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I've been thinking about this lately. The Qwen team has released several new models recently, but they appear to be holding back the 122B, 35B, 27B, and 9B versions for now. One possible reason is that these larger models performed so strongly that the team chose not to release them immediatel
Who has results for this? submitted by /u/Stock-Union6934 [link] [comments]
Sharing some testing of Qwen 3.6 27B using VLLM across the popular quants on my development system. I used llama benchy to generate the results, then fed it into an LLM to format it the tables for readibility. While NVFP4 is blazing fast, have had looping issues in copilot that I don't get with BF16
A lot of people have their own setups using pi with llama.cpp. I have my own so thought I'll share. This is the extension I and deepseek wrote: https://github.com/am17an/pi-llama-server . It allows you to do two very simple things: auto detect a llama-server running and list the models availab
Given that Alibaba went proprietary/API-only for the Qwen 3.7 Max and Plus launches back in May, do we have any rumors or roadmap for a local 9B open-weights release? In the meantime, I'm trying to figure out my next step for a small local setup. Is there any model in the ~8B to 9B class that curren
currently using Qwen3.6 35B as my main assistant model + coding agent but I think sometimes it misses basical general knowledge things, and it is more like executioner that assistant. That's why I though should I go with bigger models, But I don't want to lose speed I am on Strix Halo Having 30-40
I have an old Xeon rig with 512Gb of 4-channel DDR4 2133 memory and E5-2699v4 processor. For GPU I have GTX 1060 with 6Gb of VRAM, so I use CPU only mode. I can run GLM 5.2 with 40B active parameters in Q4_K_XL at 1.8 t/s, but as you can understand it is too slow. So I wanted to give a try to a new
I benchmarked 13 models at 65K-128K context to find out what actually matters for agentic workloads — prefill dominates everything, and KV head count beats parameter count I've been running local LLMs for agentic workflows (tool use, coding agents, RAG) and kept seeing people obsess over tg128 (toke
Not really a tutorial, but more of sharing my attempts at getting higher contexts on Q8 of Qwen3.6-27 with 32GB VRAM. Disclaimer: Not in-depth research. Crowd wisdom suggests that Qwen is more tolerant of model quantization, but my experience suggests otherwise. I have nothing quantitative to back t
deleted and reposted due to accidental inclusion of personal information in original post. Demo of an idea I had for my personal JARVIS, quickly put together with a vibe coded browser based display so I could easily see what the agent sees. Giving the agent a limited scope, a view similar to t
Brought my first 3090 and been running Qwen 27B with 200K context, couldn't be happier. I'm using the club 3090 configuration and I highly recommend it! https://github.com/noonghunna/club-3090/tree/master Thanks to the community! submitted by /u/Top_Outlandishness78 [link] [comments]
submitted by /u/johnnyApplePRNG [link] [comments]
I'm using the model with opencode and the issue is it's looping hard when reasoning. It's not a deranged babbling though, the reasoning is legit large spans of text, it just can't get outside of the loop and make a decision. So I babysit it, stop and direct it to the right path, and it mostly does o
Check it out: https://github.com/fairydreaming/llama.cpp/tree/dsv4 They are PRs #25247, #25303 (mine) and #25202 (from am17an) but I omitted some padding changes from the last one that I think are not necessary. So if it crashes for you let me know. You can now fit the antirez IQ2XXS model with 1M c
Threw together a benchmark suite (quest completion, scene endings, item/time tracking, character detection, storytelling, drafting) and ran it across 8 models people talk about a lot on here. Judged with an external LLM grader, N varies per category (shown on the chart). Overall pass rates: ge
Spent a while tuning llama.cpp for Qwen3.6 27B on a 9800X3D / 64GB / 5090 box and wanted to share the real distribution instead of just a headline number, since averages hide a lot. Ran with q8 KV cache, 192k context, MTP draft=10, spec-draft-p-min=0.5, batch/ubatch 512. Logged 6,454 samples a