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burtenshawย
updated
a
dataset
4 minutes ago
Jofthomasย
updated
a
dataset
8 minutes ago
sergiopaniegoย
updated
2
datasets
about 2 hours ago
sergiopaniegoย
posted
an
update
about 14 hours ago
Post
131
Loved this paper! โฅ๏ธ
Authors benchmark multimodal models on vision tasks (detection, segmentation...) using clever prompting tricks.
๐ Results: VLMs are solid generalists but still lag behind SOTA task-specific models โ especially on geometric tasks vs. semantic ones.
paper: How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks (2507.01955)
Authors benchmark multimodal models on vision tasks (detection, segmentation...) using clever prompting tricks.
๐ Results: VLMs are solid generalists but still lag behind SOTA task-specific models โ especially on geometric tasks vs. semantic ones.
paper: How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks (2507.01955)
sergiopaniegoย
posted
an
update
about 15 hours ago
Post
92
You can already play with two of the latest most impressive models on HF via
@novita-ai
as Inference Provider ๐จ
๐ Kimi K2: 1T params model, MoE beast for coding, reasoning and agentic tasks
๐ฎ GLM-4.1V-9B-Thinking: VLM + deep reasoning model
Kimi K2: moonshotai/Kimi-K2-Instruct
GLM-4.1V-9B-Thinking: THUDM/GLM-4.1V-9B-Thinking
๐ Kimi K2: 1T params model, MoE beast for coding, reasoning and agentic tasks
๐ฎ GLM-4.1V-9B-Thinking: VLM + deep reasoning model
Kimi K2: moonshotai/Kimi-K2-Instruct
GLM-4.1V-9B-Thinking: THUDM/GLM-4.1V-9B-Thinking
sergiopaniegoย
posted
an
update
about 21 hours ago
sergiopaniegoย
posted
an
update
6 days ago
Post
1492
Test SmolLM3, the newest fully open model released by
@HuggingFaceTB
!
It's smol (3B), multilingual (6 languages), comes with dual mode reasoning (think/no_think modes) and supports long-context (128k).
Try it now in the notebook below!! โฌ๏ธ
Colab notebook: https://colab.research.google.com/github/sergiopaniego/samples/blob/main/smollm3_3b_inference.ipynb
notebook: https://github.com/sergiopaniego/samples/blob/main/smollm3_3b_inference.ipynb
blog: https://huggingface.co/blog/smollm3
It's smol (3B), multilingual (6 languages), comes with dual mode reasoning (think/no_think modes) and supports long-context (128k).
Try it now in the notebook below!! โฌ๏ธ
Colab notebook: https://colab.research.google.com/github/sergiopaniego/samples/blob/main/smollm3_3b_inference.ipynb
notebook: https://github.com/sergiopaniego/samples/blob/main/smollm3_3b_inference.ipynb
blog: https://huggingface.co/blog/smollm3
sergiopaniegoย
posted
an
update
12 days ago
Post
1942
Updated my HF Space for vibe testing smol VLMs on object detection, visual grounding, keypoint detection & counting! ๐
๐ Compare Qwen2.5 VL 3B vs Moondream 2B side-by-side with annotated images & text outputs.
Try examples or test your own images! ๐
๐ฑSpace: sergiopaniego/vlm_object_understanding
๐ Compare Qwen2.5 VL 3B vs Moondream 2B side-by-side with annotated images & text outputs.
Try examples or test your own images! ๐
๐ฑSpace: sergiopaniego/vlm_object_understanding
burtenshawย
posted
an
update
14 days ago
Post
2736
Inference for generative ai models looks like a mine field, but thereโs a simple protocol for picking the best inference:
๐ 95% of users >> If youโre using open (large) models and need fast online inference, then use Inference providers on auto mode, and let it choose the best provider for the model. https://huggingface.co/docs/inference-providers/index
๐ท fine-tuners/ bespoke >> If youโve got custom setups, use Inference Endpoints to define a configuration from AWS, Azure, GCP. https://endpoints.huggingface.co/
๐ฆซ Locals >> If youโre trying to stretch everything you can out of a server or local machine, use Llama.cpp, Jan, LMStudio or vLLM. https://huggingface.co/settings/local-apps#local-apps
๐ช Browsers >> If you need open models running right here in the browser, use transformers.js. https://github.com/huggingface/transformers.js
Let me know what youโre using, and if you think itโs more complex than this.
๐ 95% of users >> If youโre using open (large) models and need fast online inference, then use Inference providers on auto mode, and let it choose the best provider for the model. https://huggingface.co/docs/inference-providers/index
๐ท fine-tuners/ bespoke >> If youโve got custom setups, use Inference Endpoints to define a configuration from AWS, Azure, GCP. https://endpoints.huggingface.co/
๐ฆซ Locals >> If youโre trying to stretch everything you can out of a server or local machine, use Llama.cpp, Jan, LMStudio or vLLM. https://huggingface.co/settings/local-apps#local-apps
๐ช Browsers >> If you need open models running right here in the browser, use transformers.js. https://github.com/huggingface/transformers.js
Let me know what youโre using, and if you think itโs more complex than this.
sergiopaniegoย
posted
an
update
15 days ago
Post
1028
๐ฃ CALL FOR CONTRIBUTORS! ๐ฃ
Following last weekโs full release of Gemma 3n, we launched a dedicated recipes repo to explore and share use cases. We already added some! ๐งโ๐ณ
Now weโre inviting the community to contribute and showcase how these models shine! โจ
Let them cook.
Check it out: https://github.com/huggingface/huggingface-gemma-recipes/issues/4
Following last weekโs full release of Gemma 3n, we launched a dedicated recipes repo to explore and share use cases. We already added some! ๐งโ๐ณ
Now weโre inviting the community to contribute and showcase how these models shine! โจ
Let them cook.
Check it out: https://github.com/huggingface/huggingface-gemma-recipes/issues/4
- 1 reply
thomwolfย
authored
a
paper
18 days ago
sergiopaniegoย
posted
an
update
22 days ago
Post
449
One of my favorite perks of the Hugging Face Pro plan: โจDev modeโจ
Connect your HF Space to VS Code and just build โ with hot reload out of the box.
Game changer for fast prototyping. ๐ป
Google Colab made AI accessible. Now HF Spaces are doing it too! ๐
๐ก New Hugging Face pricing: http://hf.co/pricing
๐ก More details: https://huggingface.co/learn/cookbook/en/enterprise_cookbook_dev_spaces
Connect your HF Space to VS Code and just build โ with hot reload out of the box.
Game changer for fast prototyping. ๐ป
Google Colab made AI accessible. Now HF Spaces are doing it too! ๐
๐ก New Hugging Face pricing: http://hf.co/pricing
๐ก More details: https://huggingface.co/learn/cookbook/en/enterprise_cookbook_dev_spaces
burtenshawย
posted
an
update
29 days ago
Post
942
You don't need remote APIs for a coding copliot, or the MCP Course! Set up a fully local IDE with MCP integration using Continue. In this tutorial Continue guides you through setting it up.
This is what you need to do to take control of your copilot:
1. Get the Continue extension from the [VS Code marketplace](https://marketplace.visualstudio.com/items?itemName=Continue.continue) to serve as the AI coding assistant.
2. Serve the model with an OpenAI compatible server in Llama.cpp / LmStudio/ etc.
3. Create a
4. Create a
Check out the full tutorial in the [the MCP course](https://huggingface.co/learn/mcp-course/unit2/continue-client)
This is what you need to do to take control of your copilot:
1. Get the Continue extension from the [VS Code marketplace](https://marketplace.visualstudio.com/items?itemName=Continue.continue) to serve as the AI coding assistant.
2. Serve the model with an OpenAI compatible server in Llama.cpp / LmStudio/ etc.
llama-server -hf unsloth/Devstral-Small-2505-GGUF:Q4_K_M3. Create a
.continue/models/llama-max.yaml file in your project to tell Continue how to use the local Ollama model.name: Llama.cpp model
version: 0.0.1
schema: v1
models:
- provider: llama.cpp
model: unsloth/Devstral-Small-2505-GGUF
apiBase: http://localhost:8080
defaultCompletionOptions:
contextLength: 8192
# Adjust based on the model
name: Llama.cpp Devstral-Small
roles:
- chat
- edit4. Create a
.continue/mcpServers/playwright-mcp.yaml file to integrate a tool, like the Playwright browser automation tool, with your assistant.name: Playwright mcpServer
version: 0.0.1
schema: v1
mcpServers:
- name: Browser search
command: npx
args:
- "@playwright/mcp@latest"Check out the full tutorial in the [the MCP course](https://huggingface.co/learn/mcp-course/unit2/continue-client)
- 1 reply
burtenshawย
posted
an
update
about 1 month ago
Post
1619
Brand new MCP Course has units are out, and now it's getting REAL! We've collaborated with Anthropic to dive deep into production ready and autonomous agents using MCP
๐
mcp-course
This is what the new material covers and includes:
- Use Claude Code to build an autonomous PR agent
- Integrate your agent with Slack and Github to integrate it with you Team
- Get certified on your use case and share with the community
- Build an autonomous PR cleanup agent on the Hugging Face hub and deploy it with spaces
The material goes deep into these problems and helps you to build applications that work. Weโre super excited to see what you build with it.
๐
mcp-course
This is what the new material covers and includes:
- Use Claude Code to build an autonomous PR agent
- Integrate your agent with Slack and Github to integrate it with you Team
- Get certified on your use case and share with the community
- Build an autonomous PR cleanup agent on the Hugging Face hub and deploy it with spaces
The material goes deep into these problems and helps you to build applications that work. Weโre super excited to see what you build with it.
burtenshawย
posted
an
update
about 1 month ago
Post
1518
Super excited to release Autotrain MCP. This is an MCP server for training AI models, so you can use your AI tools to train your AI models ๐คฏ.
๐ burtenshaw/autotrain-mcp
Use this MCP server with tools like Claude Desktop, Cursor, VSCode, or Continue to do this:
- Define an ML problem like Image Classification, LLM fine-tuning, Text Classification, etc.
- The AI can retrieve models and datasets from the hub using the hub MCP.
- Training happens on a Hugging Face space, so no worries about hardware restraints.
- Models are pushed to the hub to be used inference tools like Llama.cpp, vLLM, MLX, etc.
- Built on top of the AutoTrain library, so it has full integration with transformers and other libraries.
Everything is still under active development, but Iโm super excited to hear what people build, and Iโm open to contributions!
๐ burtenshaw/autotrain-mcp
Use this MCP server with tools like Claude Desktop, Cursor, VSCode, or Continue to do this:
- Define an ML problem like Image Classification, LLM fine-tuning, Text Classification, etc.
- The AI can retrieve models and datasets from the hub using the hub MCP.
- Training happens on a Hugging Face space, so no worries about hardware restraints.
- Models are pushed to the hub to be used inference tools like Llama.cpp, vLLM, MLX, etc.
- Built on top of the AutoTrain library, so it has full integration with transformers and other libraries.
Everything is still under active development, but Iโm super excited to hear what people build, and Iโm open to contributions!
- 1 reply
thomwolfย
authored
a
paper
about 1 month ago
burtenshawย
posted
an
update
about 2 months ago
Post
2644
MCP course is now LIVE! We just dropped quizzes, videos, and live streams to make it a fully interactive course:
๐ join in now:
mcp-course
- Itโs still free!
- Video 1 walks you through onboarding to the course
- The first live session is next week!
- You can now get a certificate via exam app
- We improved and written material with interactive quizzes
If youโre studying MCP and want a live, interactive, visual, certified course, then join us on the hub!
๐ join in now:
mcp-course
- Itโs still free!
- Video 1 walks you through onboarding to the course
- The first live session is next week!
- You can now get a certificate via exam app
- We improved and written material with interactive quizzes
If youโre studying MCP and want a live, interactive, visual, certified course, then join us on the hub!
Post
3505
Meet our new agentic model : ๐๐ฒ๐๐๐๐ฟ๐ฎ๐น
Devstral is an open-source LLM built software engineering tasks built under a collaboration between Mistral AI and All Hands AI ๐.
๐๐ฒ๐ ๐ณ๐ฒ๐ฎ๐๐๐ฟ๐ฒ๐ :
โข ๐ค ๐๐ด๐ฒ๐ป๐๐ : perfect for Agentic coding
โข ๐ ๐น๐ถ๐ด๐ต๐๐๐ฒ๐ถ๐ด๐ต๐: Devstral is a ๐ฎ๐ฐ๐ parameter based on Mistral small.
โข ยฉ๏ธ ๐๐ฝ๐ฎ๐ฐ๐ต๐ฒ ๐ฎ.๐ฌ, meaning fully open-source !
โข ๐ A ๐ญ๐ฎ๐ด๐ธ context window.
๐Blog : https://mistral.ai/news/devstral
โกAPI : The model is also available on our API under the name ๐ฑ๐ฒ๐๐๐๐ฟ๐ฎ๐น-๐๐บ๐ฎ๐น๐น-๐ฎ๐ฑ๐ฌ๐ฑ
๐ค repo : mistralai/Devstral-Small-2505
Can't wait to see what you will build with it !
Devstral is an open-source LLM built software engineering tasks built under a collaboration between Mistral AI and All Hands AI ๐.
๐๐ฒ๐ ๐ณ๐ฒ๐ฎ๐๐๐ฟ๐ฒ๐ :
โข ๐ค ๐๐ด๐ฒ๐ป๐๐ : perfect for Agentic coding
โข ๐ ๐น๐ถ๐ด๐ต๐๐๐ฒ๐ถ๐ด๐ต๐: Devstral is a ๐ฎ๐ฐ๐ parameter based on Mistral small.
โข ยฉ๏ธ ๐๐ฝ๐ฎ๐ฐ๐ต๐ฒ ๐ฎ.๐ฌ, meaning fully open-source !
โข ๐ A ๐ญ๐ฎ๐ด๐ธ context window.
๐Blog : https://mistral.ai/news/devstral
โกAPI : The model is also available on our API under the name ๐ฑ๐ฒ๐๐๐๐ฟ๐ฎ๐น-๐๐บ๐ฎ๐น๐น-๐ฎ๐ฑ๐ฌ๐ฑ
๐ค repo : mistralai/Devstral-Small-2505
Can't wait to see what you will build with it !
- 1 reply
burtenshawย
posted
an
update
about 2 months ago
Post
3240
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:
๐ Study Model Context Protocol in theory, design, and practice.
๐งโ๐ป Learn to use established MCP SDKs and frameworks.
๐พ Share your projects and explore applications created by the community.
๐ Participate in challenges and evaluate your MCP implementations.
๐ 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:
๐ Study Model Context Protocol in theory, design, and practice.
๐งโ๐ป Learn to use established MCP SDKs and frameworks.
๐พ Share your projects and explore applications created by the community.
๐ Participate in challenges and evaluate your MCP implementations.
๐ 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