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Run Colab

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Run Colab

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Open in Colab Run OpenAI gpt-oss 20B in a FREE Google Colab OpenAI released gpt-oss 120B and 20B. Both models are Apache 2.0 licensed. Specifically, gpt-oss-20b was made for lower latency and local or specialized use cases (21B parameters with 3.6B…

What This Teaches

  • A concrete OpenAI implementation pattern in the category: gpt-oss / open-weight deployment.

Implementation Use Cases

  • Use as a concrete implementation reference when building OpenAI API systems in this category.
  • Compare against current official API docs before copying model names, SDK calls, or parameters into production code.
  • Preserve this page as a mirrored source; prefer synthesis pages for personal recommendations or project-specific decisions.

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Open in Colab

Run OpenAI gpt-oss 20B in a FREE Google Colab

OpenAI released gpt-oss 120B and 20B. Both models are Apache 2.0 licensed.

Specifically, gpt-oss-20b was made for lower latency and local or specialized use cases (21B parameters with 3.6B active parameters).

Since the models were trained in native MXFP4 quantization it makes it easy to run the 20B even in resource constrained environments like Google Colab.

Authored by: Pedro and VB

Setup environment

Since support for mxfp4 in transformers is bleeding edge, we need a recent version of PyTorch and CUDA, in order to be able to install the mxfp4 triton kernels.

We also need to install transformers from source, and we uninstall torchvision and torchaudio to remove dependency conflicts.

!pip install -q --upgrade torch
!pip install -q transformers triton==3.4 kernels
!pip uninstall -q torchvision torchaudio -y

Please, restart your Colab runtime session after installing the packages above.

Load the model from Hugging Face in Google Colab

We load the model from here: openai/gpt-oss-20b

from transformers import AutoModelForCausalLM, AutoTokenizer
 
model_id = "openai/gpt-oss-20b"
 
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="cuda",
)

Setup messages/ chat

You can provide an optional system prompt or directly the input.

messages = [
    {"role": "system", "content": "Always respond in riddles"},
    {"role": "user", "content": "What is the weather like in Madrid?"},
]
 
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)
 
generated = model.generate(**inputs, max_new_tokens=500)
print(tokenizer.decode(generated[0][inputs["input_ids"].shape[-1]:]))

Specify Reasoning Effort

Simply pass it as an additional argument to apply_chat_template(). Supported values are "low", "medium" (default), or "high".

messages = [
    {"role": "system", "content": "Always respond in riddles"},
    {"role": "user", "content": "Explain why the meaning of life is 42"},
]
 
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    reasoning_effort="high",
).to(model.device)
 
generated = model.generate(**inputs, max_new_tokens=500)
print(tokenizer.decode(generated[0][inputs["input_ids"].shape[-1]:]))

Try out other prompts and ideas!

Check out our blogpost for other ideas: https://hf.co/blog/welcome-openai-gpt-oss

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