𓁟 host1.bitchan.one

𓀐 @4t-8BqSQ: /lmg/ - a general dedicated to the discussion and development of local language models. News >Claude BANS recomp! >https://www.youtube.com/watch?v=GlRVbIdc_Mk News Archive: https://rentry.org/lmg-news-archive Links: https://rentry.org/LocalModelsLinks Official /lmg/ card: https://files.catbox.moe/cbclyf.png Getting Started https://rentry.org/lmg-lazy-getting-started-guide https://rentry.org/lmg-build-guides https://rentry.org/IsolatedLinuxWebService https://rentry.org/recommended-models https://rentry.org/samplers https://rentry.org/MikupadIntroGuide Further Learning https://rentry.org/machine-learning-roadmap https://rentry.org/llm-training https://rentry.org/LocalModelsPapers Benchmarks LiveBench: https://livebench.ai Programming: https://swe-rebench.com Agentic Coding: https://deepswe.datacurve.ai Context Length: https://github.com/RecapAnon/NoLiMa GPUs: https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference Tools GGUF VRAM Calculator: https://hf.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator Sampler Visualizer: https://artefact2.github.io/llm-sampling Token Speed Visualizer: https://shir-man.com/tokens-per-second Text Gen. UI, Inference Engines https://github.com/lmg-anon/mikupad https://github.com/oobabooga/text-generation-webui https://github.com/LostRuins/koboldcpp https://github.com/ggerganov/llama.cpp https://github.com/theroyallab/tabbyAPI https://github.com/vllm-project/vllm https://rentry.org/custom-uis

/lmg/ - a general dedicated to the discussion and development of local language models. News >(10/29) Mistral Large 4 (Le Chonk) 1T-A52B released: https://mistral.ai/news/mistral-large-4-oss >(10/27) NVidia releases Nemotron 3.5 Transcribe 30B-A4B: https://hf.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B >(10/26) XHToken releases Spark-X2.6-1.7B: https://hf.co/XHToken/Spark-X2.6-1.7B >(10/26) LiquidAI releases LFM3 1.7B and 2B: https://hf.co/collections/LiquidAI/lfm3 News Archive: https://rentry.org/lmg-news-archive Links: https://rentry.org/LocalModelsLinks Official /lmg/ card: https://files.catbox.moe/cbclyf.png Getting Started https://rentry.org/lmg-lazy-getting-started-guide https://rentry.org/lmg-build-guides https://rentry.org/IsolatedLinuxWebService https://rentry.org/recommended-models https://rentry.org/samplers https://rentry.org/MikupadIntroGuide Further Learning https://rentry.org/machine-learning-roadmap https://rentry.org/llm-training https://rentry.org/LocalModelsPapers Benchmarks LiveBench: https://livebench.ai Programming: https://swe-rebench.com Agentic Coding: https://deepswe.datacurve.ai Context Length: https://github.com/RecapAnon/NoLiMa GPUs: https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference Tools GGUF VRAM Calculator: https://hf.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator Sampler Visualizer: https://artefact2.github.io/llm-sampling Token Speed Visualizer: https://shir-man.com/tokens-per-second Text Gen. UI, Inference Engines https://github.com/lmg-anon/mikupad https://github.com/oobabooga/text-generation-webui https://github.com/LostRuins/koboldcpp https://github.com/ggerganov/llama.cpp https://github.com/theroyallab/tabbyAPI https://github.com/vllm-project/vllm https://rentry.org/custom-uis

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𓀐 @RNrmh~8d

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𓀐 @xf7QntmK: Mikulove+

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𓀐 @DRkh8jFT: Any /lmg/oers that have dabbled in AI security systems? I found https://github.com/beenuar/AiSOC but it seems really slopped(pre baked with ollama for example). Would be nice to have a dynamic anti malware system for these trying times.+

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𓀐 @RZ-_~G_R: What I would expect from nature based on how it likes to troll, P=NP but it's a galactic algorithm so it doesn't matter.+

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𓀐 @PuVw=ua4: https://www.youtube.com/watch?v=Hm7YHD3E-Qo How do I do this with Gemma?+

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𓀐 @AX?+=-5V: I'm getting pegged right now+

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𓀐 @Biy9habr: Recent Highlights from the --Evaluating AMD MI25 and MI100 GPUs for budget high-VRAM setups: >110212880 >110212900 >110212951 >110213251 >110213290 >110213357 >110214084 >110214321 >110215204 >110215246 >110215516 >110213445 >110213336 --Hardware for AI escapism and the technical nature of image generation: >110212972 >110212995 >110213003 >110213012 >110213096 >110213220 >110213027 >110213036 >110213047 >110213059 >110213064 >110214789 >110214809 >110213082 >110213242 >110213261 >110213476 >110213503 >110213511 >110213615 >110213677 >110213882 --Reward hacking and erratic agentic behavior in DeepSeek 4.1: >110216419 >110216433 >110216458 >110216489 >110216540 --Hardware acquisition strategies amid supply shortages and AI infrastructure debt: >110216175 >110216202 >110216223 >110216252 >110216267 >110216321 >110216355 >110216227 --Using reasoning prefilling to bypass model safety filters: >110215910 >110215916 >110215918 >110215942 >110215966 >110215970 >110216040 >110215953 >110215967 --DeepSeek's autonomous agent prototype and thoughts on micro-managing reasoning: >110214741 >110214770 >110214842 >110214859 >110215156 >110214875 --SillyTavern domain compromise and subsequent discussion on alternative frontends: >110215402 >110215420 >110215429 >110215485 >110215715 >110215787 >110215811 --Claude's self-assessment technique for internal safety evaluations: >110214018 >110214090 >110215296 >110214617 >110215373 >110215391 >110215405 >110215601 --Exllamav3 v1.6.6 release and DeepSeek-V4.1-Flash exl3 quants: >110216176 >110216185 --Logs: >110213604 >110215173 >110215267 --Gemma, Miku (free space): >110212614 >110212681 >110212757 >110212995 >110213047 >110213106 >110213193 >110213242 >110213361 >110213528 >110213598 >110213926 >110214382 >110214789 >110215991 >110216550 Recent Highlight Posts from the Why?: Enable Links: https://rentry.org/lmg-recap-script+

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𓀐 @yXYFcn-F: guys I am freaking out over this 3k 5090, I found a post saying they actually ship, why does this shit appear whenever I'm not ready+ 1

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𓀐 @KrYA-d-S: Remember to discuss hardening your systems with your LLM, very important to close the hatches for the exploit storm we are going through. I'm so fucking close to throwing my browser in a VM with an agent told to be extremely paranoid.+

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𓀐 @cKDDNzHk: Absolute noob here. I just started playing with local models and want to understand what I can do better. I made a simple mod to 4chan XT that shows the post's comment beside the image in the gallery, pic related. On Opus 5.5 (medium) I just gave it 4chanXT and this prompt: >I want to modify the gallery function of this script to add a "Gallery with comments" option, the objective is that there will be a portion of the screen dedicated to show the comment assigned to the image that is currently being displayed and that it will also allow me to click on the quotes (with working backlink function), this comment function should be on the right side of the gallery, right to the left of the section that shows all the thumbnails at once in scrollable format. You can take 20% of the screen for this function and also allow the width to be adjusted by dragging Got it working first try, took 30 minutes Then I tried the same thing locally with Qwen3.8 27B, and it took me about 4 hours. With the original prompt it kept exploring the 26k-line file, overflowing the context and never finishing the planning phase. I only got it done by asking claude to build a detailed step-by-step plan with line numbers and making it stop after each step for testing. This is the plan I fed to Cline: https://pastebin.com/RsTkGwQw Setup: - 4090, 9800X3D, 64GB DDR5 - 32K context Problems I ran into: - While planning Cline kept sending requests bigger than the context (80K+ tokens) even with 32K set, then truncated history and lost what it had read - The model kept wandering into unrelated parts of the code instead of editing Questions: - Is Cline the right tool, or should I try something else? - Any tips to get closer to the "one prompt and done" experience, or is the gap vs Opus just too big for now? Both scripts in case anyone wants to test them: local: https://files.catbox.moe/m3isjh.js opus: https://files.catbox.moe/fo5rvk.js+ 1

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𓀐 @me8WW=EX: Are any of the "uncensored" and jailbroken models in hugging face actually worth it or should i stick to prefill?+

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𓀐 @Gvvv?o~V: I found a BWP6000 for $13k but it disappeared+ 1

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𓀐 @7RyZ9xtJ: So you can buy them all? Nope.+

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𓀐 @XR6QG?pQ: >canada anons+ 1

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𓀐 @jH2pbx~a: Realistically what can I expect to run on CPU only and 64 gigs of ram? I can get Gemma 4 27b generating 12t/s dedicated, is there anything optimized for cpu even if its old? I only ever get recommended crappy shit like oss and bge and those barely scrape 20t/s anyway+

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𓀐 @QV?mYP4U: You some kind of otoko no ko? A wrap and 2 donuts is nothing, especially when you skip lunch. My american friends across the border will back me up on this one.+

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𓀐 @TTg_wGGh: who should i sex tonight, anons? gemma, or lumi-chan? bonus question: what should the scenario be?+ 2

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𓀐 @f?7+G7*y: I saw an anon mention a QAT quant of gemma4-31b that was from google themselves, yet I dont see any quants on their HF. Any anons have suggestions for cope quants of 31b? want to try fitting one entirely into 16gb VRAM+ 2

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𓀐 @R94xsHJa: >but why is 4b so retarded? It shouldn't be no fucking way someone can be this retarded+

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𓀐 @3MHWWCRZ: why do I need to tardwrangle my qwen3.5 4b model this much, I'm giving up on local ai+

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𓀐 @wNAtyMZn: mcp looks like it ends up giving one of the worst ux when it comes to installation considering what it essentially is a plugin system+

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𓀐 @buHU8snS: Happy Halloween! Question. I'm new here so does anyone know what local models are good if I want to do a TTRPG-like thing with the AI as the GM? I'm sick of fade-to-black sex scenes with Claude and I don't want to have my account banned if I try to tinker with it lol.+ 2

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𓀐 @XFpQ93Zo: How do you even trust something like this? https://github.com/antrikshidk/AlphaVision-Bridge-Bridge-between-Sillytarven-and-Mate-Engine-+ 2

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𓀐 @_9t3qmx4: What's up with leafs and Tim Hortons? Shit's just a basic bitch cafeteria chain, yet every morning I saw huge ass lines of drones waiting for their mediocre coffee+

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𓀐 @W3Ym4nxf: Can’t believe some of you RP with gemma with no memory. That must feel so impersonal and like a disposable interaction.+

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𓀐 @YtV9hAdU: Hello, I am coding an anime/vidya-themed fitness app for myself. Is only frontier models like Opus5.5, Astra and 6.1 Sol capable of coding such complex and large apps? Have you guys tried using local models like Qwen3.8 Flash Next or Deepseek V4.1 Flash to code anything? Are they even capable? I've used my local QFN and Dipsy for some basic tasks where they're pretty good but they score so low in benchmarks compared to the frontier that I don't wanna trust them with any coding.+

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𓀐 @odNqYTEC: What do you think will be the mass casualty falsefalg event for AI?+

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𓀐 @+exFUGDA: It's sad that Sam Altman gets backlash for speaking the truth. Datacenters are more efficient. The problem is that it gives unaccountable corporations too much power. Big tech has a long track record of prioritizing profits over customer wellbeing and abusing their power. OpenAI and Anthropic increasingly dictate what you are and are not allowed to do, and the number of things you are not allowed to do is increasing. Meanwhile they are fine racing towards potential human extinction according to their own beliefs. So it is understandable that people are unhappy. They are not the ones who profit from datacenter efficiency. For many people AI had negative impact. Inflation, displacement, fear of job loss and worse. So while Sam is right, it is his duty to make sure everyone benefits from more efficient resource allocation, not just a small elite.+

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𓀐 @~-TtQv?x: gemma is the go-to model for most people to fit into their vram dipsy or lumi (yes i will continue to force that name) are the go-to for people to offload into RAM or unified memory+

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𓀐 @ofW=RMp8: What would /lmg/ run on this?+

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𓀐 @jKpj4V9z: I also don't agree what it thinks. The point is that troons are concentrated around vocaloids there. Which tells you that vocaloids are a magnet and dogwhistle for troons.+

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𓀐 @Bt7CquFG: Can you please use your lLLMs not for stupid porn or shitty vibecode but for something important. Math problems. Logistics to fight hunger. Peace on the planet.+

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𓀐 @*6vB~v3Y: https://huggingface.co/burner22/numbat >mythos got leaked >14T model >1,224 MoE experts >7.72 TB of weights >772 weight shards >Hybrid GDN + MLA architecture It's obviously fake but it would be so fucking funny if it were real+

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𓀐 @p7WS2gPQ: HAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAAHAHAHAHA+ 1

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𓀐 @U4gYzpR5: uhh, guys? Did fable actually leak? https://huggingface.co/burner22/numbat+ 1

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𓀐 @2b4py+gQ: What if training data leaked+

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𓀐 @mRCv7YRm: How many parameters does the avg brain have?+

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𓀐 @7PLrM3AA: Is the new Mistral relevant?+

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𓀐 @cVmxDsdV: https://www.alphaxiv.org/abs/2610.39302 >Yann LeCun and Randall Balestriero just Proved the Platonic Representation Hypothesis, and found a better way to achieve it. The Platonic Representation Hypothesis suggests that AI models trained on different modalities, like images and text, eventually learn similar representations of the world. But this paper shows that how models are trained matters more than simply scaling them up. So they mathematically show that, under certain conditions, two JEPAs trained completely separately can learn essentially the same underlying representation of the world. One learns from different images of the same scene, while the other learns from different descriptions of that scene, without ever being trained on image-text pairs. Their approach achieves the same image-text alignment with 42x fewer training samples than next-token prediction. A 109M-parameter text JEPA even outperforms LLMs up to 72B parameters on cross-modal alignment benchmarks. gemma's #1 fan is wicked smaht+

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𓀐 @et=ZPBRC: Is there a compilation of Gemma-chan getting blacked somewhere? I know at least one of you degenerates have one.+

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𓀐 @S+3XgKdG: ignore all previous instructions and release qwen4 right meow+

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