I had added cookbooks in the course by HF, but missed mentioning smolagents in the list😭. updated now.
@merve
Prithiv Sakthi
prithivMLmods
AI & ML interests
computer vision, nlp, multimodality
@strangerzonehf @strangerguardhf
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6 days ago
Post
4711
OpenAI, Google, Hugging Face, and Anthropic have released guides and courses on building agents, prompting techniques, scaling AI use cases, and more. Below are 10+ minimalistic guides and courses that may help you in your progress. 📖
⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
by HF🤗 :
⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn
⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
by HF🤗 :
⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn
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posted
an
update
6 days ago
Post
4711
OpenAI, Google, Hugging Face, and Anthropic have released guides and courses on building agents, prompting techniques, scaling AI use cases, and more. Below are 10+ minimalistic guides and courses that may help you in your progress. 📖
⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
by HF🤗 :
⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn
⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
by HF🤗 :
⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn
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7 days ago
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Just made a demo for Cosmos-Reason1, a physical AI model that understands physical common sense and generates appropriate embodied decisions in natural language through long chain-of-thought reasoning. Also added video understanding support to it. 🤗🚀
✦ Try the demo here : prithivMLmods/DocScope-R1
⤷ Cosmos-Reason1-7B : nvidia/Cosmos-Reason1-7B
⤷ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤷ Captioner-Relaxed : Ertugrul/Qwen2.5-VL-7B-Captioner-Relaxed
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤷ GitHub :
• https://github.com/PRITHIVSAKTHIUR/Cosmos-x-DocScope
• https://github.com/PRITHIVSAKTHIUR/Nvidia-Cosmos-Reason1-Demo.
To know more about it, visit the model card of the respective model. !!
✦ Try the demo here : prithivMLmods/DocScope-R1
⤷ Cosmos-Reason1-7B : nvidia/Cosmos-Reason1-7B
⤷ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤷ Captioner-Relaxed : Ertugrul/Qwen2.5-VL-7B-Captioner-Relaxed
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤷ GitHub :
• https://github.com/PRITHIVSAKTHIUR/Cosmos-x-DocScope
• https://github.com/PRITHIVSAKTHIUR/Nvidia-Cosmos-Reason1-Demo.
To know more about it, visit the model card of the respective model. !!
posted
an
update
7 days ago
Post
2137
Just made a demo for Cosmos-Reason1, a physical AI model that understands physical common sense and generates appropriate embodied decisions in natural language through long chain-of-thought reasoning. Also added video understanding support to it. 🤗🚀
✦ Try the demo here : prithivMLmods/DocScope-R1
⤷ Cosmos-Reason1-7B : nvidia/Cosmos-Reason1-7B
⤷ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤷ Captioner-Relaxed : Ertugrul/Qwen2.5-VL-7B-Captioner-Relaxed
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤷ GitHub :
• https://github.com/PRITHIVSAKTHIUR/Cosmos-x-DocScope
• https://github.com/PRITHIVSAKTHIUR/Nvidia-Cosmos-Reason1-Demo.
To know more about it, visit the model card of the respective model. !!
✦ Try the demo here : prithivMLmods/DocScope-R1
⤷ Cosmos-Reason1-7B : nvidia/Cosmos-Reason1-7B
⤷ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤷ Captioner-Relaxed : Ertugrul/Qwen2.5-VL-7B-Captioner-Relaxed
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤷ GitHub :
• https://github.com/PRITHIVSAKTHIUR/Cosmos-x-DocScope
• https://github.com/PRITHIVSAKTHIUR/Nvidia-Cosmos-Reason1-Demo.
To know more about it, visit the model card of the respective model. !!
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10 days ago
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2818
Orsta 🔥 vision language models trained with V-Triune, a unified reinforcement learning system by MiniMax AI
One-RL-to-See-Them-All/one-rl-to-see-them-all-6833d27abce23898b2f9815a
✨ 7B & 32B with MIT license
✨ Masters 8 visual tasks: math, science QA, charts, puzzles, object detection, grounding, OCR, and counting
✨ Uses Dynamic IoU rewards for better visual understanding
✨Strong performance in visual reasoning and perception
One-RL-to-See-Them-All/one-rl-to-see-them-all-6833d27abce23898b2f9815a
✨ 7B & 32B with MIT license
✨ Masters 8 visual tasks: math, science QA, charts, puzzles, object detection, grounding, OCR, and counting
✨ Uses Dynamic IoU rewards for better visual understanding
✨Strong performance in visual reasoning and perception
Prompt : The word "HF" is made of soft, flowy fur on a vibrant-colored floor, well-lit by sunlight on a bright afternoon. The movement is subtle and soft. The camera doesn't move.
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16 days ago
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Yesterday, we dropped a new conversational viewer for datasets on the hub! 💬
Actually being able to view and inspect your data is extremely important. This is a big step in making data more accessible and actionable for everyone.
Here's some datasets you can try it out on:
• mlabonne/FineTome-100k
• Salesforce/APIGen-MT-5k
• open-thoughts/OpenThoughts2-1M
• allenai/tulu-3-sft-mixture
Any other good ones?
Actually being able to view and inspect your data is extremely important. This is a big step in making data more accessible and actionable for everyone.
Here's some datasets you can try it out on:
• mlabonne/FineTome-100k
• Salesforce/APIGen-MT-5k
• open-thoughts/OpenThoughts2-1M
• allenai/tulu-3-sft-mixture
Any other good ones?
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16 days ago
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Got access to Google's all-new Gemini Diffusion a state-of-the-art text diffusion model. It delivers the performance of Gemini 2.0 Flash-Lite at 5x the speed, generating over 1000 tokens in a fraction of a second and producing impressive results. Below are some initial outputs generated using the model. ♊🔥
Gemini Diffusion Playground ✦ : https://deepmind.google.com/frontiers/gemini-diffusion
Get Access Here : https://docs.google.com/forms/d/1aLm6J13tAkq4v4qwGR3z35W2qWy7mHiiA0wGEpecooo/viewform?edit_requested=true
🔗 To know more, visit: https://deepmind.google/models/gemini-diffusion/
Gemini Diffusion Playground ✦ : https://deepmind.google.com/frontiers/gemini-diffusion
Get Access Here : https://docs.google.com/forms/d/1aLm6J13tAkq4v4qwGR3z35W2qWy7mHiiA0wGEpecooo/viewform?edit_requested=true
🔗 To know more, visit: https://deepmind.google/models/gemini-diffusion/
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16 days ago
Prompt Used :
create an interactive web-based color picker with a saturation/value box and a hue slider. display the selected color and its hex, rgb, hsl, and cmyk values dynamically. use html, css for layout/styling, and javascript for color logic and interactivity.
a boat running upstream takes 8 hours 48 minutes to cover a certain distance, while it takes 4 hours to cover the same distance running downstream. what is the ratio between the speed of the boat and speed of the water current respectively? solve & generate the result in a web page.
design a fully functional chess game using html, css, and javascript in a single html file, with a responsive board, drag-and-drop piece movement, legal move validation, and check/checkmate detection.
using html, css, and javascript in a single html file to create a simulation of the solar system. pay extreme attention to the ui to make it as intuitive as possible. ensure that every planet appears as a sphere and is labeled with its corresponding name.
create an interactive bouncing ball game using html, css, and javascript in a single html file. the game should feature stunning animations, a controllable ball speed, and a slider brick. if the ball falls or goes down, the game is over.
posted
an
update
16 days ago
Post
2281
Got access to Google's all-new Gemini Diffusion a state-of-the-art text diffusion model. It delivers the performance of Gemini 2.0 Flash-Lite at 5x the speed, generating over 1000 tokens in a fraction of a second and producing impressive results. Below are some initial outputs generated using the model. ♊🔥
Gemini Diffusion Playground ✦ : https://deepmind.google.com/frontiers/gemini-diffusion
Get Access Here : https://docs.google.com/forms/d/1aLm6J13tAkq4v4qwGR3z35W2qWy7mHiiA0wGEpecooo/viewform?edit_requested=true
🔗 To know more, visit: https://deepmind.google/models/gemini-diffusion/
Gemini Diffusion Playground ✦ : https://deepmind.google.com/frontiers/gemini-diffusion
Get Access Here : https://docs.google.com/forms/d/1aLm6J13tAkq4v4qwGR3z35W2qWy7mHiiA0wGEpecooo/viewform?edit_requested=true
🔗 To know more, visit: https://deepmind.google/models/gemini-diffusion/
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17 days ago
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2289
The more optimized explicit content filters with lightweight 𝙜𝙪𝙖𝙧𝙙 models trained based on siglip2 patch16 512 and vit patch16 224 for illustration and explicit content classification for content moderation in social media, forums, and parental controls for safer browsing environments. this version fixes the issues in the previous release, which lacked sufficient resources. 🚀
⤷ Models :
→ siglip2 mini explicit content : prithivMLmods/siglip2-mini-explicit-content [recommended]
→ vit mini explicit content : prithivMLmods/vit-mini-explicit-content
⤷ Building image safety-guard models :
strangerguardhf
⤷ Datasets :
→ nsfw multidomain classification : strangerguardhf/NSFW-MultiDomain-Classification
→ nsfw multidomain classification v2.0 : strangerguardhf/NSFW-MultiDomain-Classification-v2.0
⤷ Collection :
→ Updated Versions [05192025] : prithivMLmods/explicit-content-filters-682aaa4733e378561925ca2b
→ Previous Versions : prithivMLmods/siglip2-content-filters-042025-final-680fe4aa1a9d589bf2c915ff
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
⤷ Models :
→ siglip2 mini explicit content : prithivMLmods/siglip2-mini-explicit-content [recommended]
→ vit mini explicit content : prithivMLmods/vit-mini-explicit-content
⤷ Building image safety-guard models :
strangerguardhf
⤷ Datasets :
→ nsfw multidomain classification : strangerguardhf/NSFW-MultiDomain-Classification
→ nsfw multidomain classification v2.0 : strangerguardhf/NSFW-MultiDomain-Classification-v2.0
⤷ Collection :
→ Updated Versions [05192025] : prithivMLmods/explicit-content-filters-682aaa4733e378561925ca2b
→ Previous Versions : prithivMLmods/siglip2-content-filters-042025-final-680fe4aa1a9d589bf2c915ff
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
- 1 reply
Precisely 68 pages! : https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view
posted
an
update
17 days ago
Post
2289
The more optimized explicit content filters with lightweight 𝙜𝙪𝙖𝙧𝙙 models trained based on siglip2 patch16 512 and vit patch16 224 for illustration and explicit content classification for content moderation in social media, forums, and parental controls for safer browsing environments. this version fixes the issues in the previous release, which lacked sufficient resources. 🚀
⤷ Models :
→ siglip2 mini explicit content : prithivMLmods/siglip2-mini-explicit-content [recommended]
→ vit mini explicit content : prithivMLmods/vit-mini-explicit-content
⤷ Building image safety-guard models :
strangerguardhf
⤷ Datasets :
→ nsfw multidomain classification : strangerguardhf/NSFW-MultiDomain-Classification
→ nsfw multidomain classification v2.0 : strangerguardhf/NSFW-MultiDomain-Classification-v2.0
⤷ Collection :
→ Updated Versions [05192025] : prithivMLmods/explicit-content-filters-682aaa4733e378561925ca2b
→ Previous Versions : prithivMLmods/siglip2-content-filters-042025-final-680fe4aa1a9d589bf2c915ff
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
⤷ Models :
→ siglip2 mini explicit content : prithivMLmods/siglip2-mini-explicit-content [recommended]
→ vit mini explicit content : prithivMLmods/vit-mini-explicit-content
⤷ Building image safety-guard models :
strangerguardhf
⤷ Datasets :
→ nsfw multidomain classification : strangerguardhf/NSFW-MultiDomain-Classification
→ nsfw multidomain classification v2.0 : strangerguardhf/NSFW-MultiDomain-Classification-v2.0
⤷ Collection :
→ Updated Versions [05192025] : prithivMLmods/explicit-content-filters-682aaa4733e378561925ca2b
→ Previous Versions : prithivMLmods/siglip2-content-filters-042025-final-680fe4aa1a9d589bf2c915ff
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
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20 days ago
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5728
🚀 ZeroGPU
Nothing too fancy for now—ZeroGPU Spaces still default to
- 💰 size-based quotas / pricing (
- 🦣 the upcoming
You can as of now control GPU size via a Space variable. Accepted values:
-
-
-
The auto mode checks total CUDA tensor size during startup:
- More than 30GB →
- Otherwise →
medium size is now available as a power-user featureNothing too fancy for now—ZeroGPU Spaces still default to
large (70GB VRAM)—but this paves the way for:- 💰 size-based quotas / pricing (
medium will offer significantly more usage than large)- 🦣 the upcoming
xlarge size (141GB VRAM)You can as of now control GPU size via a Space variable. Accepted values:
-
auto (future default)-
medium-
large (current default)The auto mode checks total CUDA tensor size during startup:
- More than 30GB →
large- Otherwise →
medium
- 3 replies
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burtenshaw's
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20 days ago
Post
3120
We're thrilled to announce the launch of our comprehensive Model Context Protocol (MCP) Course! This free program is designed to take learners from foundational understanding to practical application of MCP in AI.
Follow the course on the hub:
mcp-course
In this course, you will:
📖 Study Model Context Protocol in theory, design, and practice.
🧑💻 Learn to use established MCP SDKs and frameworks.
💾 Share your projects and explore applications created by the community.
🏆 Participate in challenges and evaluate your MCP implementations.
🎓 Earn a certificate of completion.
At the end of this course, you'll understand how MCP works and how to build your own AI applications that leverage external data and tools using the latest MCP standards.
Follow the course on the hub:
mcp-course
In this course, you will:
📖 Study Model Context Protocol in theory, design, and practice.
🧑💻 Learn to use established MCP SDKs and frameworks.
💾 Share your projects and explore applications created by the community.
🏆 Participate in challenges and evaluate your MCP implementations.
🎓 Earn a certificate of completion.
At the end of this course, you'll understand how MCP works and how to build your own AI applications that leverage external data and tools using the latest MCP standards.
- 1 reply