phi2-lora-instruct

🧠 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 instruction dataset.

✅ Fine-Tuned by:

howtomakepplragequit — working on scalable, efficient LLM training for real-world instruction-following.

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🏗️ Model Architecture

• Base model: microsoft/phi-2 (2.7B parameters)
• Adapter: LoRA (Low-Rank Adaptation), trained with PEFT
• Quantization: 4-bit NF4 via bitsandbytes for efficient memory use

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📦 Dataset

• yahma/alpaca-cleaned
• Instruction-based Q&A for natural language understanding and generation
• Covers topics like science, grammar, everyday tasks, and reasoning

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🛠️ 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

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📈 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.

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🚀 Example Prompt

Instruction: Give three tips to improve time management.

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🧪 Try it Out

To use this model in your own project:

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))