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Some fixes & boost(pp) for SYCL/Intel. Merged PRs: [SYCL] Flash Attention with XMX engine via oneDNN graph API (SDPA) on KV f16 for Xe2 ; Qwen3.6-27b-Q8_0 prefill speed up x1.21 at p=512 and x4.26 at p=80k #25222 sycl: Increase minimum buffer size for USM system allocations #25525 [SYCL] Support OP
Frontier models are just so good though. Fable 5... Gemini 3.1 Pro for design critique and brainstorming. Grok for verification passes. Antigravity with Gemini 3.5 Flash for rote plan execution. Opencode with Qwen 3.6 saving me when usage credits ran out and I had plans lined up for execution.
My first time doing anything like this, and I built it because I wanted it. If anyone wants the non Heretic I'll mosey that out as compute allows. Strix Halo: 122B total · 10B active · 60.70 GiB · 28.45 tok/s · BF16 · KLD 0.100716 · PP 353.3 t/s submitted by /u/RedParaglider [link] [co
Credit goes to: kdzzzds on Bilibili Someone got access to a new version of DeepSeek V4 through A/B testing, and vibe coded / one-shot this No Man's Sky and Minecraft hybrid. Here is the chatlog: https://opncd.ai/share/fnOGJyIn And the source code: https://archive.org/details/no_mans_minecraf
I've been experimenting with interpretability on Gemma-4-31B and ended up with something cool I think you guys might like: a variant that challenges a request's premise (like fabricated tools, made-up papers, wrong assumptions stated as fact) instead of confidently going along with it. Here's the ex
Over past ~10 months I've been iterating on my memory system so I can make a proper assistant, like Rick's garage from Rick and Morty. I benched my latest iteration and it scored top out of any system I know of (470/500 on LongMemEval-S), while being way more token efficient and cheaper. The e
submitted by /u/thoquz [link] [comments]
EDIT: "near fp16 precision" I intended performance in terms of benchmarks/output. Obviously 1,0,-1 cannot be fp16. Bad word choice :) EDIT 2: Now that more people have tested it reported in, consensus (and my own stuff on more doc/retrieval tasks) is this lands better than Q2 but clearly wors
dam bois we eating good this week ngl, The velocity of the open_weight ecosystem right now is hitting a point where proprietary, closed-source APIs are losing their leverage on compute intelligence. When you have DeepSeek V4 dropping native MXFP4 mixtures of experts with massive context capabi
https://preview.redd.it/b61u04bku7dh1.png?width=741&format=png&auto=webp&s=9489ac892cfe4496b4063684cbb2a0a17ebb6722 Source: From kimi test page .They down the page now submitted by /u/External_Mood4719 [link] [comments]
Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-interleaved, which is inefficient for long-horizon agentic tasks. Recently, asynchronous RL has emerged as a more eff
👋 Training my first RL model last year was super fun, now I've RL-trained a model that RL-trains other models... wild times! The agent gets a task, writes the full training job (environment, reward, dataset, hyperparameters), and submits it to real GPUs. When the model it trained scores higher
https://huggingface.co/nvidia/Nemotron-3-Embed-8B-BF16 https://huggingface.co/nvidia/Nemotron-3-Embed-1B-BF16 Nemotron-3-Embed-8B-BF16 is a versatile text embedding model trained by NVIDIA and optimized for retrieval and semantic similarity tasks. It provides strong multilingual and cross-lingual re
Hey all, I have a 4090 + 5060ti setup with 64gb ram. I have been using opencode with MiMoV2 pro and its great but I don't think spending $20 daily is financially sound decision. I heard that using qwen3.6-27b with hermes goes into loops, have any of you successfully used any open source LLM (somethi
submitted by /u/Extraaltodeus [link] [comments]
I only spent 2 hours making the bios accept the dual gpus, only 5 hours configuring VLLM to run deepseek v4 flash dspark, but totally worth it. I truly believe in the near future we will have to rely on ourselves. submitted by /u/BitXorBit [link] [comments]
There's a push by openAI and anthropic primarily. Well more than a push just straight fear mongering about open source ai and its dangers. If it's so dangerous why would the US gov. in particular continue to allow private industry to develop and release. If I had a company OpenNuke and started makin
Here model: https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V2-GGUF Settings: temperature=0.6, top_p=0.95, top_k=20, min_p=0, presence_penalty=disabled, repeat_penalty=disabled, seed=42, thinking=enabled System Prompt: You are Qwen, a large language model created by Tong
submitted by /u/computeruser420 [link] [comments]
So like many of you I realized I was creating custom tools for each one of my AI based applications and then realized alright I guess I should go forth and build a local MCP server. SOMEONE TELL ME IF IM DOING SOMETHING WRONG but it seems like permissions control for an agent has to be managed by th
UPDATE: If you are running Qwen3.6 27b or 35b with prefix caching and spec decode, you will run into corruption problems! Take a look at https://github.com/vllm-project/vllm/pull/48375; the patch there has resolved my problems on quality decrease. Thanks to /u/buttplugs4life4me for discovering the p
TLDR: I trained Qwen3.5-4B and gemma-4-12b on self-distilled, compressed reasoning traces; compression was section-aware (compute and verification spans remain, fillers/narration/transitions get dropped/compressed). The models match or beat the originals, often by a large margin, while using 2
Hey everyone. Trying to figure out the best setup for my hardware. I've got a 64GB DDR5 RAM laptop and A 20GB 7900xt egpu. Usecase is only pi-coding-agent locally. So far, Qwen3.6 35B A3B has been the only model I've found that would work well with CPU offload (MoE) at higher quants - I've managed t
The startup, PrismML, said it has shrunk down Qwen 3.6, an open-source large language model developed by Chinese internet giant Alibaba, to run on an iPhone 17 Pro. The model has 27 billion parameters, which are roughly similar to the synapses in a brain and can help determine the complexity
Like the title say, which you prefer and why and experience if any? Deepseek Flash is definitely faster, but is text only, Mimo on the other hand is not slow but is Omnimodal so i think thats is a plus submitted by /u/Hannibalj2ca [link] [comments]
Hey everyone, I recently switched from DS4 Flash to Qwen3.5-122B on my M3 Ultra Mac Studio for long-context agentic coding. While the model fit better, I hit a wall where follow-up messages took 3-5 minutes to start generating (cold fills) despite having a "warm" context. Turns out the issue wasn't
Am I missing something? It seems like some people think distillation is magic and will raise the quality of output above what the base model is actually capable of. It's especially weird to me to see all these Fable fine-tunes, because as far as I understand it, they miss the fact that the reasoning
b9978 Claude in one sentence what does this fix llama.cpp b9978 fixes a checkpoint bug that hit agentic workloads hardest: every agent turn created a new checkpoint (bypassing min-step spacing), collapsing the coverage window so that context rewinds — common in tool-calling loops — erased all chec
submitted by /u/fallingdowndizzyvr [link] [comments]
I'm currently running an Unsloth quant of Qwen3.6-35B-A3B and I've managed to speed up my output to ~80 tk/s by offloading all experts to CPU. I'm on a Legion 7i laptop, 5080 with 16 GB VRAM. I mainly use the models for Zoo (formerly Roo) integration into VSCode. I'm running into two issues that I'm