Image created with gemini-2.5-flash-image with claude-sonnet-4-5. Image prompt: Photorealistic Times Square at golden hour completely overtaken by massive orange and gold Alibaba billboards featuring AI shopping interfaces, cloud computing visualizations, and smart logistics systems, with traditional Chinese design elements framing the screens, holographic packages floating through the scene, and the iconic orange Alibaba logo dominating every surface in warm sunset lighting
Very excited to see the Tinker release! @pcmoritz and I had a chance to experiment with the API. It does a nice job of providing flexibility while abstracting away GPU handling. Here’s a simple example showing how to generate synthetic data and fine tune a text to SQL model.”” / X https://x.com/robertnishihara/status/1973455582603649430
Tinker provides an abstraction layer that is the right one for post-training R&D — it’s the infrastructure I’ve always wanted. I’m excited to see what people build with it. “”Civilization advances by extending the number of important operations which we can perform without”” / X https://x.com/johnschulman2/status/1973450054238347314
A flexible API for fine-tuning LMs – Tinker by @thinkymachines Write a simple CPU-only script, and it runs your exact training loop on distributed GPUs. You can fine-tune open models like Llama and Qwen, up to large MoE (Qwen3-235B-A22B), switching them by changing only one https://x.com/TheTuringPost/status/1973827605448306883
Really excited and proud to see Qwen models are in the first batch of supported models for the tinker service! 🤩 we will continue to release great models to grow research in the community 😎 https://x.com/wzhao_nlp/status/1973603599616974970
I’ve been using Tinker at Redwood Research to RL-train long-context models like Qwen3-32B on difficult AI control tasks – specifically teaching models to write unsuspicious backdoors in code similar to the AI control paper. Early stages but seeing some interesting backdoors 👀”” / X https://x.com/ejcgan/status/1973449963259699284
It turns out that the AI jagged frontier worked as a reverse salient, a term from the history of science for a technology or process that holds back the whole system & thus a focus of development. Math & planning were reverse salients, so they have seen the most improvement. https://x.com/emollick/status/1973148208894451908
I had the chance to try @thinkymachines’ Tinker API for the past couple weeks. Some early impressions: Very hackable & lifts a lot of the LLM training burden, a great fit for researchers who want to focus on algs + data, not infra. My research is in RL, and many RL fine-tuning”” / X https://x.com/tyler_griggs_/status/1973450947218252224
Tinker is cool. If you’re a researcher/developer, tinker dramatically simplifies LLM post-training. You retain 90% of algorithmic creative control (usually related to data, loss function, the algorithm) while tinker handles the hard parts that you usually want to touch much less”” / X https://x.com/karpathy/status/1973468610917179630
🚀With early access to Tinker, we matched full-parameter SFT performance as in Goedel-Prover V2 (32B) (on the same 20% data) using LoRA + 20% of the data. 📊MiniF2F Pass@32 ≈ 81 (20% SFT). Next: full-scale training + RL. This is something that previously took a lot more effort”” / X https://x.com/chijinML/status/1973451597393883451
thinking-machines-lab/tinker-cookbook: Post-training with Tinker https://github.com/thinking-machines-lab/tinker-cookbook
[1 Oct 2025] Thinking Machines’ Tinker: LoRA based LLM fine-tuning API https://x.com/Smol_AI/status/1973622595124863044
Announcing Tinker – Thinking Machines Lab https://thinkingmachines.ai/blog/announcing-tinker/
One interesting “”fundamental”” reason for Tinker today is the rise of MoE. Whereas hackers used to deploy llama3-70B efficiently on one node, modern deployments of MoE models require large multinode deployments for efficiency. The underlying reason? Arithmetic intensity. (1/5) https://x.com/cHHillee/status/1973469947889422539
Very excited to see the Tinker release by @thinkymachines! @robertnishihara and I had a chance to experiment with the API, see https://x.com/pcmoritz/status/1973456462346424641
Tinker – Thinking Machines Lab https://thinkingmachines.ai/tinker/
Wow – Qwen3-Coder-30b AWQ (4bit) on a single 3090, 115 tokens per second. It just zero-shat Pac-Man. It’s no GLM4.5-Air – but, it runs on a single 3090! https://x.com/QuixiAI/status/1971427136977453184
Alibaba Group & partners unveil MMR1: Revolutionizing multimodal reasoning with less data! MMR1 introduces Variance-Aware Sampling (VAS) for stable RL fine-tuning. Tackles unstable optimization & scarce high-quality data. Releasing massive open datasets (~1.6M CoT, 15k RL QA) & https://x.com/HuggingPapers/status/1971487864807469236
AMD is using Cline as their coding agent for local models. After testing 20+ models, they found what actually works: > 32GB RAM: Qwen3-Coder 30B (4-bit) > 64GB RAM: Qwen3-Coder 30B (8-bit) > 128GB+ RAM: GLM-4.5-Air 10-minute setup with @lmstudio + Cline, linked below”” / X https://x.com/cline/status/1973035211379310708
Alibaba bets big on AI with Nvidia tie-up, new data center plans https://interestingengineering.com/culture/alibaba-nvidia-ai-partnership-expansion
People often ask about the relationships between the different Qwen models and the logic behind their naming. Currently, we have several distinct model families: LLM, Coder, VL, Omni, and a newer addition, Image. While our long-term goal is to unify them into a single, truly”” / X https://x.com/JustinLin610/status/1973974975976808808
Qwen-Image-2509 improves consistency ✨ Hope you enjoy it! 🤝 Thanks @drawthingsapp for showcasing it so well!”” / X https://x.com/Alibaba_Qwen/status/1973668568412856595
Prime-rl has now extensive support for MoE both for RL and SFT, we have been training 100B+ model with it We have support for: * Qwen3 a3-30b * GLM series and Moonlight * adding gpt oss series as we speak we end up rewriting most of the modelling code to make it works with”” / X https://x.com/samsja19/status/1973624615768674612
Proud to see Qwen in the first wave of supported models — and thrilled to empower researchers and developers with flexible, accessible fine-tuning tools. We’ll continue releasing powerful open models to support research, innovation, and collaboration across the community. 💡🤝 https://x.com/Alibaba_Qwen/status/1973665010615218421
still experimenting with LoRA based on the @thinkymachines configuration and just implemented it in colab. In this notebook I set up a fine tune of Qwen/Qwen3-0.6B on the OpenR1-Math dataset with lora rank of 1. with this setup you can get the same reward accuracy as full https://x.com/ben_burtenshaw/status/1973776491843297386
21tok/s for qwen 30b/a3b on cpu, 4tok/s on qwen 232b moe too. MoE might be like.. excellent for CPU inference? About to try GLM 4.6″” / X https://x.com/Teknium1/status/1974039942751006816
💫 Introducing RLP: Reinforcement Learning Pretraining—information-driven, verifier-free objective that teaches models to think before they predict 🔥+19% vs BASE on Qwen3-1.7B 🚀+35% vs BASE on Nemotron-Nano-12B 📄Paper: https://x.com/shrimai_/status/1973113867455832096
You can just run qwen3-coder on a macbook w/ @lmstudio”” / X https://x.com/cline/status/1971591597080064121
Qwen3 VL 235B works surprisingly well and is 10x cheaper than Sonnet”” / X https://x.com/scaling01/status/1973777774121984175
🎨Qwen-Image-Edit-2509 just redefined architectural visualization! 🏙️From sketch to stunning reality — seamlessly transfer ANY style onto buildings & cityscapes. → Traditional Chinese patterns? Done. → Photorealistic urban render? Perfect. AI doesn’t just edit images — it https://x.com/Alibaba_Qwen/status/1972558448626987264
Just dropped FP8-quantized version of Qwen/Qwen3-VL-235B-A22B-Instruct. It cuts disk size and GPU memory needs by about 50%, while maintaining >99.6% accuracy. https://x.com/RedHat_AI/status/1973932224400798163
Cool post on integrating Qwen3 Coder + Cline + LM Studio for high-quality local coding: https://x.com/awnihannun/status/1971603427131351218
