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-# GPT-OSS - Open Source ChatGPT Alternative
-A powerful open-source alternative to ChatGPT with advanced reasoning capabilities, integrated browser tools, and Python code execution — all running locally on Ollama.
-## 🚀 Quick Start
-```bash
-# Pull and run the model
-ollama pull Raiff1982/gpt-oss
-ollama run Raiff1982/gpt-oss
-```
-## 🎯 What Makes This Model Special?
-GPT-OSS provides a feature-complete ChatGPT experience with:
-- **🧠 Multi-Level Reasoning** - Built-in analysis channels for deep thinking
-- **🌐 Browser Integration** - Search, open, and find information on the web
-- **🐍 Python Execution** - Run Python code in a stateful Jupyter environment
-- **🔧 Tool Calling** - Extensible function calling framework
-- **📊 Data Persistence** - Save and load files to `/mnt/data`
-- **💭 Chain of Thought** - Transparent reasoning with configurable depth
-## 🛠️ Core Features
-### Reasoning Channels
-The model operates across multiple channels for structured thinking:
-```
-analysis     → Internal reasoning and tool usage (Python, browser)
-commentary   → Function calls and external tool integration
-final        → User-facing responses and conclusions
-```
-This architecture enables:
-- **Transparent reasoning** - See how the model thinks
-- **Tool integration** - Seamlessly use Python/browser without breaking flow
-- **Clean output** - Separate internal work from final answers
-### Browser Tools
-Built-in web browsing capabilities:
-```python
-# Search the web
-browser.search(query="latest AI research", topn=10)
-# Open specific results
-browser.open(id=3, loc=0, num_lines=50)
-# Find text on page
-browser.find(pattern="neural networks")
-```
-**Use cases:**
-- Research current events and news
-- Find technical documentation
-- Verify facts and statistics
-- Compare information across sources
-### Python Code Execution
-Stateful Jupyter notebook environment:
-```python
-# Execute code directly
-import pandas as pd
-import matplotlib.pyplot as plt
-# Load and analyze data
-df = pd.read_csv('/mnt/data/data.csv')
-df.describe()
-# Create visualizations
-plt.plot(df['x'], df['y'])
-plt.savefig('/mnt/data/plot.png')
-```
-**Capabilities:**
-- Full Python standard library
-- Data analysis (pandas, numpy)
-- Visualization (matplotlib, seaborn)
-- Machine learning (scikit-learn)
-- File persistence in `/mnt/data`
-- 120 second execution timeout
-### Reasoning Levels
-Control analysis depth with reasoning parameters:
-```
-low     → Quick, intuitive responses
-medium  → Balanced thinking (default)
-high    → Deep, thorough analysis
-```
-## 🎨 Example Use Cases
-### Research Assistant
-```
-> What are the latest developments in quantum computing?
-[Model searches web, analyzes multiple sources, synthesizes findings]
-[Cites sources with: 【6†L9-L11】 format]
-[Provides comprehensive summary with references]
-```
-### Data Analysis
-```
-> Analyze this CSV and find correlations
-[Loads data with pandas]
-[Performs statistical analysis]
-[Creates visualization]
-[Explains insights and patterns]
-```
-### Code Generation & Debugging
-```
-> Help me debug this Python function
-[Analyzes code structure]
-[Tests in Python environment]
-[Identifies issues]
-[Provides corrected version with explanation]
-```
-### Multi-Step Problem Solving
-```
-> Plan a trip to Tokyo for 5 days under $2000
-[Searches flight prices]
-[Finds accommodation options]
-[Researches local costs]
-[Creates detailed itinerary with budget breakdown]
-```
-## ⚙️ Technical Specifications
-- **Size**: ~13 GB
-- **Context Window**: 8192+ tokens
-- **Temperature**: 1.0 (balanced creativity)
-- **Knowledge Cutoff**: June 2024
-- **License**: Apache 2.0
-### System Architecture
-```
-User Query
-    ↓
-System Prompt (ChatGPT identity, tool definitions)
-    ↓
-Analysis Channel (reasoning, Python, browser tools)
-    ↓
-Commentary Channel (function calls)
-    ↓
-Final Channel (user-facing response)
-```
-## 🔧 Advanced Usage
-### Custom System Instructions
-Extend the model with additional context:
-```bash
-ollama run Raiff1982/gpt-oss "You are now a specialized Python tutor..."
-```
-### Function Calling
-Define custom functions the model can call:
-```json
-{
-  "name": "get_weather",
-  "description": "Get current weather for a location",
-  "parameters": {
-    "type": "object",
-    "properties": {
-      "location": {"type": "string"},
-      "units": {"type": "string", "enum": ["celsius", "fahrenheit"]}
-    }
-  }
-}
-```
-### API Integration
-Use with Ollama's API for programmatic access:
-```python
-import ollama
-response = ollama.chat(
-    model='Raiff1982/gpt-oss',
-    messages=[
-        {
-            'role': 'user',
-            'content': 'Write a Python script to analyze CSV data'
-        }
-    ],
-    tools=[
-        {
-            'type': 'function',
-            'function': {
-                'name': 'python',
-                'description': 'Execute Python code'
-            }
-        }
-    ]
-)
-print(response['message']['content'])
-```
-### Streaming Responses
-Get real-time output for long responses:
-```python
-stream = ollama.chat(
-    model='Raiff1982/gpt-oss',
-    messages=[{'role': 'user', 'content': 'Explain quantum mechanics'}],
-    stream=True
-)
-for chunk in stream:
-    print(chunk['message']['content'], end='', flush=True)
-```
-## 🧪 Example Interactions
-### Web Research with Citations
-```
-User: What caused the 2024 stock market volatility?
-Assistant (analysis): *searches financial news*
-Assistant (analysis): *opens top 3 results*
-Assistant (analysis): *extracts key information*
-Assistant (final): Based on recent financial reports【4†L12-L15】【7†L8】:
-1. Federal interest rate changes in Q1 2024
-2. Tech sector corrections【4†L23】
-3. Geopolitical tensions affecting energy markets【9†L5-L9】
-The primary driver was...
-```
-### Python Data Analysis
-```
-User: Load sales_data.csv and show me the top 5 products
-Assistant (analysis):
-```python
-import pandas as pd
-df = pd.read_csv('/mnt/data/sales_data.csv')
-top_5 = df.groupby('product')['revenue'].sum().sort_values(ascending=False).head(5)
-print(top_5)
-```
-Assistant (final): Here are your top 5 products by revenue:
-1. Product A: $45,230
-2. Product B: $38,920
-...
-```
-### Complex Reasoning
-```
-User: Should I invest in renewable energy stocks?
-Assistant (analysis): *thinking deeply*
-- Market trends analysis
-- Policy impact assessment
-- Risk evaluation
-- Timeline considerations
-Assistant (final): I'll break this down across several dimensions:
-**Market Analysis** [searches recent data]
-- Solar industry growth rate: 15% YoY【3†L45】
-- Wind energy investments up 23%【5†L12-L14】
-**Policy Environment**
-[Considers regulatory landscape, incentives, risks]
-**Personal Recommendation**
-Based on your [risk tolerance/timeline/goals]...
-```
-## 📊 Capabilities Matrix
-| Feature | Supported | Notes |
-|---------|-----------|-------|
-| Web Search | ✅ | Real-time information retrieval |
-| Web Browsing | ✅ | Open and parse URLs |
-| Python Execution | ✅ | Stateful Jupyter environment |
-| Code Generation | ✅ | Multiple languages |
-| Data Analysis | ✅ | Pandas, NumPy, visualization |
-| File Persistence | ✅ | `/mnt/data` directory |
-| Function Calling | ✅ | Extensible tool framework |
-| Multi-Step Reasoning | ✅ | Chain of thought |
-| Streaming | ✅ | Real-time output |
-| Citations | ✅ | Source tracking with line numbers |
-## 🔒 Privacy & Safety
-**Local Execution Benefits:**
-- All processing happens on your machine
-- No data sent to external APIs (except browser tools)
-- Full control over tool usage
-- Inspect code before execution
-**Browser Tool Considerations:**
-- Browser tools do make external web requests
-- Review URLs and search queries before execution
-- Content fetched is processed locally
-**Python Execution Safety:**
-- Sandboxed environment with 120s timeout
-- File access limited to `/mnt/data`
-- No network access from Python by default
-- Review generated code before running
-## 🚦 Best Practices
-### Effective Prompting
-```
-❌ Vague: "Tell me about AI"
-✅ Specific: "Search for recent breakthroughs in transformer architecture
-              from 2024, then summarize the top 3 findings"
-❌ Too broad: "Analyze my data"
-✅ Actionable: "Load sales.csv, calculate monthly revenue trends,
-                and create a line plot showing growth over time"
-```
-### Tool Usage
-- **Search first** - Use browser before asking knowledge questions
-- **Verify with code** - Use Python to validate calculations
-- **Cite sources** - Pay attention to citation numbers
-- **Check dates** - Knowledge cutoff is June 2024
-### Reasoning Control
-```bash
-# Quick responses
-ollama run Raiff1982/gpt-oss --reasoning low "Quick question..."
-# Deep analysis
-ollama run Raiff1982/gpt-oss --reasoning high "Complex problem..."
-```
-## 🆚 GPT-OSS vs. Other Models
-| Feature | GPT-OSS | Standard LLMs | ChatGPT Plus |
-|---------|---------|---------------|--------------|
-| Cost | Free (local) | Free/Varies | $20/month |
-| Privacy | Full privacy | Varies | Data processed externally |
-| Tools | Browser + Python | None | Browser + Python + DALL-E |
-| Reasoning | Transparent | Hidden | Partial transparency |
-| Customization | Full control | Limited | Limited |
-| Offline | After download | Varies | No |
-## 🔄 Updates & Versioning
-This model is actively maintained:
-- Base architecture follows ChatGPT design patterns
-- Tools and capabilities updated regularly
-- Community contributions welcome
-## 📚 Related Resources
-- [Ollama Documentation](https://ollama.ai/docs)
-- [Function Calling Guide](https://github.com/ollama/ollama/blob/main/docs/api.md#tools)
-- [Python Environment Details](https://jupyter.org/)
-- [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)
-## 🤝 Contributing
-Help improve GPT-OSS:
-1. Report issues with tool usage
-2. Share effective prompting strategies
-3. Contribute function definitions
-4. Document use cases and examples
-## 💡 Tips & Tricks
-### Multi-Step Workflows
-```
-> First, search for "Python data visualization libraries 2024"
-> Then, use Python to create example plots with the top 3 libraries
-> Finally, compare their strengths and weaknesses
-```
-### Data Pipeline
-```
-> Load my CSV from /mnt/data/raw.csv
-> Clean the data (handle missing values, outliers)
-> Create summary statistics
-> Save cleaned data to /mnt/data/processed.csv
-> Generate a report with key findings
-```
-### Research & Writing
-```
-> Research the history of neural networks (search 5 sources)
-> Outline a 1000-word article based on findings
-> Draft section 1 with proper citations
-> Review and refine for clarity
-```
-## 🏆 Acknowledgments
-- **OpenAI** - ChatGPT architecture inspiration
-- **Ollama Team** - Local model runtime
-- **Open Source Community** - Tool integrations and feedback
----
-**Model Page**: https://ollama.com/Raiff1982/gpt-oss
-**Created**: December 27, 2025
-**Size**: 13 GB
-**License**: Apache 2.0
-*"Open source intelligence with the power of ChatGPT, privacy of local execution, and freedom of customization."*