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