Why Hugging Face + Colab

If you don't want to sign up for a dozen platforms or bind a credit card, but still need a working free AI environment, Hugging Face's Inference API free tier plus Google Colab's free GPU is the lightest combo available.

  • Hugging Face Inference API: Sign up and get a monthly free quota (usually measured in requests or compute time), supporting open models like Llama, Mistral, and Qwen.
  • Google Colab: The free tier offers a T4 GPU (~16GB VRAM), enough to run 7B–13B quantized models locally, bypassing API quotas entirely.

Combine them: use HF API for light daily calls, switch to Colab for heavy experiments or custom prompts.

Steps

Step 1: Register on Hugging Face and get a token

  1. Go to huggingface.co and sign up (email only, no phone needed).
  2. Go to Settings → Access Tokens, create a read token.
  3. Free accounts get a monthly Inference API quota (check the site for current limits; typically a few hundred requests).

Step 2: Call the free Inference API with Python

```python

import requests

API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"

headers = {"Authorization": "Bearer hf_your_token"}

payload = {

"inputs": "Explain quantum entanglement in one sentence",

"parameters": {"max_new_tokens": 200}

}

response = requests.post(API_URL, headers=headers, json=payload)

print(response.json())

```

> Note: The free tier has rate limits (a few requests per minute). Exceeding them returns 429. Add time.sleep(2) for simple throttling.

Step 3: Run local models on Colab's free GPU

  1. Open colab.research.google.com and create a notebook.
  2. Runtime → Change runtime type → select T4 GPU (free).
  3. Install dependencies and load a quantized model:

```python

!pip install -q transformers accelerate bitsandbytes

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen2.5-7B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(

model_name,

device_map="auto",

load_in_4bit=True # 4-bit quantization fits T4

)

inputs = tokenizer("Write a haiku about spring", return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

```

Step 4: Manage quotas and pick models

  • HF API free tier: Prefer small models (e.g., google/gemma-2-2b-it, microsoft/Phi-3-mini-4k-instruct) to consume quota slower.
  • Colab: Free GPU has a daily usage limit (usually a few hours) and may disconnect during long sessions. Write scripts that can resume.
  • Model caching: Colab resets on restart. Mount Google Drive to cache models and avoid re-downloading.

Caveats

  • Hugging Face free Inference API quotas and rate limits change with platform policy. Check the official page before relying on it.
  • Colab free GPU availability is not guaranteed; T4 may be unavailable at peak times.
  • Don't run models larger than VRAM. 7B with 4-bit quantization is a safe bet.
  • Free tokens are for testing and learning. Upgrade for commercial use.

Use cases

  • Individual developers validating prompts quickly
  • Students doing coursework or final projects
  • Small teams building prototypes without long-term stable APIs
  • Learning open-model fine-tuning and inference

Summary

The Hugging Face + Colab combo doesn't promise "unlimited tokens." It gives you the lowest barrier to running an open model anytime. For occasional, experimental use, this toolchain is enough. If you need high-frequency stable calls, consider paid plans or local deployment.