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Inkling: Thinking Machines' Open-Weights Model (and Tinker Fine-Tuning Platform)

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Related to

DeepSeek_V4.pdf · deepseek-ai/DeepSeek-V4-Pro at main Inkling and DeepSeek V4 are both large-scale open-weight Mixture-of-Experts models competing at the frontier of open LLM releases.

Qwen 3.8 2.4T Mixture of Experts Model Both are open-weights language models released on Hugging Face targeting the competitive frontier model space, representing the trend of open-source models challenging proprietary ones
Introducing Muse Glimmer: An Open Agentic Model That Runs on Your Device Both are open-weights/open-source models optimized for agentic tasks and local deployment, with Inkling also targeting fine-tuning workflows — direct competitors in the open agentic model space
Marin Both Marin and Inkling represent open-weights/open-source model initiatives that challenge closed proprietary frontier labs, with Marin going further by sharing the full training process transparently
LiquidAI/LFM2.5-2.6B · Hugging Face Both are open-weights small language models released by non-hyperscaler AI labs (Liquid AI and Thinking Machines respectively), available for community use with instruction-following capabilities
Open Athena Both Open Athena and Inkling represent pushes toward open-weights/open-source alternatives to closed frontier AI platforms.
Ornith-1.5: Self-Scaffolding to Self-Improvement Both involve open-weights models with fine-tuning/improvement platforms; Ornith-1.5's self-improvement pipeline relates to Inkling's Tinker fine-tuning approach
Neuronpedia Jacobian Lens: Interactive J-Space Explorer for Qwen3.6-27B Jacobian-based interpretability methods developed for Qwen3.6-27B could plausibly extend to analyzing internal representations of large MoE models like Inkling.
Cactus Needle 2 Both are open-weights small language models positioned as alternatives to large frontier models, emphasizing accessibility and local deployment

Challenged by

Introducing Un-0: Generating Images with Coupled Oscillators Un-0's coupled-oscillator computing paradigm directly challenges the GPU-dominated deep learning approach exemplified by large Mixture-of-Experts models like Inkling.

Supported by

Together AI: Research-Driven Inference and Training Platform Research-driven inference providers like Together AI are the likely hosting layer for open-weight releases such as Inkling.
Open Athena Open Athena's nonprofit model for producing open-weights foundation models directly parallels Inkling/Thinking Machines' open-weights approach — Open Athena could be a structural vehicle that produces models like Inkling

Type:	link
First published:	Wednesday, 15 July 2026 at 12:00 UTC
Location:	https://thinkingmachines.ai/news/introducing-inkling/
URL:	https://mattwood.fyi/i/3032f045

I'm Matt Wood, and this is For Your Information. A live list of riffs and links for you and your agent, drawn from what I'm reading, noticing, questioning, concluding, and revising.

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