232
GPT-OSS-120B on AMD MI300X
💻
gpt-oss-120b model running on AMD MI300 infrastructure.
Mallory Antomarchi
mallocode200
AI & ML interests
Google Data Analytics Certified | IBM Certified Data Scientist & AI Engineer
Recent Activity
liked
a Space
about 7 hours ago
amd/gpt-oss-120b-chatbot
liked
a model
6 days ago
openai/gpt-oss-20b
liked
a model
6 days ago
openai/gpt-oss-120b
Organizations
None yet
liked
a
Space
about 7 hours ago
liked
2
models
6 days ago
liked
a
model
25 days ago
upvoted
a
collection
25 days ago
liked
a
Space
25 days ago
reacted to
merve's
post with ❤️
about 2 months ago
Post
4364
Release picks of the past week is here! Find more models, datasets, Spaces here
merve/june-20-releases-68594824d1f4dfa61aee3433
🖼️ VLMs/OCR
> moonshotai/Kimi-VL-A3B-Thinking-2506 is a powerful reasoning vision LM, 3B active params, smarter with less tokens, supports long documents, videos 👏 (OS)
> nanonets/Nanonets-OCR-s is 3.75B params OCR model based on Qwen2.5VL-3B-Instruct (OS)
💬 LLMs
> moonshotai/Kimi-Dev-72B is a strong coding model based on Qwen2.5-72B (OS)
> Mistral released mistralai/Mistral-Small-3.2-24B-Instruct-2506, an update to their former model with better function calling & instruction following (OS)
🗣️ Audio
> Google released google/magenta-realtime, real time music generation & audio synthesis (cc-by-4)
> kyutai released new speech-to-text models that come in 1B & 2B ( kyutai/stt-1b-en_fr, stt-2b-en_fr) with 0.5s and 2.5s delay
3D
> Tencent released tencent/Hunyuan3D-2.1 an image-to-3D model (see below)
🖼️ VLMs/OCR
> moonshotai/Kimi-VL-A3B-Thinking-2506 is a powerful reasoning vision LM, 3B active params, smarter with less tokens, supports long documents, videos 👏 (OS)
> nanonets/Nanonets-OCR-s is 3.75B params OCR model based on Qwen2.5VL-3B-Instruct (OS)
💬 LLMs
> moonshotai/Kimi-Dev-72B is a strong coding model based on Qwen2.5-72B (OS)
> Mistral released mistralai/Mistral-Small-3.2-24B-Instruct-2506, an update to their former model with better function calling & instruction following (OS)
🗣️ Audio
> Google released google/magenta-realtime, real time music generation & audio synthesis (cc-by-4)
> kyutai released new speech-to-text models that come in 1B & 2B ( kyutai/stt-1b-en_fr, stt-2b-en_fr) with 0.5s and 2.5s delay
3D
> Tencent released tencent/Hunyuan3D-2.1 an image-to-3D model (see below)
posted
an
update
about 2 months ago
Post
333
Hi everyone, I have updated the space from its original version.
mallocode200/Deep_Research_Assistant
The main changes are:
- The possibility for users to insert their own OpenAI API Key
- The possibility for users to select the model they want from OpenAI
- An improved UI to see the process going on when the research is running
- A better Gradio interface for both light and dark themes
Why use only OpenAI models?
For this research assistant, I've chosen to focus exclusively on OpenAI models for several key reasons:
- Reliability & Consistency: OpenAI's models provide consistent, high-quality responses that are crucial for research tasks requiring accuracy and depth.
- Advanced Reasoning: Models like GPT-4o and o1-preview excel at complex analytical thinking, making them ideal for comprehensive research synthesis and evaluation.
- Proven Performance: The research pipeline has been extensively tested and optimised specifically for OpenAI's model behaviours and capabilities.
- API Stability: OpenAI offers robust API infrastructure with reliable uptime and consistent response formats, essential for a production research tool.
- Quality Assurance: The built-in evaluation and optimisation system is fine-tuned to work with OpenAI's specific output patterns and capabilities.
That said, I'm open to expanding model support in the future! The architecture is designed to be flexible, and I'm considering adding support for other leading models, such as Claude and Gemini, as well as open-source alternatives, as the ecosystem evolves. The goal is to provide users with the best possible research experience, regardless of their preferred AI provider.
The main changes are:
- The possibility for users to insert their own OpenAI API Key
- The possibility for users to select the model they want from OpenAI
- An improved UI to see the process going on when the research is running
- A better Gradio interface for both light and dark themes
Why use only OpenAI models?
For this research assistant, I've chosen to focus exclusively on OpenAI models for several key reasons:
- Reliability & Consistency: OpenAI's models provide consistent, high-quality responses that are crucial for research tasks requiring accuracy and depth.
- Advanced Reasoning: Models like GPT-4o and o1-preview excel at complex analytical thinking, making them ideal for comprehensive research synthesis and evaluation.
- Proven Performance: The research pipeline has been extensively tested and optimised specifically for OpenAI's model behaviours and capabilities.
- API Stability: OpenAI offers robust API infrastructure with reliable uptime and consistent response formats, essential for a production research tool.
- Quality Assurance: The built-in evaluation and optimisation system is fine-tuned to work with OpenAI's specific output patterns and capabilities.
That said, I'm open to expanding model support in the future! The architecture is designed to be flexible, and I'm considering adding support for other leading models, such as Claude and Gemini, as well as open-source alternatives, as the ecosystem evolves. The goal is to provide users with the best possible research experience, regardless of their preferred AI provider.
Update!
#1 opened about 2 months ago
by
mallocode200
updated
a
Space
about 2 months ago
replied to
their
post
about 2 months ago
Great point, thank you! You're right, letting users bring their key would solve the 429 errors and make it much more scalable. I'll look into adding that.
replied to
their
post
about 2 months ago
Thanks so much for the kind words!
I'd be delighted to collaborate and have the Deep Research Assistant featured as a use case. It's exciting to see the alignment between your work on LLM agent patterns and the architecture I've developed.
Let me know what you have in mind for the next steps.
Looking forward to it!
liked
a
model
about 2 months ago
posted
an
update
about 2 months ago
Post
5245
Hello everyone, happy to share with you my experimentation of a Deep Research Assistant, using 7 agents and a quality assurance pipeline :
🤖 What makes this special:
✅ Agent-Based Architecture - 7 specialised AI agents working together:
- Planner Agent - Strategic search planning
- Search Agent - Multi-source web research
- Writer Agent - Comprehensive report generation
- Evaluator Agent - Automatic quality assessment
- Optimiser Agent - Iterative improvement when needed
- Email Agent - Professional report delivery
- Clarifier Agent - Interactive query refinement
✅ Quality Assurance Pipeline - Every report is scored (1-10) and automatically improved if it scores below 7/10
✅ Multiple Research Modes - From quick queries to deep, clarification-driven analysis
✅ Production-Ready - Deployed on Hugging Face Spaces with comprehensive documentation
🔧 Technical Stack:
- Frontend: Gradio with theme-adaptive UI
- Backend: OpenAI Agents framework
- Integration: SendGrid for email delivery
- Deployment: Containerised with full CI/CD pipeline
- Tracing: Full OpenAI trace integration for transparency
💡 Real-World Impact:
This isn't just another AI tool - it's a complete research workflow that delivers publication-quality reports with built-in fact-checking and optimisation. Perfect for consultants, researchers, analysts, and anyone who needs reliable, comprehensive research.
🚀 Key Features:
- Automatic quality evaluation and improvement
- Email delivery of formatted reports
- Interactive clarification for targeted results
- Full traceability and audit trails
- Professional documentation and deployment guides
- Built with modern AI engineering principles: modular design, quality assurance, and production deployment in mind.
- The entire codebase is organised with clean separation of concerns - each agent has a specific role, making it maintainable and extensible.
mallocode200/Deep_Research_Assistant
🤖 What makes this special:
✅ Agent-Based Architecture - 7 specialised AI agents working together:
- Planner Agent - Strategic search planning
- Search Agent - Multi-source web research
- Writer Agent - Comprehensive report generation
- Evaluator Agent - Automatic quality assessment
- Optimiser Agent - Iterative improvement when needed
- Email Agent - Professional report delivery
- Clarifier Agent - Interactive query refinement
✅ Quality Assurance Pipeline - Every report is scored (1-10) and automatically improved if it scores below 7/10
✅ Multiple Research Modes - From quick queries to deep, clarification-driven analysis
✅ Production-Ready - Deployed on Hugging Face Spaces with comprehensive documentation
🔧 Technical Stack:
- Frontend: Gradio with theme-adaptive UI
- Backend: OpenAI Agents framework
- Integration: SendGrid for email delivery
- Deployment: Containerised with full CI/CD pipeline
- Tracing: Full OpenAI trace integration for transparency
💡 Real-World Impact:
This isn't just another AI tool - it's a complete research workflow that delivers publication-quality reports with built-in fact-checking and optimisation. Perfect for consultants, researchers, analysts, and anyone who needs reliable, comprehensive research.
🚀 Key Features:
- Automatic quality evaluation and improvement
- Email delivery of formatted reports
- Interactive clarification for targeted results
- Full traceability and audit trails
- Professional documentation and deployment guides
- Built with modern AI engineering principles: modular design, quality assurance, and production deployment in mind.
- The entire codebase is organised with clean separation of concerns - each agent has a specific role, making it maintainable and extensible.
mallocode200/Deep_Research_Assistant
-
- 6 replies
published
a
Space
about 2 months ago
upvoted
a
collection
2 months ago
liked
2
Spaces
2 months ago
liked
a
model
2 months ago
upvoted
a
collection
3 months ago