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model roundup

Qwen 3.8

14 items · started 2026-09-09 · ongoing (last activity 2026-09-18)

  1. Keeping vLLM's Prefix Cache Warm Between Agent Turns From 55% to 95% cached 🔗 I’ve been playing with a few different ways to host Qwen3.8 locally. I’m aiming for something that can replace Claude Code for most of my tasks.

  2. ShapeLearn-Lite Held Up. ShapeLearn Did Better: Qwen 3.8 27B We were a little impatient.

  3. Qwen Studio offers comprehensive functionality spanning chatbot, image and video understanding, image generation, document processing, web search integration, tool utilization, and artifacts.

  4. What are your own experiences on this? What mac/specs do you have?

  5. Hi everybody, we post-trained Qwen 3.8 27B to be more efficient by figuring out which tokens were linked to overthinking and penalizing them without "attacking" the reasoning length directly then fixed the accuracy with a bit of secret sau…

  6. Operating System powered by Qwen 3.8 27B at 1950 tokens/sec! here is what 1,950 tokens/second @Alibaba_Qwen's 3.8 27b actually looks like on @cerebras: i wrote a minimal python web server that turns cerebras inference into a live operatin…

  7. AIC Evals Verified benchmark artifacts, model patches, and integrity receipts for AIC, a closed-source, host-enforced AI software engineering runtime. This repository publishes inspectable evidence from selected AIC evaluation runs.

  8. Tuning a Local Coding Agent: Oh My Pi and Qwen3.8-27B on Two RTX 3090s Running a coding agent at home 🔗 I’ve started to use Oh My Pi , usually called omp, a fork of the open-source Pi coding agent, with Qwen3.8-27B, an open-weights model t…

  9. Qwen3.8 Flash Next is now supported in DwarfStar, covering 64GB Mac systems very well and with very fast inference of 50~70 t/s and > 1400 t/s prefill. For now this is Metal only.

  10. GVS5H: Five Qwen3.8-27B Models Match Claude Fable 5 on LiveCodeBench Hard Results Abstract. Frontier coding performance is typically bought with larger proprietary models at high cost.

  11. We post-train Qwen3.8-27B for Korean response style -- verbosity, list and markdown usage, discourse structure and register -- and measure two behaviours the objective never targets: abstention on ambiguous social questions in KoBBQ, where…

  12. I build tools for AI and LLM agents — orchestration, sandboxes, and the data pipelines that feed them — with a taste for what happens under the hood in Python, Rust, and Linux. My background is in Backend and AI/Data engineering, shaped by…

  13. A follow-up to Reasoning prefills on a few open models and Stolen Thoughts This v1.1 reruns the reasoning-prefill experiment with GPT-5.5 Pro as the teacher. For each problem, I generated two responses from each target model: - an ordinary…

  14. SwarmLLM Every device brings a slice. Together they run the whole model.

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