In this tutorial, we explore how to run OpenAI’s open-weight GPT-OSS models in Google Colab with a strong focus on their technical behavior, deployment requirements, and practical inference workflows. We begin by setting up the exact dependencies needed for Transformers-based execution, verifying GPU availability, and loading openai/gpt-oss-20b with the correct configuration using native MXFP4 quantization, torch.bfloat16 activations. As we move through the tutorial, we work directly with core capabilities such as structured generation, streaming, multi-turn dialogue handling, tool execution patterns, and batch inference, while keeping in mind how open-weight models differ from closed-hosted APIs in terms of transparency, controllability, memory constraints, and local execution trade-offs. Also, we treat GPT-OSS not just as a chatbot, but as a technically inspectable open-weight LLM stack that we can configure, prompt, and extend inside a reproducible workflow.
print("🔧 Step 1: Installing required packages...")
print("=" * 70)
!pip install -q --upgrade pip
!pip install -q transformers>=4.51.0 accelerate sentencepiece protobuf
!pip install -q huggingface_hub gradio ipywidgets
!pip install -q openai-harmony
import transformers
print(f"✅ Transformers version: {transformers.__version__}")
import torch
print(f"\n🖥️ System Information:")
print(f" PyTorch version: {torch.__version__}")
print(f" CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
gpu_name = torch.cuda.get_device_name(0)
gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1e9
print(f" GPU: {gpu_name}")
print(f" GPU Memory: {gpu_memory:.2f} GB")
if gpu_memory < 15:
print(f"\n⚠️ WARNING: gpt-oss-20b requires ~16GB VRAM.")
print(f" Your GPU has {gpu_memory:.1f}GB. Consider using Colab Pro for T4/A100.")
else:
print(f"\n✅ GPU memory sufficient for gpt-oss-20b")
else:
print("\n❌ No GPU detected!")
print(" Go to: Runtime → Change runtime type → Select 'T4 GPU'")
raise RuntimeError("GPU required for this tutorial")
print("\n" + "=" * 70)
print("📚 PART 2: Loading GPT-OSS Model (Correct Method)")
print("=" * 70)
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
MODEL_ID = "openai/gpt-oss-20b"
print(f"\n🔄 Loading model: {MODEL_ID}")
print(" This may take several minutes on first run...")
print(" (Model size: ~40GB download, uses native MXFP4 quantization)")
tokenizer = AutoTokenizer.from_pretrained(
MODEL_ID,
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
print("✅ Model loaded successfully!")
print(f" Model dtype: {model.dtype}")
print(f" Device: {model.device}")
if torch.cuda.is_available():
allocated = torch.cuda.memory_allocated() / 1e9
reserved = torch.cuda.memory_reserved() / 1e9
print(f" GPU Memory Allocated: {allocated:.2f} GB")
print(f" GPU Memory Reserved: {reserved:.2f} GB")
print("\n" + "=" * 70)
print("💬 PART 3: Basic Inference Examples")
print("=" * 70)
def generate_response(messages, max_new_tokens=256, temperature=0.8, top_p=1.0):
"""
Generate a response using gpt-oss with recommended parameters.
OpenAI recommends: temperature=1.0, top_p=1.0 for gpt-oss
"""
output = pipe(
messages,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=temperature,
top_p=top_p,
pad_token_id=tokenizer.eos_token_id,
)
return output[0]["generated_text"][-1]["content"]
print("\n📝 Example 1: Simple Question Answering")
print("-" * 50)
messages = [
{"role": "user", "content": "What is the Pythagorean theorem? Explain briefly."}
]
response = generate_response(messages, max_new_tokens=150)
print(f"User: {messages[0]['content']}")
print(f"\nAssistant: {response}")
print("\n\n📝 Example 2: Code Generation")
print("-" * 50)
messages = [
]
response = generate_response(messages, max_new_tokens=300)
print(f"User: {messages[0]['content']}")
print(f"\nAssistant: {response}")
print("\n\n📝 Example 3: Creative Writing")
print("-" * 50)
messages = [
{"role": "user", "content": "Write a haiku about artificial intelligence."}
]
response = generate_response(messages, max_new_tokens=100, temperature=1.0)
print(f"User: {messages[0]['content']}")
print(f"\nAssistant: {response}")We set up the full Colab environment required to run GPT-OSS properly and verify that the system has a compatible GPU with enough VRAM. We install the core libraries, check the PyTorch and Transformers versions, and confirm that the runtime is suitable for loading an open-weight model like gpt-oss-20b. We then load the tokenizer, initialize the model with the correct technical configuration, and run a few basic inference examples to confirm that the open-weight pipeline is working end to end.
print("\n" + "=" * 70)
print("🧠 PART 4: Configurable Reasoning Effort")
print("=" * 70)
print("""
GPT-OSS supports different reasoning effort levels:
• LOW - Quick, concise answers (fewer tokens, faster)
• MEDIUM - Balanced reasoning and response
• HIGH - Deep thinking with full chain-of-thought
The reasoning effort is controlled through system prompts and generation parameters.
""")
class ReasoningEffortController:
"""
Controls reasoning effort levels for gpt-oss generations.
"""
EFFORT_CONFIGS = {
"low": {
"system_prompt": "You are a helpful assistant. Be concise and direct.",
"max_tokens": 200,
"temperature": 0.7,
"description": "Quick, concise answers"
},
"medium": {
"system_prompt": "You are a helpful assistant. Think through problems step by step and provide clear, well-reasoned answers.",
"max_tokens": 400,
"temperature": 0.8,
"description": "Balanced reasoning"
},
"high": {
"system_prompt": """You are a helpful assistant with advanced reasoning capabilities.
For complex problems:
1. First, analyze the problem thoroughly
2. Consider multiple approaches
3. Show your complete chain of thought
4. Provide a comprehensive, well-reasoned answer
Take your time to think deeply before responding.""",
"max_tokens": 800,
"temperature": 1.0,
"description": "Deep chain-of-thought reasoning"
}
}
def __init__(self, pipeline, tokenizer):
self.pipe = pipeline
self.tokenizer = tokenizer
def generate(self, user_message: str, effort: str = "medium") -> dict:
"""Generate response with specified reasoning effort."""
if effort not in self.EFFORT_CONFIGS:
raise ValueError(f"Effort must be one of: {list(self.EFFORT_CONFIGS.keys())}")
config = self.EFFORT_CONFIGS[effort]
messages = [
{"role": "system", "content": config["system_prompt"]},
{"role": "user", "content": user_message}
]
output = self.pipe(
messages,
max_new_tokens=config["max_tokens"],
do_sample=True,
temperature=config["temperature"],
top_p=1.0,
pad_token_id=self.tokenizer.eos_token_id,
)
return {
"effort": effort,
"description": config["description"],
"response": output[0]["generated_text"][-1]["content"],
"max_tokens_used": config["max_tokens"]
}
reasoning_controller = ReasoningEffortController(pipe, tokenizer)
print(f"\n🧩 Logic Puzzle: {test_question}\n")
for effort in ["low", "medium", "high"]:
result = reasoning_controller.generate(test_question, effort)
print(f"━━━ {effort.upper()} ({result['description']}) ━━━")
print(f"{result['response'][:500]}...")
print()
print("\n" + "=" * 70)
print("📋 PART 5: Structured Output Generation (JSON Mode)")
print("=" * 70)
import json
import re
class StructuredOutputGenerator:
"""
Generate structured JSON outputs with schema validation.
"""
def __init__(self, pipeline, tokenizer):
self.pipe = pipeline
self.tokenizer = tokenizer
def generate_json(self, prompt: str, schema: dict, max_retries: int = 2) -> dict:
"""
Generate JSON output in accordance with a specified schema.
Args:
prompt: The user's request
schema: JSON schema description
max_retries: Number of retries on parse failure
"""
schema_str = json.dumps(schema, indent=2)
system_prompt = f"""You are a helpful assistant that ONLY outputs valid JSON.
Your response must exactly match this JSON schema:
{schema_str}
RULES:
- Output ONLY the JSON object, nothing else
- No markdown code blocks (no ```)
- No explanations before or after
- Ensure all required fields are present
- Use correct data types as specified"""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}
]
for attempt in range(max_retries + 1):
output = self.pipe(
messages,
max_new_tokens=500,
do_sample=True,
temperature=0.3,
top_p=1.0,
pad_token_id=self.tokenizer.eos_token_id,
)
response_text = output[0]["generated_text"][-1]["content"]
cleaned = self._clean_json_response(response_text)
try:
parsed = json.loads(cleaned)
return {"success": True, "data": parsed, "attempts": attempt + 1}
except json.JSONDecodeError as e:
if attempt == max_retries:
return {
"success": False,
"error": str(e),
"raw_response": response_text,
"attempts": attempt + 1
}
messages.append({"role": "assistant", "content": response_text})
messages.append({"role": "user", "content": f"That wasn't valid JSON. Error: {e}. Please try again with ONLY valid JSON."})
def _clean_json_response(self, text: str) -> str:
"""Remove markdown code blocks and extra whitespace."""
text = re.sub(r'^```(?:json)?\s*', '', text.strip())
text = re.sub(r'\s*```$', '', text)
return text.strip()
json_generator = StructuredOutputGenerator(pipe, tokenizer)
print("\n📝 Example 1: Entity Extraction")
print("-" * 50)
entity_schema = {
"name": "string",
"type": "string (person/company/place)",
"description": "string (1-2 sentences)",
"key_facts": ["list of strings"]
}
entity_result = json_generator.generate_json(
"Extract information about: Tesla, Inc.",
entity_schema
)
if entity_result["success"]:
print(json.dumps(entity_result["data"], indent=2))
else:
print(f"Error: {entity_result['error']}")
print("\n\n📝 Example 2: Recipe Generation")
print("-" * 50)
recipe_schema = {
"name": "string",
"prep_time_minutes": "integer",
"cook_time_minutes": "integer",
"servings": "integer",
"difficulty": "string (easy/medium/hard)",
"ingredients": [{"item": "string", "amount": "string"}],
"steps": ["string"]
}
recipe_result = json_generator.generate_json(
"Create a simple recipe for chocolate chip cookies",
recipe_schema
)
if recipe_result["success"]:
print(json.dumps(recipe_result["data"], indent=2))
else:
print(f"Error: {recipe_result['error']}")We build more advanced generation controls by introducing configurable reasoning effort and a structured JSON output workflow. We define different effort modes to vary how deeply the model reasons, how many tokens it uses, and how detailed its answers are during inference. We also create a JSON generation utility that guides the open-weight model toward schema-like outputs, cleans the returned text, and retries when the response is not valid JSON.
print("\n" + "=" * 70)
print("💬 PART 6: Multi-turn Conversations with Memory")
print("=" * 70)
class ConversationManager:
"""
Manages multi-turn conversations with context memory.
Implements the Harmony format pattern used by gpt-oss.
"""
def __init__(self, pipeline, tokenizer, system_message: str = None):
self.pipe = pipeline
self.tokenizer = tokenizer
self.history = []
if system_message:
self.system_message = system_message
else:
self.system_message = "You are a helpful, friendly AI assistant. Remember the context of our conversation."
def chat(self, user_message: str, max_new_tokens: int = 300) -> str:
"""Send a message and get a response, maintaining conversation history."""
messages = [{"role": "system", "content": self.system_message}]
messages.extend(self.history)
messages.append({"role": "user", "content": user_message})
output = self.pipe(
messages,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=0.8,
top_p=1.0,
pad_token_id=self.tokenizer.eos_token_id,
)
assistant_response = output[0]["generated_text"][-1]["content"]
self.history.append({"role": "user", "content": user_message})
self.history.append({"role": "assistant", "content": assistant_response})
return assistant_response
def get_history_length(self) -> int:
"""Get number of turns in conversation."""
return len(self.history) // 2
def clear_history(self):
"""Clear conversation history."""
self.history = []
print("🗑️ Conversation history cleared.")
def get_context_summary(self) -> str:
"""Get a summary of the conversation context."""
if not self.history:
return "No conversation history yet."
summary = f"Conversation has {self.get_history_length()} turns:\n"
for i, msg in enumerate(self.history):
role = "👤 User" if msg["role"] == "user" else "🤖 Assistant"
preview = msg["content"][:50] + "..." if len(msg["content"]) > 50 else msg["content"]
summary += f" {i+1}. {role}: {preview}\n"
return summary
convo = ConversationManager(pipe, tokenizer)
print("\n🗣️ Multi-turn Conversation Demo:")
print("-" * 50)
conversation_turns = [
"Hi! My name is Alex and I'm a software engineer.",
"I'm working on a machine learning project. What framework would you recommend?",
"Good suggestion! What's my name, by the way?",
"Can you remember what field I work in?"
]
for turn in conversation_turns:
print(f"\n👤 User: {turn}")
response = convo.chat(turn)
print(f"🤖 Assistant: {response}")
print(f"\n📊 {convo.get_context_summary()}")
print("\n" + "=" * 70)
print("⚡ PART 7: Streaming Token Generation")
print("=" * 70)
from transformers import TextIteratorStreamer
from threading import Thread
import time
def stream_response(prompt: str, max_tokens: int = 200):
"""
Stream tokens as they're generated for real-time output.
"""
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
streamer = TextIteratorStreamer(
tokenizer,
skip_prompt=True,
skip_special_tokens=True
)
generation_kwargs = {
"input_ids": inputs,
"streamer": streamer,
"max_new_tokens": max_tokens,
"do_sample": True,
"temperature": 0.8,
"top_p": 1.0,
"pad_token_id": tokenizer.eos_token_id,
}
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
print("📝 Streaming: ", end="", flush=True)
full_response = ""
for token in streamer:
print(token, end="", flush=True)
full_response += token
time.sleep(0.01)
thread.join()
print("\n")
return full_response
print("\n🔄 Streaming Demo:")
print("-" * 50)
streamed = stream_response(
"Count from 1 to 10, with a brief comment about each number.",
max_tokens=250
)
We move from single prompts to stateful interactions by creating a conversation manager that stores multi-turn chat history and reuses that context in future responses. We demonstrate how we maintain memory across turns, summarize prior context, and make the interaction feel more like a persistent assistant instead of a one-off generation call. We also implement streaming generation so we can watch tokens arrive in real time, which helps us understand the model’s live decoding behavior more clearly.
print("\n" + "=" * 70)
print("🔧 PART 8: Function Calling / Tool Use")
print("=" * 70)
import math
from datetime import datetime
class ToolExecutor:
"""
Manages tool definitions and execution for gpt-oss.
"""
def __init__(self):
self.tools = {}
self._register_default_tools()
def _register_default_tools(self):
"""Register built-in tools."""
@self.register("calculator", "Perform mathematical calculations")
def calculator(expression: str) -> str:
"""Evaluate a mathematical expression."""
try:
allowed_names = {
k: v for k, v in math.__dict__.items()
if not k.startswith("_")
}
allowed_names.update({"abs": abs, "round": round})
result = eval(expression, {"__builtins__": {}}, allowed_names)
return f"Result: {result}"
except Exception as e:
return f"Error: {str(e)}"
@self.register("get_time", "Get current date and time")
def get_time() -> str:
"""Get the current date and time."""
now = datetime.now()
return f"Current time: {now.strftime('%Y-%m-%d %H:%M:%S')}"
@self.register("weather", "Get weather for a city (simulated)")
def weather(city: str) -> str:
"""Get weather information (simulated)."""
import random
temp = random.randint(60, 85)
conditions = random.choice(["sunny", "partly cloudy", "cloudy", "rainy"])
return f"Weather in {city}: {temp}°F, {conditions}"
@self.register("search", "Search for information (simulated)")
def search(query: str) -> str:
"""Search the web (simulated)."""
return f"Search results for '{query}': [Simulated results - in production, connect to a real search API]"
def register(self, name: str, description: str):
"""Decorator to register a tool."""
def decorator(func):
self.tools[name] = {
"function": func,
"description": description,
"name": name
}
return func
return decorator
def get_tools_prompt(self) -> str:
"""Generate tools description for the system prompt."""
tools_desc = "You have access to the following tools:\n\n"
for name, tool in self.tools.items():
tools_desc += f"- {name}: {tool['description']}\n"
tools_desc += """
To use a tool, respond with:
TOOL:
ARGS:
After receiving the tool result, provide your final answer to the user."""
return tools_desc
def execute(self, tool_name: str, args: dict) -> str:
"""Execute a tool with given arguments."""
if tool_name not in self.tools:
return f"Error: Unknown tool '{tool_name}'"
try:
func = self.tools[tool_name]["function"]
if args:
result = func(**args)
else:
result = func()
return result
except Exception as e:
return f"Error executing {tool_name}: {str(e)}"
def parse_tool_call(self, response: str) -> tuple:
"""Parse a tool call from model response."""
if "TOOL:" not in response:
return None, None
lines = response.split("\n")
tool_name = None
args = {}
for line in lines:
if line.startswith("TOOL:"):
tool_name = line.replace("TOOL:", "").strip()
elif line.startswith("ARGS:"):
try:
args_str = line.replace("ARGS:", "").strip()
args = json.loads(args_str) if args_str else {}
except json.JSONDecodeError:
args = {"expression": args_str} if tool_name == "calculator" else {"query": args_str}
return tool_name, args
tools = ToolExecutor()
def chat_with_tools(user_message: str) -> str:
"""
Chat with tool use capability.
"""
system_prompt = f"""You are a helpful assistant with access to tools.
{tools.get_tools_prompt()}
If the user's request can be answered directly, do so.
If you need to use a tool, indicate which tool and with what arguments."""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_message}
]
output = pipe(
messages,
max_new_tokens=200,
do_sample=True,
temperature=0.7,
pad_token_id=tokenizer.eos_token_id,
)
response = output[0]["generated_text"][-1]["content"]
tool_name, args = tools.parse_tool_call(response)
if tool_name:
tool_result = tools.execute(tool_name, args)
messages.append({"role": "assistant", "content": response})
messages.append({"role": "user", "content": f"Tool result: {tool_result}\n\nNow provide your final answer."})
final_output = pipe(
messages,
max_new_tokens=200,
do_sample=True,
temperature=0.7,
pad_token_id=tokenizer.eos_token_id,
)
return final_output[0]["generated_text"][-1]["content"]
return response
print("\n🔧 Tool Use Examples:")
print("-" * 50)
tool_queries = [
"What is 15 * 23 + 7?",
"What time is it right now?",
"What's the weather like in Tokyo?",
]
for query in tool_queries:
print(f"\n👤 User: {query}")
response = chat_with_tools(query)
print(f"🤖 Assistant: {response}")
print("\n" + "=" * 70)
print("📦 PART 9: Batch Processing for Efficiency")
print("=" * 70)
def batch_generate(prompts: list, batch_size: int = 2, max_new_tokens: int = 100) -> list:
"""
Process multiple prompts in batches for efficiency.
Args:
prompts: List of prompts to process
batch_size: Number of prompts per batch
max_new_tokens: Maximum tokens per response
Returns:
List of responses
"""
results = []
total_batches = (len(prompts) + batch_size - 1) // batch_size
for i in range(0, len(prompts), batch_size):
batch = prompts[i:i + batch_size]
batch_num = i // batch_size + 1
print(f" Processing batch {batch_num}/{total_batches}...")
batch_messages = [
[{"role": "user", "content": prompt}]
for prompt in batch
]
for messages in batch_messages:
output = pipe(
messages,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=0.7,
pad_token_id=tokenizer.eos_token_id,
)
results.append(output[0]["generated_text"][-1]["content"])
return results
print("\n📝 Batch Processing Example:")
print("-" * 50)
batch_prompts = [
"What is the capital of France?",
"What is 7 * 8?",
"Name a primary color.",
"What season comes after summer?",
"What is H2O commonly called?",
]
print(f"Processing {len(batch_prompts)} prompts...\n")
batch_results = batch_generate(batch_prompts, batch_size=2)
for prompt, result in zip(batch_prompts, batch_results):
print(f"Q: {prompt}")
print(f"A: {result[:100]}...\n") We extend the tutorial to include tool use and batch inference, enabling the open-weight model to support more realistic application patterns. We define a lightweight tool execution framework, let the model choose tools through a structured text pattern, and then feed the tool results back into the generation loop to produce a final answer. We also add batch processing to handle multiple prompts efficiently, which is useful for testing throughput and reusing the same inference pipeline across several tasks.
print("\n" + "=" * 70)
print("🤖 PART 10: Interactive Chatbot Interface")
print("=" * 70)
import gradio as gr
def create_chatbot():
"""Create a Gradio chatbot interface for gpt-oss."""
def respond(message, history):
"""Generate chatbot response."""
for user_msg, assistant_msg in history:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
output = pipe(
messages,
max_new_tokens=400,
do_sample=True,
temperature=0.8,
top_p=1.0,
pad_token_id=tokenizer.eos_token_id,
)
return output[0]["generated_text"][-1]["content"]
demo = gr.ChatInterface(
fn=respond,
title="🚀 GPT-OSS Chatbot",
description="Chat with OpenAI's open-weight GPT-OSS model!",
examples=[
"Explain quantum computing in simple terms.",
"What are the benefits of open-source AI?",
"Tell me a fun fact about space.",
],
theme=gr.themes.Soft(),
)
return demo
print("\n🚀 Creating Gradio chatbot interface...")
chatbot = create_chatbot()
print("\n" + "=" * 70)
print("🎁 PART 11: Utility Helpers")
print("=" * 70)
class GptOssHelpers:
"""Collection of utility functions for common tasks."""
def __init__(self, pipeline, tokenizer):
self.pipe = pipeline
self.tokenizer = tokenizer
def summarize(self, text: str, max_words: int = 50) -> str:
"""Summarize text to specified length."""
messages = [
{"role": "system", "content": f"Summarize the following text in {max_words} words or less. Be concise."},
{"role": "user", "content": text}
]
output = self.pipe(messages, max_new_tokens=150, temperature=0.5, pad_token_id=self.tokenizer.eos_token_id)
return output[0]["generated_text"][-1]["content"]
def translate(self, text: str, target_language: str) -> str:
"""Translate text to target language."""
messages = [
{"role": "user", "content": f"Translate to {target_language}: {text}"}
]
output = self.pipe(messages, max_new_tokens=200, temperature=0.3, pad_token_id=self.tokenizer.eos_token_id)
return output[0]["generated_text"][-1]["content"]
def explain_simply(self, concept: str) -> str:
"""Explain a concept in simple terms."""
messages = [
{"role": "system", "content": "Explain concepts simply, as if to a curious 10-year-old. Use analogies and examples."},
{"role": "user", "content": f"Explain: {concept}"}
]
output = self.pipe(messages, max_new_tokens=200, temperature=0.8, pad_token_id=self.tokenizer.eos_token_id)
return output[0]["generated_text"][-1]["content"]
def extract_keywords(self, text: str, num_keywords: int = 5) -> list:
"""Extract key topics from text."""
messages = [
{"role": "user", "content": f"Extract exactly {num_keywords} keywords from this text. Return only the keywords, comma-separated:\n\n{text}"}
]
output = self.pipe(messages, max_new_tokens=50, temperature=0.3, pad_token_id=self.tokenizer.eos_token_id)
keywords = output[0]["generated_text"][-1]["content"]
return [k.strip() for k in keywords.split(",")]
helpers = GptOssHelpers(pipe, tokenizer)
print("\n📝 Helper Functions Demo:")
print("-" * 50)
sample_text = """
Artificial intelligence has transformed many industries in recent years.
From healthcare diagnostics to autonomous vehicles, AI systems are becoming
"""
print("\n1️⃣ Summarization:")
summary = helpers.summarize(sample_text, max_words=20)
print(f" {summary}")
print("\n2️⃣ Simple Explanation:")
explanation = helpers.explain_simply("neural networks")
print(f" {explanation[:200]}...")
print("\n" + "=" * 70)
print("✅ TUTORIAL COMPLETE!")
print("=" * 70)
print("""
🎉 You've learned how to use GPT-OSS on Google Colab!
WHAT YOU LEARNED:
✓ Correct model loading (no load_in_4bit - uses native MXFP4)
✓ Basic inference with proper parameters
✓ Configurable reasoning effort (low/medium/high)
✓ Structured JSON output generation
✓ Multi-turn conversations with memory
✓ Streaming token generation
✓ Function calling and tool use
✓ Batch processing for efficiency
✓ Interactive Gradio chatbot
KEY TAKEAWAYS:
• GPT-OSS uses native MXFP4 quantization (don't use bitsandbytes)
• Recommended: temperature=1.0, top_p=1.0
• gpt-oss-20b fits on T4 GPU (~16GB VRAM)
• gpt-oss-120b requires H100/A100 (~80GB VRAM)
• Always use trust_remote_code=True
RESOURCES:
📚 GitHub: https://github.com/openai/gpt-oss
📚 Hugging Face: https://huggingface.co/openai/gpt-oss-20b
📚 Model Card: https://arxiv.org/abs/2508.10925
📚 Harmony Format: https://github.com/openai/harmony
📚 Cookbook: https://cookbook.openai.com/topic/gpt-oss
ALTERNATIVE INFERENCE OPTIONS (for better performance):
• vLLM: Production-ready, OpenAI-compatible server
• Ollama: Easy local deployment
• LM Studio: Desktop GUI application
""")
if torch.cuda.is_available():
print(f"\n📊 Final GPU Memory Usage:")
print(f" Allocated: {torch.cuda.memory_allocated() / 1e9:.2f} GB")
print(f" Reserved: {torch.cuda.memory_reserved() / 1e9:.2f} GB")
print("\n" + "=" * 70)
print("🚀 Launch the chatbot by running: chatbot.launch(share=True)")
print("=" * 70)We turn the model pipeline into a usable application by building a Gradio chatbot interface and then adding helper utilities for summarization, translation, simplified explanation, and keyword extraction. We show how the same open-weight model can support both interactive chat and reusable task-specific functions inside a single Colab workflow. We end by summarizing the tutorial, reviewing the key technical takeaways, and reinforcing how GPT-OSS can be loaded, controlled, and extended as a practical open-weight system.
In conclusion, we built a comprehensive hands-on understanding of how to use GPT-OSS as an open-source language model rather than a black-box endpoint. We loaded the model with the correct inference path, avoiding incorrect low-bit loading approaches, and worked through important implementation patterns, including configurable reasoning effort, JSON-constrained outputs, Harmony-style conversational formatting, token streaming, lightweight tool use orchestration, and Gradio-based interaction. In doing so, we saw the real advantage of open-weight models: we can directly control model loading, inspect runtime behavior, shape generation flows, and design custom utilities on top of the base model without depending entirely on managed infrastructure.
Check out the Full Code Implementation. Also, feel free to follow us on Twitter and don’t forget to join our 130k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us
