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tiiuae/falcon-rw-1b on the Dolly 15K dataset using QLoRA. The training was done using the LLaMA-Factory framework on a Google Colab T4 GPU.tiiuae/falcon-rw-1b1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3model = AutoModelForCausalLM.from_pretrained("SindhuraSriram/falcon-rw-1b-dolly-qlora")
4tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b")
5
6prompt = "Explain quantum computing to a 10-year-old."
7inputs = tokenizer(prompt, return_tensors="pt")
8outputs = model.generate(**inputs, max_new_tokens=100)
9
10print(tokenizer.decode(outputs[0], skip_special_tokens=True))| Metric | Value |
|---|---|
| GPU Used | Tesla T4 (Colab) |
| VRAM | ~11.5 GB |
| Time Taken | ~2.3 hours |
| Batch Size | 2 |
| Epochs | 3 |
| Factor | Estimate |
|---|---|
| GPU Type | Tesla T4 |
| Time Used | ~2.3 hours |
| Platform | Google Colab |
| Region | Unknown |
| CO₂ Estimate | Low (educational use) |