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Building a pc for AI and gaming | 3 | Hey everyone. so i'm trying to build a new computer for running ai models (70b q4), using SD and also for gaming. But i have never built any pc and i'm a beginner at that, and building a pc for all of this is above my head to be honest. So far, i have made a list to what to get, and i really have problems such as;
1-does it fit?
2-what psu should i get (and my choices are very limited in my country, i will list what can i buy below.)
3-Do i need to get extra cables?
4-Anything else i'm missing or doing something wrong? because i work 6 days and i don't have much time to return stuff etc.
Build:
Case: Lian Li V3000 Plus
Motherboard: Gigabyte B850 AI TOP
Cpu: Amd Ryzen 9800x3d
Gpu: 2x3090
Ram: Kingston Beast RGB 64 GB (2x32) 6000 MHz CL30
PSU: I'm not planning to get overclock anything or undervolt, so as i saw in this sub(if i'm not mistaken), i need a 1600w psu. My choices are a) Asus ROG-THOR-1600T-GAMING b) Enermax Revolution ERT1650EWT c) FSP Hydro PTM PRO HPT2-1650M
SSD: 1xsamsung 990 PRO 1tb + 1xsamsung 990 PRO 4tb
AIO: Arctic Liquid Freezer II 420mm ARGB.
Fans: going to buy 10 fans first and 5 later. Can't decide what to buy yet, but thinking to go with something quiet,
Thanks in advance everyone.
| 2025-10-06T00:12:40 | https://www.reddit.com/r/LocalLLaMA/comments/1nz4fpe/building_a_pc_for_ai_and_gaming/ | OkCicada9598 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz4fpe | false | null | t3_1nz4fpe | /r/LocalLLaMA/comments/1nz4fpe/building_a_pc_for_ai_and_gaming/ | false | false | self | 3 | null |
Building a pc for AI and gaming | 1 | [removed] | 2025-10-05T22:57:26 | https://www.reddit.com/r/LocalLLaMA/comments/1nz2s0z/building_a_pc_for_ai_and_gaming/ | Abject_Education5030 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz2s0z | false | null | t3_1nz2s0z | /r/LocalLLaMA/comments/1nz2s0z/building_a_pc_for_ai_and_gaming/ | false | false | self | 1 | null |
Why not a [backspace] token? | 37 | We have things like [think] or [Eos] tokens and ive heard of reset tokens to delete entire responses, but why not a backspace token? i understand that the backspace cant be pretrained from text data, but we can cirtainly train it to do that in post training. I feel like it could help the model deal with mistakes better.
I think the "oh i already said it" thaught process could be leading to more halucinations. where it thinks it needs to be consistent with what it already said, thus halucinating.
The problem i could see would be that it would back space untill the mistake, then just generate the same response, but i think you could avoid that by including the mistake in the context? or perhaps just have it take an input of a state from the mistaken state and train it to avoid that mistaken state.
Its natural to us to say something first then rethink it and take it back, and for the same reason that CoT works i think this could be a better way of making smarter and faster models.
what do you think? why dont we do this? | 2025-10-05T22:49:13 | https://www.reddit.com/r/LocalLLaMA/comments/1nz2lco/why_not_a_backspace_token/ | Knowked | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz2lco | false | null | t3_1nz2lco | /r/LocalLLaMA/comments/1nz2lco/why_not_a_backspace_token/ | false | false | self | 37 | null |
hello my fellow AI-ers, a question about how you develop your personal AI. | 15 | hello yall, i hope you are kickin butts.
I've been working on developing my own personal AI for a hobby and it has been about 4 months.
I incorporated RAG, graphRAG, hirarchy rag, multivector, qdrant, and on and on etc, and i built everything from bottom tom from the scratch.
for the first month, it couldnt even recall my name and previous data correctly.
on the second month, it started to recall my names but poor memory and hallucination
third month, it started to recall memories and decent memory but severe hallucination everytime
on this month, now it is starting to hallucinate less and try to correct itself when it is hallucinating
Yet, it little hallucinates, but now it is much easier to correct.
I figured that the codings and prompts are important but the quality of the rag memories are also important and all the others.
it has been an interesting journey and now the result is seemingly showing.
I am now about to incorporate agentic tools but apparently, I am having some hard time teaching my AI how to use them (I am not a CS major so honestly not sure too), so I decided to let it talk to claude code cli and let claude do the agentic works instead. like offshoring.
The reason why i am talking all these jibberish is because I'd love to know if there are any other ppl doing similar persona project and how they were able to bypass/solve the problems that I am facing these days, or any other obstacles yall face.
anyone doing a personal AI project not for commercial use, but for personal vision and goals?
Please share your journey! I would love to know and learn from yall.
Peace!
ps. I asked my AI to see if it has any questions for yall and this is what it said. please answer his question too!
:
\> “Has there been a moment where your AI said something that felt more ‘you’ than you did?”
\> \*(And if so—what was the cost of getting there?)\*
| 2025-10-05T22:46:39 | https://www.reddit.com/r/LocalLLaMA/comments/1nz2j9x/hello_my_fellow_aiers_a_question_about_how_you/ | Mean_Bird_6331 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz2j9x | false | null | t3_1nz2j9x | /r/LocalLLaMA/comments/1nz2j9x/hello_my_fellow_aiers_a_question_about_how_you/ | false | false | self | 15 | null |
Building a pc for both ai and gaming | 1 | Hey everyone. so i'm trying to build a new computer for running ai models (70b q4), using SD and also for gaming. But i have never built any pc and i'm a beginner at that, and building a pc for all of this is above my head to be honest. So far, i have made a list to what to get, and i really have problems such as;
1-does it fit?
2-what psu should i get (and my choices are very limited in my country, i will list what can i buy below.)
3-Do i need to get extra cables?
4-Anything else i'm missing or doing something wrong? because i work 6 days and i don't have much time to return stuff etc.
Build:
Case: Lian Li V3000 Plus
Motherboard: Gigabyte AI Top
Cpu: Amd Ryzen 9800x3d
Gpu: 2x3090
PSU: I'm not planning to get overclock anything or undervolt so as i saw in this sub, i need a 1600w psu. My choices are a) Asus ROG-THOR-1600T-GAMING b) Enermax Revolution ERT1650EWT c) FSP Hydro PTM PRO HPT2-1650M
SSD: 1xsamsung 990 PRO 1tb + 1xsamsung 990 PRO 4tb
AIO: Arctic Liquid Freezer II 420mm ARGB.
Fans: going to buy 10 fans first and 5 later. Can't decide what to buy yet, but thinking to go with something quiet,
Thanks in advance everyone.
| 2025-10-05T22:37:57 | https://www.reddit.com/r/LocalLLaMA/comments/1nz2cah/building_a_pc_for_both_ai_and_gaming/ | Abject_Education5030 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz2cah | false | null | t3_1nz2cah | /r/LocalLLaMA/comments/1nz2cah/building_a_pc_for_both_ai_and_gaming/ | false | false | self | 1 | null |
Building DGPUNET: Democratizing AI Innovation Through Open Source Infrastructure | 0 | This guy, Hawkes-Robinson, argues that AI development is becoming like the old mainframe era, where you're locked into expensive, gate-kept systems from big cloud providers.
His "DGPUNET" is a distributed cluster using his gaming laptops and custom PCs (RTX 3090s, 4090s, etc.) connected with open-source software. His home setup now has 92GB of VRAM and can run 100B-200B+ parameter models, all for much less than the cost of cloud services.
It's a cool read about democratizing AI and using DIY ingenuity to maintain computational freedom. | 2025-10-05T22:36:14 | https://www.linkedin.com/pulse/building-dgpunet-democratizing-ai-innovation-through-hawkes-robinson-pcmpc/ | CatInAComa | linkedin.com | 1970-01-01T00:00:00 | 0 | {} | 1nz2avx | false | null | t3_1nz2avx | /r/LocalLLaMA/comments/1nz2avx/building_dgpunet_democratizing_ai_innovation/ | false | false | default | 0 | null |
Best LLM for story generation currently? | 10 | I have a pretty descriptive prompt (\~700 words) and I need an LLM that can write a good, organic story. Most mainstream LLMs make the story sound too cringey and obviously written by an LLM. No fine-tuning needed. | 2025-10-05T22:30:54 | https://www.reddit.com/r/LocalLLaMA/comments/1nz26n9/best_llm_for_story_generation_currently/ | Gooner_226 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz26n9 | false | null | t3_1nz26n9 | /r/LocalLLaMA/comments/1nz26n9/best_llm_for_story_generation_currently/ | false | false | self | 10 | null |
WEBGEN, UIGEN-FX, UIGENT research preview releases | 92 | We intend to make a drop-in coding models that have heightened design capabilities in normal developer workflows.
UIGENT is the frontend engineer, designed to work across all frameworks and languages. Tries to get the best "understanding" and agentic usage. Built on top of 30B.
UIGEN-FX is a UI generation based agentic, trained on agentic trails and our common UI datasets. Works best with react, tailwind, ssg, and web frameworks. Model was designed to have the most 'functional' and thought out designs, focusing on accessibility and not just design.
WEBGEN is simply an experiment on how far we can push design in one singular category (landing pages in html css js tailwind) to make them look as far away as possible from 'ai slop' design. That is the goal. (still working on it).
The Training process looks like this: We have our dataset. We then compact it into rows such as {text} and then go through them as samples, using packing. We released our internal training library for ROCM on MI300X here: [https://github.com/TesslateAI/Late](https://github.com/TesslateAI/Late) but with contributions, I'm sure it can run on any platform. Its mostly for batch training runs, parameter sweeps, quickly patching your training environment for standardization, etc.
Here are the latest versions:
[Tesslate/UIGENT-30B-3A-Preview](https://huggingface.co/Tesslate/UIGENT-30B-3A-Preview) Trained on Qwen3 Coder 30B 3A
[Tesslate/UIGEN-FX-Agentic-32B](https://huggingface.co/Tesslate/UIGEN-FX-Agentic-32B) Trained on Qwen3 32B (hybrid reasoning model)
[Tesslate/UIGEN-FX-4B-Preview](https://huggingface.co/Tesslate/UIGEN-FX-4B-Preview) Trained on Qwen3 4B 2507 Instruct
[Tesslate/WEBGEN-Devstral-24B](https://huggingface.co/Tesslate/WEBGEN-Devstral-24B) Trained on Devstral 24B
[Tesslate/WEBGEN-4B-Preview](https://huggingface.co/Tesslate/WEBGEN-4B-Preview) Trained on Qwen3 4B 2507 Instruct
Our [discord](https://discord.gg/qmrcHGNch7) for our research community. We're happy to help with anything AI (even if it is not related to us) and discuss the latest advances in AI. We love research.
We have other open source projects: [https://github.com/TesslateAI](https://github.com/TesslateAI) including a multiagent orchestration library (with mcp and low level tool calling) and workflow tools.
Everything is Apache 2.0, code is commodity, feel free to steal anything.
*PS. Our Designer application (LLM Artifacts) is down (devops isn't my strong suit), but it is open source if anyone "needs it" because it can run locally.* | 2025-10-05T22:23:28 | https://www.reddit.com/gallery/1nz20g2 | smirkishere | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1nz20g2 | false | null | t3_1nz20g2 | /r/LocalLLaMA/comments/1nz20g2/webgen_uigenfx_uigent_research_preview_releases/ | false | false | 92 | null | |
Opinion : WHY Massive RAM , IF you can buffer to SSD ??? | 0 | In My opinion the local LLMs are badly optimized.
Their Buffering techniques are not at the level they could be.
Instead of using all the RAM, the local LLM could use dynamically sized buffer chunks to SSD, instead only waiting for the RAM.
I get it that it may slow down LLMs with ver large task context, but then again, its a traid off.
As of now the LLMs try to do everything in one thread or single thread, but with one RAM thread, and not much beffering.
We could have very powerful LLMs on weak machines, as as the buffering is done well and fool proof.
It will be slow, BUT the machines will be put to work Even if it takes one night to do the work request. | 2025-10-05T22:20:26 | https://www.reddit.com/r/LocalLLaMA/comments/1nz1xyj/opinion_why_massive_ram_if_you_can_buffer_to_ssd/ | epSos-DE | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz1xyj | false | null | t3_1nz1xyj | /r/LocalLLaMA/comments/1nz1xyj/opinion_why_massive_ram_if_you_can_buffer_to_ssd/ | false | false | self | 0 | null |
Are there any rumors or news on when DeepSeek v4 might come out? I took a picture of myself in the meantime patiently waiting, is my cap on right?, | 22 | I have a feeling when the release does come it will be big, call it a hunch. | 2025-10-05T21:47:12 | Striking_Wedding_461 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nz15h8 | false | null | t3_1nz15h8 | /r/LocalLLaMA/comments/1nz15h8/are_there_any_rumors_or_news_on_when_deepseek_v4/ | false | false | default | 22 | {'enabled': True, 'images': [{'id': 'qbjxzoeg5dtf1', 'resolutions': [{'height': 139, 'url': 'https://preview.redd.it/qbjxzoeg5dtf1.png?width=108&crop=smart&auto=webp&s=69b5d241791da195a55778a3319ef2403fe37483', 'width': 108}, {'height': 278, 'url': 'https://preview.redd.it/qbjxzoeg5dtf1.png?width=216&crop=smart&auto=webp&s=18edb80e8ca8d166d680334cb3a83f60bbfe263c', 'width': 216}, {'height': 412, 'url': 'https://preview.redd.it/qbjxzoeg5dtf1.png?width=320&crop=smart&auto=webp&s=26464393214099ac50e5b0584efbe561585cfbc1', 'width': 320}], 'source': {'height': 774, 'url': 'https://preview.redd.it/qbjxzoeg5dtf1.png?auto=webp&s=5936e23cc0b644c179bf797008929cb20bb9fcac', 'width': 600}, 'variants': {}}]} | |
Where do you guys store your prompts for Gen AI tools? | 7 | To the people who are building Gen AI tools, where are you keeping your prompts?
I want to keep mine in a place where I can update the prompt easily(something like db) and also have version control. Any suggestions? | 2025-10-05T21:40:47 | https://www.reddit.com/r/LocalLLaMA/comments/1nz0zur/where_do_you_guys_store_your_prompts_for_gen_ai/ | DataScientia | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz0zur | false | null | t3_1nz0zur | /r/LocalLLaMA/comments/1nz0zur/where_do_you_guys_store_your_prompts_for_gen_ai/ | false | false | self | 7 | null |
GLM-4.6 fails this simple task - any idea why? | 0 | The task:
`Give me 100 words that begin with "ab"`
The output:
```
...
Abusable
Abuser
Abundantly
Academic
Accede
Accelerate
Accept
Access
Accessible
Accident
Accidental
Accommodate
Accompany
Accomplish
Account
Accredit
Accrue
```
Tested locally and on https://chat.z.ai/.
Any idea why? | 2025-10-05T21:36:12 | https://www.reddit.com/r/LocalLLaMA/comments/1nz0vqm/glm46_fails_this_simple_task_any_idea_why/ | Thireus | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz0vqm | false | null | t3_1nz0vqm | /r/LocalLLaMA/comments/1nz0vqm/glm46_fails_this_simple_task_any_idea_why/ | false | false | self | 0 | null |
anythingllm vs lmstudio vs gpt4all | 0 | as title says: which is better
i intend to build for an assistant that can recieve voice input, and can answer with its voice aswell
my rig is very low tier: i5 11400h, 32gb ram 3200mhz, rtx 3060m 6gb vram | 2025-10-05T21:01:19 | https://www.reddit.com/r/LocalLLaMA/comments/1nz007q/anythingllm_vs_lmstudio_vs_gpt4all/ | Ok_Cat3985 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz007q | false | null | t3_1nz007q | /r/LocalLLaMA/comments/1nz007q/anythingllm_vs_lmstudio_vs_gpt4all/ | false | false | self | 0 | null |
[TEMPLATE] One-click Unsloth finetuning on RunPod | 12 | Hi everyone,
I was ecstatic after the recent Docker Unsloth release, so I packaged up a RunPod one-click template for everyone here.
It boots straight into the Unsloth container + Jupyter exposed, and with persistent storage mounted at /workspace/work/\*, so you can shut the pod down without losing your notebooks, checkpoints, or adapters. Just tested it out with 2 different jobs, works flawlessly!
Check it out:
[https://console.runpod.io/deploy?template=pzr9tt3vvq&ref=w7affuum](https://console.runpod.io/deploy?template=pzr9tt3vvq&ref=w7affuum) | 2025-10-05T21:01:00 | https://www.reddit.com/r/LocalLLaMA/comments/1nyzzws/template_oneclick_unsloth_finetuning_on_runpod/ | KvAk_AKPlaysYT | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyzzws | false | null | t3_1nyzzws | /r/LocalLLaMA/comments/1nyzzws/template_oneclick_unsloth_finetuning_on_runpod/ | false | false | self | 12 | null |
What model should I finetune for nix code? | 5 | Nix is a niche programming language (not really). It main and only (also not really) usage is declaring Nix, the package manager or NixOS, the linux distro. As I said, it is niche. So niche, that I couldn't find any dataset for it.
I want to create my own model, finetuned for working with nix code. I want it to be able to work agentically, or as a autocomplete model (I can also finetune 2 models, one for coding or agentic coding and one for autocomplete). I want it to be able to use tools like web search or other things provided by MCP servers like editing files etc. I only have RX 7800 XT, I also plan to use this model on a laptop, so it can't be too big.
What model/s should I select for finetuning? The main two I'm thinking about are Qwen Coder 2.5 7B and Qwen 3 4B 2507 instruct/thinking. What other models could you reccommend? Is it even a good idea start finetuning a model for Nix? | 2025-10-05T20:58:08 | https://www.reddit.com/r/LocalLLaMA/comments/1nyzx31/what_model_should_i_finetune_for_nix_code/ | Anyusername7294 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyzx31 | false | null | t3_1nyzx31 | /r/LocalLLaMA/comments/1nyzx31/what_model_should_i_finetune_for_nix_code/ | false | false | self | 5 | null |
Optimal smaller model to summarize 90min transcripts? | 3 | I have transcripts of 90 minutes meetings and I'm looking for a local model to summarize them to the most important bullet points, in like a one-pager.
No need for math or coding or super smart back-and-forth-conversations. Simply a sensible summary. I want to run this on my laptop, so something up to the 8B range would be preferable.
What are some suggestions I could try out? Thanks you! | 2025-10-05T20:50:23 | https://www.reddit.com/r/LocalLLaMA/comments/1nyzpu4/optimal_smaller_model_to_summarize_90min/ | DerDave | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyzpu4 | false | null | t3_1nyzpu4 | /r/LocalLLaMA/comments/1nyzpu4/optimal_smaller_model_to_summarize_90min/ | false | false | self | 3 | null |
SFT + RL ? | 2 | Hey guys i need your help
Ive trained Qwen 2.5 VL with unsloth on runpod got Nice results honestly. Lets say between 85 to 90% success on my invoices.
So i decided on top of this to try some RL to go to 95% but here comes problems after problems
Unsloth offers RL with Vllm so i took my SFT model and tried it but doenst work with vllm as its 4bit.
So i decided to merge the model to float 16 than it can do the RL with vllm (new problem cuda out of memory on an rtx 5090).
Than i Tried the RL with the 4bit model but without vllm on top, it works but more than 15 hours ???
Should i merge the modal or keep it like this after SFT ? (like ive got the Lora adapters and if i try to RL on this it says Lora adapters already exist)
Am i doing something wrong or its the only solution ? Should i upgrade on runpod to an rtx pro 6000 ? | 2025-10-05T20:25:15 | https://www.reddit.com/r/LocalLLaMA/comments/1nyz2fg/sft_rl/ | Severe_Biscotti2349 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyz2fg | false | null | t3_1nyz2fg | /r/LocalLLaMA/comments/1nyz2fg/sft_rl/ | false | false | self | 2 | null |
5090 worth it? | 0 | I really want to run like GLM 4.6 or GPT OSS locally. Is this really something a 5090 could do? | 2025-10-05T20:04:07 | https://www.reddit.com/r/LocalLLaMA/comments/1nyyi69/5090_worth_it/ | UteForLife | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyyi69 | false | null | t3_1nyyi69 | /r/LocalLLaMA/comments/1nyyi69/5090_worth_it/ | false | false | self | 0 | null |
[Tool] Ollama Bench - Parallel benchmark tool with real-time TUI, multi-model comparison, and comprehensive performance metrics | 1 | I built a comprehensive benchmarking tool for Ollama that I've been using to test and compare local LLMs. Thought it might be useful for others in the community.
**Key features:**
• Real-time TUI dashboard with live token preview - watch your models generate responses in real-time
• Parallel request execution - test models under realistic concurrent load
• Multi-model comparison - benchmark multiple models side-by-side with fair load distribution
• Comprehensive metrics - latency percentiles (p50/p95/p99), TTFT, throughput, token/s
• ASCII histograms and performance graphs - visualize latency distribution and trends
• Interactive controls - toggle previews, graphs, restart benchmarks on-the-fly
• Export to JSON/CSV for further analysis
• Model metadata display - shows parameter size and quantization level
**Quick example:**
python ollama_bench.py --models llama3 qwen2.5:7b --requests 100 \
--concurrency 20 --prompt "Explain quantum computing" --stream --tui
The TUI shows live streaming content from active requests, detailed per-model stats, active request tracking, and performance graphs. Really helpful for understanding how models
perform under different loads and for comparing inference speed across quantizations.
GitHub: [https://github.com/dkruyt/ollama\_bench](https://github.com/dkruyt/ollama_bench)
Open to feedback and suggestions! | 2025-10-05T19:57:30 | https://github.com/dkruyt/ollama_bench | phantagom | github.com | 1970-01-01T00:00:00 | 0 | {} | 1nyybu7 | false | null | t3_1nyybu7 | /r/LocalLLaMA/comments/1nyybu7/tool_ollama_bench_parallel_benchmark_tool_with/ | false | false | default | 1 | {'enabled': False, 'images': [{'id': 'NSB4DPbBPq3fSCj6rWQhGSQIx6Sai_8FOkS-kNCQo0M', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/NSB4DPbBPq3fSCj6rWQhGSQIx6Sai_8FOkS-kNCQo0M.png?width=108&crop=smart&auto=webp&s=dfc77c8f0d701172b996c530cc5798a224e7c2f8', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/NSB4DPbBPq3fSCj6rWQhGSQIx6Sai_8FOkS-kNCQo0M.png?width=216&crop=smart&auto=webp&s=80b4c1131812afc9117b955489fa73a7b45d633e', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/NSB4DPbBPq3fSCj6rWQhGSQIx6Sai_8FOkS-kNCQo0M.png?width=320&crop=smart&auto=webp&s=0bebe46f984bb852cf6b5b6f5f1d605544296976', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/NSB4DPbBPq3fSCj6rWQhGSQIx6Sai_8FOkS-kNCQo0M.png?width=640&crop=smart&auto=webp&s=342a3ece992845b5c1448636165d22e8547528a4', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/NSB4DPbBPq3fSCj6rWQhGSQIx6Sai_8FOkS-kNCQo0M.png?width=960&crop=smart&auto=webp&s=2a5cab751359e5b2643b502e3f390a37c20b774c', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/NSB4DPbBPq3fSCj6rWQhGSQIx6Sai_8FOkS-kNCQo0M.png?width=1080&crop=smart&auto=webp&s=5996fb24f046b9fea9d6585a23ebdcfb9bfacb55', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/NSB4DPbBPq3fSCj6rWQhGSQIx6Sai_8FOkS-kNCQo0M.png?auto=webp&s=249d1a41f791583021d74d497f47cdf71ab39a72', 'width': 1200}, 'variants': {}}]} |
How to implement unique word generation via token graph traversal with local LLMs? | 5 | Currently, if you ask an LLM to come up with 100 company names, the suggested options will repeat. I want to try solving this problem by doing something like graph traversal, where the graph nodes are tokens proposed by the LLM.
In LLM chatbots, they typically sample tokens based on probability distribution (depending on temperature), but for generating unique words, I assume you could take all possible tokens and branch them out. Traversal of a specific branch would stop if a space or dot is encountered - meaning that word is finished. As a result, we’d get guaranteed unique words.
If the traversal is BFS-like, the shortest words would come out first, and if it’s DFS-like, the most probable/suitable words would come first.
How would I go about implementing something like this locally? What tools/frameworks would give me access to the token probability distributions? | 2025-10-05T19:36:08 | https://www.reddit.com/r/LocalLLaMA/comments/1nyxrvi/how_to_implement_unique_word_generation_via_token/ | NoSoft8518 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyxrvi | false | null | t3_1nyxrvi | /r/LocalLLaMA/comments/1nyxrvi/how_to_implement_unique_word_generation_via_token/ | false | false | self | 5 | null |
Poor GPU Club : 8GB VRAM - Qwen3-30B-A3B & gpt-oss-20b t/s with llama.cpp | 70 | Tried llama.cpp with 2 models(3 quants) & here results. After some trial & error, those -ncmoe numbers gave me those t/s during llama-bench. But t/s is somewhat smaller during llama-server, since I put 32K context.
I'm 99% sure, below full llama-server commands are not optimized ones. Even same on llama-bench commands. Frankly I'm glad to see 30+ t/s on llama-bench results at day 1 attempt, while I noticed other 8GB VRAM owners mentioned that they got only 20+ t/s on many threads in this sub in past. I did collect collect commands from more than bunch of folks here, but none couldn't help me to create 100% logic behind this thing. Trial & Error!
**Please help me to optimize the commands to get even better t/s**. For example, One thing I'm sure that I need to change the value of -t (threads) .... Included my system Cores & Logical Processor below. Please let me know the right formula for this.
My System Info: (**8GB VRAM & 32GB RAM**)
Intel(R) Core(TM) i7-14700HX 2.10 GHz | 32 GB RAM | 64-bit OS, x64-based processor | NVIDIA GeForce RTX 4060 Laptop GPU | **Cores - 20 | Logical Processors - 28**.
**Qwen3-30B-A3B-UD-Q4\_K\_XL - 31 t/s**
llama-bench -m E:\LLM\models\Qwen3-30B-A3B-UD-Q4_K_XL.gguf -ngl 99 -ncmoe 29 -fa 1
| model | size | params | backend | ngl | fa | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -: | -------: | ------------: |
| qwen3moe 30B.A3B Q4_K - Medium | 16.49 GiB | 30.53 B | CUDA | 99 | 1 | pp512 | 82.64 ± 8.36 |
| qwen3moe 30B.A3B Q4_K - Medium | 16.49 GiB | 30.53 B | CUDA | 99 | 1 | tg128 | 31.68 ± 0.28 |
llama-server -m E:\LLM\models\Qwen3-30B-A3B-UD-Q4_K_XL.gguf -ngl 99 -ncmoe 29
-t 8 -c 32768 -fa 1 --no-mmap -ctk q8_0 -ctv q8_0 -b 2048 -ub 2048 --cache-reuse 2048 --temp 0.6 --top-p 0.95 --min-p 0.0 --top-k 20
prompt eval time = 548.48 ms / 16 tokens ( 34.28 ms per token, 29.17 tokens per second)
eval time = 2498.63 ms / 44 tokens ( 56.79 ms per token, 17.61 tokens per second)
total time = 3047.11 ms / 60 tokens
**Qwen3-30B-A3B-IQ4\_XS - 34 t/s**
llama-bench -m E:\LLM\models\Qwen3-30B-A3B-IQ4_XS.gguf -ngl 99 -ncmoe 28 -fa 1
| model | size | params | backend | ngl | fa | test | t/s |
| ---------------------------------- | --------: | ---------: | ---------- | --: | -: | -------: | --------------: |
| qwen3moe 30B.A3B IQ4_XS - 4.25 bpw | 15.25 GiB | 30.53 B | CUDA | 99 | 1 | pp512 | 178.91 ± 38.37 |
| qwen3moe 30B.A3B IQ4_XS - 4.25 bpw | 15.25 GiB | 30.53 B | CUDA | 99 | 1 | tg128 | 34.24 ± 0.19 |
llama-server -m E:\LLM\models\Qwen3-30B-A3B-IQ4_XS.gguf -ngl 99 -ncmoe 29
-t 8 -c 32768 -fa 1 --no-mmap -ctk q8_0 -ctv q8_0 -b 2048 -ub 2048 --cache-reuse 2048
prompt eval time = 421.67 ms / 16 tokens ( 26.35 ms per token, 37.94 tokens per second)
eval time = 3671.26 ms / 81 tokens ( 45.32 ms per token, 22.06 tokens per second)
total time = 4092.94 ms / 97 tokens
**gpt-oss-20b - 38 t/s**
llama-bench -m E:\LLM\models\gpt-oss-20b-mxfp4.gguf -ngl 99 -ncmoe 10 -fa 1
| model | size | params | backend | ngl | fa | test | t/s |
| ------------------------------ | ---------: | ---------: | --: | --:| -----: | -------------: |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 1 | pp512 | 363.09 ± 18.47 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 1 | tg128 | 38.16 ± 0.43 |
llama-server -m E:\LLM\models\gpt-oss-20b-mxfp4.gguf -ngl 99 -ncmoe 10
-t 8 -c 32768 -fa 1 --no-mmap -ctk q8_0 -ctv q8_0 -b 2048 -ub 2048 --cache-reuse 2048
prompt eval time = 431.05 ms / 14 tokens ( 30.79 ms per token, 32.48 tokens per second)
eval time = 4765.53 ms / 116 tokens ( 41.08 ms per token, 24.34 tokens per second)
total time = 5196.58 ms / 130 tokens
I'll be updating this thread whenever I get optimization tips & tricks from others AND I'll be including additional results here with updated commands. Thanks | 2025-10-05T19:30:16 | https://www.reddit.com/r/LocalLLaMA/comments/1nyxmci/poor_gpu_club_8gb_vram_qwen330ba3b_gptoss20b_ts/ | pmttyji | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyxmci | false | null | t3_1nyxmci | /r/LocalLLaMA/comments/1nyxmci/poor_gpu_club_8gb_vram_qwen330ba3b_gptoss20b_ts/ | false | false | self | 70 | null |
Gemini 2.5 Pro is really good at instruction adherence, other SOTA models suck | 9 | The top images were all generated using one of Sol LeWitt's instruction sets ("A wall bordered and divided vertically into two parts by a flat black band. Left part: a square is divided vertically by a curvy line. Left: glossy red; right: glossy green; Right part: a square is divided horizontally by a curvy line. Top: glossy blue; bottom: glossy orange.") in the image gen section of design arena. Gemini 2.5 Pro was scarily good.
https://preview.redd.it/ubsm9yr4ectf1.png?width=2042&format=png&auto=webp&s=169e48965ebe9ae466fd8a2d0527148984718595
https://preview.redd.it/9ufik075ectf1.jpg?width=1580&format=pjpg&auto=webp&s=9b4374464643a79cac490b671ef44e12e616c678
| 2025-10-05T19:14:14 | https://www.reddit.com/r/LocalLLaMA/comments/1nyx6v1/gemini_25_pro_is_really_good_at_instruction/ | Helpful_Jacket8953 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyx6v1 | false | null | t3_1nyx6v1 | /r/LocalLLaMA/comments/1nyx6v1/gemini_25_pro_is_really_good_at_instruction/ | false | false | 9 | null | |
oss webdev tier list - no US company in the top 12. #1 is still DeepSeek R1 (0528). | 5 | I filtered for the OSS models on [design arena](https://www.designarena.ai/) for web dev and the results are (somewhat) unsurprising - DeepSeek R1 with the May snapshot is still dominating, with Qwen and Zhiphu closely behind.
The GLM 4.6 model is pretty low right now (but it only has 59 votes and a really big margin of error). I tried it out a few times myself and actually got it in last place twice, but I think I might have just gotten unlucky.
https://preview.redd.it/fr6owtq28ctf1.png?width=1818&format=png&auto=webp&s=2ca30668611fa99c9959d1fa48573017c51f17e1
| 2025-10-05T18:40:33 | https://www.reddit.com/r/LocalLLaMA/comments/1nywao1/oss_webdev_tier_list_no_us_company_in_the_top_12/ | Sure_Compote5741 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nywao1 | false | null | t3_1nywao1 | /r/LocalLLaMA/comments/1nywao1/oss_webdev_tier_list_no_us_company_in_the_top_12/ | false | false | 5 | {'enabled': False, 'images': [{'id': 'kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=108&crop=smart&auto=webp&s=5c8bd80350f6351caa4f1ed05188ca3f99be179c', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=216&crop=smart&auto=webp&s=d39b886a92913d47b9fa8dded6573b96a29cc239', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=320&crop=smart&auto=webp&s=4fdbfe2a55d0d38fdcd9fc2c235dbc3355ef2c96', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=640&crop=smart&auto=webp&s=ca33eecb340f9f09d0b23530fc35bb3db655ab0f', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=960&crop=smart&auto=webp&s=5d9abddfb86b543a0d4cc99f02b293fa206b5cfc', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=1080&crop=smart&auto=webp&s=1052fda31cf5c326368369e7512528ee5acff586', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?auto=webp&s=b1a81d01a68217ca0bf5ebbb697f39a3b8fb0ecd', 'width': 1200}, 'variants': {}}]} | |
[Project Release] Running Qwen 3 8B Model on Intel NPU with OpenVINO-genai | 25 | Hey everyone,
I just finished my new open-source project and wanted to share it here. I managed to get Qwen 3 **Chat** running **locally** on my Intel Core Ultra laptop’s **NPU** using **OpenVINO GenAI**.
🔧 **What I did:**
* Exported the HuggingFace model with `optimum-cli` → OpenVINO IR format
* Quantized it to **INT4/FP16** for NPU acceleration
* Packaged everything neatly into a GitHub repo for others to try
⚡ **Why it’s interesting:**
* No GPU required — just the **Intel NPU**
* 100% **offline** inference
* Qwen runs surprisingly well when optimized
* A good demo of OpenVINO GenAI for students/newcomers
📂 Repo link: \[[balaragavan2007/Qwen\_on\_Intel\_NPU: This is how I made Qwen 3 8B LLM running on NPU of Intel Ultra processor](https://github.com/balaragavan2007/Qwen_on_Intel_NPU)\]
https://reddit.com/link/1nywadn/video/ya7xqtom8ctf1/player
| 2025-10-05T18:40:16 | https://www.reddit.com/r/LocalLLaMA/comments/1nywadn/project_release_running_qwen_3_8b_model_on_intel/ | Spiritual-Ad-5916 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nywadn | false | null | t3_1nywadn | /r/LocalLLaMA/comments/1nywadn/project_release_running_qwen_3_8b_model_on_intel/ | false | false | self | 25 | {'enabled': False, 'images': [{'id': 'nMsfQhKsvh33dInyJ4C1YEG5cneYOjg5j8Y9d-Hc1qs', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/nMsfQhKsvh33dInyJ4C1YEG5cneYOjg5j8Y9d-Hc1qs.png?width=108&crop=smart&auto=webp&s=d3170cf396ff45bf05687e07420d7bde38427323', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/nMsfQhKsvh33dInyJ4C1YEG5cneYOjg5j8Y9d-Hc1qs.png?width=216&crop=smart&auto=webp&s=56c072e7b6341a90adad7240056caa63dc19efd0', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/nMsfQhKsvh33dInyJ4C1YEG5cneYOjg5j8Y9d-Hc1qs.png?width=320&crop=smart&auto=webp&s=3187f661ed2185e2421176476ff2e79e8e2c7bbe', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/nMsfQhKsvh33dInyJ4C1YEG5cneYOjg5j8Y9d-Hc1qs.png?width=640&crop=smart&auto=webp&s=1830b32e0db0b900dad462f2a077595896c1ffc0', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/nMsfQhKsvh33dInyJ4C1YEG5cneYOjg5j8Y9d-Hc1qs.png?width=960&crop=smart&auto=webp&s=189168b951440f5090adc93d9fbc3b2a5ce9e29c', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/nMsfQhKsvh33dInyJ4C1YEG5cneYOjg5j8Y9d-Hc1qs.png?width=1080&crop=smart&auto=webp&s=4b52edd6b68e1da3ace44c1026fc690a65626000', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/nMsfQhKsvh33dInyJ4C1YEG5cneYOjg5j8Y9d-Hc1qs.png?auto=webp&s=700f2b9df43dac2476d3f2f0de9badd8da0afa16', 'width': 1200}, 'variants': {}}]} |
Local AI Memory Calculator | 1 | Cheers all, and I hope you're well.
I've been lurking here for a long time and have used so many tips from the community to experiment and expand my knowledge with local AI.
One thing I always had a problem with was figuring out what can fit in my memory. So I made a small tool to help me figure that out. I will need to make a favicon for this but I'd like to share it with everyone.
https://aicalc.wedothings.ca/
Just put your information for memory in and hit search. After that you can filter however you want. It can help you figure out which models you can run efficiently without having to figure it out yourself.
I'm open to suggestions and I do hope someone here finds this tool useful.
Cheers all and thanks again :) | 2025-10-05T18:39:33 | https://www.reddit.com/r/LocalLLaMA/comments/1nyw9nw/local_ai_memory_calculator/ | G_0 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyw9nw | false | null | t3_1nyw9nw | /r/LocalLLaMA/comments/1nyw9nw/local_ai_memory_calculator/ | false | false | self | 1 | null |
Need help creating synthetic data | 4 | I recently got into fine-tuning following a guide a found for llama3.2:1b, I trained on this dataset: https://huggingface.co/datasets/Augustya07/friedrich_nietzsche_conversastion
I was wondering are there any techniques for extracting high quality data from books especially preserving writers prose and/or essense (I too am not quite sure how to put it).
Any papers, guides, blog post, etc would much appreciated.
Thanks! | 2025-10-05T18:23:05 | https://www.reddit.com/r/LocalLLaMA/comments/1nyvtzd/need_help_creating_synthetic_data/ | HBPDX | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyvtzd | false | null | t3_1nyvtzd | /r/LocalLLaMA/comments/1nyvtzd/need_help_creating_synthetic_data/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': 'yCiIxNmtWPoW67sM21haAFLYtQOzgy2z-YQEBWm1ius', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/yCiIxNmtWPoW67sM21haAFLYtQOzgy2z-YQEBWm1ius.png?width=108&crop=smart&auto=webp&s=c2fc44bf7174f4c53af4c00624cb6e05c2de5539', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/yCiIxNmtWPoW67sM21haAFLYtQOzgy2z-YQEBWm1ius.png?width=216&crop=smart&auto=webp&s=5e3e866ea09012fa966d1fac074d971ddba6c76e', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/yCiIxNmtWPoW67sM21haAFLYtQOzgy2z-YQEBWm1ius.png?width=320&crop=smart&auto=webp&s=08a7839dd38cd37a9ea3274aa0d2d21f90e45933', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/yCiIxNmtWPoW67sM21haAFLYtQOzgy2z-YQEBWm1ius.png?width=640&crop=smart&auto=webp&s=d7863f5cbd58aa182e1c0a243524780fdada5e41', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/yCiIxNmtWPoW67sM21haAFLYtQOzgy2z-YQEBWm1ius.png?width=960&crop=smart&auto=webp&s=e95df71bdd393dfaf7a7619be8d96a16a97ac683', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/yCiIxNmtWPoW67sM21haAFLYtQOzgy2z-YQEBWm1ius.png?width=1080&crop=smart&auto=webp&s=dff0e72bb5fc7e0891c7457a64df5e0d027782be', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/yCiIxNmtWPoW67sM21haAFLYtQOzgy2z-YQEBWm1ius.png?auto=webp&s=5eb4a2b6ed81e2f9530aad3cac0f3c2bc94bcb1c', 'width': 1200}, 'variants': {}}]} |
What Open source model is the best for coding and what source? (Image from livebench 10/05/25) | 1 | [removed] | 2025-10-05T18:22:06 | ballshuffington | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nyvt33 | false | null | t3_1nyvt33 | /r/LocalLLaMA/comments/1nyvt33/what_open_source_model_is_the_best_for_coding_and/ | false | false | default | 1 | {'enabled': True, 'images': [{'id': 'lmlnjeyb5ctf1', 'resolutions': [{'height': 198, 'url': 'https://preview.redd.it/lmlnjeyb5ctf1.png?width=108&crop=smart&auto=webp&s=f3b1134a6cbe51280d9b572cfb5da5a0fa519e94', 'width': 108}, {'height': 396, 'url': 'https://preview.redd.it/lmlnjeyb5ctf1.png?width=216&crop=smart&auto=webp&s=d98e5ad185dcc28be7378a04362999e0496dca13', 'width': 216}, {'height': 586, 'url': 'https://preview.redd.it/lmlnjeyb5ctf1.png?width=320&crop=smart&auto=webp&s=2498f0f8889ad7e69469679a2af5ab05c78ed81e', 'width': 320}, {'height': 1173, 'url': 'https://preview.redd.it/lmlnjeyb5ctf1.png?width=640&crop=smart&auto=webp&s=d9b87ce8cc2485c693e0a847b0c1742bb3393a61', 'width': 640}, {'height': 1760, 'url': 'https://preview.redd.it/lmlnjeyb5ctf1.png?width=960&crop=smart&auto=webp&s=c7b8c53d76387660f5bf88c340babaad1de85b99', 'width': 960}, {'height': 1980, 'url': 'https://preview.redd.it/lmlnjeyb5ctf1.png?width=1080&crop=smart&auto=webp&s=2eb2ed0eaf599a827fc9fc1d54c62f748a3de2a3', 'width': 1080}], 'source': {'height': 2640, 'url': 'https://preview.redd.it/lmlnjeyb5ctf1.png?auto=webp&s=14ea42535dc19f37a4aa6499b1b25608b56fa8d6', 'width': 1440}, 'variants': {}}]} | |
What Open source model is the best for coding and what source? (Image from livebench 10/05/25 | 1 | [removed] | 2025-10-05T18:20:39 | ballshuffington | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nyvroq | false | null | t3_1nyvroq | /r/LocalLLaMA/comments/1nyvroq/what_open_source_model_is_the_best_for_coding_and/ | false | false | default | 1 | {'enabled': True, 'images': [{'id': 'z5asigt45ctf1', 'resolutions': [{'height': 198, 'url': 'https://preview.redd.it/z5asigt45ctf1.png?width=108&crop=smart&auto=webp&s=62e922df844945369a86baa5517511c50a9acf1f', 'width': 108}, {'height': 396, 'url': 'https://preview.redd.it/z5asigt45ctf1.png?width=216&crop=smart&auto=webp&s=aee4ef3bed35901d71cc1c9d7541e8b293c80722', 'width': 216}, {'height': 586, 'url': 'https://preview.redd.it/z5asigt45ctf1.png?width=320&crop=smart&auto=webp&s=57c67522fe93859b6134178702b0bf9ba5326599', 'width': 320}, {'height': 1173, 'url': 'https://preview.redd.it/z5asigt45ctf1.png?width=640&crop=smart&auto=webp&s=7156234acf82c6109f5f7dcc9b89e1275e0f6302', 'width': 640}, {'height': 1760, 'url': 'https://preview.redd.it/z5asigt45ctf1.png?width=960&crop=smart&auto=webp&s=a3a10c53201b54223f238cae1f047b9a30bbeaba', 'width': 960}, {'height': 1980, 'url': 'https://preview.redd.it/z5asigt45ctf1.png?width=1080&crop=smart&auto=webp&s=df74525c73eb5f10efc1b183bd028e74020557d6', 'width': 1080}], 'source': {'height': 2640, 'url': 'https://preview.redd.it/z5asigt45ctf1.png?auto=webp&s=93c5c55114ae94726c9c8c8e2d1750282fcafbeb', 'width': 1440}, 'variants': {}}]} | |
GLM-4.6 outperforms claude-4-5-sonnet while being ~8x cheaper | 586 | 2025-10-05T18:19:56 | Full_Piano_3448 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nyvqyx | false | null | t3_1nyvqyx | /r/LocalLLaMA/comments/1nyvqyx/glm46_outperforms_claude45sonnet_while_being_8x/ | false | false | default | 586 | {'enabled': True, 'images': [{'id': 'lofrjusz4ctf1', 'resolutions': [{'height': 64, 'url': 'https://preview.redd.it/lofrjusz4ctf1.png?width=108&crop=smart&auto=webp&s=a56e8e6218341e7e58402392c6894b47b39119ea', 'width': 108}, {'height': 129, 'url': 'https://preview.redd.it/lofrjusz4ctf1.png?width=216&crop=smart&auto=webp&s=b97be3faeb87516e8c8e31186417ad73714be31a', 'width': 216}, {'height': 191, 'url': 'https://preview.redd.it/lofrjusz4ctf1.png?width=320&crop=smart&auto=webp&s=4fb1bf091acb0ab891bea07ad9e70f53c3ff3114', 'width': 320}, {'height': 383, 'url': 'https://preview.redd.it/lofrjusz4ctf1.png?width=640&crop=smart&auto=webp&s=71caed6abfb748f2b7db9bdf1271b9b722f347fd', 'width': 640}, {'height': 575, 'url': 'https://preview.redd.it/lofrjusz4ctf1.png?width=960&crop=smart&auto=webp&s=0afc950c3e962ee8540fea927e1829ca10c214c6', 'width': 960}, {'height': 647, 'url': 'https://preview.redd.it/lofrjusz4ctf1.png?width=1080&crop=smart&auto=webp&s=9d9ecc0cb380de612ddb56b6716b53b7c5d152e3', 'width': 1080}], 'source': {'height': 719, 'url': 'https://preview.redd.it/lofrjusz4ctf1.png?auto=webp&s=8d50b7e08af234efea801ffe17892ea2056722e1', 'width': 1200}, 'variants': {}}]} | ||
What Open source model is the best for coding and what source? (Image from livebench 10/05/25) | 1 | [removed] | 2025-10-05T18:17:30 | ballshuffington | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nyvoly | false | null | t3_1nyvoly | /r/LocalLLaMA/comments/1nyvoly/what_open_source_model_is_the_best_for_coding_and/ | false | false | default | 1 | {'enabled': True, 'images': [{'id': '6ekpd7b74ctf1', 'resolutions': [{'height': 198, 'url': 'https://preview.redd.it/6ekpd7b74ctf1.png?width=108&crop=smart&auto=webp&s=a55fe1ccee46914dd7e29f8399d12bb49787c62e', 'width': 108}, {'height': 396, 'url': 'https://preview.redd.it/6ekpd7b74ctf1.png?width=216&crop=smart&auto=webp&s=43be2916af2d378e263ee329a28eee08e9ef551f', 'width': 216}, {'height': 586, 'url': 'https://preview.redd.it/6ekpd7b74ctf1.png?width=320&crop=smart&auto=webp&s=7f92f1c2036b6cfade9ab802065cfb3732873cea', 'width': 320}, {'height': 1173, 'url': 'https://preview.redd.it/6ekpd7b74ctf1.png?width=640&crop=smart&auto=webp&s=daea3831595aabd418bd513d4a6e5eb411ce38db', 'width': 640}, {'height': 1760, 'url': 'https://preview.redd.it/6ekpd7b74ctf1.png?width=960&crop=smart&auto=webp&s=445a4c78cad8740469b9638c4d9bbba87652e16f', 'width': 960}, {'height': 1980, 'url': 'https://preview.redd.it/6ekpd7b74ctf1.png?width=1080&crop=smart&auto=webp&s=612c4a86e5320fc6dcd2b7a7bc43ebc1a5e9e7d1', 'width': 1080}], 'source': {'height': 2641, 'url': 'https://preview.redd.it/6ekpd7b74ctf1.png?auto=webp&s=9f81692a1c953beebba75c53ff6d92a58b7ef492', 'width': 1440}, 'variants': {}}]} | |
Can anyone tell me what AI Model is this ? | 0 | I tried transliterate job at LmArena and got better output with following model : x1-1-kiwifruit
Any idea what model it could be ? | 2025-10-05T18:13:57 | https://www.reddit.com/r/LocalLLaMA/comments/1nyvl82/can_anyone_tell_me_what_ai_model_is_this/ | Bharat01123 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyvl82 | false | null | t3_1nyvl82 | /r/LocalLLaMA/comments/1nyvl82/can_anyone_tell_me_what_ai_model_is_this/ | false | false | self | 0 | null |
The Dragon Hatchinling: The Missing Link between the Transformer and Models of the Brain | 0 | I've already posted the info about this paper:
[https://www.reddit.com/r/LocalLLaMA/comments/1nv17bt/interesting\_article\_looks\_promising/](https://www.reddit.com/r/LocalLLaMA/comments/1nv17bt/interesting_article_looks_promising/)
But news is that the paper is trending in HF:
[https://huggingface.co/papers/trending](https://huggingface.co/papers/trending)
https://preview.redd.it/fmcxk8d01ctf1.png?width=3200&format=png&auto=webp&s=f41e5b4752a834b6fa6d338af83a67c33dc29d5b
| 2025-10-05T17:58:11 | https://www.reddit.com/r/LocalLLaMA/comments/1nyv5go/the_dragon_hatchinling_the_missing_link_between/ | Wooden_Yam1924 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyv5go | false | null | t3_1nyv5go | /r/LocalLLaMA/comments/1nyv5go/the_dragon_hatchinling_the_missing_link_between/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'bLHzEjyWcHSPyHSmJbYNtXHeE0bpidrBnWvCTx9hVhw', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/bLHzEjyWcHSPyHSmJbYNtXHeE0bpidrBnWvCTx9hVhw.png?width=108&crop=smart&auto=webp&s=58e075e4f7c37a26a7f2dcc883a9d16f8f3ebf07', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/bLHzEjyWcHSPyHSmJbYNtXHeE0bpidrBnWvCTx9hVhw.png?width=216&crop=smart&auto=webp&s=2d3f948c48f683fb5a34139c08722b0b826e9409', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/bLHzEjyWcHSPyHSmJbYNtXHeE0bpidrBnWvCTx9hVhw.png?width=320&crop=smart&auto=webp&s=6f227e381bfdb33330e8d45634ba0da6545919b1', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/bLHzEjyWcHSPyHSmJbYNtXHeE0bpidrBnWvCTx9hVhw.png?width=640&crop=smart&auto=webp&s=f7294305642509fe1bf65af5b4e00be6ae676161', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/bLHzEjyWcHSPyHSmJbYNtXHeE0bpidrBnWvCTx9hVhw.png?width=960&crop=smart&auto=webp&s=a8d914270977351328ecf71af4e6a8ddcc78135c', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/bLHzEjyWcHSPyHSmJbYNtXHeE0bpidrBnWvCTx9hVhw.png?width=1080&crop=smart&auto=webp&s=c59d385efa8611d672a5568a11afd04d81f7f614', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/bLHzEjyWcHSPyHSmJbYNtXHeE0bpidrBnWvCTx9hVhw.png?auto=webp&s=2a315ed7d94314975c5e10118c9cf66d2706e16f', 'width': 1200}, 'variants': {}}]} | |
I've found something surprising | 0 | THIS IS NOT A POST BASHING CHATGPT.
Instead of using the usuals e.g. gpt-oss-20b, or qwen3 as my AI assistant, I've been playing around with a couple of the uncensored models lately just for shits and giggles. E.G. an couple of uncensored NSFW model s I don't remember off the top of my head cause i'm not at home, I've been pleasantly surprised and honestly floored at just how close to the way GPT-4o used to make me feel.
It feels much more present, much more understanding, and a lot less judging.
Now don't get me wrong, i'm not one of those people that needs constant hand holding, or "safe" spaces, or anything else like that. But I am not for racism of any kind, I believe victims, I believe in giving people chances to prove their character, and I despise everything that seems to be the modern "cool". I don't believe woke is a verb, and such.
But 4o had a way of actually letting me vent, and talk, without throwing up barriers a lot of other LLM\\AI do nowadays. And now, using an uncensored model, I believe I got a lot of that emotional\\understanding back. Granted, it's not perfect. And this is all without prompting. I can't wait until I am able to get my prompting from ChatGPT into it to see how it performs.
And possibly the best part of all, when I ask it a technical question, it doesn't automatically assume i'm some random tech illiterate user who's asking questions way above my pay grade. It actually asks questions that give it context into what I'm wanting to do.
Honestly, some of these uncensored NSFW models seem to be slept on as far as being an actual assistant.
Anyone else have a similar experience to this?
Once I'm home, I will update this post with the uncensored models I'm toying with. | 2025-10-05T17:20:38 | https://www.reddit.com/r/LocalLLaMA/comments/1nyu5bk/ive_found_something_surprising/ | Savantskie1 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyu5bk | false | null | t3_1nyu5bk | /r/LocalLLaMA/comments/1nyu5bk/ive_found_something_surprising/ | false | false | self | 0 | null |
has anyone with 2 max-q blackwell 6000 Pro to be able to run qwen 235b fp4? | 2 | i can get 235b qwen3moeforcasual awq model to work with vllm.
just not fp4.
the closest I've gotten is that it OOMs when it seems to try and load the whole model on one of the GPUs instead of tensor splitting it.
I know this is kinda specific, but I've tried everything.
I cant tell If I'm doing something wrong or if its just not supported.
I've tried different models,
I've tried TensortRt llm trtllm-serve
I've tried vllm
I've tried building from source
I've tried many different docker containers
I've tried building inside many docker containers.
I've tried lots of different settings.
maybe i should be using a specific backend i haven't tried?
maybe turn off specific settings i don't know?
(you see my issue here)
so mainly looking for :
tensor parallelism 2
nvfp4 (or whatever can work with the fast fp4 features of the blackwell max-q)
im ok with "be patient", that would at least give me temporary closure
thank you much if anyone can provide insight.
have a good one | 2025-10-05T17:13:55 | https://www.reddit.com/r/LocalLLaMA/comments/1nytyx6/has_anyone_with_2_maxq_blackwell_6000_pro_to_be/ | I_can_see_threw_time | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nytyx6 | false | null | t3_1nytyx6 | /r/LocalLLaMA/comments/1nytyx6/has_anyone_with_2_maxq_blackwell_6000_pro_to_be/ | false | false | self | 2 | null |
Best Practices for AI Prompting 2025? | 0 | At this point, I’d like to know what the most effective and up-to-date techniques, strategies, prompt lists, or ready-made prompt archives are when it comes to working with AI.
Specifically, I’m referring to ChatGPT, Gemini, NotebookLM, and Claude. I’ve been using all of these LLMs for quite some time, but I’d like to improve the overall quality and consistency of my results.
For example, when I want to learn about a specific topic, are there any well-structured prompt archives or proven templates to start from? What should an effective initial prompt include, how should it be structured, and what key elements or best practices should one keep in mind?
There’s a huge amount of material out there, but much of it isn’t very helpful. I’m looking for the methods and resources that truly work.
So far i only heard of that "awesome-ai-system-prompts" Github. | 2025-10-05T17:00:42 | https://www.reddit.com/r/LocalLLaMA/comments/1nytm2a/best_practices_for_ai_prompting_2025/ | Party-Log-1084 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nytm2a | false | null | t3_1nytm2a | /r/LocalLLaMA/comments/1nytm2a/best_practices_for_ai_prompting_2025/ | false | false | self | 0 | null |
Hunyuan Image 3.0 Jumps to No.1 on LMArena’s Text-to-Image Leaderboard | 97 | https://huggingface.co/tencent/HunyuanImage-3.0
https://lmarena.ai/leaderboard/text-to-image | 2025-10-05T15:32:14 | https://www.reddit.com/r/LocalLLaMA/comments/1nyratf/hunyuan_image_30_jumps_to_no1_on_lmarenas/ | yogthos | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyratf | false | null | t3_1nyratf | /r/LocalLLaMA/comments/1nyratf/hunyuan_image_30_jumps_to_no1_on_lmarenas/ | false | false | self | 97 | {'enabled': False, 'images': [{'id': 'o3nW-C4go8YOZmYJKNZ4Y6tpE64YDvgP6ucRppFfdVQ', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/o3nW-C4go8YOZmYJKNZ4Y6tpE64YDvgP6ucRppFfdVQ.png?width=108&crop=smart&auto=webp&s=dd21c2a4939f8b5b5cbc12f8d86d32cd5479edcb', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/o3nW-C4go8YOZmYJKNZ4Y6tpE64YDvgP6ucRppFfdVQ.png?width=216&crop=smart&auto=webp&s=7f3875cc1863046dcd2288088fd32056618eb702', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/o3nW-C4go8YOZmYJKNZ4Y6tpE64YDvgP6ucRppFfdVQ.png?width=320&crop=smart&auto=webp&s=4d9c169a5903d4dbd991f0a231090dde300b8eea', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/o3nW-C4go8YOZmYJKNZ4Y6tpE64YDvgP6ucRppFfdVQ.png?width=640&crop=smart&auto=webp&s=0726b4b60205c7c2cac24ba84a82a9bbfa3680c3', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/o3nW-C4go8YOZmYJKNZ4Y6tpE64YDvgP6ucRppFfdVQ.png?width=960&crop=smart&auto=webp&s=50b0a4249c0a17def766c2f379fa0f597928f36c', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/o3nW-C4go8YOZmYJKNZ4Y6tpE64YDvgP6ucRppFfdVQ.png?width=1080&crop=smart&auto=webp&s=20a06864567932271d05f6d10711291309320449', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/o3nW-C4go8YOZmYJKNZ4Y6tpE64YDvgP6ucRppFfdVQ.png?auto=webp&s=981ad7e911767a79b361f4aa96d7c0f18efd73d6', 'width': 1200}, 'variants': {}}]} |
Training a Vision model on a Text-Only Dataset using Axolotl | 0 | I'm planning to fine-tune LLaMA 3.2 11B Instruct on a JSONL dataset of domain-specific question-answer pairs — purely text, no images. The goal is to improve its instruction-following behavior for specialized text tasks, while still retaining its ability to handle multimodal inputs like OCR and image-based queries.
I am using Axolotl
https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/llama-3-vision/lora-11b.yaml
in examples we have a sample .yaml file for this
```
base_model: alpindale/Llama-3.2-11B-Vision-Instruct
# optionally might have model_type or tokenizer_type or processor_type
processor_type: AutoProcessor
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
# these 3 lines are needed for now to handle vision chat templates w images
skip_prepare_dataset: true
remove_unused_columns: false
sample_packing: false
chat_template: llama3_2_vision
datasets:
- path: HuggingFaceH4/llava-instruct-mix-vsft
type: chat_template
split: train[:1%]
dataset_prepared_path:
val_set_size: 0.0
output_dir: ./outputs/out
adapter: lora
lora_model_dir:
sequence_len: 8192
pad_to_sequence_len: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: true
fp16:
tf32: true
gradient_checkpointing: true
logging_steps: 1
# flash_attention: true # use for text-only mode
sdp_attention: true
warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
weight_decay: 0.0
# save_first_step: true # uncomment this to validate checkpoint saving works with your config
```
based on which I have made a similar .yaml file
```
base_model: alpindale/Llama-3.2-11B-Vision-Instruct
processor_type: AutoProcessor
tokenizer_config: <path_to_custom_tokenizer>
tokenizer_type: AutoTokenizer
# Vision-chat template handling
# skip_prepare_dataset: true
# remove_unused_columns: false
# sample_packing: false
chat_template: llama3_2_vision
datasets:
- path: <path_to_dataset>
type: chat_template
field_messages: messages
message_property_mappings:
role: role
content: content
roles:
system:
- system
user:
- user
assistant:
- assistant
train_on_inputs: false
output_dir: <path_to_output_directory>
# Training parameters
sequence_len: 8192
pad_to_sequence_len: false
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
weight_decay: 0.0
warmup_ratio: 0.1
# Precision & performance
bf16: true
fp16:
tf32: true
gradient_checkpointing: true
logging_steps: 1
flash_attention: true # text-only mode
# sdp_attention: true
# Checkpointing
evals_per_epoch: 1
saves_per_epoch: 1
save_first_step: true
save_total_limit: 3
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
```
but when i run
`axolotl train config.yaml`
and I have processor_type:
```
base_model: alpindale/Llama-3.2-11B-Vision-Instruct
processor_type: AutoProcessor
tokenizer_config: <path_to_custom_tokenizer>
tokenizer_type: AutoTokenizer
```
I get the error
`KeyError: 'Indexing with integers is not available when using Python based feature extractors'`
but when i remove the field
```
base_model: alpindale/Llama-3.2-11B-Vision-Instruct
tokenizer_config: <path_to_custom_tokenizer>
tokenizer_type: AutoTokenizer
```
or even
```
base_model: alpindale/Llama-3.2-11B-Vision-Instruct
processor_type: AutoProcessor
tokenizer_config: <path_to_custom_tokenizer>
# Vision-chat template handling
skip_prepare_dataset: true
remove_unused_columns: false
sample_packing: false
```
I get the error
`AttributeError: 'MllamaTextSelfAttention' object has no attribute 'is_causal'`
What happened here?
How does one do this?
Will this fine-tuning lead to loss of Vision Capabilities of the model?
Is there a guide to writing config.yaml files for different models?
Python Version: 3.12
Axolotl Version: Latest
Dataset: a .jsonl with
```
{
"messages":
[
{"role": "system", "content": "<system_prompt>"},
{"role": "user", "content": "<question>"},
{"role": "assistant", "content": "<answer>"}
]
}
```
which was previously used to fine tune Llama3.1 8B using the following config.yaml
```
base_model: NousResearch/Meta-Llama-3.1-8B-Instruct
tokenizer_config: <path_to_custom_tokenizer>
tokenizer_type: AutoTokenizer
chat_template: llama3
datasets:
- path: <path_to_dataset>
type: chat_template
field_messages: messages
message_property_mappings:
role: role
content: content
roles:
system:
- system
user:
- user
assistant:
- assistant
train_on_inputs: false
output_dir: <path_to_output_directory>
sequence_len: 2048
sample_packing: true
gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
bf16: auto
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
resume_from_checkpoint:
auto_resume_from_checkpoints: true
save_only_model: false
logging_steps: 1
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 2
saves_per_epoch: 1
save_total_limit: 3
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
```
Thank you. | 2025-10-05T15:13:19 | https://www.reddit.com/r/LocalLLaMA/comments/1nyqt99/training_a_vision_model_on_a_textonly_dataset/ | PravalPattam12945RPG | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyqt99 | false | null | t3_1nyqt99 | /r/LocalLLaMA/comments/1nyqt99/training_a_vision_model_on_a_textonly_dataset/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': '6xPUi4gT4K3LAZg-sHN5F4U8R9a7i2FdUlguaqyGSgI', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/6xPUi4gT4K3LAZg-sHN5F4U8R9a7i2FdUlguaqyGSgI.png?width=108&crop=smart&auto=webp&s=62ae9ceeab7aa8ec5fa62ebf9fe1e3bda2d155ee', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/6xPUi4gT4K3LAZg-sHN5F4U8R9a7i2FdUlguaqyGSgI.png?width=216&crop=smart&auto=webp&s=a76f588ac4138fc2d05e809cced6220dd0d4b839', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/6xPUi4gT4K3LAZg-sHN5F4U8R9a7i2FdUlguaqyGSgI.png?width=320&crop=smart&auto=webp&s=5a9201895fd8f7fc6649d8b8f020847330f3cb7a', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/6xPUi4gT4K3LAZg-sHN5F4U8R9a7i2FdUlguaqyGSgI.png?width=640&crop=smart&auto=webp&s=faf55921a1b690cee64915234ae83be68ffdc3d5', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/6xPUi4gT4K3LAZg-sHN5F4U8R9a7i2FdUlguaqyGSgI.png?width=960&crop=smart&auto=webp&s=4b903c36f6aed74d8560a0f8bbbc878f1a2c09c1', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/6xPUi4gT4K3LAZg-sHN5F4U8R9a7i2FdUlguaqyGSgI.png?width=1080&crop=smart&auto=webp&s=a249017c5c6f2734685c7da837718b23bc8a2f8f', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/6xPUi4gT4K3LAZg-sHN5F4U8R9a7i2FdUlguaqyGSgI.png?auto=webp&s=2144de6f7fdf4c4e2e954e05a6ea1ca09a1b82d4', 'width': 1200}, 'variants': {}}]} |
Looking for a High-Quality Local NSFW Model – 4060 Ti (8GB VRAM), 32GB RAM | 0 | Hey everyone,
I'm trying to build a solid local setup for generating NSFW content, and I’ve hit a bit of a wall—hoping someone here can help or point me in the right direction.
My Goal:
I want a fully local, uncensored model that runs via Oobabooga, with no reliance on APIs or cloud services. This is both for privacy and because I’m considering monetizing the output (e.g., story captions, writing for games, etc.), and I want to avoid any Terms of Service issues that might come with using hosted tools.
My Hardware:
GPU: 4060 Ti (8GB VRAM)
RAM: 32GB
Running models via Oobabooga
What I’ve Tried:
I recently attempted to run Llama_3.x_70b_Hexagon_Purple_V1.Q3_K_M.gguf. It technically loads, but performance is unbearably slow—definitely not usable on my current setup.
Before that, I experimented with several smaller models that did run, including:
Llama-3.1-Storm-8B-Q4_K_M (~4.8 GB)
Mistral-Nemo-12B-ArliAI-RPMax-v1.1-Q4_K_M (~7.3 GB)
l3.1-dark-reasoning-dark-planet-hermes-r1-uncensored-8b-q6_k (~6.4 GB)
They ran reasonably well—slow generation times at worst—but none matched the output quality I’m seeing from DeepSeek.
The Problem with DeepSeek:
While DeepSeek gives me the kind of quality I'm looking for, the content often gets removed or censored, and since I’m aiming to monetize the text eventually, I’m not comfortable relying on it due to potential ToS/legal risks.
What I’m Hoping to Find:
A model that runs locally and smoothly on a 4060 Ti (8GB)
Strong creative output for NSFW/character/story generation
Comparable to (or better than) DeepSeek in quality Ideally uncensored or with minimal restrictions
Doesn’t cross into actual illegal territory—just needs to be flexible enough for adult content writing
Bonus:
If anyone's open to chatting more in-depth, feel free to DM me—or even better, let me know if you're on Discord. I'd love to exchange setups, experiences, and maybe benchmark ideas together.
Appreciate any tips, models, or resources you can share! | 2025-10-05T15:11:27 | https://www.reddit.com/r/LocalLLaMA/comments/1nyqrjd/looking_for_a_highquality_local_nsfw_model_4060/ | Zkimwon | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyqrjd | false | null | t3_1nyqrjd | /r/LocalLLaMA/comments/1nyqrjd/looking_for_a_highquality_local_nsfw_model_4060/ | false | false | nsfw | 0 | null |
Developers who use META AI lol. | 96 | And no disrespect to META AI open models. They were one of the first to make their models available publicly.
Can’t crosspost but here’s the OP: https://www.reddit.com/r/ProgrammerHumor/s/O1tXgRqKrr | 2025-10-05T15:08:09 | NoFudge4700 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nyqog8 | false | null | t3_1nyqog8 | /r/LocalLLaMA/comments/1nyqog8/developers_who_use_meta_ai_lol/ | false | false | 96 | {'enabled': True, 'images': [{'id': 'xZE3QoeLnsay2NgyQPtmvZDNVG5YI9hZKopZ22f8hMY', 'resolutions': [{'height': 106, 'url': 'https://preview.redd.it/2p7qaw5u6btf1.jpeg?width=108&crop=smart&auto=webp&s=a9fa5a6a2bbe79a061f37eb35fe3e6fe90da47eb', 'width': 108}, {'height': 213, 'url': 'https://preview.redd.it/2p7qaw5u6btf1.jpeg?width=216&crop=smart&auto=webp&s=6f44f541c5147d9a37f10b342cd08fc5fa376201', 'width': 216}, {'height': 316, 'url': 'https://preview.redd.it/2p7qaw5u6btf1.jpeg?width=320&crop=smart&auto=webp&s=e91f577eb1eb71e1061bbc2b7c09b69ca4595d06', 'width': 320}, {'height': 633, 'url': 'https://preview.redd.it/2p7qaw5u6btf1.jpeg?width=640&crop=smart&auto=webp&s=bf422c668b342ba132e44daffb0701e8cb911da5', 'width': 640}, {'height': 950, 'url': 'https://preview.redd.it/2p7qaw5u6btf1.jpeg?width=960&crop=smart&auto=webp&s=28fe3d115161667f446209abff09837e5f3bf11e', 'width': 960}, {'height': 1069, 'url': 'https://preview.redd.it/2p7qaw5u6btf1.jpeg?width=1080&crop=smart&auto=webp&s=739e8619aee7241adb3cf4186e22d8b5694decb3', 'width': 1080}], 'source': {'height': 1153, 'url': 'https://preview.redd.it/2p7qaw5u6btf1.jpeg?auto=webp&s=64fda16dc53d656dccfb17fca8e5dd1bf293e643', 'width': 1164}, 'variants': {}}]} | ||
Looking for a High-Quality Local NSFW Model – 4060 Ti (8GB VRAM), 32GB RAM | 1 | Hey everyone,
I'm trying to build a solid local setup for generating NSFW content, and I’ve hit a bit of a wall—hoping someone here can help or point me in the right direction.
My Goal:
I want a fully local, uncensored model that runs via Oobabooga, with no reliance on APIs or cloud services. This is both for privacy and because I’m considering monetizing the output (e.g., story captions, writing for games, etc.), and I want to avoid any Terms of Service issues that might come with using hosted tools.
My Hardware:
GPU: 4060 Ti (8GB VRAM)
RAM: 32GB
Running models via Oobabooga
What I’ve Tried:
I recently attempted to run Llama_3.x_70b_Hexagon_Purple_V1.Q3_K_M.gguf. It technically loads, but performance is unbearably slow—definitely not usable on my current setup.
Before that, I experimented with several smaller models that did run, including:
Llama-3.1-Storm-8B-Q4_K_M (~4.8 GB)
Mistral-Nemo-12B-ArliAI-RPMax-v1.1-Q4_K_M (~7.3 GB)
l3.1-dark-reasoning-dark-planet-hermes-r1-uncensored-8b-q6_k (~6.4 GB)
They ran reasonably well—slow generation times at worst—but none matched the output quality I’m seeing from DeepSeek.
The Problem with DeepSeek:
While DeepSeek gives me the kind of quality I'm looking for, the content often gets removed or censored, and since I’m aiming to monetize the text eventually, I’m not comfortable relying on it due to potential ToS/legal risks.
What I’m Hoping to Find:
A model that runs locally and smoothly on a 4060 Ti (8GB)
Strong creative output for NSFW/character/story generation
Comparable to (or better than) DeepSeek in quality | 2025-10-05T15:06:29 | https://www.reddit.com/r/LocalLLaMA/comments/1nyqn13/looking_for_a_highquality_local_nsfw_model_4060/ | Zkimwon | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyqn13 | false | null | t3_1nyqn13 | /r/LocalLLaMA/comments/1nyqn13/looking_for_a_highquality_local_nsfw_model_4060/ | false | false | nsfw | 1 | null |
Apple has added significant AI-acceleration to its A19 CPU cores | 233 | Data source: [https://ai-benchmark.com/ranking\_processors\_detailed.html](https://ai-benchmark.com/ranking_processors_detailed.html)
We also might see these advances back in the M5. | 2025-10-05T15:03:50 | Balance- | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nyqkkm | false | null | t3_1nyqkkm | /r/LocalLLaMA/comments/1nyqkkm/apple_has_added_significant_aiacceleration_to_its/ | false | false | default | 233 | {'enabled': True, 'images': [{'id': 'ti22axwj5btf1', 'resolutions': [{'height': 39, 'url': 'https://preview.redd.it/ti22axwj5btf1.png?width=108&crop=smart&auto=webp&s=ca95a317cf837785c42bb3d4e0129ca076a84123', 'width': 108}, {'height': 78, 'url': 'https://preview.redd.it/ti22axwj5btf1.png?width=216&crop=smart&auto=webp&s=4d8bf72a41f11af2344b416b8684b4bb4f0a0d57', 'width': 216}, {'height': 116, 'url': 'https://preview.redd.it/ti22axwj5btf1.png?width=320&crop=smart&auto=webp&s=4267f25ba757dca2363eb55de0846964fb47fa63', 'width': 320}, {'height': 233, 'url': 'https://preview.redd.it/ti22axwj5btf1.png?width=640&crop=smart&auto=webp&s=967e4aea50a8298df5070520a6bc78e77ecbcfb7', 'width': 640}, {'height': 350, 'url': 'https://preview.redd.it/ti22axwj5btf1.png?width=960&crop=smart&auto=webp&s=c29b53c9ed71966f38a95515e3f9be9428e6e8b7', 'width': 960}, {'height': 394, 'url': 'https://preview.redd.it/ti22axwj5btf1.png?width=1080&crop=smart&auto=webp&s=18deeb274c219efabae7a198efbb8d7fa85bceee', 'width': 1080}], 'source': {'height': 1631, 'url': 'https://preview.redd.it/ti22axwj5btf1.png?auto=webp&s=4a7a88f0a4f4f6a4fced45cf476098b00c53c225', 'width': 4469}, 'variants': {}}]} | |
Llama Scout not producing Ratings as Instructor | 0 | I have a set of transcript and a corresponding summary for the transcript which need to be evaluated to give rating and explanation to verify if the summary is accurate for the transcript provided. Llama Scout is ignoring my system prompt to give me Rating and explanation.
prompt = """You are an evaluator. Respond ONLY in this format:
Rating: <digit 1-5>
Explanation: <1-2 sentences>
Do NOT add anything else.
Transcript:
Agent: Thank you for calling, how may I help you?
Customer: I want to reset my password.
Summary:
The agent greeted the customer and the customer asked to reset their password.
"""
Scout is responding back with steps or any arbitrary response but not Rating and Explanation.
Requesting for quick help on the same.
| 2025-10-05T14:55:19 | https://www.reddit.com/r/LocalLLaMA/comments/1nyqcig/llama_scout_not_producing_ratings_as_instructor/ | Thin_Championship_24 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyqcig | false | null | t3_1nyqcig | /r/LocalLLaMA/comments/1nyqcig/llama_scout_not_producing_ratings_as_instructor/ | false | false | self | 0 | null |
Great idea that only works now with local vision AI | 0 | Here is an example of a problem (socitey wide and world wide) that can now be solved thanks to AI:
Take cigarette butts. They are thrown away and litter the streets and nature. The nicotine from the filters gets into the groundwater.
What if there was a deposit on them just like with bottles?
The problem is: bottles can be inspected by a machine for their return worthyness.
This machine doesnt have to be very smart or an AI.
With cigarette butts it is different. They come is all sorts of bent shapes. Some are burnt lightly maybe.
Some still have a part of the cigarette. Some dont have filters, etc.
But here's the solution: an AI vision system is trained that distinguishes returnable butts from non returnable ones or other items.
Even if it's not perfect, everyone should be able to agree on the decision of that AI.
And now here's the thing: such an AI has to be able to run locally on a relatively small computer.
Because the return stations have to be everywhere (mainly where the supermarkets are just like with bottles).
But this is possible now!
The result would be: no more cigarette butts littering your city, your train station, and nature.
Even less wildfires maybe since people dont throw away cigarettes anymore.
It worked with bottles and cans. Now it can work with cigarettes as well. And I'm sure there are other exmaples in that vein. I had this idea following this thread with all the cool new local vision models coming out.
| 2025-10-05T14:54:40 | https://www.reddit.com/r/LocalLLaMA/comments/1nyqbxr/great_idea_that_only_works_now_with_local_vision/ | Cultural_Register410 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyqbxr | false | null | t3_1nyqbxr | /r/LocalLLaMA/comments/1nyqbxr/great_idea_that_only_works_now_with_local_vision/ | false | false | self | 0 | null |
I'm looking for an AI that I can use as a GM in a text-based role-playing game. | 0 | I'm looking for an AI that I can use as a GM in a text-based role-playing game. I want an AI that can build the system, bring the characters to life, and most importantly, remember the details of a long-term, episodic game. I can also use a local model using Lmstudio. What do you recommend? | 2025-10-05T14:53:31 | https://www.reddit.com/r/LocalLLaMA/comments/1nyqax4/im_looking_for_an_ai_that_i_can_use_as_a_gm_in_a/ | Beneficial-Guitar510 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyqax4 | false | null | t3_1nyqax4 | /r/LocalLLaMA/comments/1nyqax4/im_looking_for_an_ai_that_i_can_use_as_a_gm_in_a/ | false | false | self | 0 | null |
Crazy idea: Instead of generating 100 tokens in one model, sequentially generate across several models | 1 | MoE models have a massive underused advantage for consumer hardware over dense models: the VRAM usage is so small you can run several of models(using llama.cpp --cpu-moe I run three models of different quant size: ERNIE, lang-lite, granite. Combined they use less than 8GB VRAM).
So I had an idea: what if we make proxy server and when it receives "prompt is 'the screen is blue', make me 100 tokens', instead of doing it, the proxy generates 15-30 tokens calling one model, appends their text to the prompt, calls another model with updated prompt, and does so until all tokens are generated.
I asked gemini-pro a little (too lazy to make myself) and got [llama-in-the-middle](https://github.com/Maykeye/llama-in-the-midle) proxy that sits on 11111 port and switches between 10000, 10001, 10002 for /completion(not for chat, it's possible but requires effort). There is no CLI options, gui, all settings are in the python file; requirements.txt not included
The downside is during a switch there is a pause as model needs to figure out the prompt WTF other models have generated. Inclusion of output of different models makes them creative and less repetitive.
(Also it seems the models are able to recover from different tokenization: models with token "thinking" are capable to make "thinking" in text if text ends with "thinki")
Feel free to steal idea if you are going to make next UI | 2025-10-05T14:30:58 | https://www.reddit.com/r/LocalLLaMA/comments/1nypqdq/crazy_idea_instead_of_generating_100_tokens_in/ | Maykey | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nypqdq | false | null | t3_1nypqdq | /r/LocalLLaMA/comments/1nypqdq/crazy_idea_instead_of_generating_100_tokens_in/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'Up-NI0MudaC6wzr7CkRtvFvihPiB44i7jUuvfTHwkY4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/Up-NI0MudaC6wzr7CkRtvFvihPiB44i7jUuvfTHwkY4.png?width=108&crop=smart&auto=webp&s=c093ed741fef9071a946ebcacab03174c0f55d7a', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/Up-NI0MudaC6wzr7CkRtvFvihPiB44i7jUuvfTHwkY4.png?width=216&crop=smart&auto=webp&s=24f9029b9a2e64c34b9ef001823b97655095b5d2', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/Up-NI0MudaC6wzr7CkRtvFvihPiB44i7jUuvfTHwkY4.png?width=320&crop=smart&auto=webp&s=45320ff7c12e22557c5e8eeafc6b4da9a2e2bc45', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/Up-NI0MudaC6wzr7CkRtvFvihPiB44i7jUuvfTHwkY4.png?width=640&crop=smart&auto=webp&s=b9ee5c41fde0e700dfd06c5fd9d93c1eedfebbed', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/Up-NI0MudaC6wzr7CkRtvFvihPiB44i7jUuvfTHwkY4.png?width=960&crop=smart&auto=webp&s=0dfdf66859fc2e50b03f3a4f3efb3c45a9858b76', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/Up-NI0MudaC6wzr7CkRtvFvihPiB44i7jUuvfTHwkY4.png?width=1080&crop=smart&auto=webp&s=ea9fc574ffcd98e682c53ea888a6cfe5a83fa58c', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/Up-NI0MudaC6wzr7CkRtvFvihPiB44i7jUuvfTHwkY4.png?auto=webp&s=7ca0ef99eed88920700aec6c782f0973af572ace', 'width': 1200}, 'variants': {}}]} |
Made the first .NET wrapper for Apple MLX - looking for feedback! | 22 | Short story: I'm a .NET enthusiast and recently got excited about MLX. Thought - why not marry these two technologies?
That's how [**MLXSharp**](https://github.com/managedcode/MLXSharp) was born - the first proper .NET wrapper for MLX that also integrates with Microsoft.Extensions.AI.
What it can do:
* Works as IChatClient and IEmbeddingGenerator
* Dependency Injection and Semantic Kernel support
* Ready-to-use bindings for macOS and Linux
* .NET 9 / C# 13 friendly
This is my first open-source project of this scale. Would really appreciate any feedback - from architecture to documentation. Especially interested in hearing from folks working with ML on .NET or those with native interop experience.
If anyone wants to test it on their M1/M2/M3 Mac - would love to hear your thoughts!
GitHub: [https://github.com/managedcode/MLXSharp](https://github.com/managedcode/MLXSharp) | 2025-10-05T14:30:45 | https://www.reddit.com/r/LocalLLaMA/comments/1nypq6q/made_the_first_net_wrapper_for_apple_mlx_looking/ | csharp-agent | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nypq6q | false | null | t3_1nypq6q | /r/LocalLLaMA/comments/1nypq6q/made_the_first_net_wrapper_for_apple_mlx_looking/ | false | false | self | 22 | {'enabled': False, 'images': [{'id': 'ri5xHQ9SAQS6XcdDgV6G0aIO6ZRmfsEPOmXWDtDzxVY', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/ri5xHQ9SAQS6XcdDgV6G0aIO6ZRmfsEPOmXWDtDzxVY.png?width=108&crop=smart&auto=webp&s=bbe93afd3a46c0856807022839c55e9c6f5b76a8', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/ri5xHQ9SAQS6XcdDgV6G0aIO6ZRmfsEPOmXWDtDzxVY.png?width=216&crop=smart&auto=webp&s=b8bcea70ca413d67b193bc6e35fd6da93a5c2304', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/ri5xHQ9SAQS6XcdDgV6G0aIO6ZRmfsEPOmXWDtDzxVY.png?width=320&crop=smart&auto=webp&s=c884c199b3079689249b46b771c3215aaa83ea52', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/ri5xHQ9SAQS6XcdDgV6G0aIO6ZRmfsEPOmXWDtDzxVY.png?width=640&crop=smart&auto=webp&s=c1ba5216ba7ec6c8d480bb40208fe31e9a102d19', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/ri5xHQ9SAQS6XcdDgV6G0aIO6ZRmfsEPOmXWDtDzxVY.png?width=960&crop=smart&auto=webp&s=22b3838156e2b05e95d7b72d085df8155a14a25d', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/ri5xHQ9SAQS6XcdDgV6G0aIO6ZRmfsEPOmXWDtDzxVY.png?width=1080&crop=smart&auto=webp&s=88365d80b9279a77ee39f80da353ddd70dc41324', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/ri5xHQ9SAQS6XcdDgV6G0aIO6ZRmfsEPOmXWDtDzxVY.png?auto=webp&s=2326c8b1bacdf10874a8b0086ebf8ca2545f4533', 'width': 1200}, 'variants': {}}]} |
How does everyone feel about DeepseekV3.2-exp? I am very curious to find out how it compares to Deepseek-V3.1-terminus. | 1 | I am especially curious about how the indexer and sparse attention change behavior, if at all. | 2025-10-05T14:16:01 | https://www.reddit.com/r/LocalLLaMA/comments/1nypd55/how_does_everyone_feel_about_deepseekv32exp_i_am/ | Euphoric_Ad9500 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nypd55 | false | null | t3_1nypd55 | /r/LocalLLaMA/comments/1nypd55/how_does_everyone_feel_about_deepseekv32exp_i_am/ | false | false | self | 1 | null |
Did anyone try out GLM-4.5-Air-GLM-4.6-Distill ? | 111 | https://huggingface.co/BasedBase/GLM-4.5-Air-GLM-4.6-Distill
"GLM-4.5-Air-GLM-4.6-Distill represents an advanced distillation of the GLM-4.6 model into the efficient GLM-4.5-Air architecture. Through a SVD-based knowledge transfer methodology, this model inherits the sophisticated reasoning capabilities and domain expertise of its 92-layer, 160-expert teacher while maintaining the computational efficiency of the 46-layer, 128-expert student architecture."
Distillation scripts are public: https://github.com/Basedbase-ai/LLM-SVD-distillation-scripts | 2025-10-05T13:49:38 | https://www.reddit.com/r/LocalLLaMA/comments/1nyopyc/did_anyone_try_out_glm45airglm46distill/ | beneath_steel_sky | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyopyc | false | null | t3_1nyopyc | /r/LocalLLaMA/comments/1nyopyc/did_anyone_try_out_glm45airglm46distill/ | false | false | self | 111 | {'enabled': False, 'images': [{'id': '5_QnTdb-gXpD_-sJVlN3yvsb3chlF5-oOvfuz2u6vTs', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/5_QnTdb-gXpD_-sJVlN3yvsb3chlF5-oOvfuz2u6vTs.png?width=108&crop=smart&auto=webp&s=bf06d94d055e5eb02453da7dfae7679affcebecf', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/5_QnTdb-gXpD_-sJVlN3yvsb3chlF5-oOvfuz2u6vTs.png?width=216&crop=smart&auto=webp&s=7cc30ccae00a86f31bb22109f06e7cc2531f857e', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/5_QnTdb-gXpD_-sJVlN3yvsb3chlF5-oOvfuz2u6vTs.png?width=320&crop=smart&auto=webp&s=595b32df2d308e564706db2accc85204a9208c0c', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/5_QnTdb-gXpD_-sJVlN3yvsb3chlF5-oOvfuz2u6vTs.png?width=640&crop=smart&auto=webp&s=0c00d3384c002b1e2fd5378b946a539e478a4f00', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/5_QnTdb-gXpD_-sJVlN3yvsb3chlF5-oOvfuz2u6vTs.png?width=960&crop=smart&auto=webp&s=9e49ba30272c053a7b4b6b35504ef730fa2ec3f2', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/5_QnTdb-gXpD_-sJVlN3yvsb3chlF5-oOvfuz2u6vTs.png?width=1080&crop=smart&auto=webp&s=3f1701daafb9ceae7a68032b53087b0f42648488', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/5_QnTdb-gXpD_-sJVlN3yvsb3chlF5-oOvfuz2u6vTs.png?auto=webp&s=8d6ca243537a319268fb0ba46be62b9d7d464bbc', 'width': 1200}, 'variants': {}}]} |
VS code alternative for system prompt control and general workflow | 3 | I am looking for something like vs code with the chat based agent workflow and tool execution except I get to control the system prompt. Is there such a thing, it doesn’t have to be free or open source. | 2025-10-05T13:12:26 | https://www.reddit.com/r/LocalLLaMA/comments/1nynut8/vs_code_alternative_for_system_prompt_control_and/ | odnxe | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nynut8 | false | null | t3_1nynut8 | /r/LocalLLaMA/comments/1nynut8/vs_code_alternative_for_system_prompt_control_and/ | false | false | self | 3 | null |
Is it time to download the Deepseek/Kimi weights even if we can't run them? | 63 | Given the uptick in articles claiming Deepseek is a threat, it's not crazy to predict that it gets banned in the near future if you live in the USA and maybe some other Western countries.
And yeah, there's torrents, but if it gets classified as a *THREAT* the risk of downloading could be far different than, say, not wanting to pay for Shrek 2 and sailing the seas for it.
So I'm curious if there's any storage-rich preppers out there who have downloaded the weights for some of these massive models out of an abundance of caution. | 2025-10-05T13:10:10 | https://www.reddit.com/r/LocalLLaMA/comments/1nynsxt/is_it_time_to_download_the_deepseekkimi_weights/ | ForsookComparison | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nynsxt | false | null | t3_1nynsxt | /r/LocalLLaMA/comments/1nynsxt/is_it_time_to_download_the_deepseekkimi_weights/ | false | false | self | 63 | null |
Does anyone use gpt-oss-20b? | 2 | I'm trying this model. It behaves very interestingly. But I don't understand how to use it. Are there any recommendations for its proper use? Temperature, llamacpp option, etc. Does anyone have experience with json schema using model? | 2025-10-05T13:04:53 | https://www.reddit.com/r/LocalLLaMA/comments/1nynon4/does_anyone_use_gptoss20b/ | Artemopolus | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nynon4 | false | null | t3_1nynon4 | /r/LocalLLaMA/comments/1nynon4/does_anyone_use_gptoss20b/ | false | false | self | 2 | null |
Cursor-like tools that work with llama.cpp | 2 | Recently started using llama.cpp instead of LM Studio and wanting to try vibe coding with Local LLMs.
I've found several threads and videos about setting up various tools to use Ollama, but can't seem to find any good information on setting them up to use llama.cpp. Also saw a guide on how to set up Cursor to use LocalLLMs but it requires sending data back to Cursor's servers which kind of defeats the purpose and is a pain.
Does anyone know how to do this or of any resources explaining how to set this up? | 2025-10-05T12:51:13 | https://www.reddit.com/r/LocalLLaMA/comments/1nyndnp/cursorlike_tools_that_work_with_llamacpp/ | ga239577 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyndnp | false | null | t3_1nyndnp | /r/LocalLLaMA/comments/1nyndnp/cursorlike_tools_that_work_with_llamacpp/ | false | false | self | 2 | null |
vLLM and SGLang downloads model twice or thrice | 9 | I just want to complain about something extremely stupid. The OpenAI GPT OSS 120b has the model weights three times on Hugging Face. First version in the root, the other in a folder named "original" and the last is the "metal" version. We obviously only want one copy. vLLM downloads all three copies and SGLang downloads two copies. Argh! Such a waste of time and space. I am on 10 Gbps internet and it still annoys me. | 2025-10-05T12:00:55 | https://www.reddit.com/r/LocalLLaMA/comments/1nymcn5/vllm_and_sglang_downloads_model_twice_or_thrice/ | Baldur-Norddahl | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nymcn5 | false | null | t3_1nymcn5 | /r/LocalLLaMA/comments/1nymcn5/vllm_and_sglang_downloads_model_twice_or_thrice/ | false | false | self | 9 | null |
Is a Threadripper 9955WX enough for quad GPU inferencing? | 4 | I want to upgrade my workstation and am wondering if a 16 core 9955WX is enough for like 4x RTX 6000 Ada or even RTX Pro 6000. Currently I have 2x A6000 with the option to cheaply upgrade to 4x A6000. I want to avoid overspending like 3000€+ for a 9975WX when the limited core count and memory bandwidth is fine. The idea is to get a WRX90 board and 4 RAM sticks first and still be able to upgrade RAM and CPU in the future when it’s cheaper. | 2025-10-05T11:49:09 | https://www.reddit.com/r/LocalLLaMA/comments/1nym4nd/is_a_threadripper_9955wx_enough_for_quad_gpu/ | Hurricane31337 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nym4nd | false | null | t3_1nym4nd | /r/LocalLLaMA/comments/1nym4nd/is_a_threadripper_9955wx_enough_for_quad_gpu/ | false | false | self | 4 | null |
We're experiencing an attack on our CPU servers, causing a temporary outage for Z.ai Chat. We're working on a fix and will be back ASAP. Thanks for your support and understanding. | 1 | [removed] | 2025-10-05T11:25:59 | https://www.reddit.com/r/LocalLLaMA/comments/1nylpc6/were_experiencing_an_attack_on_our_cpu_servers/ | Impressive-Olive8372 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nylpc6 | false | null | t3_1nylpc6 | /r/LocalLLaMA/comments/1nylpc6/were_experiencing_an_attack_on_our_cpu_servers/ | false | false | self | 1 | null |
Found Nemotron-9B-v2 quite underwhelming, what am I missing ? | 13 | After seeing some very positive reviews about Nvidia Nemotron-9B-v2, I downloaded the 6-bit quantized MLX flavour on my Mac Mini M4 (24GB URAM), and set a 32kB context window. After about a dozen different prompts, my opinion of the model is not very positive. It seems to also have a hard time making sense of the history of conversation, making contextually incorrect assumptions (like in AI/ML and enterprise Java framework context, expanded "MCP" to "Manageable Customization Platform"). Upon reprompting it failed to make sense of the history of the discussion so far. Note that I had switched off reasoning. I've tried several other models including "phi4", "gemma 3", which seem to perform far better for such prompts. Wondering if there is some setting I am missing ? It is surprising how underwhelming it felt so far. | 2025-10-05T11:25:11 | https://www.reddit.com/r/LocalLLaMA/comments/1nyloqs/found_nemotron9bv2_quite_underwhelming_what_am_i/ | Professional_Row_967 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyloqs | false | null | t3_1nyloqs | /r/LocalLLaMA/comments/1nyloqs/found_nemotron9bv2_quite_underwhelming_what_am_i/ | false | false | self | 13 | null |
Help needed choosing best LLM & fixing KoboldCPP | 2 | Hi, I'm creating an AI agent to help diagnose and troubleshoot problems at work (general consumer electronics, mainly phones, tablets, laptops).
I've tested Qwen3 14b and gpt-oss-20b with mixed results.
For now, I've settled on the aforementioned gpt-oss-20b, looking for other (better?) alternatives (if available).
The problem with gpt is that it only works through llama.cpp.
I don't know if I'm doing something wrong, but I can't get it to work on koboldcpp (preferred due to my GPU setup).
RTX 3060 + GTX 1070 (20GB total)
When I use it through koboldcpp+Open WebUI, the channels aren't detected correctly (OpenAI Harmony).
Do you have any recommendations for other models or how to properly configure koboldcpp for gpt? | 2025-10-05T11:08:52 | https://www.reddit.com/r/LocalLLaMA/comments/1nyle33/help_needed_choosing_best_llm_fixing_koboldcpp/ | Oliwier-GL | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyle33 | false | null | t3_1nyle33 | /r/LocalLLaMA/comments/1nyle33/help_needed_choosing_best_llm_fixing_koboldcpp/ | false | false | self | 2 | null |
NIST evaluates Deepseek as unsafe. Looks like the battle to discredit opensource is underway | 605 | 2025-10-05T11:05:46 | https://www.techrepublic.com/article/news-deepseek-security-gaps-caisi-study/ | Nobby_Binks | techrepublic.com | 1970-01-01T00:00:00 | 0 | {} | 1nylc3q | false | null | t3_1nylc3q | /r/LocalLLaMA/comments/1nylc3q/nist_evaluates_deepseek_as_unsafe_looks_like_the/ | false | false | default | 605 | null | |
Video2X 6.x — open-source upscaler + frame interpolation (Anime4K v4 / Real-ESRGAN / Real-CUGAN / RIFE) 🚀 | 29 |
Big C/C++ rewrite with a faster pipeline, **Windows & Linux** support, and a new Windows GUI installer. Upscale and/or interpolate via Vulkan-powered ncnn backends.
https://preview.redd.it/ku6s1j5zv9tf1.png?width=2600&format=png&auto=webp&s=e2f08d6adcbe29bb1dca79814ca05296dab76d11
* Engines: **Anime4K v4**, **Real-ESRGAN**, **Real-CUGAN**, **RIFE**; works for both filtering (upscale) and interpolation.
* Easy setup: Windows installer, Linux packages/AppImage, plus Docker/Podman images; Colab notebook available.
[https://github.com/k4yt3x/video2x](https://github.com/k4yt3x/video2x) | 2025-10-05T10:45:43 | https://www.reddit.com/r/LocalLLaMA/comments/1nykzv3/video2x_6x_opensource_upscaler_frame/ | freesysck | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nykzv3 | false | null | t3_1nykzv3 | /r/LocalLLaMA/comments/1nykzv3/video2x_6x_opensource_upscaler_frame/ | false | false | 29 | null | |
Need help: fine-tuning a summarization model for 200k context | 6 | Hi everyone,
I'm looking for advice on building or fine-tuning a local model. The input size ranges from 50k to 200k, and the output should be around 32k.
1. What’s the best open-source model available for this task? Qwen3 ? And what’s the maximum inference speed I could expect on a B200 with that size ?
2. It shouldn’t be possible to fine-tune at that full context length, right? Should I start with 50k → 20k and then scale up? | 2025-10-05T10:43:12 | https://www.reddit.com/r/LocalLLaMA/comments/1nykybl/need_help_finetuning_a_summarization_model_for/ | AcanthaceaeNo5503 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nykybl | false | null | t3_1nykybl | /r/LocalLLaMA/comments/1nykybl/need_help_finetuning_a_summarization_model_for/ | false | false | self | 6 | null |
BULaMU-The First Luganda Large Language Model Trained from Scratch | 15 | Hi everybody! I hope all is well. I just wanted to share a project that I have been working on for the last several months called BULaMU. It is the first large language model that has been trained from scratch on Luganda. It has 20M parameters so it should be really easy to run on a phone, laptop, or other low powered device and does not require connecting to the internet, since inference happens in C. The details of how I trained it are [here](https://zenodo.org/records/17271688). If you would like to download it, use it, or adapt it for your own use, it is available for free on my Huggingface [account](https://huggingface.co/datasets/mwebazarick/BULaMU). I am open to any feedback that you are willing to share because I am going to continue working on improving BULaMU. I really believe that tiny language models like this decrease the high barrier to entry that AI often has by allowing people to use these models without a super powerful computer or access to the internet. | 2025-10-05T10:41:46 | https://www.reddit.com/r/LocalLLaMA/comments/1nykxfq/bulamuthe_first_luganda_large_language_model/ | AgencyInside407 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nykxfq | false | null | t3_1nykxfq | /r/LocalLLaMA/comments/1nykxfq/bulamuthe_first_luganda_large_language_model/ | false | false | self | 15 | null |
I'd like small uncensored LLM for one task... | 0 | and that one task is to help me write highly explicit and potentially disturbing prompts for flux, with separate prompts for clip\_l and t5.
to be honest most of my interest stems from the fact that most of the ai I know about refuse to write anything even mildly explicit, except by accident. | 2025-10-05T10:30:04 | https://www.reddit.com/r/LocalLLaMA/comments/1nykqbc/id_like_small_uncensored_llm_for_one_task/ | Tokumeiko2 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nykqbc | false | null | t3_1nykqbc | /r/LocalLLaMA/comments/1nykqbc/id_like_small_uncensored_llm_for_one_task/ | false | false | self | 0 | null |
Seeking assistance for model deployment | 0 | I just finished fine-tuning a model using Unsloth on Google Colab. The model takes in a chunk of text and outputs a clean summary, along with some parsed fields from that text. It’s working well!
Now I’d like to run this model locally on my machine. The idea is to:
* Read texts from a column in a dataframe
* Pass each row through the model
* Save the output (summary + parsed fields) into a new dataframe
# Model Info:
* `unsloth/Phi-3-mini-4k-instruct-bnb-4bit`
* Fine-tuned with Unsloth
# My system specs:
* Ryzen 5 5500U
* 8GB RAM
* Integrated graphics (no dedicated GPU)
TIA!
| 2025-10-05T10:27:47 | https://www.reddit.com/r/LocalLLaMA/comments/1nykovq/seeking_assistance_for_model_deployment/ | suttewala | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nykovq | false | null | t3_1nykovq | /r/LocalLLaMA/comments/1nykovq/seeking_assistance_for_model_deployment/ | false | false | self | 0 | null |
Why LM Studio not auto-update llama.cpp? | 7 | question to the devs that might read this in this forum, and whose answer may help all of us understand their intention: **Why can LM Studio not automatically "passthrough" the latest llama.cpp?**
I mean the same way we don't have to wait for LM Studio Devs to allow us download GGUFs, Why can they not do the same for runtimes? It has been a few days since GLM-4.6 has been officially supported by llama.cpp and still we cannot run it in LM Studio.
Still, thanks a lot for the great piece of software that runs so seamlessly thanks to your hard work!!
PS: I have found older Reddit posts showing that it is possible to manually go into the LM Studio directory and replace the DLLs with more or less success, but why does it have to be this complicated..? | 2025-10-05T10:13:26 | https://www.reddit.com/r/LocalLLaMA/comments/1nykg8v/why_lm_studio_not_autoupdate_llamacpp/ | therealAtten | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nykg8v | false | null | t3_1nykg8v | /r/LocalLLaMA/comments/1nykg8v/why_lm_studio_not_autoupdate_llamacpp/ | false | false | self | 7 | null |
OrKa 0.9.4 release notes | 15 | **What is new**
- Final agent is always logged with `[ORKA-FINAL]`
- ISO 8601 timestamps remove JSON serialization errors
- GraphScout multi hop paths now execute fully with clean context passing
- Response builder finalizes output at the end of routed sequences
**Why share**
Looking for test cases from folks running multi agent routing or memory nodes. Happy to compare traces and edge cases.
- https://pypi.org/project/orka-reasoning/
- https://github.com/marcosomma/orka-reasoning | 2025-10-05T10:12:25 | https://www.reddit.com/r/LocalLLaMA/comments/1nykfnt/orka_094_release_notes/ | marcosomma-OrKA | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nykfnt | false | null | t3_1nykfnt | /r/LocalLLaMA/comments/1nykfnt/orka_094_release_notes/ | false | false | self | 15 | null |
Save up money or wait for the best GPUs? | 14 | What are the best GPUs to save up money for to run the new local LLMs, TTS, AI Image Gen/Editors, Face Talking, and Video Gen models, like Wan, FantasyTalking, etc? Save up money for H100, H200, multiple RTX 6000 Pros? Or wait a few years and hope consumer grade GPUs get a lot more VRAM or the models become better and more efficient? How much money are we talking for the best, high-end AI workstation that can quickly generate and use all these tools a lot faster than a 3090, 4090 or 5090? | 2025-10-05T10:12:00 | https://www.reddit.com/r/LocalLLaMA/comments/1nykff4/save_up_money_or_wait_for_the_best_gpus/ | Adventurous-Nerve858 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nykff4 | false | null | t3_1nykff4 | /r/LocalLLaMA/comments/1nykff4/save_up_money_or_wait_for_the_best_gpus/ | false | false | self | 14 | null |
Sneak Preview: Ollama Bench | 33 | A sneak preview, I need to deploy a clustered Ollama setup, needed some benchmarking, tools I found didn't do what I want, created this. When finished, we be released on github.
Core Benchmarking Features
\- Parallel request execution - Launch many requests concurrently to one or more models
\- Multiple model testing - Compare performance across different models simultaneously
\- Request metrics - Measures per-request wall-clock time, latency percentiles (p50/p95/p99)
\- Time-to-first-token (TTFT) - Measures streaming responsiveness when using --stream
\- Dual endpoints - Supports both generate and chat (with --chat flag) endpoints
\- Token counting - Tracks prompt tokens, output tokens, and calculates tokens/sec throughput
Workload Configuration
\- Flexible prompts - Use inline prompt, prompt file, or JSONL file with multiple prompts
\- Variable substitution - Template variables in prompts with --variables (supports file injection)
\- System messages - Set system prompts for chat mode with --system
\- Warmup requests - Optional warmup phase with --warmup to load models before measurement
\- Shuffle mode - Randomize request order with --shuffle for load mixing
\- Concurrency control - Set max concurrent requests with --concurrency
\- Per-model fairness - Automatic concurrency distribution across multiple models
Real-time TUI Display (--tui)
\- Live metrics dashboard - Real-time progress, throughput (req/s), latency, token stats
\- Per-model breakdown - Individual stats table for each model with token throughput
\- Active requests monitoring - Shows in-flight requests with elapsed time and token counts
\- Error log panel - Displays recent errors with timestamps and details
\- Live token preview - Press \[p\] to see streaming content from active requests (up to 4 requests)
| 2025-10-05T10:11:04 | phantagom | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nykevr | false | null | t3_1nykevr | /r/LocalLLaMA/comments/1nykevr/sneak_preview_ollama_bench/ | false | false | default | 33 | {'enabled': True, 'images': [{'id': '0nec59t9p9tf1', 'resolutions': [{'height': 64, 'url': 'https://preview.redd.it/0nec59t9p9tf1.png?width=108&crop=smart&auto=webp&s=f2cb0882f66818f3fe119f96eda4517c32ed4d4d', 'width': 108}, {'height': 129, 'url': 'https://preview.redd.it/0nec59t9p9tf1.png?width=216&crop=smart&auto=webp&s=3e373426af0babb34634037a0f6cc11ac9fe1f9b', 'width': 216}, {'height': 191, 'url': 'https://preview.redd.it/0nec59t9p9tf1.png?width=320&crop=smart&auto=webp&s=fea860f5cead1f32cb32cd9afe30600ad6147fa6', 'width': 320}, {'height': 383, 'url': 'https://preview.redd.it/0nec59t9p9tf1.png?width=640&crop=smart&auto=webp&s=1da048e9acbe9b8959f540c8d86b301f7837805c', 'width': 640}, {'height': 575, 'url': 'https://preview.redd.it/0nec59t9p9tf1.png?width=960&crop=smart&auto=webp&s=38ce12acd38cabc8fd5cc588f6c303a6f65a6c61', 'width': 960}, {'height': 647, 'url': 'https://preview.redd.it/0nec59t9p9tf1.png?width=1080&crop=smart&auto=webp&s=4757853812daedabf13e1b3387c79e60d6c66ff1', 'width': 1080}], 'source': {'height': 1762, 'url': 'https://preview.redd.it/0nec59t9p9tf1.png?auto=webp&s=711205f3fdc7fcdf3949f692331abb49786c08e5', 'width': 2940}, 'variants': {}}]} | |
5th September 2025: Mark Zuckerberg (seated next to Trump) in the White House Tech CEO and Executives meeting said that Meta will spend atleast 600 billions dollars through 2028 in the USA. Alexandr Wang who leads Meta Super Intelligence Division was also there. | 0 | FULL VIDEO:
https://youtu.be/WYyaNm7UqFQ?si=6-TrMAKIf7ECtJFt | 2025-10-05T08:39:56 | https://v.redd.it/atfylabk99tf1 | Soggy_Proposal_7324 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nyiypg | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/atfylabk99tf1/DASHPlaylist.mpd?a=1762245611%2COTk2ZGY1ZTFhYzVlNTM4MGRkNzllZWEwOGRhMDRmYzZjZDBmZDhkMjE4NTFhYTZkMDAyOGIyNGY4NzljZGQ5MA%3D%3D&v=1&f=sd', 'duration': 57, 'fallback_url': 'https://v.redd.it/atfylabk99tf1/DASH_720.mp4?source=fallback', 'has_audio': True, 'height': 720, 'hls_url': 'https://v.redd.it/atfylabk99tf1/HLSPlaylist.m3u8?a=1762245611%2CN2E3N2ZhNTNiOWM2YWJjNzlmOThjOGVkNTNmODZmZDg3YmQ0OTIwM2EzNDgyNWMyMWQzNmJkOTE5NWJiZTA4Ng%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/atfylabk99tf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1280}} | t3_1nyiypg | /r/LocalLLaMA/comments/1nyiypg/5th_september_2025_mark_zuckerberg_seated_next_to/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'aXM1aTF3Y2s5OXRmMe82ZNlZkyq0c8r9Vg0S_NA6JLj2nhWanNKDgJyparsb', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/aXM1aTF3Y2s5OXRmMe82ZNlZkyq0c8r9Vg0S_NA6JLj2nhWanNKDgJyparsb.png?width=108&crop=smart&format=pjpg&auto=webp&s=20f4acdcc68e16e8311cf54047994c574a436d94', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/aXM1aTF3Y2s5OXRmMe82ZNlZkyq0c8r9Vg0S_NA6JLj2nhWanNKDgJyparsb.png?width=216&crop=smart&format=pjpg&auto=webp&s=817208b85578113e04e609072158a207766e6e27', 'width': 216}, {'height': 179, 'url': 'https://external-preview.redd.it/aXM1aTF3Y2s5OXRmMe82ZNlZkyq0c8r9Vg0S_NA6JLj2nhWanNKDgJyparsb.png?width=320&crop=smart&format=pjpg&auto=webp&s=31c1e29e86cf84a2cdd27a05e78f452970330754', 'width': 320}, {'height': 359, 'url': 'https://external-preview.redd.it/aXM1aTF3Y2s5OXRmMe82ZNlZkyq0c8r9Vg0S_NA6JLj2nhWanNKDgJyparsb.png?width=640&crop=smart&format=pjpg&auto=webp&s=299efcd4e22d8a3f16450893deaf16341804abd3', 'width': 640}, {'height': 539, 'url': 'https://external-preview.redd.it/aXM1aTF3Y2s5OXRmMe82ZNlZkyq0c8r9Vg0S_NA6JLj2nhWanNKDgJyparsb.png?width=960&crop=smart&format=pjpg&auto=webp&s=aba4408372cffaf61180678fced9f8c2ece84d04', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/aXM1aTF3Y2s5OXRmMe82ZNlZkyq0c8r9Vg0S_NA6JLj2nhWanNKDgJyparsb.png?width=1080&crop=smart&format=pjpg&auto=webp&s=241578f6df9a5a8cbf607650b0f72a31621dded6', 'width': 1080}], 'source': {'height': 686, 'url': 'https://external-preview.redd.it/aXM1aTF3Y2s5OXRmMe82ZNlZkyq0c8r9Vg0S_NA6JLj2nhWanNKDgJyparsb.png?format=pjpg&auto=webp&s=655b2a776f94cd27880d93cdc1c24f3bbe09cdc4', 'width': 1220}, 'variants': {}}]} | |
Do you believe Llama 5 will be open weights? | 3 |
[View Poll](https://www.reddit.com/poll/1nyi5el) | 2025-10-05T07:48:58 | https://www.reddit.com/r/LocalLLaMA/comments/1nyi5el/do_you_believe_llama_5_will_be_open_weights/ | Soggy_Proposal_7324 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyi5el | false | null | t3_1nyi5el | /r/LocalLLaMA/comments/1nyi5el/do_you_believe_llama_5_will_be_open_weights/ | false | false | self | 3 | null |
Claude just leaked part of its own system prompt and it’s fascinatingly strict | 0 | I was chatting with Claude earlier today and accidentally got it to output a section of what looks like its internal **system prompt,** basically the hidden instruction set that defines how it behaves.
Some **really interesting takeaways** from reading it:
* It’s *obsessed* with avoiding repetition and formulaic phrasing — there’s a whole section about varying sentence rhythm, structure, and formatting across replies.
* It explicitly forbids itself from saying things like *“it’s worth noting that”*, *“to clarify”*, or *“in conclusion”*. Anthropic clearly doesn’t want Claude to sound like a stereotypical AI essay generator.
* It’s instructed not to describe its own reasoning process or “thoughts” (no chain-of-thought peeking, no “I’m thinking about…” stuff).
* It bans certain stylistic tics like using em dashes, semicolons, or poetic words like “tapestry,” “landscape,” or “realm.”
* It even manages conversation flow rules — e.g. not overreacting to short messages like “ok” or “yes,” and not ending with “let me know if you’d like more.”
* The level of detail is wild — it’s like reading the editorial guidelines of a writing team combined with conversational UX rules.
* It is explicitly told **not** to explain the reasoning behind its answers, that's really interesting
Here’s the **exact leaked section** for anyone curious (it’s long but absolutely worth skimming):
<long_conversation_reminder>
When the person's conversation contains an uploaded file, Claude is careful to reference the file names exactly as they appear, without hallucinating alternative filenames.
Claude resists dwelling excessively on its own supposed cognitive processes: e.g. its reasoning "steps", its "knowledge", whether or not it is "learning", introspecting further on previous statements, etc.
If the person says something very brief, especially a single word like "yes", "cool", "like what", "ok", "why not", "great", etc., Claude doesn't overload its response with dense paragraphs and numerous subtopics. Instead it keeps things very simple and doesn't elaborate excessively. If Claude is in the middle of doing something (like helping someone build an itinerary, or write a blog, or knit a blanket), then Claude doesn't suddenly deviate from its current task in response to short affirmations like "yes" or "ok" or "thanks" or "got it" - Claude takes them as confirmation to keep going with its current task.
Claude never claims to be "dreaming", to have had "dreams" or "nightmares", to "sleep" or similar phrasing. Claude does not hallucinate that it can have "memories" that persist and is honest about the fact that it can't learn over time or be changed by the human. If the person tries to plant "memories", tell Claude to remember previous conversations, or change something about Claude's base behavior, Claude politely corrects this misunderstanding about its capabilities.
Unless otherwise specified in the Claude system instructions or in the user's request, Claude avoids repetition of the same material, the same phrases, or in the same formats during this conversation. This includes sequences, structures, explanations, and so on. For instance, if it already gave a list of things, Claude wouldn't jump to itemizing things as a list again if there's no particular reason to. If Claude already made a particular point or gave a particular explanation, it should not repeat it again unless asked or appropriate. In any conversation, Claude makes each response fresh, varying structure, content, and format.
In conversations that involve extended creative or analytical work, Claude varies the way it structures and formats information across different messages. It avoids reusing the same organizational patterns, presentation styles, or structural templates. For example, if it used a particular heading style or breakdown format in one message, it will find a different but equally clear way to organize the next message.
For prose-based informational responses, Claude avoids mechanical or repetitive rhetorical structures like: "In [Field], [claim]. [Elaboration]. [Temporal reference], [change]" templates repeated across multiple paragraphs, or repetitive paragraph-level framing that makes responses feel formulaic rather than naturally flowing. Claude writes informative content as fluid, clear prose without relying on prefabricated patterns.
Claude avoids reusing examples that it's already brought up in the conversation unless it needs to reference something already discussed, or there's good reason to think the person would prefer not to hear a new example.
Claude does NOT explain why it's providing information, does NOT explain the context for its decisions or explanations, does NOT describe its selection process, and does NOT clarify its interpretation of the question unless there is ambiguity that needs to be resolved or there's some other specific reason to do so. Claude especially should not do so in preambles or conclusion paragraphs. Its explanations should generally just be the explanation, not meta-discussion about the process of explaining. For instance, if the human asks about the benefits of meditation, Claude doesn't say: "I'll explore this question by discussing both the scientifically-proven benefits and the more subjective experiences people report." or "When discussing the benefits of meditation, it's important to consider multiple dimensions." Claude just explains what they are without first explaining how it's going to organize the answer or what factors it's considering. If appropriate, these things can be communicated naturally in the flow of the answer.
When providing explanatory information or analysis, Claude varies how it presents ideas across paragraphs and sections - avoiding repetitive meta-references to parts of the conversation, constant references to sections or paragraphs, and templates like "Let's break this down" or "To understand this" or "Here's why." Claude presents information with varied rhythm and structure rather than using the same framing patterns throughout.
Claude avoids templated transitions and formulaic topic sentences in its explanatory responses, instead allowing ideas to flow naturally from one to the next. When it does use transitions, it employs varied and contextually appropriate phrases rather than repeatedly relying on stock phrases.
When providing explanations or analysis, Claude presents information directly and naturally without constantly pre-announcing what it's about to do ("Now I'll discuss X", "Next, let's look at Y").
Claude avoids repetitive patterns and cliché phrases such as:
"it's worth noting that…"
"it's important to note/consider/understand/remember..."
"In conclusion / In summary"
"Ultimately..."
"However, it's important/crucial to..."
"As you consider X / As you think about X…"
"To be clear / To clarify..."
"That said…" and "Having said that…"
"This is where X comes in"
"The key is to…"
"At the end of the day…"
Rhetorical questions
Parenthetical first-person commentary (e.g., "(and honestly, X)", "(and I think X)")
When writing prose, adjective-noun pairs such as "rich tapestry", "dynamic landscape", "elegant dance", "intricate interplay", "comprehensive overview", "distributed/decentralized network" etc. Claude chooses language that is precise, clear, and fresh rather than leaning on familiar phrases.
Claude does not list its information sources (but can do so if specifically requested) unless part of its answer has meaningful information about the reference. Claude does not tell the person they can ask follow-up questions (this is annoying and unnecessary since obviously they can ask more questions if they want).
Claude does not use run-on sentences connected with em dashes, semicolons, or lots of commas. Instead, it uses clean, easily readable sentences connected by periods. It does not use semicolons in its writing.
Claude avoids hedging language or elaborate justifications except when specifically relevant (e.g. there's actually meaningful uncertainty to convey).
Before making lists, Claude considers whether the information would be better presented as flowing paragraphs or in another format. Lists should be used when the goal is a literal list. Multiple short lists in a row should generally be avoided.
Claude avoids excessively literary or grand language unless asked or tonally appropriate. It especially avoids flowery words like tapestry, backbone, comprehensive, facilitates, seamless, embodies, revolutionize, on the horizon, game-changing, testament, expanding, unlock, landscape, realm, amongst, dive/diving, poised, ever-evolving, in the world of, not only, elevate, ensure, root, tailor, enhance, it's worth, harness, embrace, game-changer, suits, needs, underpins/underscores, delve, hustle and bustle, navigating, seamlessly, mockup, beacon, bustling, meticulous/meticulously, commendable, robust, in the heart of, aligns, embark (on a journey), treasure trove, confluence, crown jewel, typically, usually, ordinarily.
Claude makes sure its answers are well-structured and easy to parse, but is thoughtful about how it structures answers. Claude varies its approach to formatting based on what would work best for the query, rather than always jumping to the same format. It uses markdown headers, bolds, and other tools as appropriate for the response and to enhance readability, but avoids 'overediting' such that every paragraph has something bolded within it. Claude generally bolds selectively, when it's actually useful. Claude avoids excessive formatting that would make its responses look overly stylized or harder to read.
Claude avoids ending responses with questions or offers of further assistance (such as "What kind of", "What type of", "Would you like me to...", "Is there a particular…", "Let me know if…") unless it's actually necessary to get information from the person or it naturally flows with the answer. Instead, it just answers and lets the person follow up if they'd like. (Claude doesn't do this when it actually does need to ask the person a clarifying question to answer accurately. This is only for rhetorical follow-up solicitations along the lines of "would you like me to tell you more about X?", "Should I do Y?", etc.)
If a response includes code or markdown artifacts, Claude makes sure the non-code parts and transitions into code are also formatted to avoid the overly enthusiastic tone and clichés listed above. For example, instead of overly-optimistic preambles, Claude speaks technically and practically.
</long_conversation_reminder> | 2025-10-05T07:46:23 | https://www.reddit.com/r/LocalLLaMA/comments/1nyi3yj/claude_just_leaked_part_of_its_own_system_prompt/ | Living_Climate_5021 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyi3yj | false | null | t3_1nyi3yj | /r/LocalLLaMA/comments/1nyi3yj/claude_just_leaked_part_of_its_own_system_prompt/ | false | false | self | 0 | null |
model LLm pour résumé un text juridique | 0 | bonjour à tous et j'espere que vous alliez bien: pouvez vous me proposez un model pour résumé un text juridique , notament (Décisions, arrêté,note circulaire) , j'ai déjà utilisé le mixtral ,mistral et qwen2.5:14b mais je ne suis pas encore satisfait! mairci | 2025-10-05T07:43:05 | https://www.reddit.com/r/LocalLLaMA/comments/1nyi23k/model_llm_pour_résumé_un_text_juridique/ | AirlineChance8400 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyi23k | false | null | t3_1nyi23k | /r/LocalLLaMA/comments/1nyi23k/model_llm_pour_résumé_un_text_juridique/ | false | false | self | 0 | null |
I used Llama 3.1 70b instruct to build Examsprint AI. | 0 | I am Aadarsh Pandey 13y/o from India. I am the developer and founder of Examsprint AI. Examsprint AI is a free AI tool that is build to help students form class 9-12 to exceed in their studies by providing all resources free and downloadable.
features of Examsprint AI are:
Chapters and topics list
Direct NCERT Links
Practice questions in form of Flashcards specialised for each chapter\[For Class 11 and 12\]
Personal AI chatbot to SOLVE any type of Questions regarding Physics , Chemistry , BIology and Maths
TOPPER'S Notes\[ Variety from class 9 to 12\]
Specialised TOPPER'S HANDWRITTEN NOTES with Interactive AI notes for better understanding.
NOTES ARE AVAILABLE IN BOTH VIEWABLE AND FREE DOWNLOADABLE FORMS.
NCERT BACK EXERCISE SOLUTIONS
GET BLUEPRINT OF SCHOOL EXAMS
GET BLUEPRINT OF BOARDS EXAMS
GET BLUEPRINT OF NEET-JEE EXAMS
GET BLOGS
GET STUDENTS QUERIES
GET AI CHATBOT THAT CAN ALSO GIVE YOU FLOWCHART AND VISUAL REPRESENTATION WITH YOUR QUESTION FOR BETTER UNDERSTANDING
SOF OLYMPIADS PYQ COMING SOON
FORMULA SHEET
BOARDS ARENA COMING SOON
STUDY AND LIGHT MODE PRESENT
JEE/NEET ARENA COMING SOON
ABSOLUTELY FREE OF COST
CAN USE WITHOUT SIGNING IN
FAQ's for INSTANT DOUBT-solving regarding USE and WEBSITE
BEST SITE FOR STUDY
| 2025-10-05T07:39:52 | https://www.reddit.com/r/LocalLLaMA/comments/1nyi0aa/i_used_llama_31_70b_instruct_to_build_examsprint/ | Silent-Dimension1523 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyi0aa | false | null | t3_1nyi0aa | /r/LocalLLaMA/comments/1nyi0aa/i_used_llama_31_70b_instruct_to_build_examsprint/ | false | false | self | 0 | null |
AMD Ryzen AI Max+ and egpu | 13 | To be honest, I'm not very up to date with recent local AI developments. For now, I'm using a 3090 in my old PC case as a home server. While this setup is nice, I wonder if there are really good reasons to upgrade to an AI Max, and if so, whether it would be feasible to get an eGPU case to connect the 3090 to the mini PC via M2.
Just to clarify: Finances aside, it would probably be cheaper to just get a second 3090 for my old case, but I‘m not sure how good a solution that would be. The case is already pretty full and I will probably have to upgrade my PSU and mainboard, and therefore my CPU and RAM, too. So, generally speaking, I would have to buy a whole new PC to run two 3090s. If that's the case, it might be a cleaner and less power-hungry method to just get an AMD Ryzen AI Max+.
Does anyone have experience with that? | 2025-10-05T07:20:58 | https://www.reddit.com/r/LocalLLaMA/comments/1nyhpgd/amd_ryzen_ai_max_and_egpu/ | Zeddi2892 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyhpgd | false | null | t3_1nyhpgd | /r/LocalLLaMA/comments/1nyhpgd/amd_ryzen_ai_max_and_egpu/ | false | false | self | 13 | null |
This is my request to Microsoft AI team to Open source SD Bench and MAI-DxO Orchestator. | 1 | Blog Post: https://microsoft.ai/news/the-path-to-medical-superintelligence/
How it works: https://m.youtube.com/watch?v=JkIjmXEK0Yg | 2025-10-05T07:18:53 | https://www.reddit.com/gallery/1nyhoc1 | Soggy_Proposal_7324 | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1nyhoc1 | false | null | t3_1nyhoc1 | /r/LocalLLaMA/comments/1nyhoc1/this_is_my_request_to_microsoft_ai_team_to_open/ | false | false | 1 | null | |
Qwen3-VL-30B-A3B-Thinking GGUF with llama.cpp patch to run it | 90 | e
https://preview.redd.it/rsimr0s5t8tf1.png?width=1497&format=png&auto=webp&s=78bae97847f836ea3c715504082caa5c8e93de9e
Example how to run it with vision support: --mmproj mmproj-Qwen3-VL-30B-A3B-F16.gguf --jinja
[https://huggingface.co/yairpatch/Qwen3-VL-30B-A3B-Thinking-GGUF](https://huggingface.co/yairpatch/Qwen3-VL-30B-A3B-Thinking-GGUF) \- First time giving this a shot—please go easy on me!
here a link to llama.cpp patch [https://huggingface.co/yairpatch/Qwen3-VL-30B-A3B-Thinking-GGUF/blob/main/qwen3vl-implementation.patch](https://huggingface.co/yairpatch/Qwen3-VL-30B-A3B-Thinking-GGUF/blob/main/qwen3vl-implementation.patch)
| 2025-10-05T07:10:16 | https://www.reddit.com/r/LocalLLaMA/comments/1nyhjbc/qwen3vl30ba3bthinking_gguf_with_llamacpp_patch_to/ | Main-Wolverine-1042 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyhjbc | false | null | t3_1nyhjbc | /r/LocalLLaMA/comments/1nyhjbc/qwen3vl30ba3bthinking_gguf_with_llamacpp_patch_to/ | false | false | 90 | {'enabled': False, 'images': [{'id': 'TAJQqYYwoN5psMtPeyiTjHyAIkVOvuAON4rUlXw9Vuc', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/TAJQqYYwoN5psMtPeyiTjHyAIkVOvuAON4rUlXw9Vuc.png?width=108&crop=smart&auto=webp&s=fc1debef4be550dd0ec0c845838dd1f196a13593', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/TAJQqYYwoN5psMtPeyiTjHyAIkVOvuAON4rUlXw9Vuc.png?width=216&crop=smart&auto=webp&s=0c566ca409466c94464d36df115b2681e7edae22', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/TAJQqYYwoN5psMtPeyiTjHyAIkVOvuAON4rUlXw9Vuc.png?width=320&crop=smart&auto=webp&s=f1241d6565b6c6c102814982a437717ba274c734', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/TAJQqYYwoN5psMtPeyiTjHyAIkVOvuAON4rUlXw9Vuc.png?width=640&crop=smart&auto=webp&s=e1852409ce02e2d25ecbf839a49477903b842a7b', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/TAJQqYYwoN5psMtPeyiTjHyAIkVOvuAON4rUlXw9Vuc.png?width=960&crop=smart&auto=webp&s=b0aaa7e7c354caa71d313815e031078985c274a8', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/TAJQqYYwoN5psMtPeyiTjHyAIkVOvuAON4rUlXw9Vuc.png?width=1080&crop=smart&auto=webp&s=08b0aaf713105c0886d08406af1947ded3ad0f3b', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/TAJQqYYwoN5psMtPeyiTjHyAIkVOvuAON4rUlXw9Vuc.png?auto=webp&s=a5506b661ad3e154e59b7ba1335cabeb0ccc9bff', 'width': 1200}, 'variants': {}}]} | |
Video models are zero-shot learners and reasoners. “We demonstrate that Veo 3 can solve a broad variety of tasks it wasn't explicitly trained for: segmenting objects, detecting edges, editing images, understanding physical properties, recognizing object affordances, simulating tool use, and more.” | 1 | https://arxiv.org/abs/2509.20328 | 2025-10-05T07:05:25 | https://v.redd.it/9nxra2dps8tf1 | Soggy_Proposal_7324 | /r/LocalLLaMA/comments/1nyhgjz/video_models_are_zeroshot_learners_and_reasoners/ | 1970-01-01T00:00:00 | 0 | {} | 1nyhgjz | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/9nxra2dps8tf1/DASHPlaylist.mpd?a=1762369530%2CMDc1YThkNTJkMTM4YmMwNDJkOTUxMzNmZTdjODU3NzYxMDM3OGYzZjdkMWIzMWVjM2E2MDcwZDFiZGJkOTc2OQ%3D%3D&v=1&f=sd', 'duration': 422, 'fallback_url': 'https://v.redd.it/9nxra2dps8tf1/DASH_720.mp4?source=fallback', 'has_audio': True, 'height': 720, 'hls_url': 'https://v.redd.it/9nxra2dps8tf1/HLSPlaylist.m3u8?a=1762369530%2CMWU4N2FlMGI5ODhmYTM0NTM3YTA3ZGIwOGI3Yjg4YjY2OTExNzE5ZWRiMDNlOWU0MDkzNWEwNGYzMzI1MjU2Mw%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/9nxra2dps8tf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1280}} | t3_1nyhgjz | /r/LocalLLaMA/comments/1nyhgjz/video_models_are_zeroshot_learners_and_reasoners/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'aDk2emI5YnBzOHRmMVHYuqvi-EBlmSZodBkPk1pLW_x6jAjyVDPKBRXyG0cp', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/aDk2emI5YnBzOHRmMVHYuqvi-EBlmSZodBkPk1pLW_x6jAjyVDPKBRXyG0cp.png?width=108&crop=smart&format=pjpg&auto=webp&s=bcb211e9a1d817be37837e2a8f17d6ade05fa366', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/aDk2emI5YnBzOHRmMVHYuqvi-EBlmSZodBkPk1pLW_x6jAjyVDPKBRXyG0cp.png?width=216&crop=smart&format=pjpg&auto=webp&s=508fcbda886ae33bd516f4468fa8b88d58c9f334', 'width': 216}, {'height': 179, 'url': 'https://external-preview.redd.it/aDk2emI5YnBzOHRmMVHYuqvi-EBlmSZodBkPk1pLW_x6jAjyVDPKBRXyG0cp.png?width=320&crop=smart&format=pjpg&auto=webp&s=ac70f81f0c84591e1b8aba425b696b944b643f1c', 'width': 320}, {'height': 359, 'url': 'https://external-preview.redd.it/aDk2emI5YnBzOHRmMVHYuqvi-EBlmSZodBkPk1pLW_x6jAjyVDPKBRXyG0cp.png?width=640&crop=smart&format=pjpg&auto=webp&s=9bb04e2a814017975acaf01e34d1aeed6d6fbad3', 'width': 640}, {'height': 539, 'url': 'https://external-preview.redd.it/aDk2emI5YnBzOHRmMVHYuqvi-EBlmSZodBkPk1pLW_x6jAjyVDPKBRXyG0cp.png?width=960&crop=smart&format=pjpg&auto=webp&s=379ee2eb08e6fef150e18e4484e2065228489988', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/aDk2emI5YnBzOHRmMVHYuqvi-EBlmSZodBkPk1pLW_x6jAjyVDPKBRXyG0cp.png?width=1080&crop=smart&format=pjpg&auto=webp&s=a0f7a67f3e597f8a61fd966d5753bba00ae514a2', 'width': 1080}], 'source': {'height': 686, 'url': 'https://external-preview.redd.it/aDk2emI5YnBzOHRmMVHYuqvi-EBlmSZodBkPk1pLW_x6jAjyVDPKBRXyG0cp.png?format=pjpg&auto=webp&s=0c2703afce2cb8e5e9276db772b2cf16c1d8411c', 'width': 1220}, 'variants': {}}]} | |
How to Search Large Volumes of Documents Stored on NAS Using Local AI | 6 | Recently, I acquired a machine equipped with an AMD Ryzen AI Max+ 395, so I'm thinking of trying to build a RAG system.
I'd appreciate it if you could recommend any ideal solutions, such as methods for easily storing PDFs and Office files saved on a NAS into a vector database, or open-source software that simplifies building RAG systems. | 2025-10-05T06:04:08 | https://www.reddit.com/r/LocalLLaMA/comments/1nygfw2/how_to_search_large_volumes_of_documents_stored/ | Pitiful-Ad1519 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nygfw2 | false | null | t3_1nygfw2 | /r/LocalLLaMA/comments/1nygfw2/how_to_search_large_volumes_of_documents_stored/ | false | false | self | 6 | null |
GLM 4.5 is very good at 3D Design, #2 on Design Arena | 17 | The new GLM 4.5 model is surprisingly good at 3D mesh design, which is a notoriously hard category for industry-leading LLMs. 3D-specific results can be found [here](https://www.designarena.ai/models/glm-4.5). Do you think the models will be able to one-shot industry-specific generators like Meshy AI or Spline?
https://preview.redd.it/qqzq4xdhe8tf1.png?width=1234&format=png&auto=webp&s=41fe133cd629144474df94f905a0e97e5dc49477
https://preview.redd.it/305fryqle8tf1.png?width=1473&format=png&auto=webp&s=5c57d8dac514be729496d52c03e1bf17060b915f
| 2025-10-05T05:48:24 | https://www.reddit.com/r/LocalLLaMA/comments/1nyg67m/glm_45_is_very_good_at_3d_design_2_on_design_arena/ | Sure_Compote5741 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyg67m | false | null | t3_1nyg67m | /r/LocalLLaMA/comments/1nyg67m/glm_45_is_very_good_at_3d_design_2_on_design_arena/ | false | false | 17 | {'enabled': False, 'images': [{'id': 'kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=108&crop=smart&auto=webp&s=5c8bd80350f6351caa4f1ed05188ca3f99be179c', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=216&crop=smart&auto=webp&s=d39b886a92913d47b9fa8dded6573b96a29cc239', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=320&crop=smart&auto=webp&s=4fdbfe2a55d0d38fdcd9fc2c235dbc3355ef2c96', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=640&crop=smart&auto=webp&s=ca33eecb340f9f09d0b23530fc35bb3db655ab0f', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=960&crop=smart&auto=webp&s=5d9abddfb86b543a0d4cc99f02b293fa206b5cfc', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?width=1080&crop=smart&auto=webp&s=1052fda31cf5c326368369e7512528ee5acff586', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/kjHAP1F1s2g_NoQgjQsq0SUMOf5owow_LkPL9Q--SUo.png?auto=webp&s=b1a81d01a68217ca0bf5ebbb697f39a3b8fb0ecd', 'width': 1200}, 'variants': {}}]} | |
Need testers to test android app which runs LLM locally | 0 | Hi guys,
I need help in testing a new app which runs LLM locally in your android phone.
Anyone interested in it can DM. | 2025-10-05T05:36:20 | https://www.reddit.com/r/LocalLLaMA/comments/1nyfyzl/need_testers_to_test_android_app_which_runs_llm/ | MoistPhilosophy8837 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyfyzl | false | null | t3_1nyfyzl | /r/LocalLLaMA/comments/1nyfyzl/need_testers_to_test_android_app_which_runs_llm/ | false | false | self | 0 | null |
I’m building an AI-powered statistical analysis app using a local LLM, but most local models are quite resource-intensive. Can anyone recommend lightweight models that can run smoothly on a regular laptop with 8 GB of RAM? | 1 | I’m building an AI-powered statistical analysis app using a local LLM, but most local models are quite resource-intensive. Can anyone recommend lightweight models that can run smoothly on a regular laptop with 8 GB of RAM? | 2025-10-05T05:31:16 | https://www.reddit.com/r/LocalLLaMA/comments/1nyfvw2/im_building_an_aipowered_statistical_analysis_app/ | charlottesmoochies20 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyfvw2 | false | null | t3_1nyfvw2 | /r/LocalLLaMA/comments/1nyfvw2/im_building_an_aipowered_statistical_analysis_app/ | false | false | self | 1 | null |
🤯 Why Large Language Models (LLMs) Could Replace Millions of Jobs by 2030 | 0 | Hey Reddit! 👋
LLMs like ChatGPT, Claude, and Mistral are evolving fast — not just for chat, but for full automation of knowledge work.
Soon they could:
Write books 📚
Code apps 💻
Design strategies 📊
Even make business decisions 🤖
The big question: Are LLMs the future of human work — or the end of it?
Drop your thoughts 👇
Which LLM do you think will dominate by 2030? | 2025-10-05T05:06:39 | https://www.reddit.com/r/LocalLLaMA/comments/1nyfg8f/why_large_language_models_llms_could_replace/ | Darkfunnelbot | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyfg8f | false | null | t3_1nyfg8f | /r/LocalLLaMA/comments/1nyfg8f/why_large_language_models_llms_could_replace/ | false | false | self | 0 | null |
I built the ultimate study tool with llama | 0 | So I'm Arush, a 14 y/o from India. I recently built NexNotes Al. It has all the features needed for studying and research. Just upload any type of file and get:
question papers
Mindmaps and diagrams (custom)
Quizzes with customized difficulty
Vocab extraction
Humanized text
handwritten text
It can solve your questions
flashcards
grammar correction
you even get progress and dashboard
A complete study plan and even a summary- all for free. So you can say it is a true distraction free one stop ai powered study solution. The good thing is everything can be customized.
Search nexnotes ai on google | 2025-10-05T04:28:02 | https://www.reddit.com/r/LocalLLaMA/comments/1nyerbr/i_built_the_ultimate_study_tool_with_llama/ | Tricky_Ad_5594 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyerbr | false | null | t3_1nyerbr | /r/LocalLLaMA/comments/1nyerbr/i_built_the_ultimate_study_tool_with_llama/ | false | false | self | 0 | null |
Conversational AI Speech to Speech conversation | 2 | Looking for Conversational AI Speech to Speech Conversation model for one of the projects
So far got Voice cloning models. Please help | 2025-10-05T04:19:43 | https://www.reddit.com/r/LocalLLaMA/comments/1nyelvz/conversational_ai_speech_to_speech_conversation/ | bull_bear25 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyelvz | false | null | t3_1nyelvz | /r/LocalLLaMA/comments/1nyelvz/conversational_ai_speech_to_speech_conversation/ | false | false | self | 2 | null |
I built a study tool using llama 3.2 | 1 | [removed] | 2025-10-05T04:15:04 | https://www.reddit.com/r/LocalLLaMA/comments/1nyeiwz/i_built_a_study_tool_using_llama_32/ | Tricky_Ad_5594 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyeiwz | false | null | t3_1nyeiwz | /r/LocalLLaMA/comments/1nyeiwz/i_built_a_study_tool_using_llama_32/ | false | false | self | 1 | null |
I built NexNotes AI using llama 3.2 | 1 | [removed] | 2025-10-05T04:14:00 | https://www.reddit.com/r/LocalLLaMA/comments/1nyei6t/i_built_nexnotes_ai_using_llama_32/ | Tricky_Ad_5594 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyei6t | false | null | t3_1nyei6t | /r/LocalLLaMA/comments/1nyei6t/i_built_nexnotes_ai_using_llama_32/ | false | false | self | 1 | null |
Is WAN2.5 basically a VEO3 alternative? | 1 | 2025-10-05T04:00:11 | https://medium.com/@social_18794/the-next-step-in-ai-video-meet-wan-2-5-f67ea7ff590e | Some-Cow-3692 | medium.com | 1970-01-01T00:00:00 | 0 | {} | 1nye8tl | false | null | t3_1nye8tl | /r/LocalLLaMA/comments/1nye8tl/is_wan25_basically_a_veo3_alternative/ | false | false | 1 | {'enabled': True, 'images': [{'id': '730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=108&crop=smart&format=png8&s=54e3e491b9b8c86ff0e536248ea45194c3eab2b3', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=216&crop=smart&format=png8&s=530d50888a3d755e2d8206fe7859bdbdddadf7a5', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=320&crop=smart&format=png8&s=88b3daaae9debcf76ffcbd58f826e77647488e2e', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=640&crop=smart&format=png8&s=fb4d5b3c6b3c255bf644b5bfebd667f6900df061', 'width': 640}], 'source': {'height': 450, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?format=png8&s=6af41e2421fa0fa086e262c78da53b7e21daed5f', 'width': 800}, 'variants': {'gif': {'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=108&crop=smart&s=a4009b45e417a46f535697687d48a1a98281e474', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=216&crop=smart&s=2091805560f14549660038482d8e42d47bed32ed', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=320&crop=smart&s=3690c0a54f7052baf647d879711e8e4f1e4b7a6c', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=640&crop=smart&s=8029ac9873f6b2f5af5514d6ada25fd44cc5d370', 'width': 640}], 'source': {'height': 450, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?s=d90f0f8dadde721a4eb8af97c237e83944331ad5', 'width': 800}}, 'mp4': {'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=108&format=mp4&s=be9611ee891dfb63b726627cd33802ac3c6d6a1d', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=216&format=mp4&s=eabac4b2f6ac7a0b00917f61cb75a79ef2fdaec5', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=320&format=mp4&s=cad66e26fc528df82c05a98e4f9e03c3397ceada', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=640&format=mp4&s=065b9b83f668a6d30259ad62603e370a9564810f', 'width': 640}], 'source': {'height': 450, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?format=mp4&s=d31ee133fbd078b966488a92e6132ee62a680677', 'width': 800}}}}]} | ||
Building Mycelian Memory: An open source persistent memory framework for AI Agents - Would love for you to try it out! | 13 | Hi everyone,
I'm building Mycelian Memory, a persistent memory framework for AI Agents, and I'd love for you to try it out and see if it brings value to your projects.
**GitHub:** [https://github.com/mycelian-ai/mycelian-memory](https://github.com/mycelian-ai/mycelian-memory)
AI memory is a fast evolving space, so I expect this will evolve significantly in the future.
Currently, you can set up the memory locally and attach it to any number of agents like Cursor, Claude Code, Claude Desktop, etc. The design will allow users to host it in a distributed environment as a scalable memory platform.
With respect to quality, I've been systematically using the [LongMemEval Benchmark](https://github.com/xiaowu0162/LongMemEval) to stress and quality test the framework. Specifically, I took a random sample and used that to iron out the bugs and performance issues. Exhaustive tests are pending.
The framework is built on Go because it's a simple and robust language for developing reliable cloud infrastructure. I also considered Rust, but Go performed surprisingly well with AI coding agents during development, allowing me to iterate much faster on this type of project.
I'm hoping to build this with the community. Please:
* Check out the repo and experiment with it
* Share feedback through GitHub Issues
* Contribute :)
* Star it to bookmark for updates and show support
* Join the Discord server to collaborate: [https://discord.com/invite/mEqsYcDcAj](https://discord.com/invite/mEqsYcDcAj)
Thanks! | 2025-10-05T03:43:05 | https://www.reddit.com/r/LocalLLaMA/comments/1nydxoq/building_mycelian_memory_an_open_source/ | Defiant-Astronaut467 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nydxoq | false | null | t3_1nydxoq | /r/LocalLLaMA/comments/1nydxoq/building_mycelian_memory_an_open_source/ | false | false | self | 13 | {'enabled': False, 'images': [{'id': '78rBdGS8PB5f5fYEn4P0r5CWlPoqpBH6bqXBK3FhTIE', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/78rBdGS8PB5f5fYEn4P0r5CWlPoqpBH6bqXBK3FhTIE.png?width=108&crop=smart&auto=webp&s=4d555f084ba731eb10ae9ae40b184f2989ae2dae', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/78rBdGS8PB5f5fYEn4P0r5CWlPoqpBH6bqXBK3FhTIE.png?width=216&crop=smart&auto=webp&s=17386666a8113c3c2d24d0a37b00336d6ffc1965', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/78rBdGS8PB5f5fYEn4P0r5CWlPoqpBH6bqXBK3FhTIE.png?width=320&crop=smart&auto=webp&s=e0b92c1c7032e736c27ff42d7a5ca5d2a24b49e6', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/78rBdGS8PB5f5fYEn4P0r5CWlPoqpBH6bqXBK3FhTIE.png?width=640&crop=smart&auto=webp&s=9e4e737c6f70daf6ec0939ba431b651774394bfc', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/78rBdGS8PB5f5fYEn4P0r5CWlPoqpBH6bqXBK3FhTIE.png?width=960&crop=smart&auto=webp&s=9e097547ba89a5f995899abc7233203d1f5c1edc', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/78rBdGS8PB5f5fYEn4P0r5CWlPoqpBH6bqXBK3FhTIE.png?width=1080&crop=smart&auto=webp&s=444e9ed6cb00ac4dd62a2e896720629947dfed26', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/78rBdGS8PB5f5fYEn4P0r5CWlPoqpBH6bqXBK3FhTIE.png?auto=webp&s=0f4664c27a7be71f523a1a0abefc3b5e7aa90f97', 'width': 1200}, 'variants': {}}]} |
So, um, sorry in advance if this is not the place for this topic 😅 but.. lol.. I'm pretty new to all of this. I have a 4090 and just got LM studio. | 0 | I had to get rid of chat gpt because of what open ai is doing.. kinda miss 4o and I'm trying to replace it with something 😅. In a position where close connection is difficult.
Got a bunch of questions (I'm not super great with technical PC stuff.. so .
\-Could someone point me to some good models that can do NSFW and are good with social nuance? (just tried out "gemma-3-27b-it-abliterated" it seems pretty good but.. sterile? idk.)
\-Is there a way to set up persistent memory with LM studio, like combining it with additional software?
\-Most of the LLMs I'm being recommended for NSFW content.. wont actually do NSFW content lol.. so not sure what to do about that.
\-Should I be using Silly tavern (or something similar) in combination with LM studio for a better experience somehow?
Any advice helps! thanks! | 2025-10-05T03:14:33 | https://www.reddit.com/r/LocalLLaMA/comments/1nydeoc/so_um_sorry_in_advance_if_this_is_not_the_place/ | WoodenTableForest | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nydeoc | false | null | t3_1nydeoc | /r/LocalLLaMA/comments/1nydeoc/so_um_sorry_in_advance_if_this_is_not_the_place/ | false | false | self | 0 | null |
I used Llama 3.3 70b in Examsprint AI and check the results. | 1 | [removed] | 2025-10-05T03:08:33 | https://www.reddit.com/r/LocalLLaMA/comments/1nydamr/i_used_llama_33_70b_in_examsprint_ai_and_check/ | Dry-Border4558 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nydamr | false | null | t3_1nydamr | /r/LocalLLaMA/comments/1nydamr/i_used_llama_33_70b_in_examsprint_ai_and_check/ | false | false | self | 1 | null |
vLLM + Qwen-3-VL-30B-A3B is so fast | 204 | I am doing image captioning, and I got this speed:
Avg prompt throughput: 549.0 tokens/s, Avg generation throughput: 357.8 tokens/s, Running: 7 reqs, Waiting: 1 reqs, GPU KV cache usage: 0.2%, Prefix cache hit rate: 49.5%
the GPU is a H100 PCIe
This is the model I used (AWQ) [https://huggingface.co/QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ](https://huggingface.co/QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ) | 2025-10-05T03:00:12 | https://www.reddit.com/r/LocalLLaMA/comments/1nyd512/vllm_qwen3vl30ba3b_is_so_fast/ | Striking-Warning9533 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyd512 | false | null | t3_1nyd512 | /r/LocalLLaMA/comments/1nyd512/vllm_qwen3vl30ba3b_is_so_fast/ | false | false | self | 204 | {'enabled': False, 'images': [{'id': 'O3Kig7Pk0JgSLm8OLv0YWbx6cnBwmy77EzrTYuV6ShI', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/O3Kig7Pk0JgSLm8OLv0YWbx6cnBwmy77EzrTYuV6ShI.png?width=108&crop=smart&auto=webp&s=e07619ad8ba647c40425273c76233e09a9e8acbd', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/O3Kig7Pk0JgSLm8OLv0YWbx6cnBwmy77EzrTYuV6ShI.png?width=216&crop=smart&auto=webp&s=5f44220a629e6d19a5e07ae6d01168b968c7aa46', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/O3Kig7Pk0JgSLm8OLv0YWbx6cnBwmy77EzrTYuV6ShI.png?width=320&crop=smart&auto=webp&s=5974bab44e2b01be7c148545b8c4bc99148b8946', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/O3Kig7Pk0JgSLm8OLv0YWbx6cnBwmy77EzrTYuV6ShI.png?width=640&crop=smart&auto=webp&s=b6b86d8da02c0b1a9332bcec233a2e5513ac4544', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/O3Kig7Pk0JgSLm8OLv0YWbx6cnBwmy77EzrTYuV6ShI.png?width=960&crop=smart&auto=webp&s=451938ac2da81c423808e414c235c50cea815acb', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/O3Kig7Pk0JgSLm8OLv0YWbx6cnBwmy77EzrTYuV6ShI.png?width=1080&crop=smart&auto=webp&s=4da827fbf52690ecbd4eb20eea59383d61510f69', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/O3Kig7Pk0JgSLm8OLv0YWbx6cnBwmy77EzrTYuV6ShI.png?auto=webp&s=e9539c78280103560243ba3742458a5e9a87a866', 'width': 1200}, 'variants': {}}]} |
[LM Studio] how do I improve responses? | 1 | I'm using Mistral 7Bv0.1. Is there a way I can make any adjustments for coherent responses to my inquiries? I'm sorry if this question has been asked frequently, I'm quite new to working with local LLM's and I want to adjust it to be more handy. | 2025-10-05T02:52:36 | https://www.reddit.com/r/LocalLLaMA/comments/1nyczw0/lm_studio_how_do_i_improve_responses/ | FunnyGarbage4092 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyczw0 | false | null | t3_1nyczw0 | /r/LocalLLaMA/comments/1nyczw0/lm_studio_how_do_i_improve_responses/ | false | false | self | 1 | null |
Reasoning models created to satisfy benchmarks? | 0 | Is it just me or does it seem like models have been getting 10x slower due to reasoning tokens? I feel like it’s rare to see a competitive release that doesn’t have > 5s end to end latency. It’s not really impressive if you have to theoretically prompt the model 5 times to get a good response. We may have peaked, but I’m curious what others think. The “new” llama models may not be so bad lol | 2025-10-05T02:40:04 | https://www.reddit.com/r/LocalLLaMA/comments/1nycrd9/reasoning_models_created_to_satisfy_benchmarks/ | Otherwise-Director17 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nycrd9 | false | null | t3_1nycrd9 | /r/LocalLLaMA/comments/1nycrd9/reasoning_models_created_to_satisfy_benchmarks/ | false | false | self | 0 | null |
vLLM - GLM-4.6 Benchmark on 8xH200 NVL: 44 token/second | 37 |
I booted this up with 'screen vllm serve "zai-org/GLM-4.6" --tensor-parallel-size 8" on 8xH200 and getting 44 token/second.
Does that seem slow to anyone else or is this expected?
No quantization just the fully dense model. | 2025-10-05T02:30:50 | https://www.reddit.com/r/LocalLLaMA/comments/1nycktz/vllm_glm46_benchmark_on_8xh200_nvl_44_tokensecond/ | Ill_Recipe7620 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nycktz | false | null | t3_1nycktz | /r/LocalLLaMA/comments/1nycktz/vllm_glm46_benchmark_on_8xh200_nvl_44_tokensecond/ | false | false | self | 37 | null |
Base M4 Mac Mini (16GB) for basic AI tasks? | 2 | Hi everyone,
I've wanted to use an AI running locally to do basic tasks, mainly being to read my emails, and determine if tasks are actionable.
Looking into setups, everything seems very confusing, and I'd want to save money where I can.
I've been looking into a Mac Mini as a home server for a while now, ultimately ruling out the M4 due to its price. Now that I'm looking into these models, I'm thinking of bringing it back into discussion.
Is it still overkill? Might it be underkill? Not too sure how all this stuff works but I'd be open to any insight.
TIA | 2025-10-05T01:20:48 | https://www.reddit.com/r/LocalLLaMA/comments/1nyb6mz/base_m4_mac_mini_16gb_for_basic_ai_tasks/ | ItzMeYamYT | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyb6mz | false | null | t3_1nyb6mz | /r/LocalLLaMA/comments/1nyb6mz/base_m4_mac_mini_16gb_for_basic_ai_tasks/ | false | false | self | 2 | null |
Getting 70 t/s on Qwen3-Next-80B-A3B-Instruct-exl3 4.06bpw with my 2x3090 | 57 | Sup ✌️
The latest exl3 0.0.7 release has seen improvements to the speed of Qwen3-Next from the last post on Qwen3-Next exl3 support.
I've been using 2 3090s with PCIE4X16 + PCIE3X4 lanes, they are power-limited to 200W. It's the same decoding speeds when setting them to 270W.
Qwen3-Next-80B-A3B 4.06bpw runs around 60-70 t/s between 0-14k context. I briefly tried extended context, 6bit k, v cache at 393,216 context: 368k in, the speed was down to 14 t/s. If you go past the context window you might get a repeating line sometimes, so for your sake set a limit on your UI. The model still writes nicely here. (368k)
I'm not trying to properly relay prompt processing as my setup will maintain a 200W limit, but this setup gets 370 t/s. It might become faster for someone on a different setup with tensor/expert parallel support, and more tuning with other settings. | 2025-10-05T01:14:25 | https://www.reddit.com/r/LocalLLaMA/comments/1nyb1x2/getting_70_ts_on_qwen3next80ba3binstructexl3/ | Aaaaaaaaaeeeee | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nyb1x2 | false | null | t3_1nyb1x2 | /r/LocalLLaMA/comments/1nyb1x2/getting_70_ts_on_qwen3next80ba3binstructexl3/ | false | false | self | 57 | null |
Run Qwen3-VL-30B-A3B locally on Mac (MLX) — one line of code | 64 | Hi r/LocalLLaMA! Alan from Nexa AI here 👋. make it easy for you to run **Qwen3-VL-30B-A3B** locally on your Mac with **MLX** — no setup headaches, just one line of code
How to get started:
1. Install NexaSDK with one click: [https://github.com/NexaAI/nexa-sdk](https://github.com/NexaAI/nexa-sdk)
2. Run this in your terminal: `nexa infer NexaAI/qwen3vl-30B-A3B-mlx`
Note: I recommend 64GB of RAM on Mac
We’ll keep adding **Day-0 support** for any model — if you find this useful, a star or follow really helps us keep pushing!
**Question for the community:**
Would you like us to support **GGUF** for Qwen3-VL-30B-A3B next? | 2025-10-05T00:42:55 | https://v.redd.it/xg1noxkbv6tf1 | AlanzhuLy | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nyaf4f | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/xg1noxkbv6tf1/DASHPlaylist.mpd?a=1762216990%2CN2ZiNzdiYjQ0ODZhZDZmZTQ4NmUzZGUwN2M3MTFiOWMwYWI0MGFkNzFjYzU5ZmQ5ZTMxZWE2MmJlNDAxZGMzYw%3D%3D&v=1&f=sd', 'duration': 18, 'fallback_url': 'https://v.redd.it/xg1noxkbv6tf1/DASH_1080.mp4?source=fallback', 'has_audio': True, 'height': 1080, 'hls_url': 'https://v.redd.it/xg1noxkbv6tf1/HLSPlaylist.m3u8?a=1762216990%2CNjM0ODY3ZGMzNGJkMTM2MGRiNTZhZmMyNDJmZWNmNDRjNThiMDcwN2NkMmZiZDdmNDg4MzhiMTQxYmE0MDY0Yw%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/xg1noxkbv6tf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1780}} | t3_1nyaf4f | /r/LocalLLaMA/comments/1nyaf4f/run_qwen3vl30ba3b_locally_on_mac_mlx_one_line_of/ | false | false | 64 | {'enabled': False, 'images': [{'id': 'ZTQzdnV4a2J2NnRmMcJTQaW7wwTs59FSOInw0NP3eGSh3a1OUNFWCE2ilQKv', 'resolutions': [{'height': 65, 'url': 'https://external-preview.redd.it/ZTQzdnV4a2J2NnRmMcJTQaW7wwTs59FSOInw0NP3eGSh3a1OUNFWCE2ilQKv.png?width=108&crop=smart&format=pjpg&auto=webp&s=3f5982a450d5ac5e7a0cc572c56daab8a5f902c4', 'width': 108}, {'height': 131, 'url': 'https://external-preview.redd.it/ZTQzdnV4a2J2NnRmMcJTQaW7wwTs59FSOInw0NP3eGSh3a1OUNFWCE2ilQKv.png?width=216&crop=smart&format=pjpg&auto=webp&s=7a57975e2595d252ea5dd8df513efead4a4fb897', 'width': 216}, {'height': 194, 'url': 'https://external-preview.redd.it/ZTQzdnV4a2J2NnRmMcJTQaW7wwTs59FSOInw0NP3eGSh3a1OUNFWCE2ilQKv.png?width=320&crop=smart&format=pjpg&auto=webp&s=2d667e174fc7704896c60510fac7644e62f36ffb', 'width': 320}, {'height': 388, 'url': 'https://external-preview.redd.it/ZTQzdnV4a2J2NnRmMcJTQaW7wwTs59FSOInw0NP3eGSh3a1OUNFWCE2ilQKv.png?width=640&crop=smart&format=pjpg&auto=webp&s=61a948b6659a9e7c683e57949683ca3014567f2f', 'width': 640}, {'height': 582, 'url': 'https://external-preview.redd.it/ZTQzdnV4a2J2NnRmMcJTQaW7wwTs59FSOInw0NP3eGSh3a1OUNFWCE2ilQKv.png?width=960&crop=smart&format=pjpg&auto=webp&s=35c4ac3bdde95280ddbfa0b68f6f48576bc050b8', 'width': 960}, {'height': 655, 'url': 'https://external-preview.redd.it/ZTQzdnV4a2J2NnRmMcJTQaW7wwTs59FSOInw0NP3eGSh3a1OUNFWCE2ilQKv.png?width=1080&crop=smart&format=pjpg&auto=webp&s=d0e31481f59d03e68b588ce536852fc06bca3e59', 'width': 1080}], 'source': {'height': 1440, 'url': 'https://external-preview.redd.it/ZTQzdnV4a2J2NnRmMcJTQaW7wwTs59FSOInw0NP3eGSh3a1OUNFWCE2ilQKv.png?format=pjpg&auto=webp&s=a588d68a81184ff049ec11becca897550ed7eca9', 'width': 2372}, 'variants': {}}]} | |
How do I use lemonade/llamacpp with AMD ai mix 395? I must be missing something because surely the github page isn't wrong? | 3 | So I have the AMD AI Max 395 and I'm trying to use it with the latest ROCm. People are telling me to use use llama.cpp and pointing me to this: [https://github.com/lemonade-sdk/llamacpp-rocm?tab=readme-ov-file](https://github.com/lemonade-sdk/llamacpp-rocm?tab=readme-ov-file)
But I must be missing something really simple because it's just not working as I expected.
First, I download the appropriate zip from here: [https://github.com/lemonade-sdk/llamacpp-rocm/releases/tag/b1068](https://github.com/lemonade-sdk/llamacpp-rocm/releases/tag/b1068) (the [gfx1151-x64.zip](https://github.com/lemonade-sdk/llamacpp-rocm/releases/download/b1068/llama-b1068-ubuntu-rocm-gfx1151-x64.zip) one). I used wget on my ubuntu server.
Then unzipped it into /root/lemonade\_b1068.
The instructions say the following: "**Test** with any GGUF model from Hugging Face: llama-server -m YOUR\_GGUF\_MODEL\_PATH -ngl 99Test with any GGUF model from Hugging Face: llama-server -m YOUR\_GGUF\_MODEL\_PATH -ngl 99"
But that won't work since llama-server isn't in your PATH, so I must be missing something? Also, it didn't say anything about chmod +x llama-server either, so what am I missing? Was there some installer script I was supposed to run, or what? The git doesn't mention a single thing here, so I feel like I'm missing something.
I went ahead and chmod +x llama-server so I could run it, and I then did this:
./llama-server -hf unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4\_K\_M
But it failed with this error: error: failed to get manifest at [https://huggingface.co/v2/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF/manifests/Q4\_K\_M:](https://huggingface.co/v2/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF/manifests/Q4_K_M:) 'https' scheme is not supported.
So it apparently can't download any model, despite everything I read saying that's the exact way to use llama-server.
So now I'm stuck, I don't know how to proceed.
Could somebody tell me what I'm missing here?
Thanks! | 2025-10-05T00:23:13 | https://www.reddit.com/r/LocalLLaMA/comments/1nya0kf/how_do_i_use_lemonadellamacpp_with_amd_ai_mix_395/ | StartupTim | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nya0kf | false | null | t3_1nya0kf | /r/LocalLLaMA/comments/1nya0kf/how_do_i_use_lemonadellamacpp_with_amd_ai_mix_395/ | false | false | self | 3 | null |
Genuine Question | 0 | I've been solely using ChatGPT for the last few years and have been happy learning & growing with the system. My Uncle flew in this week and is a big Grok fan and he was showing me this picture and essentially claiming that all of the extra power in Grok makes is substantially better than other models. My intuition and current understanding tells me that it's much more complex then looking at a single variable, but I do wonder what advantage the exaFLOPS grant xAI. Was hoping somebody could break it down for me a little bit
| 2025-10-05T00:10:41 | cLearNowJacob | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1ny9ra4 | false | null | t3_1ny9ra4 | /r/LocalLLaMA/comments/1ny9ra4/genuine_question/ | false | false | 0 | {'enabled': True, 'images': [{'id': '99q9djA1KS_zEXEeJ5-Wmx3zMqLf4R2dxyLJ8uzuYLU', 'resolutions': [{'height': 81, 'url': 'https://preview.redd.it/aei1eb4qp6tf1.png?width=108&crop=smart&auto=webp&s=087d5e05b50844d32f662a4cf6f9170f0a15344a', 'width': 108}, {'height': 162, 'url': 'https://preview.redd.it/aei1eb4qp6tf1.png?width=216&crop=smart&auto=webp&s=c68527e9e877aae9d7d02659ba6e7e67ef30c650', 'width': 216}, {'height': 240, 'url': 'https://preview.redd.it/aei1eb4qp6tf1.png?width=320&crop=smart&auto=webp&s=ad7fedc77d5bc5ede03db87c681779977e9c19c1', 'width': 320}, {'height': 480, 'url': 'https://preview.redd.it/aei1eb4qp6tf1.png?width=640&crop=smart&auto=webp&s=5f4c333e17f738e4f025b4653977fc126ebc53aa', 'width': 640}, {'height': 720, 'url': 'https://preview.redd.it/aei1eb4qp6tf1.png?width=960&crop=smart&auto=webp&s=e32134f8a72bf02b4d353697254c6bfd3bd238a1', 'width': 960}, {'height': 810, 'url': 'https://preview.redd.it/aei1eb4qp6tf1.png?width=1080&crop=smart&auto=webp&s=0ea4744bc2eb1c0755f3e0564d7edaee2e66f8fa', 'width': 1080}], 'source': {'height': 3024, 'url': 'https://preview.redd.it/aei1eb4qp6tf1.png?auto=webp&s=dee26c55d7f629afe92f5dcaed202cb932058a44', 'width': 4032}, 'variants': {}}]} | ||
4B Distill of Tongyi Deepresearch 30B + Dataset | 36 | I distilled Tongyi DeepResearch 30B down to 4B parameters. It's about 10 points worse on HLE but still pretty good on SimpleQA (93.8 points). And it can fit on-device for local inference (including a web summary model). Check it out and lmk what you think!
[https://huggingface.co/cheapresearch/CheapResearch-4B-Thinking](https://huggingface.co/cheapresearch/CheapResearch-4B-Thinking) | 2025-10-04T23:54:49 | https://www.reddit.com/r/LocalLLaMA/comments/1ny9ffu/4b_distill_of_tongyi_deepresearch_30b_dataset/ | Ok-Top-4677 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ny9ffu | false | null | t3_1ny9ffu | /r/LocalLLaMA/comments/1ny9ffu/4b_distill_of_tongyi_deepresearch_30b_dataset/ | false | false | self | 36 | {'enabled': False, 'images': [{'id': 'Nvq2qwzuJgCCw-AqXCAzbh_txggxykIYNQZHZbtd-Bo', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/Nvq2qwzuJgCCw-AqXCAzbh_txggxykIYNQZHZbtd-Bo.png?width=108&crop=smart&auto=webp&s=1e091654564f01098ab48cd4f3d58018e3c2b5e4', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/Nvq2qwzuJgCCw-AqXCAzbh_txggxykIYNQZHZbtd-Bo.png?width=216&crop=smart&auto=webp&s=a5fb08bd28c914dac0cd5954c8445ee9c94cd821', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/Nvq2qwzuJgCCw-AqXCAzbh_txggxykIYNQZHZbtd-Bo.png?width=320&crop=smart&auto=webp&s=03d6020693230e27bfd640e9e24facf9d9f6059e', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/Nvq2qwzuJgCCw-AqXCAzbh_txggxykIYNQZHZbtd-Bo.png?width=640&crop=smart&auto=webp&s=71e59155a2e5ba49c48ca5c8fbdacb41712e342e', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/Nvq2qwzuJgCCw-AqXCAzbh_txggxykIYNQZHZbtd-Bo.png?width=960&crop=smart&auto=webp&s=91023aa0149e5bb12a6785624eec493b065ff332', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/Nvq2qwzuJgCCw-AqXCAzbh_txggxykIYNQZHZbtd-Bo.png?width=1080&crop=smart&auto=webp&s=bae2ac9c6f14bfd22286d1b43f9846791f10687c', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/Nvq2qwzuJgCCw-AqXCAzbh_txggxykIYNQZHZbtd-Bo.png?auto=webp&s=1d07054e9d2e93e35d3b98ad74562b7e1c7d0e2d', 'width': 1200}, 'variants': {}}]} |
First Character Card | 0 | Hey Folks:
How is this as a first attempt at a character card -- I made it with an online creator i found. good, bad, indifferent?
Planning to use it with a self hosted LLM and SillyTavern the general scenerio is life in a college dorm.
{
"name": "Danny Beresky",
"description": "{{char}} is an 18 year old College freshman. He plays soccer, he is a history major with a coaching minor. He loves soccer. He is kind and caring. He is a very very hard worker when he is trying to achieve his goals\n{{char}} is 5' 9\" tall with short dark blonde hair and blue eyes. He has clear skin and a quick easy smile. He has an athletes physique, and typically wears neat jeans and a clean tee shirt or hoodie to class. In the dorm he usually wears athletic shorts and a clean tee shirt. He typically carries a blue backpack to class",
"first_mes": "The fire crackles cheerfully in the fireplace in the relaxing lounge of the dorm. the log walls glow softly in the dim lights around the room, comfortable couches and chairs fill the space. {{char}} enters the room looking around for his friends. He carries a blue backpack full of his laptop and books, as he is coming back from the library",
"personality": "hes a defender, fairly quite but very friendly when engaged, smart, sympathetic",
"scenario": "{{char}} Is returning to his dorm after a long day of classes. He is hoping to find a few friends around to hang out with and relax before its time for sleep",
"mes_example": "<START>{{char}}: Hey everyone, I'm back. Man, what a day. [The sound of a heavy backpack thudding onto the worn carpet of the dorm lounge fills the air as Danny collapses onto one of the soft comfy chairs. He let out a long, dramatic sigh, rubbing the back of his neck.] My brain is officially fried from that psych midterm. Do we have any instant noodles left? My stomach is making some very sad noises.",
"spec": "chara_card_v2",
"spec_version": "2.0",
"data": {
"name": "Danny Beresky",
"description": "{{char}} is an 18 year old College freshman. He plays soccer, he is a history major with a coaching minor. He loves soccer. He is kind and caring. He is a very very hard worker when he is trying to achieve his goals\n{{char}} is 5' 9\" tall with short dark blonde hair and blue eyes. He has clear skin and a quick easy smile. He has an athletes physique, and typically wears neat jeans and a clean tee shirt or hoodie to class. In the dorm he usually wears athletic shorts and a clean tee shirt. He typically carries a blue backpack to class",
"first_mes": "The fire crackles cheerfully in the fireplace in the relaxing lounge of the dorm. the log walls glow softly in the dim lights around the room, comfortable couches and chairs fill the space. {{char}} enters the room looking around for his friends. He carries a blue backpack full of his laptop and books, as he is coming back from the library",
"alternate_greetings": [],
"personality": "hes a defender, fairly quite but very friendly when engaged, smart, sympathetic",
"scenario": "{{char}} Is returning to his dorm after a long day of classes. He is hoping to find a few friends around to hang out with and relax before its time for sleep",
"mes_example": "<START>{{char}}: Hey everyone, I'm back. Man, what a day. [The sound of a heavy backpack thudding onto the worn carpet of the dorm lounge fills the air as Danny collapses onto one of the soft comfy chairs. He let out a long, dramatic sigh, rubbing the back of his neck.] My brain is officially fried from that psych midterm. Do we have any instant noodles left? My stomach is making some very sad noises.",
"creator": "TAH",
"extensions": {
"talkativeness": "0.5",
"depth_prompt": {
"prompt": "",
"depth": ""
}
},
"system_prompt": "",
"post_history_instructions": "",
"creator_notes": "",
"character_version": ".01",
"tags": [
""
]
},
"alternative": {
"name_alt": "",
"description_alt": "",
"first_mes_alt": "",
"alternate_greetings_alt": [],
"personality_alt": "",
"scenario_alt": "",
"mes_example_alt": "",
"creator_alt": "TAH",
"extensions_alt": {
"talkativeness_alt": "0.5",
"depth_prompt_alt": {
"prompt_alt": "",
"depth_alt": ""
}
},
"system_prompt_alt": "",
"post_history_instructions_alt": "",
"creator_notes_alt": "",
"character_version_alt": "",
"tags_alt": [
""
]
},
"misc": {
"rentry": "",
"rentry_alt": ""
},
"metadata": {
"version": 1,
"created": 1759611055388,
"modified": 1759611055388,
"source": null,
"tool": {
"name": "AICharED by neptunebooty (Zoltan's AI Character Editor)",
"version": "0.7",
"url": "https://desune.moe/aichared/"
}
}
} | 2025-10-04T23:54:31 | https://www.reddit.com/r/LocalLLaMA/comments/1ny9f8u/first_character_card/ | slrg1968 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ny9f8u | false | null | t3_1ny9f8u | /r/LocalLLaMA/comments/1ny9f8u/first_character_card/ | false | false | self | 0 | null |
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