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cfahlgren1ย  submitted a paper about 2 months ago
From AGI to ASI
cfahlgren1ย  submitted a paper 6 months ago
How AI Impacts Skill Formation
ariG23498ย  authored a paper 10 months ago
FineVision: Open Data Is All You Need
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pcuenqย 
posted an update 7 months ago
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5663
๐Ÿ‘‰ What happened in AI in 2025? ๐Ÿ‘ˆ

We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!

Play with it here:
2025-ai-timeline/2025-ai-timeline

Here's my personal quarterly TL;DR:

1๏ธโƒฃ Q1 โ€” Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.

Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)

2๏ธโƒฃ Q2 โ€” Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.

Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4

3๏ธโƒฃ Q3 โ€” "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.

Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5

4๏ธโƒฃ Q4 โ€” Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!

Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 ๐Ÿคฏ

Credits
๐Ÿ™ NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline

๐Ÿซก @reach-vb for the original idea, design and recipe

๐Ÿ™Œ @ariG23498 and yours truly for compiling and verifying the 2025 edition

๐Ÿฅณ Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! ๐Ÿฅ‚
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ariG23498ย 
posted an update 11 months ago
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2695
New post is live!

This time we cover some major updates to transformers.

๐Ÿค—
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ariG23498ย 
posted an update about 1 year ago
cfahlgren1ย 
posted an update about 1 year ago
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1345
I ran the Anthropic Misalignment Framework for a few top models and added it to a dataset: cfahlgren1/anthropic-agentic-misalignment-results

You can read the reasoning traces of the models trying to blackmail the user and perform other actions. It's very interesting!!

ariG23498ย 
posted an update about 1 year ago
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1810
๐Ÿšจ Implement KV Cache from scratch in pure PyTorch. ๐Ÿšจ

We have documented all of our learning while implementing KV Cache to nanoVLM. Joint work with @kashif @lusxvr @andito @pcuenq

Blog: hf.co/blog/kv-cache
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cfahlgren1ย 
posted an update about 1 year ago
cfahlgren1ย 
posted an update about 1 year ago
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1745
Yesterday, we dropped a new conversational viewer for datasets on the hub! ๐Ÿ’ฌ

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:
โ€ข mlabonne/FineTome-100k
โ€ข Salesforce/APIGen-MT-5k
โ€ข open-thoughts/OpenThoughts2-1M
โ€ข allenai/tulu-3-sft-mixture

Any other good ones?
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cfahlgren1ย 
posted an update over 1 year ago
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2358
If you haven't seen yet, we just released Inference Providers ๐Ÿ”€

> 4 new serverless inference providers on the Hub ๐Ÿคฏ
> Use your HF API key or personal key with all providers ๐Ÿ”‘
> Chat with Deepseek R1, V3, and more on HF Hub ๐Ÿ‹
> We support Sambanova, TogetherAI, Replicate, and Fal.ai ๐Ÿ’ช

Best of all, we don't charge any markup on top of the provider ๐Ÿซฐ Have you tried it out yet? HF Pro accounts get $2 of free usage for the provider inference.
ariG23498ย 
posted an update over 1 year ago
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2865
Tried my hand at simplifying the derivations of Direct Preference Optimization.

I cover how one can reformulate RLHF into DPO. The idea of implicit reward modeling is chef's kiss.

Blog: https://huggingface.co/blog/ariG23498/rlhf-to-dpo
ariG23498ย 
posted an update over 1 year ago
cfahlgren1ย 
posted an update over 1 year ago
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1797
Wow, I just added Langfuse tracing to the Deepseek Artifacts app and it's really nice ๐Ÿ”ฅ

It allows me to visualize and track more things along with the cfahlgren1/react-code-instructions dataset.

It was just added as a one click Docker Space template, so it's super easy to self host ๐Ÿ’ช
cfahlgren1ย 
posted an update over 1 year ago
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2285
You'll notice the AI in the SQL Console is much better at working with chatml conversations:

Here's example of unnesting the cfahlgren1/react-code-instructions in less than 10 seconds by asking it. Check it out here: cfahlgren1/react-code-instructions

- "show me the average assistant response length"
- "extract user, system, and assistant messages into separate columns"

It's super easy to work with conversational datasets now with natural language ๐Ÿ—ฃ๏ธ





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cfahlgren1ย 
posted an update over 1 year ago
ariG23498ย 
posted an update over 1 year ago
cfahlgren1ย 
posted an update over 1 year ago
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You can just ask things ๐Ÿ—ฃ๏ธ

"show me messages in the coding category that are in the top 10% of reward model scores"

Download really high quality instructions from the Llama3.1 405B synthetic dataset ๐Ÿ”ฅ

argilla/magpie-ultra-v1.0