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.gitattributes

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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text

.gitignore

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+checkpoint-*
+runs/

README.md

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+# GG Team Instruction-Tuned Adapters (LLaMA 3.2-3B)
+
+This repository provides a collection of PEFT adapters (LoRA) trained on various instruction-tuning datasets using the base model **LLaMA 3.2-3B**. These adapters are developed by **GG Team - CSE476 @ Arizona State University**.
+
+## Adapter Variants
+
+| Folder | Dataset(s) Used | Description |
+|--------|------------------|-------------|
+| `llama-3.2-3B-sft` | Alpaca | Fine-tuned only on the original Alpaca dataset |
+| `llama-3.2-3B-sft-dolly` | Alpaca + Dolly | Fine-tuned on Databricks' Dolly dataset |
+| `llama-3.2-3B-sft-FLAN` | Alpaca + Dolly + FLAN | Fine-tuned on FLAN and Alpaca mixed |
+| `sft_a_d` | Alpaca + Dolly | Combined dataset fine-tuning (Alpaca + Dolly) |
+| `sft_a_d1` | Alpaca(cleaned) + Dolly | Combined dataset fine-tuning (Alpaca + Dolly) |
+
+---
+
+## 🛠️ Usage (with `peft`)
+
+Here's an example of loading one of the adapters using 🤗 Transformers and PEFT:
+
+```python
+from peft import PeftModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-3B")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-3B")
+
+# Load adapter (choose one)
+model = PeftModel.from_pretrained(base_model, "gg-cse476/gg/sft_a_d")
+
+# Inference
+prompt = "Explain how a rocket works in simple terms."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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