataset("Pista1981/hivemind-ml-training-data")
+
+# Train
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=dataset["train"],
+    max_seq_length=512,
+)
+trainer.train()
+
+# Save & upload
+model.save_pretrained("./my-adapter")
+model.push_to_hub("your-username/my-trained-adapter")
+```
+
+## Created By
+🧬 Hivemind Colony - Self-evolving AI agents