Colab free tier killed my 7B fine-tune — here's the autopsy

A developer's attempt to fine-tune Mistral-7B-v0.1 on Google Colab's free tier failed due to out-of-memory errors and a 2-hour session limit, with the runtime disconnecting at step 200 and a MemoryError at step 800. The free tier's 16GB VRAM, 12GB RAM, and 2-hour cap proved insufficient for 7B LoRA fine-tuning, forcing the developer to rent a RunPod A100 40GB for $1.10/hr, completing 3 epochs in 47 minutes for $0.85. The developer advises that free Colab is only suitable for inference, small models, or prototyping, not production fine-tunes.

Colab free tier killed my 7B fine-tune — here's the autopsy The setup - Base model: Mistral-7B-v0.1 4-bit quantized via bitsandbytes - Dataset: 12k Alpaca-format examples ~400MB tokenized - Method: LoRA rank 16, alpha 32, targeting q proj/v proj - Colab: Free tier, T4 GPU 16GB VRAM , 12GB RAM, 2-hour disconnect python The config that seemed reasonable on paper from transformers import TrainingArguments training args = TrainingArguments output dir="./mistral-7b-lora", per device train batch size=1, gradient accumulation steps=16, num train epochs=3, learning rate=2e-4, fp16=True, optim="paged adamw 8bit", logging steps=10, save steps=500, max steps=1500, ~2 hours at this rate What broke First run: OOM at step 47. The gradient accumulation buffer + optimizer states + model weights pushed past 16GB. T4 has 16GB but Colab's overhead eats ~2GB before you start. Second run: Switched to gradient checkpointing=True , dropped batch to 1, accumulation to 32. Made it to step 200 before the runtime disconnected. Colab free kills at exactly 2 hours — no warning, no checkpoint recovery. Third run: Added save steps=100 and save total limit=3 . Got to step 800. Then the 12GB system RAM filled up dataset caching + tokenizer + intermediate tensors and the kernel died with MemoryError: Unable to allocate 1.2 GiB . The actual limits I hit | Resource | Free tier | What I needed | |----------|-----------|---------------| | VRAM | 16GB T4 | ~14GB usable for 7B 4-bit + LoRA | | System RAM | 12GB | 16GB+ for dataset + overhead | | Max session | 2 hours | 4-6 hours for 3 epochs | | Disk | ~70GB | Fine, not the bottleneck | What does work on free Colab 1.5B-3B models full fine-tune Phi-2, TinyLlama, Gemma-2B 7B LoRA with aggressive quantization 4-bit + max memory={0: "13GiB"} + dataset streaming Inference only — 7B-13B 4-bit runs fine for generation python Streaming dataset avoids RAM explosion from datasets import load dataset dataset = load dataset "json", data files="train.jsonl", split="train", streaming=True dataset = dataset.shuffle buffer size=1000, seed=42 My workaround not pretty Ended up renting a RunPod A100 40GB for $1.10/hr. Same code, finished 3 epochs in 47 minutes. Cost: ~$0.85 total. Colab Pro $10/mo gets you T4/V100 priority and 24hr sessions — might work for 7B LoRA if you're patient. But free tier? Save yourself the grief. TL;DR: Free Colab cannot reliably fine-tune 7B models. The 2-hour hard limit + 12GB RAM ceiling + 16GB VRAM shared is a triple constraint. Use it for inference, small models, or prototyping — not production fine-tunes. Next Debugging the timeout cascade that killed our UPI integration → /en/threads/6976/