Hugging Face

Hugging Face (Twitter) RT @ariG23498: Hugging Face blogs will now feature articles from Team and Enterprise subs with 30+ seats! 🤩 This has been a proven source of impact and visibility for model releases! If you 🫵🏻 are from such a company reading this, bookmark this and use it.

Hugging Face (Twitter) RT @ClementDelangue: I don't think there's a more diverse and international platform in AI than @huggingface! Current trending models are coming from all over the world in all sorts of modalities & sizes. That is AI maturing at the speed of light!

Hugging Face (Twitter) RT @novita_labs: 🤗 Give GLM‑4.6V a try on @huggingface , supported by Novita.

Hugging Face (Twitter) RT @ClementDelangue: 13 secs for 5 GB between @huggingface & @googlecloud thanks to our new collaboration 🤯🤯🤯

Hugging Face (Twitter) RT @gordic_aleksa: sweet we're trending on @huggingface ! ^^

Hugging Face (Twitter) RT @ailozovskaya: Reachy Mini is my new gaming buddy 😁

Hugging Face (Twitter) RT @AdinaYakup: Great LoRA by @dx8152🔥

‌Hugging Face (Twitter) RT @_akhaliq: GLM-4.6V-Flash is out https://huggingface.co/zai-org/GLM-4.6V-Flash

Hugging Face (Twitter) RT @Tu7uruu: Just dropped on HF: YODAS2-Sido a multilingual, massive-scale speech dataset. > 67+ languages with balanced speaker diversity > High-quality, natural conversational audio > Ideal for ASR, TTS, speech-to-speech, and audio agents > Clean annotations with ready-to-train splits > Strong fit for multimodal LLM alignment work You can easily load it with Hugging Face’s datasets library!

Hugging Face (Twitter) RT @akshay_pachaar: HuggingFace just made fine-tuning 10x easier! One line of English to fine-tune any open-source LLM. They released a new "skill" you can plug into Claude or any coding agent. It doesn't just write training scripts, but actually submits jobs to cloud GPUs, monitors progress, and pushes finished models to the Hub. Here's how it works: You say something like: "Fine-tune Qwen3-0.6B on the open-r1/codeforces-cots dataset" And Claude will: ↳ Validate your dataset format ↳ Select appropriate GPU hardware ↳ Submit the job to Hugging Face Jobs ↳ Monitor training progress ↳ Push the finished model to the Hub The model trains on Hugging Face GPUs while you do other things. When it's done, your fine-tuned model appears on the Hub, ready to use. This isn't a toy demo. The skill supports production training methods: SFT, DPO, and GRPO. You can train models from 0.5B to 70B parameters, convert them to GGUF for local deployment, and run... Перейти на оригинальный пост

Hugging Face (Twitter) RT @Weyaxi: I’m thrilled to announce that I’ve officially joined the @huggingface Fellows program! 🚀 From building community leaderboards to pushing the boundaries of LLM fine-tuning, I can't wait to do even more for the open-source ecosystem. Let’s build! 🦾

Hugging Face (Twitter) RT @Zai_org: GLM-4.6V Series is here🚀 - GLM-4.6V (106B): flagship vision-language model with 128K context - GLM-4.6V-Flash (9B): ultra-fast, lightweight version for local and low-latency workloads First-ever native Function Calling in the GLM vision model family Weights: http://huggingface.co/collections/zai-org/glm-46v Try GLM-4.6V now: chat.z.ai API: http://docs.z.ai/guides/vlm/glm-4.6v Tech Blog: z.ai/blog/glm-4.6v API Pricing (per 1M tokens): - GLM-4.6V: $0.6 input / $0.9 output - GLM-4.6V-Flash: Free