howtomakepplragequit
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phi2-lora-instruct

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 ---
+license: mit
+tags:
+  - generated-from-train
+  - instruction-tuned
+  - phi2
+  - lora
+  - low-resource
+  - fine-tuning
+datasets:
+  - yahma/alpaca-cleaned
+base_model: microsoft/phi-2
+widget:
+  - text: "### Instruction:\nExplain the concept of gravity.\n\n### Response:"
 ---
+# 🧠 phi2-lora-instruct
+This is a **LoRA fine-tuned version of Microsoft’s Phi-2** model trained on 500 examples from the [`yahma/alpaca-cleaned`](https://huggingface.co/datasets/yahma/alpaca-cleaned) instruction dataset.
+### ✅ Fine-Tuned by:
+**[howtomakepplragequit](https://huggingface.co/howtomakepplragequit)** — working on scalable, efficient LLM training for real-world instruction-following.
+---
+## 🏗️ Model Architecture
+- **Base model**: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) (2.7B parameters)
+- **Adapter**: LoRA (Low-Rank Adaptation), trained with [PEFT](https://github.com/huggingface/peft)
+- **Quantization**: 4-bit NF4 via `bitsandbytes` for efficient memory use
+---
+## 📦 Dataset
+- [`yahma/alpaca-cleaned`](https://huggingface.co/datasets/yahma/alpaca-cleaned)
+- Instruction-based Q&A for natural language understanding and generation
+- Covers topics like science, grammar, everyday tasks, and reasoning
+---
+## 🛠️ Training Details
+- **Training platform**: Google Colab (Free T4 GPU)
+- **Epochs**: 2
+- **Batch size**: 2 (with gradient accumulation)
+- **Optimizer**: AdamW (via Transformers `Trainer`)
+- **Training time**: ~20–30 mins
+---
+## 📈 Intended Use
+- Ideal for **instruction-following tasks**, such as:
+  - Explanation
+  - Summarization
+  - List generation
+  - Creative writing
+- Can be adapted to **custom domains** (health, code, manufacturing) by adding your own prompts + responses.
+---
+## 🚀 Example Prompt
+Instruction:
+Give three tips to improve time management.
+---
+## 🧪 Try it Out
+To use this model in your own project:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("howtomakepplragequit/phi2-lora-instruct")
+tokenizer = AutoTokenizer.from_pretrained("howtomakepplragequit/phi2-lora-instruct")
+input_text = "### Instruction:\nExplain how machine learning works.\n\n### Response:"
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(output[0], skip_special_tokens=True))