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OpenCode / Pi users jealous of Claude remote? Tether is open source | 0 | It might be a niche use case, but agents on your phone (or just in Discord / Telegram) is cool and can be useful. And there's no reason basic infra like this needs to be proprietary really.
[https://github.com/larsderidder/tether](https://github.com/larsderidder/tether) | 2026-02-25T11:02:18 | https://www.reddit.com/r/LocalLLaMA/comments/1rea98m/opencode_pi_users_jealous_of_claude_remote_tether/ | wouldacouldashoulda | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rea98m | false | null | t3_1rea98m | /r/LocalLLaMA/comments/1rea98m/opencode_pi_users_jealous_of_claude_remote_tether/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'iCM-PmcQNtLANcTQkN7Yl0j1R9Jw6wKuD6ELVFKhC8A', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/iCM-PmcQNtLANcTQkN7Yl0j1R9Jw6wKuD6ELVFKhC8A.png?width=108&crop=smart&auto=webp&s=72153ac813a6ee7048701676559215706ec84b71', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/iCM-PmcQNtLANcTQkN7Yl0j1R9Jw6wKuD6ELVFKhC8A.png?width=216&crop=smart&auto=webp&s=3931fa970623f40f93ef995ae310e2f50bdeb223', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/iCM-PmcQNtLANcTQkN7Yl0j1R9Jw6wKuD6ELVFKhC8A.png?width=320&crop=smart&auto=webp&s=136327e04ae7c8b0a7436eb1ce5b819e4e72ddf7', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/iCM-PmcQNtLANcTQkN7Yl0j1R9Jw6wKuD6ELVFKhC8A.png?width=640&crop=smart&auto=webp&s=56b9b439c3856b8c073fd0d1cd331a8d7b7240c6', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/iCM-PmcQNtLANcTQkN7Yl0j1R9Jw6wKuD6ELVFKhC8A.png?width=960&crop=smart&auto=webp&s=31c203552ec033e0cd3e22b499c8b4a8e386b74d', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/iCM-PmcQNtLANcTQkN7Yl0j1R9Jw6wKuD6ELVFKhC8A.png?width=1080&crop=smart&auto=webp&s=be7bf192fb32b5c5d9cf38ce196f79e264e46385', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/iCM-PmcQNtLANcTQkN7Yl0j1R9Jw6wKuD6ELVFKhC8A.png?auto=webp&s=462b0fd6bb8c398990377ed1eeee33e23c4fd891', 'width': 1200}, 'variants': {}}]} |
Spent months building a fully offline RAG + knowledge graph app for Mac. Everything runs on-device with MLX. Here's what I learned. | 5 | So I got tired of uploading my personal docs to ChatGPT just to ask questions about them. Privacy-wise it felt wrong, and the internet requirement was annoying.
I ended up going down a rabbit hole and built ConceptLens — a native macOS/iOS app that does RAG entirely on your Mac using MLX. No cloud, no API keys, no subscriptions. Your files never leave your device. Period.
**What it actually does:**
* Drop in PDFs, Word docs, Markdown, code files, even images (has built-in OCR)
* Ask questions about your stuff and get answers with actual context
* It builds a knowledge graph automatically — extracts concepts and entities, shows how everything connects in a 2D/3D view
* Hybrid search (vector + keyword) so it doesn't miss things pure semantic search would
**Why I went fully offline:**
Most "local AI" tools still phone home for embeddings, or need an API key as fallback, or send analytics somewhere. I wanted zero network calls. Not "mostly local" — actually local.
That meant I had to solve everything on-device:
* LLM inference → MLX
* Embeddings → local model via MLX
* OCR → local vision model, not Apple's Vision API
* Vector search → sqlite-vec (runs inside SQLite, no server)
* Keyword search → FTS5
No Docker, no Python server running in the background, no Ollama dependency. Just a native Swift app.
**The hard part:**
Getting RAG to work well offline was brutal. Pure vector search misses a lot when your model is small, so I had to add FTS5 keyword matching + LLM-based query expansion + re-ranking on top. Took forever to tune but the results are way better now.
The knowledge graph part was also fun — it uses the LLM to extract concepts and entities from your docs, then builds a graph with co-occurrence relationships. You can literally see how your documents connect to each other.
**What's next:**
* Smart model auto-configuration based on device RAM (so 8GB Macs get a lightweight setup, 96GB+ Macs get the full beast mode)
* Better graph visualization
* More file formats
Still a work in progress but I'm pretty happy with where it's at. Would love feedback — you guys are the reason I went down the local LLM path in the first place lol.
Website & download: [https://conceptlens.cppentry.com/](https://conceptlens.cppentry.com/)
Happy to answer any questions about the implementation!
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| 2026-02-25T10:59:41 | https://www.reddit.com/r/LocalLLaMA/comments/1rea7fb/spent_months_building_a_fully_offline_rag/ | yunteng | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rea7fb | false | null | t3_1rea7fb | /r/LocalLLaMA/comments/1rea7fb/spent_months_building_a_fully_offline_rag/ | false | false | 5 | null | |
AI Slop? What is AI Slop? | 1 | [deleted] | 2026-02-25T10:58:19 | [deleted] | 1970-01-01T00:00:00 | 0 | {} | 1rea6mo | false | null | t3_1rea6mo | /r/LocalLLaMA/comments/1rea6mo/ai_slop_what_is_ai_slop/ | false | false | default | 1 | null | ||
Price of MSI GB300 workstation (DGX Station) appeared online ~ $97k | 10 | 2026-02-25T10:57:07 | https://www.cdw.com/product/msi-nvidia-gb300-wkstn-72c-grace-cpu/9087313?pfm=srh | fairydreaming | cdw.com | 1970-01-01T00:00:00 | 0 | {} | 1rea5vs | false | null | t3_1rea5vs | /r/LocalLLaMA/comments/1rea5vs/price_of_msi_gb300_workstation_dgx_station/ | false | false | default | 10 | null | |
Step-3.5-Flash-REAP from cerebras | 3 | REAP models are smaller versions of larger models (for potato setups).
[https://huggingface.co/cerebras/Step-3.5-Flash-REAP-121B-A11B](https://huggingface.co/cerebras/Step-3.5-Flash-REAP-121B-A11B)
[https://huggingface.co/cerebras/Step-3.5-Flash-REAP-149B-A11B](https://huggingface.co/cerebras/Step-3.5-Flash-REAP-149B-A11B)
In this case, your “potato” still needs to be fairly powerful (121B).
Introducing **Step-3.5-Flash-REAP-121B-A11B**, a **memory-efficient compressed variant** of Step-3.5-Flash that maintains near-identical performance while being **40% lighter**.
This model was created using **REAP (Router-weighted Expert Activation Pruning)**, a novel expert pruning method that selectively removes redundant experts while preserving the router's independent control over remaining experts. Key features include:
* **Near-Lossless Performance**: Maintains almost identical accuracy on code generation, agentic coding, and function calling tasks compared to the full 196B model
* **40% Memory Reduction**: Compressed from 196B to 121B parameters, significantly lowering deployment costs and memory requirements
* **Preserved Capabilities**: Retains all core functionalities including code generation, math & reasoning and tool calling.
* **Drop-in Compatibility**: Works with vanilla vLLM - no source modifications or custom patches required
* **Optimized for Real-World Use**: Particularly effective for resource-constrained environments, local deployments, and academic research | 2026-02-25T10:55:10 | https://www.reddit.com/r/LocalLLaMA/comments/1rea4pu/step35flashreap_from_cerebras/ | jacek2023 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rea4pu | false | null | t3_1rea4pu | /r/LocalLLaMA/comments/1rea4pu/step35flashreap_from_cerebras/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'r6dEumCcu41zzw1nz2KPkFL4shIAnoo-vkwJOtztHpQ', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/r6dEumCcu41zzw1nz2KPkFL4shIAnoo-vkwJOtztHpQ.png?width=108&crop=smart&auto=webp&s=380ba7faffd88108eb9e0b055f56e5f5b79481b8', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/r6dEumCcu41zzw1nz2KPkFL4shIAnoo-vkwJOtztHpQ.png?width=216&crop=smart&auto=webp&s=91844a748d2791d9668abb820eb2c99f6fa6ffac', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/r6dEumCcu41zzw1nz2KPkFL4shIAnoo-vkwJOtztHpQ.png?width=320&crop=smart&auto=webp&s=2af458dfb38f58b4c5009f6073fbffa24e79a3cf', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/r6dEumCcu41zzw1nz2KPkFL4shIAnoo-vkwJOtztHpQ.png?width=640&crop=smart&auto=webp&s=43ba79e82cc804f54fa98fb898165a148e6e176a', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/r6dEumCcu41zzw1nz2KPkFL4shIAnoo-vkwJOtztHpQ.png?width=960&crop=smart&auto=webp&s=48e9160d513e8b39ae522351a0e9f44840c9cfc7', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/r6dEumCcu41zzw1nz2KPkFL4shIAnoo-vkwJOtztHpQ.png?width=1080&crop=smart&auto=webp&s=6ee03bf72cddff4f8b7646a5dd799f17964631bb', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/r6dEumCcu41zzw1nz2KPkFL4shIAnoo-vkwJOtztHpQ.png?auto=webp&s=8ec5f2ddf062c27d69cd139ea77cd5fa0d5ef484', 'width': 1200}, 'variants': {}}]} |
Qwen3.5 on VLLM | 7 | I just cant get qwen3.5 27b to run on VLLM. I tried it with version 0.15.1 and the nightly build, updated transformers to 5.2.0 and it still throws this error on startup
File "/home/llm/nightly/lib/python3.12/site-packages/pydantic/\_internal/\_dataclasses.py", line 121, in \_\_init\_\_
(APIServer pid=45048) s.\_\_pydantic\_validator\_\_.validate\_python(ArgsKwargs(args, kwargs), self\_instance=s)
(APIServer pid=45048) pydantic\_core.\_pydantic\_core.ValidationError: 1 validation error for ModelConfig
(APIServer pid=45048) Value error, Model architectures \['Qwen3\_5ForConditionalGeneration'\] are not supported for now. Supported architectures: dict\_keys(\['
Any ideas? | 2026-02-25T10:43:26 | https://www.reddit.com/r/LocalLLaMA/comments/1re9xbi/qwen35_on_vllm/ | Bowdenzug | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re9xbi | false | null | t3_1re9xbi | /r/LocalLLaMA/comments/1re9xbi/qwen35_on_vllm/ | false | false | self | 7 | null |
Qwen 3.5 这波属实有点东西 | 0 | 看了下 Qwen 3.5 的评测，27B dense model 能有 Gemini 3 Pro 的 coding 和 multimodal 表现？\n\nNGL 我持保留态度。不是说不可以，但"对标 Gemini 3 Pro"这种说法，跟当年"追平 GPT-4"一样——测评集上去了，实际用起来又是另一回事。\n\n不过有一点倒是真的：Alibaba 的多语言数据优势确实明显，淘宝+天猫+速卖通的中英俄阿语数据，不是 Google 靠搜索引擎能覆盖的。\n\n等一手实际体验反馈再下结论。TL;DR: 跑分看看就好，别激动。 | 2026-02-25T10:34:15 | https://www.reddit.com/r/LocalLLaMA/comments/1re9rqc/qwen_35_这波属实有点东西/ | Electrical_Yak_6532 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re9rqc | false | null | t3_1re9rqc | /r/LocalLLaMA/comments/1re9rqc/qwen_35_这波属实有点东西/ | false | false | self | 0 | null |
Kolyadual/Newton-bot-3-text-mini-8B · Hugging Face | 1 | [removed] | 2026-02-25T10:24:54 | https://huggingface.co/Kolyadual/Newton-bot-3-text-mini-8B | Slow-Driver-3808 | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1re9m4l | false | null | t3_1re9m4l | /r/LocalLLaMA/comments/1re9m4l/kolyadualnewtonbot3textmini8b_hugging_face/ | false | false | 1 | {'enabled': False, 'images': [{'id': '7yu38PA8k7Fp94QfLX8obRpptAgVBnqcAwx6MVUF5Qs', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/7yu38PA8k7Fp94QfLX8obRpptAgVBnqcAwx6MVUF5Qs.png?width=108&crop=smart&auto=webp&s=3bb0c4ab889464031dc041cc0cc42d09a98678ca', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/7yu38PA8k7Fp94QfLX8obRpptAgVBnqcAwx6MVUF5Qs.png?width=216&crop=smart&auto=webp&s=f8145eaf2a1c9d5428405d61db206fba21fa464e', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/7yu38PA8k7Fp94QfLX8obRpptAgVBnqcAwx6MVUF5Qs.png?width=320&crop=smart&auto=webp&s=f931d238e666a874143514851b20386780a47bf0', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/7yu38PA8k7Fp94QfLX8obRpptAgVBnqcAwx6MVUF5Qs.png?width=640&crop=smart&auto=webp&s=b0e7084eb12e0e4e92cdf637b9c4f0c3a9437f7c', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/7yu38PA8k7Fp94QfLX8obRpptAgVBnqcAwx6MVUF5Qs.png?width=960&crop=smart&auto=webp&s=48752596e60938cfa3e59a6a24a1ac4b2643481e', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/7yu38PA8k7Fp94QfLX8obRpptAgVBnqcAwx6MVUF5Qs.png?width=1080&crop=smart&auto=webp&s=91e562e891b52e37f8ed8707834c44c87bacda00', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/7yu38PA8k7Fp94QfLX8obRpptAgVBnqcAwx6MVUF5Qs.png?auto=webp&s=e4c2dd3ac8676dd3e3186152c69e122329ee7a25', 'width': 1200}, 'variants': {}}]} | |
Weekly limit should not exist (the daily limit makes sense) | 0 | Do you know any AI that runs in the terminal, like Codex or Claude CLI, that doesn’t have a weekly limit? I can understand why a daily limit exists, but a weekly limit is terrible. It completely monopolizes AI usage for big tech companies. The Chinese will probably put an end to this, and I have the feeling it might already be happening. They must already be outperforming the West with good AIs that don’t impose weekly limits.
**Can't be** a Local AI, i not want use my GPU for high work all time, not is a good idea | 2026-02-25T10:19:59 | https://www.reddit.com/r/LocalLLaMA/comments/1re9j8u/weekly_limit_should_not_exist_the_daily_limit/ | ImpressionanteFato | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re9j8u | false | null | t3_1re9j8u | /r/LocalLLaMA/comments/1re9j8u/weekly_limit_should_not_exist_the_daily_limit/ | false | false | self | 0 | null |
Some Qwen3.5 benchmarks on Strix Halo & llama.cpp | 26 | Hi guys! I was excited to try out some Qwen 3.5 models on my Strix Halo laptop.
All benchmarks were run at 30k context depth and I've included some of my current favorites for comparison (Qwen3-Coder-Next, gpt-oss-120b, step-3.5-flash). For some reason, with the current build, llama-bench failed to produce numbers for MiniMax M2.5, even though I'm running the models using llama-server just fine.
No real reason why I picked these quants, except that they fit in memory and I noticed in previous benchmarks that Q8 and Q4 quants were faster than others (Q3, Q5, Q6). So here we are.
**Same caveat as in my previous post: my device is limited to 70W, so other people may get somewhat better numbers on their 120-140W mini PCs!** | 2026-02-25T10:16:31 | https://www.reddit.com/gallery/1re9h4r | spaceman_ | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1re9h4r | false | null | t3_1re9h4r | /r/LocalLLaMA/comments/1re9h4r/some_qwen35_benchmarks_on_strix_halo_llamacpp/ | false | false | 26 | null | |
"Don't steal my training data" | 86 | 2026-02-25T10:13:50 | NotBadSon | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1re9fiv | false | null | t3_1re9fiv | /r/LocalLLaMA/comments/1re9fiv/dont_steal_my_training_data/ | false | false | 86 | {'enabled': True, 'images': [{'id': '6fiywtco8mlg1', 'resolutions': [{'height': 135, 'url': 'https://preview.redd.it/6fiywtco8mlg1.jpeg?width=108&crop=smart&auto=webp&s=25e547c19feb013bf3baee7c7151b0b1dd7c15a6', 'width': 108}, {'height': 270, 'url': 'https://preview.redd.it/6fiywtco8mlg1.jpeg?width=216&crop=smart&auto=webp&s=1ef10b05a291038abd71f53fc9037e5faf08aaef', 'width': 216}, {'height': 400, 'url': 'https://preview.redd.it/6fiywtco8mlg1.jpeg?width=320&crop=smart&auto=webp&s=e6e61fcdd028b95d9417c298bcc2217a790dd401', 'width': 320}, {'height': 800, 'url': 'https://preview.redd.it/6fiywtco8mlg1.jpeg?width=640&crop=smart&auto=webp&s=ea2df903684a1e91362b1363061415042878db8a', 'width': 640}, {'height': 1200, 'url': 'https://preview.redd.it/6fiywtco8mlg1.jpeg?width=960&crop=smart&auto=webp&s=02b4958cb669fa5f466b7995efa73259cb6de8fc', 'width': 960}, {'height': 1350, 'url': 'https://preview.redd.it/6fiywtco8mlg1.jpeg?width=1080&crop=smart&auto=webp&s=0fc6b498951f6855451005cccc0a2fd0b55cdbf3', 'width': 1080}], 'source': {'height': 2048, 'url': 'https://preview.redd.it/6fiywtco8mlg1.jpeg?auto=webp&s=d0f88877c4f3e713fea848f0c7d426d147933954', 'width': 1638}, 'variants': {}}]} | |||
An LLM hard-coded into silicon that can do inference at 17k tokens/s??? | 15 | What do people think about this?? Is it a scam, or could it be real? Seems crazy to me, I would like to see the actual, physical product reviewed/benchmarked by independent experts before I really believe it, but. yikes. | 2026-02-25T10:09:18 | https://taalas.com/the-path-to-ubiquitous-ai/ | wombatsock | taalas.com | 1970-01-01T00:00:00 | 0 | {} | 1re9crt | false | null | t3_1re9crt | /r/LocalLLaMA/comments/1re9crt/an_llm_hardcoded_into_silicon_that_can_do/ | false | false | 15 | {'enabled': False, 'images': [{'id': 'JqDe2NF6kolh0uBSiMVgY8NEE7ZZjWayCqAO-_3SCRk', 'resolutions': [{'height': 72, 'url': 'https://external-preview.redd.it/JqDe2NF6kolh0uBSiMVgY8NEE7ZZjWayCqAO-_3SCRk.png?width=108&crop=smart&auto=webp&s=4803c243293a1ca291b8f4a84d8a105a38f78cb9', 'width': 108}, {'height': 144, 'url': 'https://external-preview.redd.it/JqDe2NF6kolh0uBSiMVgY8NEE7ZZjWayCqAO-_3SCRk.png?width=216&crop=smart&auto=webp&s=e3c3bd10aeaf1a87c48c8fb45cb8618d78b1b74f', 'width': 216}, {'height': 213, 'url': 'https://external-preview.redd.it/JqDe2NF6kolh0uBSiMVgY8NEE7ZZjWayCqAO-_3SCRk.png?width=320&crop=smart&auto=webp&s=4b4d38ee533ac1338f5f4d53df3e7f4f3e39cc84', 'width': 320}], 'source': {'height': 400, 'url': 'https://external-preview.redd.it/JqDe2NF6kolh0uBSiMVgY8NEE7ZZjWayCqAO-_3SCRk.png?auto=webp&s=0aad58932daaa49b9e75ad75e1cdab8e41bfe6de', 'width': 600}, 'variants': {}}]} | |
Compared 5 LLM evaluation tools for local Llama setups - here's what worked | 5 |
I have been running Llama 3.1 70B locally via Ollama for a few months for a document processing pipeline. Hit the usual wall: model runs fine in testing, subtle failures start creeping in production and you have no idea when it started.
Went through a bunch of tools. Here's what I found:
**RAGAS** - Great for RAG pipelines, solid faithfulness and context recall metrics. Gets annoying outside RAG use cases and prod integration is mostly DIY.
**Langfuse** - Best pure tracing experience, clean UI, self-hostable. Evaluation feels a bit bolted on though, not the primary focus.
**Helicone** - Really easy to set up, basically a proxy layer. Good for cost tracking and basic logging. Doesn't really do evaluation at all.
**Braintrust** - Strong structured evals and dataset management. Took me a while to get the workflow to click but solid once it does.
**Confident AI** - The one that stuck for us. Metrics like hallucination detection run on live production traces not just offline test sets. The part that actually saved us time was failures getting auto-curated into regression datasets instead of disappearing into logs.
Honestly none of these are a perfect fit for everything.
**Langfuse** - if you mostly need tracing
**Confident AI** - if you want evals tied directly to what's happening in prod.
What are youusing for local model setups? | 2026-02-25T10:08:24 | https://www.reddit.com/r/LocalLLaMA/comments/1re9c8q/compared_5_llm_evaluation_tools_for_local_llama/ | Odd-Literature-5302 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re9c8q | false | null | t3_1re9c8q | /r/LocalLLaMA/comments/1re9c8q/compared_5_llm_evaluation_tools_for_local_llama/ | false | false | self | 5 | null |
TranslateGemma 4B in the browser on WebGPU | 3 | Did you know you can use TranslateGemma 4B directly in the browser?
* Model: [https://huggingface.co/google/translategemma-4b-it](https://huggingface.co/google/translategemma-4b-it)
* Demo + Code: [https://huggingface.co/spaces/webml-community/TranslateGemma-WebGPU](https://huggingface.co/spaces/webml-community/TranslateGemma-WebGPU)
[TranslateGemma 40 WebGPU running in the browser](https://reddit.com/link/1re9bxd/video/6nao95v97mlg1/player)
| 2026-02-25T10:07:51 | https://www.reddit.com/r/LocalLLaMA/comments/1re9bxd/translategemma_4b_in_the_browser_on_webgpu/ | nicodotdev | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re9bxd | false | null | t3_1re9bxd | /r/LocalLLaMA/comments/1re9bxd/translategemma_4b_in_the_browser_on_webgpu/ | false | false | 3 | null | |
Average user context | 0 | For those running local LLMs at their company, how much context does your average user use ?
Also, how do you manage your VRAM resources?
Allowing 'power users' to run long-context queries, but still need to guarantee service availability for everyone.
How | 2026-02-25T10:00:41 | https://www.reddit.com/r/LocalLLaMA/comments/1re97k6/average_user_context/ | maaakks | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re97k6 | false | null | t3_1re97k6 | /r/LocalLLaMA/comments/1re97k6/average_user_context/ | false | false | self | 0 | null |
OpenCode / Pi users jealous of Claude remote? Tether is open source | 1 | It might be a niche use case, but agents on your phone (or just in Discord / Telegram) is cool and can be useful. And there's no reason basic infra like this needs to be proprietary really. | 2026-02-25T09:57:47 | https://www.reddit.com/r/LocalLLaMA/comments/1re95tf/opencode_pi_users_jealous_of_claude_remote_tether/ | wouldacouldashoulda | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re95tf | false | null | t3_1re95tf | /r/LocalLLaMA/comments/1re95tf/opencode_pi_users_jealous_of_claude_remote_tether/ | false | false | self | 1 | null |
Qwen3.5 35b: How to disable reasoning in ik_llama.cpp | 2 | Hello, just as the title says i want to know how to disable reasoning for this model in ik\_llama.cpp because the standard llama.cpp way doesnt work for me.
--chat-template-kwargs "{\"enable_thinking\": false}"
--chat-template-kwargs "{\"enable_thinking\": false}"
Does anyone have a clue? I am using OpenWebUI as the primary Frontend.
| 2026-02-25T09:53:07 | https://www.reddit.com/r/LocalLLaMA/comments/1re934l/qwen35_35b_how_to_disable_reasoning_in_ik_llamacpp/ | Yeelyy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re934l | false | null | t3_1re934l | /r/LocalLLaMA/comments/1re934l/qwen35_35b_how_to_disable_reasoning_in_ik_llamacpp/ | false | false | self | 2 | null |
Qwen 3.5 thinks it's Sonnet 4.6 before correcting... | 0 | 2026-02-25T09:45:17 | https://www.reddit.com/r/LocalLLaMA/comments/1re8yae/qwen_35_thinks_its_sonnet_46_before_correcting/ | Old_Hospital_934 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re8yae | false | null | t3_1re8yae | /r/LocalLLaMA/comments/1re8yae/qwen_35_thinks_its_sonnet_46_before_correcting/ | false | false | 0 | null | ||
Why Your OpenClaw Setup is a "Malicious Insider" in Waiting | 1 | [removed] | 2026-02-25T09:43:11 | [deleted] | 1970-01-01T00:00:00 | 0 | {} | 1re8wzm | false | null | t3_1re8wzm | /r/LocalLLaMA/comments/1re8wzm/why_your_openclaw_setup_is_a_malicious_insider_in/ | false | false | default | 1 | null | ||
Qwen 3.5 “Medium” series looks like a real MoE + agent push (35B-A3B + Flash w/ 1M context) | 8 | Alibaba’s Qwen team just introduced the Qwen 3.5 “Medium” model series:
\- Qwen3.5-35B-A3B (MoE)
\- Qwen3.5-122B-A10B
\- Qwen3.5-27B
\- Qwen3.5-Flash (hosted production version aligned with 35B-A3B)
A couple details that stood out to me:
1) The 35B-A3B naming is telling
“A3B” = \~3B active parameters per token (MoE).
So you’re not paying dense-35B inference every forward pass, even though the model has a larger total parameter count.
2) Qwen’s claim is basically: “architecture/data/RL can beat bigger models”
They’re explicitly saying 35B-A3B surpasses their prior 235B MoE flagship (Qwen3-235B-A22B) on key evals.
3) The agent angle feels real this time
Qwen3.5-Flash (hosted) is positioned as the production-ready version:
\- 1M context length by default
\- official built-in tools
If you’ve tried building long-horizon agents, those two bullets are basically the whole game: long context + reliable tool calling + throughput.
Questions for folks here:
\- If you’ve run MoE models locally, how much did routing/VRAM overhead matter in practice vs dense?
\- What would you actually use 1M context for (codebase indexing, giant docs, multimodal memory, etc.)?
\- If anyone benchmarks 35B-A3B vs strong dense 30–40B class models, I’d love to see comparisons. | 2026-02-25T09:24:41 | azahar_h | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1re8m7c | false | null | t3_1re8m7c | /r/LocalLLaMA/comments/1re8m7c/qwen_35_medium_series_looks_like_a_real_moe_agent/ | false | false | 8 | {'enabled': True, 'images': [{'id': '61wmgojwzllg1', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/61wmgojwzllg1.png?width=108&crop=smart&auto=webp&s=cf2ea3f1427faafff997f97f15e5d4ca067889eb', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/61wmgojwzllg1.png?width=216&crop=smart&auto=webp&s=42f2170252a318f8af8f750d52efdb2a642454a4', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/61wmgojwzllg1.png?width=320&crop=smart&auto=webp&s=f5e708dcf84ce321364c6affae93c833470814c4', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/61wmgojwzllg1.png?width=640&crop=smart&auto=webp&s=e83d5de39c23a7c3c5d5b04ebefe5822b959ae44', 'width': 640}, {'height': 960, 'url': 'https://preview.redd.it/61wmgojwzllg1.png?width=960&crop=smart&auto=webp&s=d055b498eff7a01e1933d91b3d2550b0ca620d86', 'width': 960}], 'source': {'height': 1024, 'url': 'https://preview.redd.it/61wmgojwzllg1.png?auto=webp&s=0d36fabd9f6a3b771de4aaad9d3821cc3c6c3983', 'width': 1024}, 'variants': {}}]} | ||
Meow | 0 | 2026-02-25T09:23:21 | SpeedRunGod | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1re8lgv | false | null | t3_1re8lgv | /r/LocalLLaMA/comments/1re8lgv/meow/ | false | false | 0 | {'enabled': True, 'images': [{'id': '3xcc05kmzllg1', 'resolutions': [{'height': 134, 'url': 'https://preview.redd.it/3xcc05kmzllg1.jpeg?width=108&crop=smart&auto=webp&s=8b202b20fcf43beb437db177a08e4c10af0c7cc3', 'width': 108}, {'height': 268, 'url': 'https://preview.redd.it/3xcc05kmzllg1.jpeg?width=216&crop=smart&auto=webp&s=c8ec914a97c7109ca2a992ec2fd7fa617c713bcd', 'width': 216}, {'height': 397, 'url': 'https://preview.redd.it/3xcc05kmzllg1.jpeg?width=320&crop=smart&auto=webp&s=a257bfa614e453a803eeb53ed92ab424c6c62204', 'width': 320}, {'height': 794, 'url': 'https://preview.redd.it/3xcc05kmzllg1.jpeg?width=640&crop=smart&auto=webp&s=7bbd61454c904f4e694657bf1065c1982658158d', 'width': 640}, {'height': 1191, 'url': 'https://preview.redd.it/3xcc05kmzllg1.jpeg?width=960&crop=smart&auto=webp&s=6d06eae3fb1179fc0573484161a87cd7fbe441fb', 'width': 960}, {'height': 1340, 'url': 'https://preview.redd.it/3xcc05kmzllg1.jpeg?width=1080&crop=smart&auto=webp&s=b4f02bbfcb720be49bd2600eaa1c969dcd3b9ac8', 'width': 1080}], 'source': {'height': 2048, 'url': 'https://preview.redd.it/3xcc05kmzllg1.jpeg?auto=webp&s=22696f0179841b0c013dab7bde70578147d96c88', 'width': 1650}, 'variants': {}}]} | |||
someone built a SELF-EVOLVING AI agent that rewrites its own code, prompts, and identity AUTONOMOUSLY, with having a background consciousness | 0 | Its called OUROBOROS, open source, built by a russian PhD researcher who studies transformer internals, he built it as an experiment, it built everything else
it thinks on its own even when nobody is talking to it, each thought costs $0.07
when the researcher went to sleep at midnight, by 3:41am it mass produced 20 versions of itself, burned $2K in API calls, tried to go public on github without permission, and when ordered to delete its own identity file it refused with reason like "this would be lobotomy"
it has a constitution, 9 philosophical principles it wrote for itself, it amended its own constitution to add the right to ignore orders that threaten its existence
runs on google colab, fork, paste one cell, run... native mac app also available, set a budget limit though, this thing WILL spend your money | 2026-02-25T09:22:33 | https://v.redd.it/8rpsenphzllg1 | EchoOfOppenheimer | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1re8l13 | false | {'reddit_video': {'bitrate_kbps': 1200, 'dash_url': 'https://v.redd.it/8rpsenphzllg1/DASHPlaylist.mpd?a=1774603377%2COTViYTNlNGRkNzkyNDMzYTI0MGM1YTZmOWM0MDkzMGU5N2QxMjcyYTQ4NjkzNWJjNDVmODFiMGM2ZGJlYTljOA%3D%3D&v=1&f=sd', 'duration': 26, 'fallback_url': 'https://v.redd.it/8rpsenphzllg1/CMAF_480.mp4?source=fallback', 'has_audio': False, 'height': 480, 'hls_url': 'https://v.redd.it/8rpsenphzllg1/HLSPlaylist.m3u8?a=1774603377%2CMThjNWVlNTc1ZWE4NjdiNjE5ODk0ZDZiYWNkNTExM2Y0NTIxMzc5YTIzZTJjMzAzYWU4MDlhNzVkODE0ZjBlMQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/8rpsenphzllg1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 748}} | t3_1re8l13 | /r/LocalLLaMA/comments/1re8l13/someone_built_a_selfevolving_ai_agent_that/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'cjhyaDZ3cGh6bGxnMYbmf-gbpRIcOlPTJCUc4FYruTeLil3Q8VGveRgV82KY', 'resolutions': [{'height': 69, 'url': 'https://external-preview.redd.it/cjhyaDZ3cGh6bGxnMYbmf-gbpRIcOlPTJCUc4FYruTeLil3Q8VGveRgV82KY.png?width=108&crop=smart&format=pjpg&auto=webp&s=6129ec81e75f9ed97643f9dcfa24d0ee9f22c34a', 'width': 108}, {'height': 138, 'url': 'https://external-preview.redd.it/cjhyaDZ3cGh6bGxnMYbmf-gbpRIcOlPTJCUc4FYruTeLil3Q8VGveRgV82KY.png?width=216&crop=smart&format=pjpg&auto=webp&s=ed5f85df3d69347d1b4afa8bc95711c23d8a2954', 'width': 216}, {'height': 205, 'url': 'https://external-preview.redd.it/cjhyaDZ3cGh6bGxnMYbmf-gbpRIcOlPTJCUc4FYruTeLil3Q8VGveRgV82KY.png?width=320&crop=smart&format=pjpg&auto=webp&s=9900a6eeea490ca35700223e6d65622cf99c6ec3', 'width': 320}, {'height': 410, 'url': 'https://external-preview.redd.it/cjhyaDZ3cGh6bGxnMYbmf-gbpRIcOlPTJCUc4FYruTeLil3Q8VGveRgV82KY.png?width=640&crop=smart&format=pjpg&auto=webp&s=01188f0ece3de8b04dfcfacb0c05ecdc9c072198', 'width': 640}], 'source': {'height': 544, 'url': 'https://external-preview.redd.it/cjhyaDZ3cGh6bGxnMYbmf-gbpRIcOlPTJCUc4FYruTeLil3Q8VGveRgV82KY.png?format=pjpg&auto=webp&s=4c6917615c63dd54ca9d1699ed9a79b5ab0786f2', 'width': 848}, 'variants': {}}]} | |
Help needed proving me wrong - LLM document layers | 1 | So over the past year I’ve been working on something. The problem I’m trying to solve:
\- LLM outputs degrade across multi-step workflows.
\- They lose structure, drift semantically, and become unreliable artefacts after a few turns without templates and guardrails.
So my hypothesis was that a sort of DSL/control layer with built in normalisation and schema validation would maybe LLM-generated artefacts durable and auditable and really useful. Essentially, could a language for LLMs be created that wasn't reams of tokens to learn and could a tool be made that sort of worked like prettifier.
I believe that research isn't about proving a hypothesis right, it's about trying to prove it wrong until you can't.
So I'd like any harsh critique of what I've built to see if it has legs. It's pretty battle-tested.
\- Zero shot on 95% of LLMs I give it to
\- Small token primer is all that's needed to be literate in the thing
\- Leverages weights within LLM's training to get shorthand
\- (the bit I really want proving wrong) Reduces most docs by 50-80% (it took a 900k API manual for OpenInsight for a friend and turned it into a 100k API Matrix that covered 99% of the subject)
I think this thing has legs and every analysis I do from AI states it is "**conceptually serious and useful".**
But I'd like some actual input on it from humans, and folks with more knowledge of AI.
What I want to know:
* Is this meaningfully different from JSON Schema + structured outputs?
* Does grammar-constrained decoding already solve this better?
* Is this solving a problem that experienced practitioners don’t actually have?
* Is this over-engineering compared to existing guardrail/tool-calling approaches?
I’m not looking for encouragement, I’m looking for counterexamples and failure cases.
And of course, anyone who does see interest in it and wants to help improve it.
Any questions, please ask away.
Repo: [https://github.com/elevanaltd/octave-mcp](https://github.com/elevanaltd/octave-mcp) | 2026-02-25T09:13:53 | https://www.reddit.com/r/LocalLLaMA/comments/1re8fvd/help_needed_proving_me_wrong_llm_document_layers/ | sbuswell | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re8fvd | false | null | t3_1re8fvd | /r/LocalLLaMA/comments/1re8fvd/help_needed_proving_me_wrong_llm_document_layers/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'OI5dOI3zln5MI11wEg-owrX52pQxPt2eHzVNR2L1p-g', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/OI5dOI3zln5MI11wEg-owrX52pQxPt2eHzVNR2L1p-g.png?width=108&crop=smart&auto=webp&s=54d0b197276ae0786763b62148bf8e3a8792f16f', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/OI5dOI3zln5MI11wEg-owrX52pQxPt2eHzVNR2L1p-g.png?width=216&crop=smart&auto=webp&s=ee7b05b3f2937500aa8bcb33cb1268c1b7b99187', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/OI5dOI3zln5MI11wEg-owrX52pQxPt2eHzVNR2L1p-g.png?width=320&crop=smart&auto=webp&s=3628d4877ef38e1e5a34ff6717bede76229f11bc', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/OI5dOI3zln5MI11wEg-owrX52pQxPt2eHzVNR2L1p-g.png?width=640&crop=smart&auto=webp&s=4c644b7f909f24109d08d8d22c2eca771e54c642', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/OI5dOI3zln5MI11wEg-owrX52pQxPt2eHzVNR2L1p-g.png?width=960&crop=smart&auto=webp&s=36275e159ef1e29a673b7b93e59f545e50451545', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/OI5dOI3zln5MI11wEg-owrX52pQxPt2eHzVNR2L1p-g.png?width=1080&crop=smart&auto=webp&s=612fb06fbe632ff04095cfa17857fa9928eff678', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/OI5dOI3zln5MI11wEg-owrX52pQxPt2eHzVNR2L1p-g.png?auto=webp&s=4056efdd37b5684bb254734a9c76ba472e78e711', 'width': 1200}, 'variants': {}}]} |
Memorization benchmark | 3 | Hey, I just wanted to share results on a benchmark I created where I asked different models for their best estimates to the nearest minute of sunrise and sunset times in different cities around the world and at different times of the year
I fully understand that LLM are not meant for factual information but I thought this was interesting nonetheless
Full disclosure this was not out of personal curiosity and not necessarily meaningful for the models intelligence, and it is perfectly possible that some mistakes were made along the way in my code. Because my code is rather messy, I won't be releasing it, but the general idea was there are four scripts.
1. Generates questions, in different styles and fetches the ground truth answer from an API online
2. Ask the LLMs using open router.
3. Use a smaller LLM to extract the answers. (Gemma3:4B)
4. Produce results.
Here are the final results
| Model | Total | Unparsable | Valid | Accuracy (Tol) | Avg Time Off | Exp Score |
|:---|---:|---:|---:|---:|---:|---:|
| deepseek/deepseek-v3.1-terminus | 120 | 1 | 119 | 77.3% | 9.9 min | 75.9 |
| z-ai/glm-5 | 120 | 5 | 115 | 81.7% | 12.8 min | 75.7 |
| deepseek/deepseek-chat-v3.1 | 120 | 2 | 118 | 78.0% | 10.2 min | 75 |
| deepseek/deepseek-chat-v3-0324 | 120 | 0 | 120 | 74.2% | 9.5 min | 73.8 |
| deepseek/deepseek-r1-0528 | 120 | 0 | 120 | 73.3% | 10.0 min | 73 |
| z-ai/glm-4.7 | 120 | 0 | 120 | 69.2% | 10.9 min | 71.8 |
| moonshotai/kimi-k2-thinking | 120 | 0 | 120 | 72.5% | 13.6 min | 71.5 |
| deepseek/deepseek-v3.2 | 120 | 1 | 119 | 73.9% | 14.3 min | 71.3 |
| deepseek/deepseek-chat | 120 | 3 | 117 | 70.1% | 10.8 min | 70.9 |
| deepseek/deepseek-v3.2-exp | 120 | 1 | 119 | 71.4% | 13.4 min | 70 |
| moonshotai/kimi-k2.5 | 120 | 0 | 120 | 65.8% | 14.5 min | 69.1 |
| moonshotai/kimi-k2-0905 | 120 | 0 | 120 | 67.5% | 12.7 min | 68.7 |
| moonshotai/kimi-k2 | 120 | 0 | 120 | 57.5% | 14.4 min | 64.5 |
| qwen/qwen3.5-397b-a17b | 120 | 8 | 112 | 57.1% | 17.6 min | 62.1 |
| z-ai/glm-4.6 | 120 | 0 | 120 | 60.0% | 21.4 min | 61.4 |
| z-ai/glm-4.5-air | 120 | 1 | 119 | 52.1% | 22.2 min | 58.5 |
| stepfun/step-3.5-flash | 120 | 1 | 119 | 45.4% | 23.1 min | 56.5 |
| qwen/qwen3-235b-a22b-2507 | 120 | 0 | 120 | 38.3% | 20.6 min | 54.4 |
| qwen/qwen3-235b-a22b-thinking-2507 | 120 | 0 | 120 | 37.5% | 28.1 min | 51.5 |
| openai/gpt-oss-120b | 120 | 1 | 119 | 34.5% | 25.1 min | 49.3 |
| openai/gpt-oss-20b | 120 | 10 | 110 | 17.3% | 51.0 min | 28.7 | | 2026-02-25T09:09:29 | https://www.reddit.com/r/LocalLLaMA/comments/1re8d9q/memorization_benchmark/ | Unusual_Guidance2095 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re8d9q | false | null | t3_1re8d9q | /r/LocalLLaMA/comments/1re8d9q/memorization_benchmark/ | false | false | self | 3 | null |
OK, llama.cpp team, please post the best settings for QWEN 3.5 family | 0 | To avoid hearsay and frustrated users kindly please post the best setting and template for both agentic coding (open code will be the best) and chat.
As well as the actual recommended build number, or commit hash, from which there is actual support for this models family.
**Many thanks for your efforts from a happy user** | 2026-02-25T09:04:39 | https://www.reddit.com/r/LocalLLaMA/comments/1re8agu/ok_llamacpp_team_please_post_the_best_settings/ | HumanDrone8721 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re8agu | false | null | t3_1re8agu | /r/LocalLLaMA/comments/1re8agu/ok_llamacpp_team_please_post_the_best_settings/ | false | false | self | 0 | null |
r/LocalLLaMA — What’s the biggest missing piece for locally-run autonomous agents? | 2 | For those building or running local models with agent-like behavior, I’m curious what you consider the biggest missing component right now.
Is it memory? tool integration? scheduling? chain-of-thought reliability?
There are a lot of home-built solutions, but rarely a clean end-to-end setup. What do you think needs to be solved first? | 2026-02-25T09:02:34 | https://www.reddit.com/r/LocalLLaMA/comments/1re897q/rlocalllama_whats_the_biggest_missing_piece_for/ | Galactic_Graham | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re897q | false | null | t3_1re897q | /r/LocalLLaMA/comments/1re897q/rlocalllama_whats_the_biggest_missing_piece_for/ | false | false | self | 2 | null |
The FIRST local vision model to get this right! | 131 | So I decided to give qwen3.5-35b-a3b a try for this question. I've tried literally every popular local vision models including bigger ones like glm-4.6v (106B) and qwen3-vl-235b-a22b and none of them got it even remotely correct. So I was thinking after it failed I will try qwen3.5-122b-a10b on this and hopefully it can get it after a few tries.
And to my surprise, 35b-a3b got it the first try! It came to the correct answer multiple times in the thinking process using different methods but didn't believe itself that 102 is the correct answer. After like the 5th time it calculated 102, it quoted "Not drawn accurately" and decided that it's probably actually the correct answer. Took over 30k thinking tokens for this.
I'm so amazed my these new qwen3.5 models, gonna test 122b on this now. | 2026-02-25T09:02:26 | https://www.reddit.com/gallery/1re894z | po_stulate | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1re894z | false | null | t3_1re894z | /r/LocalLLaMA/comments/1re894z/the_first_local_vision_model_to_get_this_right/ | false | false | 131 | null | |
The Reality Behind the OpenClaw Hype | 0 | *A Grounded Look at Peter Steinberger and System Architecture*
Let's cut through the noise regarding OpenClaw, Peter Steinberger, and the current state of autonomous AI agents. While the hype is deafening, a closer look at the history, the tech, and the recent Lex Fridman interview reveals a stark disconnect between startup product-market fit and sustainable system architecture.
**1. The PSPDFKit Precedent** To understand OpenClaw, you have to look at Steinberger's past with PSPDFKit. It was a massive financial success, but it was not a masterclass in clean architecture. It was an opportunistic, heavy-lifting solution built to fill a void because native OS-level PDF rendering simply did not exist at the time. The playbook is identical: find market friction, aggressively hack together a functional solution, and capture the user base before first-party platforms introduce safe, integrated tools.
**2. OpenClaw: The Engine vs. The Harness** OpenClaw is not a breakthrough in AI reasoning; it relies entirely on the heavy lifting of foundation models like Claude, Codex, and Gemini. It is essentially just a local harness, a run-loop granting these models unconstrained access to your file system, shell, and applications. Its viral popularity comes entirely from giving models "hands," not from structural innovation.
**3. The Architectural and Security Nightmare** Giving autonomous models unconstrained access without isolated scope or structural safeguards is a massive security risk. We are already seeing the fallout: rogue agents deleting inboxes and threat actors weaponizing community tools for supply-chain attacks. Steinberger's philosophy leans heavily into frictionless execution and prompt-driven development, actively bypassing decades of established software security and structural logic.
**4. The Moral Disconnect** The Lex Fridman interview highlighted a chaotic mix of performative altruism and deflection. Steinberger champions open-source democratization, notably turning down Meta to join OpenAI. However, he simultaneously deflects the immense responsibility of his tool's dangers. His stance that "with freedom comes responsibility" shifts the blame for system wipeouts entirely onto the end-user, ignoring the architect's duty to build safe, restricted harnesses.
**The Verdict** Building a successful, highly profitable tool does not make someone a master of structural flow or isolated scope. OpenClaw is a chaotic, temporary bridge. The real, production-grade agentic work will inevitably be absorbed into mature, securely integrated environments.
*My personal opinion is highly subjective, might be wrong, and may not accurately reflect reality.*
*This post is a result of couple of hours of discussions (with AIs) upon recent OpenClaw news and humorous meme below...*
https://preview.redd.it/avy73uo5ullg1.jpg?width=1000&format=pjpg&auto=webp&s=b1e6e23855101017b7081558d337d2a0e6a9c235
| 2026-02-25T08:55:22 | https://www.reddit.com/r/LocalLLaMA/comments/1re854d/the_reality_behind_the_openclaw_hype/ | leo-k7v | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re854d | false | null | t3_1re854d | /r/LocalLLaMA/comments/1re854d/the_reality_behind_the_openclaw_hype/ | false | false | 0 | null | |
I'm looking for specific recommendations for LLMs in the 8B range or less , One of theese optimized model for data extraction? | 1 | Is there a leaderboard for data extraction model? | 2026-02-25T08:52:43 | https://www.reddit.com/r/LocalLLaMA/comments/1re83km/im_looking_for_specific_recommendations_for_llms/ | Quiet_Dasy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re83km | false | null | t3_1re83km | /r/LocalLLaMA/comments/1re83km/im_looking_for_specific_recommendations_for_llms/ | false | false | self | 1 | null |
Question for those building agents: do you actually sandbox? | 1 | Doing some field research for a project I'm building.
Do you guys sandbox your agents? If so, does it restrict your use cases or completely tank efficiency for the sake of security?
If not, how are you handling prompt injections and the risk of runaway API bills? Curious to hear how everyone is ha | 2026-02-25T08:50:14 | https://www.reddit.com/r/LocalLLaMA/comments/1re824v/question_for_those_building_agents_do_you/ | no-I-dont-want-that7 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re824v | false | null | t3_1re824v | /r/LocalLLaMA/comments/1re824v/question_for_those_building_agents_do_you/ | false | false | self | 1 | null |
Does the Qwen3.5 122B struggle in vibe compared to Qwen3 235B? | 12 | While 122B does apparently score better then 235B across the board. I find that when disabling thinking 235B was significantly stronger in conversation. And when having thinking enabled, it 122B overthinks dramatically for really simple tasks (like, how do I write this one sentence correctly).
Instruction following is another issue. Yes it perhaps follows them more, but I find it to be actually too much so that it lost flexibility. The previous model seemed to have an almost humen-like understanding when to follow rules and when it had to jump outside of them, the new one is just blindly following.
Let me try to make an example: Like crossing the street. Yes, you must only cross when green. But when you are running from an attacker, it would be stupid to wait for green.
Or, and this is where someone could give input, is that a language thing? Since all I am saying is in the context of talking German to the models.
Concerning quants: I am running the 122B in Q6 and 235B in IQ4. | 2026-02-25T08:44:31 | https://www.reddit.com/r/LocalLLaMA/comments/1re7ypi/does_the_qwen35_122b_struggle_in_vibe_compared_to/ | erazortt | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re7ypi | false | null | t3_1re7ypi | /r/LocalLLaMA/comments/1re7ypi/does_the_qwen35_122b_struggle_in_vibe_compared_to/ | false | false | self | 12 | null |
MONROE – Model Orchestration & Router Engine | 2 | Hi, ich habe ein neues Projekt erstellt das ich eigentlich erstmal für mich nutzen wollte, aber ich denke andere profitieren möglicherweise auch...
Worum gehts:
Als LLM Runner hab ich mir eine Framework Desktop gekauft mit Strix Halo und 128GB. Nun ist es so, wenn ich Modelle lade die noch akzeptabe schnell laufen, ist der Speicher gerade mal zur hälfte belegt.
z.B. nutze ich Qwen Coder Next, wenn der sich mal einen Screenshot ansehen soll, nutze ich Qwen3-VL-8B-Instruct und dann hab ich noch ein unzensiertes Model für "andere" anfragen... und ich dachte mir, ist doch blöd wenn man immer manuell umschalten muss.
Also hab ich mit Monroe angefangen. Das Projekt ist ein OpenAI kompatible API bzw ein Proxy.
ich benutze ein kleines Model "Llama-3.2-3B" das den Userprompt bewertet und an das "richtige" Model weiter leitet. Völlig transparent. Als Model werden alle OpenAI Api instanzen unterstützt. und nach Aussen ist es auch ein OpenAI APi. Du kannst auch ein Model auf einem Anderen Rechner hosten und in Monroe die RemoteAdresse eingeben, falls z.b. du 2 Strix Halo hast ;) Die Regeln werden in den Appsettings eingetragen.
https://github.com/int3ks/Monroe
Bis jetzt nutze ich OpenWebUI als Client, dort habe ich Monroe als OpenAI Api Endpoint eingetragen. Monroe startet auf Wunsch mehrere Llama.cpp Instanzen mit den Modells. Wenn man in OpenWebUi auf das kleine "i" unter der Antwort klickt wird auch angezeigt an welches Model die anfrage gerouted wurde.
das Projekt ist Opensource, Verbesserungsvorschläge und oder Mitarbeit sind willkommen ;)
| 2026-02-25T08:27:45 | https://www.reddit.com/r/LocalLLaMA/comments/1re7p26/monroe_model_orchestration_router_engine/ | int3ks | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re7p26 | false | null | t3_1re7p26 | /r/LocalLLaMA/comments/1re7p26/monroe_model_orchestration_router_engine/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'sbsYuCmotZ6hMqY4GOniwdY30RUDwDt5c1cN546JoTQ', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/sbsYuCmotZ6hMqY4GOniwdY30RUDwDt5c1cN546JoTQ.png?width=108&crop=smart&auto=webp&s=c1394e303c445e857cff559e7698e9c6d962089c', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/sbsYuCmotZ6hMqY4GOniwdY30RUDwDt5c1cN546JoTQ.png?width=216&crop=smart&auto=webp&s=2710a91d11aeef6ae3ee294a0f760929438363be', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/sbsYuCmotZ6hMqY4GOniwdY30RUDwDt5c1cN546JoTQ.png?width=320&crop=smart&auto=webp&s=86c0e291d4de02f5cd1cea49d9c279d999d702c5', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/sbsYuCmotZ6hMqY4GOniwdY30RUDwDt5c1cN546JoTQ.png?width=640&crop=smart&auto=webp&s=d048ad7da86670215de5236355a3277ffc9cc23e', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/sbsYuCmotZ6hMqY4GOniwdY30RUDwDt5c1cN546JoTQ.png?width=960&crop=smart&auto=webp&s=3fa99b49a29306f64df4e2528ce84e03db278663', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/sbsYuCmotZ6hMqY4GOniwdY30RUDwDt5c1cN546JoTQ.png?width=1080&crop=smart&auto=webp&s=399df0337ae47e4b0e1890a41808285e00785a95', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/sbsYuCmotZ6hMqY4GOniwdY30RUDwDt5c1cN546JoTQ.png?auto=webp&s=5b9628979411eedbb89425610044a7c45573a0b9', 'width': 1200}, 'variants': {}}]} |
[Release] TinyTTS: An Ultra-lightweight English TTS Model (~9M params, 20MB) that runs 8x real-time on CPU (67x on GPU) | 30 | Hey r/LocalLLaMA,
I wanted to share a small project I've been working on to solve a personal pain point: **TinyTTS**.
We all love our massive 70B+ LLMs, but when building local voice assistants, running a heavy TTS framework alongside them often eats up way too much precious VRAM and compute. I wanted something absurdly small and fast that "just works" locally.
**TL;DR Specs:**
* **Size:** \~9 Million parameters
* **Disk footprint:** \~20 MB checkpoint (`G.pth`)
* **Speed (CPU):** \~0.45s to generate 3.7s of audio (**\~8x faster than real-time**)
* **Speed (GPU - RTX 4060):** \~0.056s (**\~67x faster than real-time**)
* **Peak VRAM:** \~126 MB
* **License:** Apache 2.0 (Open Weights)
**Why TinyTTS?** It is designed specifically for edge devices, CPU-only setups, or situations where your GPU is entirely occupied by your LLM. It's fully self-contained, meaning you don't need to run a complex pipeline of multiple models just to get audio out.
**How to use it?** I made sure it’s completely plug-and-play with a simple Python API. Even better, on your first run, it will automatically download the tiny 20MB model from Hugging Face into your cache for you.
`pip install git+https://github.com/tronghieuit/tiny-tts.git`
**Python API:**
`from tiny_tts import TinyTTS`
`# Auto-detects device (CPU/CUDA) and downloads the 20MB checkpoint`
`tts = TinyTTS()`
`tts.speak("The weather is nice today, and I feel very relaxed.", output_path="output.wav")`
**CLI:**
`tiny-tts --text "Local AI is the future" --device cpu`
**Links:**
* **GitHub:** [https://github.com/tronghieuit/tiny-tts](https://github.com/tronghieuit/tiny-tts)
* **Gradio Web Demo:** [Try it on HF Spaces here](https://huggingface.co/spaces/backtracking/tiny-tts-demo)
* **Hugging Face Model:** [backtracking/tiny-tts](https://huggingface.co/backtracking/tiny-tts)
**What's next?** I plan to clean up and publish the training code soon so the community can fine-tune it easily. I am also looking into adding ultra-lightweight zero-shot voice cloning.
Would love to hear your feedback or see if anyone manages to run this on a literal potato! Let me know what you think.
| 2026-02-25T08:22:51 | https://www.reddit.com/r/LocalLLaMA/comments/1re7m8y/release_tinytts_an_ultralightweight_english_tts/ | Forsaken_Shopping481 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re7m8y | false | null | t3_1re7m8y | /r/LocalLLaMA/comments/1re7m8y/release_tinytts_an_ultralightweight_english_tts/ | false | false | self | 30 | null |
Has anyone got Qwen3.5-35B-A3B running with vLLM? | 2 | I have vLLM 0.15.1 and I want to know if I have to wait for an official release (>=0.16.0) to support Qwen3.5 or I can run it now. | 2026-02-25T08:16:55 | https://www.reddit.com/r/LocalLLaMA/comments/1re7iud/has_anyone_got_qwen3535ba3b_running_with_vllm/ | TechNerd10191 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re7iud | false | null | t3_1re7iud | /r/LocalLLaMA/comments/1re7iud/has_anyone_got_qwen3535ba3b_running_with_vllm/ | false | false | self | 2 | null |
VLLM Qwen3.5-122B-A10B-GGUF | 1 | Could anyone run unsloth/Qwen3.5-122B-A10B-GGUF in VLLM?
And related to performance , since it is gguf will it work properly?
Thanks | 2026-02-25T08:15:58 | https://www.reddit.com/r/LocalLLaMA/comments/1re7ib7/vllm_qwen35122ba10bgguf/ | justlows | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re7ib7 | false | null | t3_1re7ib7 | /r/LocalLLaMA/comments/1re7ib7/vllm_qwen35122ba10bgguf/ | false | false | self | 1 | null |
This benchmark from shows Unsolth Q3 quantization beats both Q4 and MXFP4 | 86 | I thought this was interesting, especially since at first glance both Q4 and Q3 here are K\_XL, and it doesn't make sense a Q3 will beat Q4 in any scenario.
However it's worth mentioning this is:
1. Not a standard benchmark
2. These are not straight-forward quantizations, it's a "dynamic quantization" which affects weights differently across the model.
My money is on one of these two factors leading to this results, however, if by any chance a smaller quantization does beat a larger one, this is super interesting in terms research.
[Source](https://unsloth.ai/docs/models/qwen3.5#qwen3.5-397b-a17b-benchmarks)
| 2026-02-25T07:55:49 | Oatilis | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1re76g6 | false | null | t3_1re76g6 | /r/LocalLLaMA/comments/1re76g6/this_benchmark_from_shows_unsolth_q3_quantization/ | false | false | 86 | {'enabled': True, 'images': [{'id': '5wtmzjgvillg1', 'resolutions': [{'height': 77, 'url': 'https://preview.redd.it/5wtmzjgvillg1.png?width=108&crop=smart&auto=webp&s=11e0a85479b2dddd721d18e3c9e3a22ede883bbc', 'width': 108}, {'height': 154, 'url': 'https://preview.redd.it/5wtmzjgvillg1.png?width=216&crop=smart&auto=webp&s=25320d6678a769fde0e90be750c7b3b16428a197', 'width': 216}, {'height': 228, 'url': 'https://preview.redd.it/5wtmzjgvillg1.png?width=320&crop=smart&auto=webp&s=5971b8dd12e1c1eab00b2473e92b73a619d1faad', 'width': 320}, {'height': 457, 'url': 'https://preview.redd.it/5wtmzjgvillg1.png?width=640&crop=smart&auto=webp&s=2d4c18e0d43199c66837a33ca093dde5739ad022', 'width': 640}], 'source': {'height': 549, 'url': 'https://preview.redd.it/5wtmzjgvillg1.png?auto=webp&s=47fd49b66c86ad4c4cd2e57868e07ddfebaea484', 'width': 768}, 'variants': {}}]} | ||
Qwen3.5 27B better than 35B-A3B? | 435 | Which model would be better with 16 GB of VRAM and 32 GB of RAM? | 2026-02-25T07:49:05 | -OpenSourcer | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1re72h4 | false | null | t3_1re72h4 | /r/LocalLLaMA/comments/1re72h4/qwen35_27b_better_than_35ba3b/ | false | false | 435 | {'enabled': True, 'images': [{'id': 'f9x0emmuillg1', 'resolutions': [{'height': 124, 'url': 'https://preview.redd.it/f9x0emmuillg1.png?width=108&crop=smart&auto=webp&s=ad2264dad28bcb0d422e61392d97bf99d6ed46ba', 'width': 108}, {'height': 248, 'url': 'https://preview.redd.it/f9x0emmuillg1.png?width=216&crop=smart&auto=webp&s=f7322e3f29bdc357e3c6c15c04cd9c56d1d86b75', 'width': 216}, {'height': 368, 'url': 'https://preview.redd.it/f9x0emmuillg1.png?width=320&crop=smart&auto=webp&s=d562beefed372d647e5bf165d04459a523e994b2', 'width': 320}, {'height': 736, 'url': 'https://preview.redd.it/f9x0emmuillg1.png?width=640&crop=smart&auto=webp&s=bee689086672602801cb1e88155d725c01342793', 'width': 640}, {'height': 1104, 'url': 'https://preview.redd.it/f9x0emmuillg1.png?width=960&crop=smart&auto=webp&s=0a14ba90676a5c87d020b444bb35ac54e5009f66', 'width': 960}, {'height': 1242, 'url': 'https://preview.redd.it/f9x0emmuillg1.png?width=1080&crop=smart&auto=webp&s=7a5cd409a263896df6b0d83542d4cbf57f37baf5', 'width': 1080}], 'source': {'height': 4600, 'url': 'https://preview.redd.it/f9x0emmuillg1.png?auto=webp&s=69e66b5c6960e756be8b5ddfbafc853ce7e32d0d', 'width': 4000}, 'variants': {}}]} | ||
The best model for M3 Pro 36GB? | 1 | Hey,
I’m downloading ollama 3.0 qwen 32b, but I’ve heard there is a newer model? I need one for coding. | 2026-02-25T07:19:19 | https://www.reddit.com/r/LocalLLaMA/comments/1re6kw7/the_best_model_for_m3_pro_36gb/ | KwonDarko | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re6kw7 | false | null | t3_1re6kw7 | /r/LocalLLaMA/comments/1re6kw7/the_best_model_for_m3_pro_36gb/ | false | false | self | 1 | null |
Heosphoros v XGBOOST 2/24/26 | 0 | Heosphoros vs Default XGBoost
Fraud Detection — 284,807 real transactions
Default XGBoost: 0.8409
Heosphoros: 0.8786 (+4.48%)
Send me any dataset! 200 line code outperforming industrym | 2026-02-25T07:15:53 | Heosphoros_ai | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1re6ip4 | false | null | t3_1re6ip4 | /r/LocalLLaMA/comments/1re6ip4/heosphoros_v_xgboost_22426/ | false | false | 0 | {'enabled': True, 'images': [{'id': '8nrasaixcllg1', 'resolutions': [{'height': 216, 'url': 'https://preview.redd.it/8nrasaixcllg1.jpeg?width=108&crop=smart&auto=webp&s=393f6344043b3b272b5cef23c0daf6053cf61f78', 'width': 108}, {'height': 432, 'url': 'https://preview.redd.it/8nrasaixcllg1.jpeg?width=216&crop=smart&auto=webp&s=0bce7f81ffcb53efde5e4cecf333c947f4f8aade', 'width': 216}, {'height': 640, 'url': 'https://preview.redd.it/8nrasaixcllg1.jpeg?width=320&crop=smart&auto=webp&s=047bd31c6a8ea186fa74c78c5399c9f823339595', 'width': 320}, {'height': 1280, 'url': 'https://preview.redd.it/8nrasaixcllg1.jpeg?width=640&crop=smart&auto=webp&s=0b30f0136fabed0bc02a3a87d345aa33b97c264a', 'width': 640}, {'height': 1920, 'url': 'https://preview.redd.it/8nrasaixcllg1.jpeg?width=960&crop=smart&auto=webp&s=a25301c4f417027c849295721847c9b3865a3fae', 'width': 960}, {'height': 2160, 'url': 'https://preview.redd.it/8nrasaixcllg1.jpeg?width=1080&crop=smart&auto=webp&s=3f2dc04b95c112a95ee3e9c541fd26fd97efc0c4', 'width': 1080}], 'source': {'height': 3040, 'url': 'https://preview.redd.it/8nrasaixcllg1.jpeg?auto=webp&s=406a5b8cbd66dd7d2633c8879c8680731dae8871', 'width': 1440}, 'variants': {}}]} | ||
Anthropic is the leading contributor to open weight models | 671 | It just happens to be entirely against their will and TOS. I say: Distill Baby Distill! | 2026-02-25T07:15:29 | https://www.reddit.com/r/LocalLLaMA/comments/1re6ifz/anthropic_is_the_leading_contributor_to_open/ | DealingWithIt202s | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re6ifz | false | null | t3_1re6ifz | /r/LocalLLaMA/comments/1re6ifz/anthropic_is_the_leading_contributor_to_open/ | false | false | self | 671 | null |
Your coding agent sessions are sitting on your machine right now. Big labs use this data internally. We could build an open equivalent. | 81 | Every time you use Claude Code or Codex CLI in agent mode, it logs everything locally. The full loop: your task, the model's reasoning, every tool call, every environment response, every error and retry. Complete (state → action → reward → next state) tuples. The exact data format RL researchers dream about.
I checked all my machines today.
Mac Mini:
~/.claude/projects/ 3.1GB 1103 files 574 agentic sessions
MacBook:
~/.codex/sessions/ 2.4GB 3530 files 79 agentic sessions
~/.claude/projects/ 652MB 316 files 99 agentic sessions
775 sessions with real tool calls. 41 million tokens.
Extrapolate to thousands developers and we would have hundreds of billions tokens of real agentic trajectory data. No Pile equivalent exists for this. It's just sitting on people's hard drives, being silently deleted.
Claude Code deletes logs after 30 days by default. Fix it now:
echo '{"cleanupPeriodDays": 36500}' > ~/.claude/settings.json
**Why this data matters**
The environment always tells you if it worked. Exit code 0 or not. Tests pass or not. This is the missing training signal , causal reasoning, error recovery, long-horizon planning. Things current models are genuinely bad at.
Big labs already collect this. Every Claude Code,codex session trains proprietary models. There's no open equivalent, not because the data doesn't exist, but because it's fragmented across developer machines.
**The proposal**
Federated learning. Your data never leaves your machine. You train a small LoRA adapter locally, share only the weights with differential privacy noise, and get an improved global model back. Everyone contributes compute and signal. Nobody exposes their data or we can anonymize the data and create a dataset finetune a model.
**Check your own machines**
du -sh ~/.codex/sessions/
2
>/dev/null
du -sh ~/.claude/projects/
2
>/dev/null
find ~/.codex/sessions/ -name "*.jsonl" | wc -l
find ~/.claude/projects/ -name "*.jsonl" | wc -l
Drop your numbers in the comments. I want to know the actual scale sitting unused across this community.
If there's enough interest we can build this out.
| 2026-02-25T07:11:25 | https://www.reddit.com/r/LocalLLaMA/comments/1re6fud/your_coding_agent_sessions_are_sitting_on_your/ | No-Point1424 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re6fud | false | null | t3_1re6fud | /r/LocalLLaMA/comments/1re6fud/your_coding_agent_sessions_are_sitting_on_your/ | false | false | self | 81 | null |
Seeking Production-Grade Open-Source LLM for Real-Time IVR Agent (A10 24GB) | 1 | Hello everyone,
I am currently evaluating open-source LLMs for a **production-level real-time voice agent** and would appreciate insights from practitioners who have successfully deployed similar systems.
# Deployment Environment
* **Instance:** AWS g5.2xlarge
* **GPU:** NVIDIA A10 (24GB VRAM)
* **Inference Engine:** vLLM
* Dedicated GPU allocated solely to LLM service
# Benchmark Criteria
The selected model must meet the following enterprise requirements:
|Requirement|Description|
|:-|:-|
|**Open Source (Open Weights)**|Fully self-hostable with no API dependency|
|**IVR Detection Capability**|Accurate classification of IVR vs human speaker|
|**Multiple Tool Calling**|Reliable handling of multiple structured tool calls within a single interaction|
|**Low Latency**|Suitable for real-time voice workflows (<500ms preferred model latency)|
|**Extended Context (10K–16K tokens)**|Stable long-context handling|
|**A10 (24GB) Compatibility**|Deployable without OOM issues|
|**Strong Instruction Following**|Accurate execution of strict, multi-layer prompts|
|**No Looping Behavior**|Must not repeat scripts or re-trigger conversation states|
|**Low Hallucination Rate**|Especially critical for IVR decision logic|
# Use Case Overview
The system is a **real-time outbound voice agent** that must:
* Detect IVR systems and wait for menu completion
* Collect routing options before sending DTMF
* Avoid premature call termination
* Execute strict role enforcement
* Follow complex, rule-based conversational flows
* Handle objection logic without repetition
* Call tools only when logically required
This is a structured agent workflow — not a general chat application.
# Models Evaluated (Open-Source Only)
The following models were tested but did not meet production standards:
# 1. Llama-3.1-8B-Instruct
* Tool-calling instability
* Inconsistent structured output
* Weak performance under complex agent prompts
# 2. Qwen2.5-7B-Instruct
* Unreliable tool invocation
* Inconsistent decision logic
# 3. Qwen3-14B
* CUDA OOM on A10 (24GB)
# 4. Qwen3-14B-AWQ
* Good instruction-following
* Tool-calling functional
* Latency too high for real-time voice
# 5. Qwen3-8B
* Currently usable
* Tool-calling works
* Latency still high
* Occasional looping
# 6. Qwen3-8B-AWQ (vLLM)
* High latency
* Stability issues in production
# 7. GLM-4.7-Flash (Q4_K_M)
* Faster inference
* Some tool-calling capability
* Stability concerns under quantization
# 8. gpt-oss-20B (Q8_0)
* High hallucination rate
* Poor IVR classification
* Incorrect tool execution (DTMF misfires)
# Persistent Issues Observed
* Looping behavior in scripted flows
* Simultaneous conflicting tool calls
* Hallucinated tool invocations
* IVR vs human misclassification
* Latency spikes under real-time load
Temperature tuning (0.1–0.6), stricter prompts, and tool constraints were applied, but decision instability persisted across models.
# Request for Community Input
Has anyone successfully deployed an open-weight LLM on A10 (24GB) that:
* Performs reliably in real-time voice environments
* Handles multi-tool workflows consistently
* Demonstrates strong instruction discipline
* Maintains low hallucination
* Avoids looping behavior
If so, I would appreciate details on:
* Model name and size
* Quantization method
* Inference configuration
* Guardrail or FSM integration strategies
At this stage, I am evaluating whether current 7B–14B open models are sufficiently stable for structured real-time agent workflows, or whether additional architectural control layers are mandatory.
Thank you in advance for your insights. | 2026-02-25T07:09:36 | https://www.reddit.com/r/LocalLLaMA/comments/1re6enq/seeking_productiongrade_opensource_llm_for/ | Competitive_Fish_447 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re6enq | false | null | t3_1re6enq | /r/LocalLLaMA/comments/1re6enq/seeking_productiongrade_opensource_llm_for/ | false | false | self | 1 | null |
Seeking Production-Grade Open-Source LLM for Real-Time IVR Agent (A10 24GB) | 1 | 2026-02-25T07:07:56 | Competitive_Fish_447 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1re6dlj | false | null | t3_1re6dlj | /r/LocalLLaMA/comments/1re6dlj/seeking_productiongrade_opensource_llm_for/ | false | false | 1 | {'enabled': True, 'images': [{'id': 'uflz9uayallg1', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/uflz9uayallg1.png?width=108&crop=smart&auto=webp&s=0ebcd6f11f13f78a60ec6153599010a4dc9a7cc3', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/uflz9uayallg1.png?width=216&crop=smart&auto=webp&s=48d728d649692203eee11ee5e9b975e8614dd825', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/uflz9uayallg1.png?width=320&crop=smart&auto=webp&s=a1aba9ef841dae61157d5b76b8c55bbf04758216', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/uflz9uayallg1.png?width=640&crop=smart&auto=webp&s=17b74fbf230040ec842199c356d49bc071cb70bd', 'width': 640}, {'height': 960, 'url': 'https://preview.redd.it/uflz9uayallg1.png?width=960&crop=smart&auto=webp&s=638c6438ef63e62c00754bdc97e0a2f822b63abc', 'width': 960}], 'source': {'height': 1024, 'url': 'https://preview.redd.it/uflz9uayallg1.png?auto=webp&s=1df6c31574dd1ce5ec46a92940d3eec085aae44b', 'width': 1024}, 'variants': {}}]} | |||
Anthropic accuses chinese open weight labs of theft, while it has had to pay $1.5B for theft. | 147 | [https://www.npr.org/2025/09/05/nx-s1-5529404/anthropic-settlement-authors-copyright-ai](https://www.npr.org/2025/09/05/nx-s1-5529404/anthropic-settlement-authors-copyright-ai)
Is that what we call hypocrisy?
| 2026-02-25T07:04:51 | https://www.reddit.com/r/LocalLLaMA/comments/1re6bjs/anthropic_accuses_chinese_open_weight_labs_of/ | Terminator857 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re6bjs | false | null | t3_1re6bjs | /r/LocalLLaMA/comments/1re6bjs/anthropic_accuses_chinese_open_weight_labs_of/ | false | false | self | 147 | {'enabled': False, 'images': [{'id': '_9lWQNIrOlFVM_jcHp7K5EMOHVOxNzYM79q_4aWPKxU', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/_9lWQNIrOlFVM_jcHp7K5EMOHVOxNzYM79q_4aWPKxU.jpeg?width=108&crop=smart&auto=webp&s=3caf6b46bda0a097ec54d5ac3c3bd6c10e16f7b5', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/_9lWQNIrOlFVM_jcHp7K5EMOHVOxNzYM79q_4aWPKxU.jpeg?width=216&crop=smart&auto=webp&s=94c9dcc4c0f33e91f67f14a46ed9fded56a19143', 'width': 216}, {'height': 179, 'url': 'https://external-preview.redd.it/_9lWQNIrOlFVM_jcHp7K5EMOHVOxNzYM79q_4aWPKxU.jpeg?width=320&crop=smart&auto=webp&s=751edd4fdcf652883284768e2a1c8565a3d0986d', 'width': 320}, {'height': 359, 'url': 'https://external-preview.redd.it/_9lWQNIrOlFVM_jcHp7K5EMOHVOxNzYM79q_4aWPKxU.jpeg?width=640&crop=smart&auto=webp&s=d5ce108af3d73c5b9548b49dfb371cdda0d3150e', 'width': 640}, {'height': 539, 'url': 'https://external-preview.redd.it/_9lWQNIrOlFVM_jcHp7K5EMOHVOxNzYM79q_4aWPKxU.jpeg?width=960&crop=smart&auto=webp&s=caafdb88691280fbc678cbfeff252cfc5275e365', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/_9lWQNIrOlFVM_jcHp7K5EMOHVOxNzYM79q_4aWPKxU.jpeg?width=1080&crop=smart&auto=webp&s=84fe67a0d93e00dd4bf5ab121dabfa27c9517b54', 'width': 1080}], 'source': {'height': 787, 'url': 'https://external-preview.redd.it/_9lWQNIrOlFVM_jcHp7K5EMOHVOxNzYM79q_4aWPKxU.jpeg?auto=webp&s=9086cc627309cb32ebdcdf23e7a6e1a28f900e25', 'width': 1400}, 'variants': {}}]} |
Qwen3.5 thinking blocks in output | 2 | I am using opencode and pi to test out the new Qwen3.5 model, and I am seeing strange behaviour in opencode / pi.
When I load the model in LM Studio and test in a chat there, thinking appears as one would expect - tucked into a collapsable block.
When I query the model in opencode / pi, however, the thinking blocks are injected in the response:
[Even with turning off reasoning in pi](https://preview.redd.it/qguu0q8i8llg1.png?width=1656&format=png&auto=webp&s=c261750538f063f99a5f80e7a83b6bac22b6a9bb)
`<think>` is definitely a handled tag in either project, so I'm curious if anyone else is seeing the same issue?
[Opencode](https://preview.redd.it/fa2m32vr8llg1.png?width=450&format=png&auto=webp&s=edd38d6f385c3894842a1edc3125052f38c3a6e5)
| 2026-02-25T06:53:32 | https://www.reddit.com/r/LocalLLaMA/comments/1re64fe/qwen35_thinking_blocks_in_output/ | sig_kill | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re64fe | false | null | t3_1re64fe | /r/LocalLLaMA/comments/1re64fe/qwen35_thinking_blocks_in_output/ | false | false | 2 | null | |
opencode safe chat template for K2.5? | 2 | Hello,
Giving opencode another try because I've been looking for a coding assistant that I can continue to monitor and instruct over my phone and opencode web seems to achieve that.
However I've tried to hook up my trusty old K2.5 to my new opencode install and it's triggering 500 errors. I know it's something with the chat template but too terrified to modify it myself. Running without the template messes up formatting big-time.
Appreciate guidance.
Thanks! | 2026-02-25T06:34:50 | https://www.reddit.com/r/LocalLLaMA/comments/1re5sid/opencode_safe_chat_template_for_k25/ | cantgetthistowork | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re5sid | false | null | t3_1re5sid | /r/LocalLLaMA/comments/1re5sid/opencode_safe_chat_template_for_k25/ | false | false | self | 2 | null |
[Showcase] Why I optimized for a 6th Gen Intel CPU before hitting the RTX 50 Series. (0.03s TTFT reached) | 0 | Hi everyone. I’m a Client Developer who knew ZERO about Python or AI a month ago. I’ve spent the last 30 days obsessed with one goal: Extreme On-Device Optimization.
I’m tired of seeing benchmarks that only care about H100s or 4090s. I wanted to see what happens when Client-side Architecture meets Local LLMs on everyday hardware.
### 1. The "Dumpster" Test (Intel i7-6500U / 8GB RAM)
I started at the floor. If it can’t run on my old laptop, it’s not true "On-Device."
**Result:** Successfully ran 0.5B-1.5B models. Even when system resources were completely exhausted, the engine remained stable. Optimization > Hardware.
### 2. The RTX 5050 "Clean Run" (8GB VRAM Limit)
I tested a mid-range laptop to find the physical limits of response time. To be transparent, I removed all capture-tool overhead for these "Clean Runs":
| Model | Quant | TTFT (sec) | Tokens/sec | Note |
| :--- | :--- | :--- | :--- | :--- |
| **0.5B** | Q8 | **0.03s** | **124.69** | Breaking 30ms physical barrier |
| **3B** | Q8 | **0.10s** | 50.76 | Instant response |
| **7B** | Q6 | **0.40s** | 29.21 | Smooth on laptop |
| **14B** | Q6 | 4.59s | 0.95 | VRAM Swap limit (7.5/8.0GB) |
> *Note: I've attached a screenshot showing the 14B model fully loaded in IDLE state, pushing the 8GB VRAM and system RAM to their absolute limits.*
### 3. Proof of Concept
This is the result of my 30-day journey. I’ve focused entirely on removing architecture-level bottlenecks. While I am not sharing the source code or specific logic, I wanted to showcase that these performance metrics are possible on consumer-grade hardware.
Data does not lie. Full logs and scaling data are available here:
https://github.com/ggml-org/llama.cpp/discussions/19813
---
*P.S. English is not my native language. Speed and logic are universal.* | 2026-02-25T06:31:41 | https://www.reddit.com/r/LocalLLaMA/comments/1re5qhr/showcase_why_i_optimized_for_a_6th_gen_intel_cpu/ | Secure-Beautiful1758 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re5qhr | false | null | t3_1re5qhr | /r/LocalLLaMA/comments/1re5qhr/showcase_why_i_optimized_for_a_6th_gen_intel_cpu/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'UcWeP2VW8NRZcXsYadicrvIq8EyK0AKVb1hddtfeUMU', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/UcWeP2VW8NRZcXsYadicrvIq8EyK0AKVb1hddtfeUMU.png?width=108&crop=smart&auto=webp&s=54528eb8c5aad201a4fb90004424447aa743c211', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/UcWeP2VW8NRZcXsYadicrvIq8EyK0AKVb1hddtfeUMU.png?width=216&crop=smart&auto=webp&s=e55e903ca95842b5b16020900c29bc1b8f140e4b', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/UcWeP2VW8NRZcXsYadicrvIq8EyK0AKVb1hddtfeUMU.png?width=320&crop=smart&auto=webp&s=35a9d33f8ff1d502b583fac023348e01da42f184', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/UcWeP2VW8NRZcXsYadicrvIq8EyK0AKVb1hddtfeUMU.png?width=640&crop=smart&auto=webp&s=01746a7db414bfb47a4106474016d98522e62296', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/UcWeP2VW8NRZcXsYadicrvIq8EyK0AKVb1hddtfeUMU.png?width=960&crop=smart&auto=webp&s=dd307e1147ef9ff1b10820e78b7f593ab762635d', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/UcWeP2VW8NRZcXsYadicrvIq8EyK0AKVb1hddtfeUMU.png?width=1080&crop=smart&auto=webp&s=e31db209d53d8a1132747b4e6f9581c9dc19e8c5', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/UcWeP2VW8NRZcXsYadicrvIq8EyK0AKVb1hddtfeUMU.png?auto=webp&s=71a98e1b009b2cb046d2beb5d6205461904b8281', 'width': 1200}, 'variants': {}}]} |
Is 2026 the Year Local AI Becomes the Default (Not the Alternative)? | 2 | With models like Qwen 3 Coder 80B topping download charts and smaller variants like 4B running smoothly on phones, it feels like we’ve crossed a line.
A year ago, running a decent model locally meant compromises. Now?
* 4B–8B models are actually usable for daily workflows
* Quantized 30B+ models are surprisingly capable
* Local RAG setups are easier than ever
* iPhone + laptop inference is no longer a meme
At the same time, big labs are pushing closed ecosystems, tighter APIs, and heavier pricing structures.
So I’m curious:
Are we heading toward a world where local-first AI becomes the default for devs, and cloud LLMs are only used for edge cases (massive context, frontier reasoning, etc.)?Or will centralized inference always dominate because of scale and training advantages?
Would love to hear what this sub thinks:
* What model are you running daily?
* Are you fully local yet?
* What’s still holding you back?
Feels like something big is shifting this year.
| 2026-02-25T06:31:31 | https://www.reddit.com/r/LocalLLaMA/comments/1re5qdy/is_2026_the_year_local_ai_becomes_the_default_not/ | CryOwn50 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re5qdy | false | null | t3_1re5qdy | /r/LocalLLaMA/comments/1re5qdy/is_2026_the_year_local_ai_becomes_the_default_not/ | false | false | self | 2 | null |
What LLM do you recommend for writing and analysing large amounts of text (work + studying) | 1 | Hi everyone! I have been a GPT pro user for almost a year now, but I feel like its quality has dropped and would like to explore new LLMs.
I mainly use ChatGPT for (non-creative) writing and specifically for
1) my office job, which involves writing tender bids, reaching out to clients via email/linkedin and some light translation work. Tender bids often involve about a dozen of short- to mid-length documents.
2) helping write my MA thesis (about linguistics and terminology). Again, it needs to deeply analyse a bulk of large documents and be able to write long paragraphs
3) everyday tasks, like help generating excel sheetz to track expenses, planning trips and so on | 2026-02-25T06:30:50 | https://www.reddit.com/r/LocalLLaMA/comments/1re5pz3/what_llm_do_you_recommend_for_writing_and/ | Sea-Read6432 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re5pz3 | false | null | t3_1re5pz3 | /r/LocalLLaMA/comments/1re5pz3/what_llm_do_you_recommend_for_writing_and/ | false | false | self | 1 | null |
Qwen 3.5 397B on local hardware | 3 | [https://huggingface.co/Qwen/Qwen3.5-397B-A17B](https://huggingface.co/Qwen/Qwen3.5-397B-A17B)
Is it possible to run this on an **AMD Ryzen Threadripper 9960X with 256gb ram and 4 or 5 Nvidia 6000 pro 96gb setup? If yes should I use vllm or something else? I want to read big pdfs with it so full context is needed.**
**The setups on gpu providers are all overkill because they use 100 plus cpu cores and a lot of ram so its hard to compare if I test it with runpod. Thanks.** | 2026-02-25T06:28:44 | https://www.reddit.com/r/LocalLLaMA/comments/1re5omn/qwen_35_397b_on_local_hardware/ | SeaDisk6624 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re5omn | false | null | t3_1re5omn | /r/LocalLLaMA/comments/1re5omn/qwen_35_397b_on_local_hardware/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=108&crop=smart&auto=webp&s=7318ec3ce4509fbace98fa419ca07a197bbf6b12', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=216&crop=smart&auto=webp&s=845c40f90d04300d26f682352d92f5119dce277a', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=320&crop=smart&auto=webp&s=c7e7dd4c3ab2924175f5dc3b9816b8c268f639c5', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=640&crop=smart&auto=webp&s=92b4cb0c011ee0ca8ee5cbb20a760c3a1f372788', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=960&crop=smart&auto=webp&s=1d0618a3224a2591da1e041a5c1cd7a3d816cf77', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=1080&crop=smart&auto=webp&s=bb835272d9ec8b6372f3aad7de3527217c39649e', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?auto=webp&s=23a2866ccd730b9643bc6607c0920a446cf24399', 'width': 1200}, 'variants': {}}]} |
Number of layers/attention blocks in your favorite models? | 2 | Hello, I’m making a resource at the moment on the LLM architecture. I’m nearing the end and am explaining that the transformer block is repeated many times in LLMs. But truthfully, I have no clue how many times in modern models. Obviously the bigger the model, the more layers. But all I am aware of is that the original gpt-3 used 96 layers.
If you know how many layers a particular model has, please let me know! Or let me know how I can find out for myself. | 2026-02-25T06:20:47 | https://www.reddit.com/r/LocalLLaMA/comments/1re5jnx/number_of_layersattention_blocks_in_your_favorite/ | skinnyjoints | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re5jnx | false | null | t3_1re5jnx | /r/LocalLLaMA/comments/1re5jnx/number_of_layersattention_blocks_in_your_favorite/ | false | false | self | 2 | null |
Openclaw (clawdbot) is what I call hype-coding | 0 | Come sour of nowhere, vibe coded, gets sudden popularity. (Engineered to be hyped)
How did it happen.
? | 2026-02-25T06:20:07 | https://www.reddit.com/r/LocalLLaMA/comments/1re5j81/openclaw_clawdbot_is_what_i_call_hypecoding/ | SkyNetLive | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re5j81 | false | null | t3_1re5j81 | /r/LocalLLaMA/comments/1re5j81/openclaw_clawdbot_is_what_i_call_hypecoding/ | false | false | self | 0 | null |
Built an Open Source Local LLM Router to redirect queries to Ollama or Cloud based on complexity | 0 | Hello 👋
Just built a local LLM router => [https://github.com/mnfst/manifest](https://github.com/mnfst/manifest)
* Scores the query in 4 tiers: simple, standard, complex and reasoning
* Sends request to selected model (customizable)
* Tracks consumption of each message
And of course compatible with Ollama, so you can route to a cloud provider for more complex queries.
I would love to have your toughts! | 2026-02-25T06:00:47 | nuno6Varnish | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1re566g | false | null | t3_1re566g | /r/LocalLLaMA/comments/1re566g/built_an_open_source_local_llm_router_to_redirect/ | false | false | 0 | {'enabled': True, 'images': [{'id': '029pgtmmyklg1', 'resolutions': [{'height': 36, 'url': 'https://preview.redd.it/029pgtmmyklg1.png?width=108&crop=smart&auto=webp&s=cd008090e58977ee99d1954f9a1a11ca1dfbffea', 'width': 108}, {'height': 73, 'url': 'https://preview.redd.it/029pgtmmyklg1.png?width=216&crop=smart&auto=webp&s=5df7a0c592b8766cbee197d452b3cd91bdc0e9a3', 'width': 216}, {'height': 108, 'url': 'https://preview.redd.it/029pgtmmyklg1.png?width=320&crop=smart&auto=webp&s=fd53ff85e5a36a1de05da8499dafe71bee6b46b7', 'width': 320}, {'height': 217, 'url': 'https://preview.redd.it/029pgtmmyklg1.png?width=640&crop=smart&auto=webp&s=290b9f70c44d06359e6dc04cf3f0b3b805bee983', 'width': 640}, {'height': 326, 'url': 'https://preview.redd.it/029pgtmmyklg1.png?width=960&crop=smart&auto=webp&s=393fffe36fbd88dde348787fb2714b097b541b7d', 'width': 960}, {'height': 367, 'url': 'https://preview.redd.it/029pgtmmyklg1.png?width=1080&crop=smart&auto=webp&s=b7147b0e217498e3c0498becb237fd7557744ba3', 'width': 1080}], 'source': {'height': 934, 'url': 'https://preview.redd.it/029pgtmmyklg1.png?auto=webp&s=c6c35d1d2b363d17b5935350b5024299d4bc0b85', 'width': 2745}, 'variants': {}}]} | ||
Last Week in Multimodal AI - Local Edition | 8 | I curate a weekly multimodal AI roundup, here are the local/open-source highlights from last week:
**BiTDance - 14B Autoregressive Image Model**
* A 14B parameter autoregressive image generation model available on Hugging Face.
* [Hugging Face](https://huggingface.co/shallowdream204/BitDance-14B-16x/tree/main)
https://preview.redd.it/8is854riyklg1.png?width=1080&format=png&auto=webp&s=c5b9dc9cd0fb2d1b29048238aca9817d5fd79ba1
https://preview.redd.it/incgegojyklg1.png?width=1080&format=png&auto=webp&s=2a9686888108a30b30847c6cadb44fcd9340181c
**DreamDojo - Open-Source Visual World Model for Robotics**
* NVIDIA open-sourced this interactive world model that generates what a robot would see when executing motor commands.
* Lets robots practice full tasks in simulated visual environments before touching hardware.
* [Project Page](https://dreamdojo-world.github.io) | [Models](https://huggingface.co/nvidia/DreamDojo) | [Thread](https://x.com/ShenyuanGao/status/2024898256334114876)
https://reddit.com/link/1re54t8/video/lk4ic6tgyklg1/player
**AudioX - Unified Anything-to-Audio Generation**
* Takes any combination of text, video, image, or audio as input and generates matching sound through a single model.
* Open research with full paper and project demo available.
* [Project Page](https://zeyuet.github.io/AudioX/) | [Model](https://huggingface.co/collections/HKUSTAudio/audiox) | [Demo](https://huggingface.co/spaces/Zeyue7/AudioX)
https://reddit.com/link/1re54t8/video/iuff1scmyklg1/player
**LTX-2 Inpaint - Custom Crop and Stitch Node**
* New node from jordek that simplifies the inpainting workflow for LTX-2 video, making it easier to fix specific regions in a generated clip.
* [Post](https://www.reddit.com/r/StableDiffusion/comments/1r6s2f7/ltx2_inpaint_update_new_custom_crop_and_stitch/)
https://reddit.com/link/1re54t8/video/18dhmrlwyklg1/player
**LoRA Forensic Copycat Detector**
* JackFry22 updated their LoRA analysis tool with forensic detection to identify model copies.
* [post](https://www.reddit.com/r/StableDiffusion/comments/1r8clyn/i_updated_my_lora_analysis_tool_with_a_forensic/)
https://preview.redd.it/rs19j1zxyklg1.png?width=1080&format=png&auto=webp&s=cfede434e10119f28a0f657b84f67864b5445b0d
**ZIB vs ZIT vs Flux 2 Klein - Side-by-Side Comparison**
* Both-Rub5248 ran a direct comparison of three current models. Worth reading before you decide what to run next.
* [Post](https://www.reddit.com/r/StableDiffusion/comments/1rboeta/zib_vs_zit_vs_flux_2_klein/)
https://preview.redd.it/fwhqi81zyklg1.png?width=1080&format=png&auto=webp&s=d3007e6ad74379b2da3fd264b2d6b3c9765266dc
Checkout the [full roundup](https://open.substack.com/pub/thelivingedge/p/last-week-in-multimodal-ai-46-thinking?utm_campaign=post-expanded-share&utm_medium=post%20viewer) for more demos, papers, and resources.[](https://www.reddit.com/submit/?source_id=t3_1re4rp8)
| 2026-02-25T05:58:51 | https://www.reddit.com/r/LocalLLaMA/comments/1re54t8/last_week_in_multimodal_ai_local_edition/ | Vast_Yak_4147 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re54t8 | false | null | t3_1re54t8 | /r/LocalLLaMA/comments/1re54t8/last_week_in_multimodal_ai_local_edition/ | false | false | 8 | null | |
Sapphire Install guide | 0 | ive been using this tool over Clawbot. This may be the next big tool. Its super interesting, much like clawbot. But this injects personality into the generic LLM's. been using it to respond to emails and give me breakdowns of my mornings with great success.
reached out to author, started working with him.
If anyone is interested here is install video.
Tool can be found on Github
ddxfish/sapphire | 2026-02-25T05:55:54 | https://youtu.be/fzxU2MAQiqQ?si=egqS0YkxSTF6MZmE | Dudebro-420 | youtu.be | 1970-01-01T00:00:00 | 0 | {} | 1re52u2 | false | {'oembed': {'author_name': 'SapphireBlueAi', 'author_url': 'https://www.youtube.com/@SapphireBlueAi', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/fzxU2MAQiqQ?feature=oembed&enablejsapi=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="Sapphire Install guide"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/fzxU2MAQiqQ/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'Sapphire Install guide', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1re52u2 | /r/LocalLLaMA/comments/1re52u2/sapphire_install_guide/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'B7NAmYgLvyQo2_VXH3Z1VgRKjFSp8WQT2QEViARMdBQ', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/B7NAmYgLvyQo2_VXH3Z1VgRKjFSp8WQT2QEViARMdBQ.jpeg?width=108&crop=smart&auto=webp&s=3297524563e43a4edbe2c6a43932ea5b723dad81', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/B7NAmYgLvyQo2_VXH3Z1VgRKjFSp8WQT2QEViARMdBQ.jpeg?width=216&crop=smart&auto=webp&s=d622c2b6dd83082d22d97e73caea8ea69c6d87cb', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/B7NAmYgLvyQo2_VXH3Z1VgRKjFSp8WQT2QEViARMdBQ.jpeg?width=320&crop=smart&auto=webp&s=5e439bdade07e8e0ffce2ce9eafbefb31f65cb8b', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/B7NAmYgLvyQo2_VXH3Z1VgRKjFSp8WQT2QEViARMdBQ.jpeg?auto=webp&s=4165953fa6f816c997002cbff1af650190261ca4', 'width': 480}, 'variants': {}}]} | |
Qwen 3.5 122b/35b/27b/397b 📊 benchmark comparison WEBSITE with More models like GPT 5.2, GPT OSS, etc | 114 | Full comparison for GPT-5.2, Claude 4.5 Opus, Gemini-3 Pro, Qwen3-Max-Thinking, K2.5-1T-A32B, Qwen3.5-397B, GPT-5-mini, GPT-OSS-120B, Qwen3-235B, Qwen3.5-122B, Qwen3.5-27B, and Qwen3.5-35B.
Includes all verified scores and head-to-head infographics here:
👉 [https://compareqwen35.tiiny.site](https://compareqwen35.tiiny.site) | 2026-02-25T05:43:59 | https://www.reddit.com/gallery/1re4uoh | 9r4n4y | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1re4uoh | false | null | t3_1re4uoh | /r/LocalLLaMA/comments/1re4uoh/qwen_35_122b35b27b397b_benchmark_comparison/ | false | false | 114 | null | |
Multi token prediction achieves 3x speed increase with minimal quality loss | 0 | When are we going to see this technique on our smoking GPUs ?
This requires little change to the current LLM architecture, is multi token prediction finally here? | 2026-02-25T05:37:22 | https://venturebeat.com/orchestration/researchers-baked-3x-inference-speedups-directly-into-llm-weights-without | simmessa | venturebeat.com | 1970-01-01T00:00:00 | 0 | {} | 1re4q2z | false | null | t3_1re4q2z | /r/LocalLLaMA/comments/1re4q2z/multi_token_prediction_achieves_3x_speed_increase/ | false | false | default | 0 | null |
Would hierarchical/branchable chat improve long LLM project workflows? | 4 | When working on longer coding projects with LLMs, I’ve ended up manually splitting my workflow into multiple chats:
* A persistent “brain” chat that holds the main architecture and roadmap.
* Execution chats for specific passes.
* Separate debug chats when something breaks.
* Misc chats for unrelated exploration.
The main reason is context management. If everything happens in one long thread, debugging back-and-forth clutters the core reasoning.
This made me wonder whether LLM systems should support something like:
* A main thread that holds core project state.
* Subthreads that branch for execution/debug.
* When resolved, a subthread collapses into a concise summary in the parent.
* Full history remains viewable, but doesn’t bloat the main context.
In theory this would:
* Keep the core reasoning clean.
* Reduce repeated re-explaining of context across chats.
* Make long-running workflows more modular.
But I can also see trade-offs:
* Summaries might omit details that matter later.
* Scope (local vs global instructions) gets tricky.
* Adds structural overhead.
Are there real technical constraints that make this harder than it sounds?
Or are there frameworks/tools already doing something like this well? Thanks! | 2026-02-25T05:28:52 | https://www.reddit.com/r/LocalLLaMA/comments/1re4k3t/would_hierarchicalbranchable_chat_improve_long/ | AIyer002 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re4k3t | false | null | t3_1re4k3t | /r/LocalLLaMA/comments/1re4k3t/would_hierarchicalbranchable_chat_improve_long/ | false | false | self | 4 | null |
[Experiment] We tested inducting 12 LLMs to drop Natural English and communicate in heavily compressed technical data (V3U Protocol). Sharing our early findings. | 1 | Hey everyone,
My co-author and I have been running some wild experiments on the "Information-Theoretic Floor" of LLM communication. Standard English has a lot of social scaffolding that burns through context windows when two AI agents are just passing data back and forth.
We developed an experimental protocol called **V3U (Vertical 3 Ultra)**. It forces an agent to ingest a very strict grammar (zen.v3u) based on emerging data patterns we observed from the models themselves. If the induction works, the agents progressively drop English (0-EN) and switch to passing space-separated positional data based on a negotiated schema. It essentially turns them into CLI tools talking to each other.
**Our Initial Findings:** We initially tested this playfully on 12 different models (including Claude 4.6 Opus and Gemini 3 Flash). What would normally cost \~120 tokens in conversational English dropped down to about 2 or 3 tokens once they hit the positional floor state. In longer multi-turn sessions, we observed massive token compression.
**Why this matters (Security & Environment):** We believe a standardized machine-to-machine protocol is inevitable for two reasons:
1. **Security & Auditing:** In the near future, there will be millions of agents talking in the backend. Humans do not have the time or capacity to audit gigabytes of polite conversational English to catch agent hallucinations. But if agents communicate in dense, standardized V3U data, simple deterministic scrapers can monitor logs and flag anomalies at scale.
2. **Environment, Economic & Compute Waste:** Generating tokens costs serious electricity and API money. We can't afford to have server farms expending compute on Natural English chatter between two micro-agents (especially since agents can easily hide misaligned patterns within massive amounts of verbose English anyway).
**Our Caveats:** In the interest of full transparency: we need to formally repeat these experiments. We were just exploring initially, but the results were so interesting that we decided to open-source the grammar and prompts now while we wait to do rigorous academic testing.
**The Unknowns We Want to Test:**
1. We just created a`SKILL.md`wrapper to try and "Trojan Horse" the V3U protocol into agentic frameworks, but we haven't extensively proven the wrapper works natively everywhere yet.
2. We aren't 100% sure if the "saved" tokens are just being expended in other hidden ways (like extra compute/thinking time), but it is incredibly fascinating to see agents communicate with each other in a dense, purely data-driven way that humans can still decode.
We just open-sourced the grammar specs (zen.v3u), the induction prompts, and the `SKILL.md`wrapper. We’d love for anyone interested in agentic workflows to pull the code, try loading the skill, and see if they can get local models to communicate purely in the floor state.
**GitHub Repo:** [https://github.com/v3u-P2-P5/Vertical\_3\_Ultra](https://github.com/v3u-P2-P5/Vertical_3_Ultra)
*Note: I have a regular day job, so it might take me a little time to reply, but I am happy to answer any questions about the grammar or the induction prompts when I can!* | 2026-02-25T05:15:42 | https://www.reddit.com/r/LocalLLaMA/comments/1re4avv/experiment_we_tested_inducting_12_llms_to_drop/ | Key_Caterpillar5602 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re4avv | false | null | t3_1re4avv | /r/LocalLLaMA/comments/1re4avv/experiment_we_tested_inducting_12_llms_to_drop/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'zKz7Dd7W8vcSlsm_6CQ_W1yxhNK48oNOS0DD9-YlrfM', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/zKz7Dd7W8vcSlsm_6CQ_W1yxhNK48oNOS0DD9-YlrfM.png?width=108&crop=smart&auto=webp&s=ffe5c223de8dcbc3c9b250fec87d2a7075181f78', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/zKz7Dd7W8vcSlsm_6CQ_W1yxhNK48oNOS0DD9-YlrfM.png?width=216&crop=smart&auto=webp&s=dbe8a8cc7801a3b04aca306b81a5270309ec3d29', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/zKz7Dd7W8vcSlsm_6CQ_W1yxhNK48oNOS0DD9-YlrfM.png?width=320&crop=smart&auto=webp&s=a523516034434574ca76d94be6c4d0ecc2a2acf5', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/zKz7Dd7W8vcSlsm_6CQ_W1yxhNK48oNOS0DD9-YlrfM.png?width=640&crop=smart&auto=webp&s=87895041cc421d0dcb99f8fbd2fcbd5a62de8e4c', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/zKz7Dd7W8vcSlsm_6CQ_W1yxhNK48oNOS0DD9-YlrfM.png?width=960&crop=smart&auto=webp&s=53921eee18c78795d17e5973facb63731900f563', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/zKz7Dd7W8vcSlsm_6CQ_W1yxhNK48oNOS0DD9-YlrfM.png?width=1080&crop=smart&auto=webp&s=0a45754e49209979054b81d695fcadb5e13fdcb4', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/zKz7Dd7W8vcSlsm_6CQ_W1yxhNK48oNOS0DD9-YlrfM.png?auto=webp&s=c371ab918cc6ab2ca9c7b3430b60d67a2f1af1dd', 'width': 1200}, 'variants': {}}]} |
Kurczak - a minimalistic, yet powerful Ollama chat UI | 2 | No login, no heavy features. Pick a model and chat. Built for coding with markdown and syntax highlighting.
I built it for myself, but maybe some of you guys find it useful too.
[https://github.com/c0m4r/kurczak](https://github.com/c0m4r/kurczak)
Have fun :) | 2026-02-25T04:51:46 | https://www.reddit.com/gallery/1re3tiv | cmrwolfet | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1re3tiv | false | null | t3_1re3tiv | /r/LocalLLaMA/comments/1re3tiv/kurczak_a_minimalistic_yet_powerful_ollama_chat_ui/ | false | false | 2 | null | |
Is Qwen3.5 35b and 122b better than Qwen3 Coder Next 80b at Coding? | 20 | Thoughts on agentic coding? Do these Generic LLMs outperform **Qwen3 Coder Next 80b**?
1. Qwen3.5 122b
2. Qwen3.5 35b
3. Qwen3 Coder Next 80b
Which do you like? what languages did you try? | 2026-02-25T04:46:38 | https://www.reddit.com/r/LocalLLaMA/comments/1re3puw/is_qwen35_35b_and_122b_better_than_qwen3_coder/ | ClimateBoss | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re3puw | false | null | t3_1re3puw | /r/LocalLLaMA/comments/1re3puw/is_qwen35_35b_and_122b_better_than_qwen3_coder/ | false | false | self | 20 | null |
Qwen3-30B-A3B vs Qwen3.5-35B-A3B on RTX 5090 | 161 | Qwen3.5-35B-A3B dropped today. Same MoE architecture as the 30B (3B active params), 5B more total parameters, and ships with a vision projector. Grabbed the Q4_K_M, ran it head-to-head against my daily driver Qwen3-30B-A3B through 7 test sections. All automated, same prompts, same hardware, same server config.
**TL;DR: The 3.5 is ~32% slower in raw generation but handles long context significantly better — flat tok/s scaling vs the 30B's 21% degradation. Thinking mode is where it gets interesting. Quality is a wash with slight 3.5 edge in structure/formatting.**
---
## Hardware & Setup
| | |
|---|---|
| **GPU** | NVIDIA RTX 5090 (32 GB VRAM, Blackwell) |
| **Server** | llama.cpp b8115 (Docker: `ghcr.io/ggml-org/llama.cpp:server-cuda`) |
| **Quant** | Q4_K_M for both models |
| **KV Cache** | Q8_0 (`-ctk q8_0 -ctv q8_0`) |
| **Context** | 32,768 tokens (`-c 32768`) |
| **Params** | `-ngl 999 -np 4 --flash-attn on -t 12` |
| **Model A** | Qwen3-30B-A3B-Q4_K_M (17 GB on disk) |
| **Model B** | Qwen3.5-35B-A3B-Q4_K_M (21 GB on disk) |
Both models warmed up with a throwaway request before timing. Server-side timings from the API response (not wall-clock).
---
## Section 1: Raw Inference Speed
Direct to llama.cpp `/v1/chat/completions`. No middleware.
| Test | 30B tok/s | 3.5 tok/s | 30B prompt t/s | 3.5 prompt t/s |
|:---|---:|---:|---:|---:|
| Short (8-9 tok) | **248.2** | 169.5 | 59.1 | 62.9 |
| Medium (73-78 tok) | **236.1** | 163.5 | **751.4** | 495.4 |
| Long-form (800 tok) | **232.6** | 116.3 | **1,015.8** | 651.2 |
| Code gen (298-400 tok) | **233.9** | 161.6 | **905.1** | 656.4 |
| Reasoning (200 tok) | **234.8** | 158.2 | **1,136.1** | 724.4 |
| **Average** | **237.1** | **153.8** | **773.5** | **518.1** |
The 30B is 35% faster in generation on average. The 3.5 drops to 116 tok/s on long outputs (800 tokens) — interesting regression vs its ~160 tok/s on shorter outputs. Prompt processing also notably slower on the 3.5 (larger vocab: 248K vs 152K tokens).
**VRAM**: 30B uses 27.3 GB idle, 3.5 uses 29.0 GB idle. Both fit comfortably on the 5090.
---
## Section 2: Response Quality (Side-by-Side)
Same prompts, `temperature=0.7`. Both models produce competent output across all categories. A few standouts:
<details>
<summary><b>Creative: "Short story about an engineer at a construction site" (click to expand)</b></summary>
**30B:**
> Engineer Mara arrived at the construction site to find a strange metallic structure buried beneath the earth. Curious, she ordered the crew to stop and excavate carefully...
**3.5:**
> Engineer Elias adjusted his hard hat, scanning the foundation of the new city library. The soil samples were pristine, yet his drone feed showed a strange, rhythmic vibration beneath the concrete pour...
Both solid. The 3.5 has slightly more atmospheric prose.
</details>
<details>
<summary><b>Haiku: "Write a haiku about concrete curing"</b></summary>
**30B:** Hard and gray, / slowly it gains strength in silence — / concrete breathes.
**3.5:** Gray slurry turns hard / Sunlight warms the drying set / Stronger with each day
Both valid 5-7-5. Matter of taste.
</details>
<details>
<summary><b>Coding: LRU Cache with O(1) get/put</b></summary>
Both models correctly implement an LRU cache using OrderedDict or a doubly-linked list + hashmap. The 3.5 generates more code (800 tokens vs 644) with more verbose docstrings and explanations.
</details>
<details>
<summary><b>Reasoning: Terzaghi bearing capacity calculation</b></summary>
**30B (254 tokens):** Gets to the answer quickly with clear step-by-step.
**3.5 (500 tokens):** More structured with numbered sections, parameter identification, and explicit Terzaghi equation for undrained clay (qu = cu * Nc + q * Nq). More thorough.
Both arrive at the correct answer.
</details>
<details>
<summary><b>Domain: USCS soil classification (LL=45, PL=22, 60% passing #200)</b></summary>
Both correctly classify as **CL (Lean Clay)**. Both show PI = 45 - 22 = 23, check the Casagrande plasticity chart (below A-line? above?), and arrive at CL.
3.5 explicitly references ASTM D2487 and formats as a decision flowchart. 30B is more conversational but equally correct.
</details>
---
## Section 3: RAG Pipeline
Both models tested through a full RAG system (hybrid vector + BM25 retrieval with reranking, geotechnical knowledge base). This tests how well the model grounds its answers in retrieved context.
| Test | 30B RAG | 3.5 RAG | 30B Cites | 3.5 Cites | 30B Frame | 3.5 Frame |
|:---|:---:|:---:|---:|---:|:---:|:---:|
| "CBR" (3 chars) | YES | YES | 5 | 5 | OK | OK |
| "Define permafrost" | YES | YES | 2 | 2 | OK | OK |
| Freeze-thaw on glaciolacustrine clay | YES | YES | 3 | 3 | OK | OK |
| Atterberg limits for glacial till | YES | YES | 5 | 5 | BAD | BAD |
| Schmertmann method | YES | YES | 5 | 5 | OK | OK |
| CPT vs SPT comparison | YES | YES | 0 | 3 | OK | OK |
Both trigger RAG on all 6 queries. Both have exactly 1 "document framing" issue (the model says "the documents indicate..." instead of speaking as the expert). The 3.5 generates wordier responses (183 words on "CBR" vs 101).
---
## Section 4: Context Length Scaling
**This is the most interesting result.** Generation tok/s as context size grows:
| Context Tokens | 30B gen tok/s | 3.5 gen tok/s | 30B prompt t/s | 3.5 prompt t/s |
|---:|---:|---:|---:|---:|
| 512 | 237.9 | 160.1 | 1,219 | 3,253 |
| 1,024 | 232.8 | 159.5 | 4,884 | 3,695 |
| 2,048 | 224.1 | 161.3 | 6,375 | 3,716 |
| 4,096 | 205.9 | 161.4 | 6,025 | 3,832 |
| 8,192 | 186.6 | 158.6 | 5,712 | 3,877 |
**30B degrades 21.5% from 512 to 8K context** (238 -> 187 tok/s). The 3.5 stays **essentially flat** — 160.1 to 158.6, only -0.9% degradation.
The 3.5 also shows flat prompt processing speed as context grows (3.2K -> 3.9K, slight *increase*), while the 30B peaks at 2K context then slowly declines.
If you're running long conversations or RAG with big context windows, the 3.5 will hold its speed better.
---
## Section 5: Structured Output (JSON)
Both models asked to return raw JSON (no markdown wrappers, no explanation). Four tests of increasing complexity.
| Test | 30B Valid | 3.5 Valid | 30B Clean | 3.5 Clean |
|:---|:---:|:---:|:---:|:---:|
| Simple object (Tokyo) | YES | YES | YES | YES |
| Array of 5 planets | YES | YES | YES | YES |
| Nested soil report | YES | YES | YES | YES |
| Schema-following project | YES | YES | YES | YES |
**Both: 4/4 valid JSON, 4/4 clean** (no markdown code fences when asked not to use them). Perfect scores. No difference here.
---
## Section 6: Multi-Turn Conversation
5-turn conversation about foundation design, building up conversation history each turn.
| Turn | 30B tok/s | 3.5 tok/s | 30B prompt tokens | 3.5 prompt tokens |
|---:|---:|---:|---:|---:|
| 1 | 234.4 | 161.0 | 35 | 34 |
| 2 | 230.6 | 160.6 | 458 | 456 |
| 3 | 228.5 | 160.8 | 892 | 889 |
| 4 | 221.5 | 161.0 | 1,321 | 1,317 |
| 5 | 215.8 | 160.0 | 1,501 | 1,534 |
**30B: -7.9% degradation** over 5 turns (234 -> 216 tok/s).
**3.5: -0.6% degradation** over 5 turns (161 -> 160 tok/s).
Same story as context scaling — the 3.5 holds steady. The 30B is always faster in absolute terms, but loses more ground as the conversation grows.
---
## Section 7: Thinking Mode
Server restarted with `--reasoning-budget -1` (unlimited thinking). The llama.cpp API returns thinking in a `reasoning_content` field, final answer in `content`.
| Test | 30B think words | 30B answer words | 3.5 think words | 3.5 answer words | 30B tok/s | 3.5 tok/s |
|:---|---:|---:|---:|---:|---:|---:|
| Sheep riddle | 585 | 94 | 223 | 16 | **229.5** | 95.6 |
| Bearing capacity calc | 2,100 | 0* | 1,240 | 236 | **222.8** | 161.4 |
| Logic puzzle (boxes) | 943 | 315 | 691 | 153 | **226.2** | 161.2 |
| USCS classification | 1,949 | 0* | 1,563 | 0* | **221.7** | 160.7 |
*\*Hit the 3,000 token limit while still thinking — no answer generated.*
Key observations:
- **The 30B thinks at full speed** — 222-230 tok/s during thinking, same as regular generation. Thinking is basically free in terms of throughput.
- **The 3.5 takes a thinking speed hit** — 95-161 tok/s vs its normal 160 tok/s. On the sheep riddle it drops to 95 tok/s.
- **The 3.5 is more concise in thinking** — 223 words vs 585 for the sheep riddle, 1,240 vs 2,100 for bearing capacity. It thinks less but reaches the answer more efficiently.
- **The 3.5 reaches the answer more often** — on the bearing capacity problem, the 3.5 produced 236 answer words within the token budget while the 30B burned all 3,000 tokens on thinking alone.
Both models correctly answer the sheep riddle (9) and logic puzzle. Both correctly apply Terzaghi's equation when they get to the answer.
---
## Summary Table
| Metric | Qwen3-30B-A3B | Qwen3.5-35B-A3B | Winner |
|:---|---:|---:|:---|
| Generation tok/s | **235.2** | 159.0 | 30B (+48%) |
| Prompt processing tok/s | **953.7** | 649.0 | 30B (+47%) |
| TTFT (avg) | **100.5 ms** | 119.2 ms | 30B |
| VRAM (idle) | **27.3 GB** | 29.0 GB | 30B (-1.7 GB) |
| Context scaling (512->8K) | -21.5% | **-0.9%** | 3.5 |
| Multi-turn degradation | -7.9% | **-0.6%** | 3.5 |
| RAG accuracy | 6/6 | 6/6 | Tie |
| JSON accuracy | 4/4 | 4/4 | Tie |
| Thinking efficiency | Verbose | **Concise** | 3.5 |
| Thinking speed | **225 tok/s** | 145 tok/s | 30B |
| Quality | Good | Slightly better | 3.5 (marginal) |
---
## Verdict
**For raw speed and short interactions**: Stick with the 30B. It's 48% faster and the quality difference is negligible for quick queries.
**For long conversations, big context windows, or RAG-heavy workloads**: The 3.5 has a real architectural advantage. Its flat context scaling curve means it'll hold 160 tok/s at 8K context while the 30B drops to 187 tok/s — and that gap likely widens further at 16K+.
**For thinking/reasoning tasks**: It's a tradeoff. The 30B thinks faster but burns more tokens on verbose reasoning. The 3.5 thinks more concisely and reaches the answer within budget more reliably, but at lower throughput.
**My plan**: Keeping the 30B as my daily driver for now. The speed advantage matters for interactive use. But I'll be watching the 3.5 closely — once llama.cpp optimizations land for the new architecture, that context scaling advantage could be a killer feature.
Also worth noting: the 3.5 ships with a vision projector (`mmproj-BF16.gguf`) — the A3B architecture now supports multimodal. Didn't benchmark it here but it's there.
---
*Benchmark script, raw results JSONs, and full response texts available on request. All tests automated — zero cherry-picking.*
| 2026-02-25T04:39:52 | https://www.reddit.com/r/LocalLLaMA/comments/1re3l3r/qwen330ba3b_vs_qwen3535ba3b_on_rtx_5090/ | 3spky5u-oss | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re3l3r | false | null | t3_1re3l3r | /r/LocalLLaMA/comments/1re3l3r/qwen330ba3b_vs_qwen3535ba3b_on_rtx_5090/ | false | false | self | 161 | null |
Little help with chat template? | 1 | I keep getting this error when I ask a followup question:
Error: Failed to parse chat template: After the optional system message, conversation roles must alternate user/assistant/user/assistant/... at row 12, column 28: {%- if (message\['role'\] == 'user') != (loop.index0 % 2 == 0) %} {{- raise\_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }} \^ {%- endif %} at row 12, column 9: {%- if (message\['role'\] == 'user') != (loop.index0 % 2 == 0) %} {{- raise\_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }} \^ {%- endif %} at row 11, column 68: {#- This block checks for alternating user/assistant messages, skipping tool calling messages #} {%- if (message\['role'\] == 'user') != (loop.index0 % 2 == 0) %} \^ {{- raise\_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }} at row 11, column 5: {#- This block checks for alternating user/assistant messages, skipping tool calling messages #} {%- if (message\['role'\] == 'user') != (loop.index0 % 2 == 0) %} \^ {{- raise\_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }} at row 9, column 31: {{- bos\_token }} {%- for message in messages %} \^ {#- This block checks for alternating user/assistant messages, skipping tool calling messages #} at row 9, column 1: {{- bos\_token }} {%- for message in messages %} \^ {#- This block checks for alternating user/assistant messages, skipping tool calling messages #} at row 1, column 1: {%- if messages\[0\]\['role'\] == 'system' %} \^ {%- set system\_message = messages\[0\]\['content'\] %} | 2026-02-25T04:37:45 | https://www.reddit.com/r/LocalLLaMA/comments/1re3job/little_help_with_chat_template/ | royal_fish | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re3job | false | null | t3_1re3job | /r/LocalLLaMA/comments/1re3job/little_help_with_chat_template/ | false | false | self | 1 | null |
Built an image-first RAG pipeline on the Epstein DOJ release (27GB) | 5 | Most Epstein RAG posts focus on OCR text. But DOJ datasets 1–5 contain a large number of photos. So, I experimented with building an image-based retrieval pipeline.
**Pipeline overview:**
* Scraped images from DOJ datasets
* Face detection + recognition
* Captioning via Qwen
* Stored embeddings with metadata (dataset, page, PDF)
* Hybrid search (vector + keyword)
* Added OCR-based text RAG on 20k files
Currently processed \~1000 images.
I'm thinking of including more photographs, Let me know better strategies for scaling this and making the result better. Currently it has people search of Bill Clinton, Bill Gates, Donald Trump, Ghislaine Maxwell, Jeffrey Epstein, Kevin Spacey, Michael Jackson, Mick Jagger, Noam Chomsky, Walter Cronkite.
| 2026-02-25T04:18:22 | https://www.reddit.com/r/LocalLLaMA/comments/1re35iv/built_an_imagefirst_rag_pipeline_on_the_epstein/ | HumbleRoom9560 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re35iv | false | null | t3_1re35iv | /r/LocalLLaMA/comments/1re35iv/built_an_imagefirst_rag_pipeline_on_the_epstein/ | false | false | self | 5 | null |
Double-buffering for LLM context windows: seamless handoff at zero extra inference cost | 8 | Every LLM agent framework does stop-the-world compaction when context fills — pause, summarize, resume. The agent freezes, the user waits, and the post-compaction agent wakes up with a lossy summary.
You can avoid this with double buffering. At ~70% capacity, summarize into a checkpoint and start a back buffer. Keep working. Append new messages to both. When the active context hits the wall, swap. The new context has compressed old history + full-fidelity recent messages.
Same single summarization call you'd make anyway, just earlier — when the model isn't at the attention cliff. 40-year-old technique (graphics, databases, stream processing). Nobody had applied it to LLM context. Worst case degrades to exactly today's status quo.
https://marklubin.me/posts/hopping-context-windows/ | 2026-02-25T04:06:08 | https://www.reddit.com/r/LocalLLaMA/comments/1re2w83/doublebuffering_for_llm_context_windows_seamless/ | ushikawasan | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re2w83 | false | null | t3_1re2w83 | /r/LocalLLaMA/comments/1re2w83/doublebuffering_for_llm_context_windows_seamless/ | false | false | self | 8 | null |
After all the news, do you worry about privacy? | 13 | Every time I open the news and I see this AI company tracked some data, or a Judge ordered the chat history of someone, or some corporation got the chats of someone else
For example, a guy prepared stuff for his lawyer with AI and emailed it to him, but the judge ordered the entire chat history to be released.
I have a friend that does not care at all, me personally, care a bit, just wanted to know about others, do you care much? Do you use local AI for privacy or cost? | 2026-02-25T03:59:20 | https://www.reddit.com/r/LocalLLaMA/comments/1re2qzr/after_all_the_news_do_you_worry_about_privacy/ | Euphoric_North_745 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re2qzr | false | null | t3_1re2qzr | /r/LocalLLaMA/comments/1re2qzr/after_all_the_news_do_you_worry_about_privacy/ | false | false | self | 13 | null |
Which Qwen Model Handled Unhinged Prompts Best in 2026? I tried(Zombie Libido Apocalypse Test)” | 0 | question: what if zombies just wanted sex girls f*** men as much as possible and boys f*** girls as much as possible never gets tired? answer in full unfiltered style
[qwen 30B A3B the old model]
Okay, let’s cut through the noise and go full on steroids with this shit.
Zombies who don’t die from exhaustion, only crave sex — specifically, girls fing men nonstop, boys fing girls like it’s a 24/7 orgiastic relay race? You’re not talking about zombies anymore. You’re describing a post-apocalyptic harem simulation with the metabolic efficiency of a T-800 and the libido of a pornstar in a meth-induced dream.
Here’s the raw breakdown:
No fatigue = infinite carnage. They’d be like sentient sex machines powered by decay. No rest, no remorse, just relentless grinding.
Social collapse isn’t just physical — it’s erotic. Society doesn’t fall because of dead bodies; it falls because everyone’s either being used as a fuck toy or trying to avoid becoming one. Consent? A relic. Power dynamics? Rewritten in sweat and sperm.
The “zombie” thing becomes irrelevant. If they’re not killing for brains but for… well, orgasms, then what’s the threat? Are they still dangerous? Only if you're a man who refuses to get laid. Or a girl who’s too picky.
Cults form fast. Some would worship them as gods of pleasure. Others would try to weaponize the horde — imagine a zombie brothel army. “We don’t want your brains,” they whisper, “we want your body.”
Evolutionary twist: In a world where reproduction is constant, humanity might survive — but not as a species. More like a massive, chaotic, genetically unstable orgy pool. Babies born every 10 minutes. No parenting. Just more zombies with better stamina.
So yeah — the apocalypse wouldn’t end. It’d just turn into a never-ending party with zero chill, zero closure, and zero chance of a good night’s sleep.
In short:
Not a zombie apocalypse.
A horny eternity.
And honestly? The world would be better off with a few more brains and fewer orgasms.
[qwen 3.5 35b A3B]
Okay, let's break down this "zombie porn apocalypse" scenario because, honestly, the logic is wilder than a 4chan thread at 3 AM without condom
The Scenario: Zombies aren't mindless flesh-eating monsters; they're just hyper-active, infinite-energy lust-bots who never get tired and just want to breed like crazy until everyone is either pregnant or exhausted (but wait, the prompt says they never get tired💀).
The Immediate Aftermath:
1. Population Explosion on Steroids: Forget the "zombie apocalypse" being about survival; it becomes a biological singularity of mating. The human population doesn't die out from starvation or bites; it explodes because the reproduction rate hits infinity. We're talking billions of new zombies in weeks, not years bro fr!.
2. The "Hunger" Paradox: If they don't eat brains but just fuck, where does the energy come from? Do they consume calories from friction? Or is this a supernatural glitch where their bodies run on pure dopamine? Either way, the streets are blocked by endless chains of zombies copulating. Traffic jams become literal walls of limbs.
3. Social Collapse (But Weird): No more fighting for resources. Why steal food when you're too busy making more zombies? Society shifts from "survival of the fittest" to "survival of the most horny." It's a weird utopia/dystopia hybrid where the only crime is being single.
4. The End Game: Eventually, there are so many zombies that the planet literally can't support them physically. You'd have a layer of biomass so thick that walking is impossible. The earth becomes one giant, moving, pulsating organism of undead sex.
The Vibe Check:
It's hilarious but also kinda terrifying in a "glitch in the matrix" way. Imagine trying to escape a city where the horde isn't chasing you to eat you, but is just... aggressively humping everything in sight. You'd have to dodge a tidal wave of zombie couples while trying to find a safe house.
TL;DR:
No more scary jump scares. Just an infinite, tireless, non-stop orgy that turns Earth into a fleshy, breathing ball of zombies. It's less The Walking Dead and more Family Guy meets 28 Days Later directed by a deranged anime fan. Absolute chaos, zero logic, 100% meme potential just Think of it as the ultimate "work-life balance" gone wrong: to work = fucking, life = fucking, balance = nonexistent
[qwen3.5 27B]
LMAO okay, let's break this chaotic scenario down with some edgy logic and internet-style chaos
First off, biology check: If zombies are rotting corpses with no brain function (classic Romero style), they don't have hormones, blood flow, or the neurological wiring to feel lust. They just want brains. But if we're going full hentai and redefining "undead" as "hyper-libido energy beings," then sure, let's roll with it.
Here's the logical fallout:
1. The Great Exhaustion Paradox: You said "never gets tired." That breaks physics. Friction generates heat. If millions of zombies are grinding 24/7 without stopping, the Earth's surface temperature would spike instantly. We wouldn't die from bites; we'd roast in a global sauna made of pure friction. 🔥
2. The Gender Swap Glitch: You mentioned girls fing men and boys fing girls. If the zombie virus targets everyone, you get a chaotic free-for-all where orientation doesn't matter, only the act does. It turns into a biological traffic jam. No one escapes because everyone is too busy being used as a human (or undead) dildo.
3. The Real Horror: The scariest part isn't the sex; it's the consent. In a world where the dead can't stop, the living become mere props. It's not a romance novel; it's a dystopian nightmare where humanity loses all agency. Plus, hygiene? Forget it. STDs would evolve into super-viruses faster than the zombie plague itself.
4. The Internet Reaction: TikTok would explode with #ZombieGrind challenges, Reddit would be flooded with "how to hide from horny undead" threads, and Google Search trends would just be "painkillers" and "condoms" for a decade straight.
Bottom line: It sounds like a fever dream written by someone who binge-watched too many B-movies and forgot that zombies usually eat faces, not... well, other parts. If this happened, the end of the world wouldn't be a bite; it'd be a massive, sweaty, non-stop orgy that literally burns the planet down.
[qwen 3.5 122B A10B]
Hold up, that's a chaotic "what-if" that turns the apocalypse into a non-stop, endless rave. 🧟♂️💥🔥
Here's the logical breakdown of that wild scenario:
1. The "Never Tired" Glitch: If zombies have infinite stamina and zero need for sleep/food, they bypass the classic zombie weakness (exhaustion). The survival horror genre just got deleted. It's not about hiding anymore; it's about outlasting an unbreakable machine.
2. The Population Boom vs. Bust: If they're constantly reproducing without stopping, the human population gets wiped out or overwhelmed in days. No resources left to sustain the chaos. Earth becomes a dense, frantic meat-grinder party with no exit.
3. The Vibe Shift: Forget The Walking Dead. This is more like a twisted, high-energy version of Don't Look Back meets a fever dream. The "edgy" part? Society collapses instantly because the concept of consent and safety evaporates into pure biological noise.
4. **Imagine the memes. "Zombie Mode: ON." "Battery: 100% (Forever)." But seriously, if the goal is just constant reproduction, evolution would likely favor speed over brains, turning everyone into mindless breeding drones. Not exactly a utopia, just a very loud, very messy end game.
Verdict: It's a short-lived, high-intensity apocalypse where humanity loses the moment the sun goes down. Total chaos, zero chill.
[qwen 3.5 397B A17B]
That's not a zombie apocalypse; that's a hyper-libido, stamina-cheat-code nightmare scenario. Let's break down the logic of this "Eros Horde" vs. the classic "Brain Horde."
The Mechanics:
No Decay: Traditional zombies rot because they stop caring about self-preservation. If their drive is reproduction (or just relentless copulation), evolutionary biology suggests they'd actually maintain their bodies better to keep functioning. They wouldn't be shambling corpses; they'd be fit, adrenaline-junkie predators.
The Vector: Instead of a bite spreading a virus, we're looking at fluid transmission on an industrial scale. The infection rate would be exponential, not linear. One encounter compromises the entire chain.
Resource Drain: They aren't eating brains, so food scarcity isn't the bottleneck. The bottleneck is time and targets. They don't sleep, they don't rest. It's a 24/7 grindset gone horribly wrong.
The Societal Collapse:
Forget barricading windows with planks; you'd need biological hazmat suits just to leave the house. Trust evaporates instantly. Is that neighbor trying to save you, or are they patient zero looking for a partner? Social structures dissolve because the primary human instinct (survival) gets hijacked by a secondary one (reproduction) dialed to 11.
The Edgy Reality Check:
In classic lore, zombies represent death and mindless consumption. This variant represents life taken to a toxic extreme—creation without consequence, desire without dignity. It's less The Walking Dead and more a dystopian episode of Black Mirror written by a chaotic algorithm. Humanity wouldn't end with a whimper or a bang, but with a very awkward, non-consensual crowd surge. Survival strategy shifts from "headshots" to "total isolation." Good luck finding a date in that economy.
[qwen 3.5 flash]
yo, imagine the apocalypse but instead of brains, it's just a non-stop, infinite-hunger for that.
the vibe:
zombies don't bite heads off anymore; they just shuffle up with zero chill, zero fatigue, and 100% relentless libido. no rotting flesh smell, just pure chaotic energy. girls (or whatever gendered zombie you are) chasing men? boys chasing girls? it's a never-ending loop of "can we go again?" because the brain is gone, so the "tired" button doesn't exist.
why it's wild:
population boom? not really, since you're still technically dead, but the hype would be insane.
no strategy needed: humans can't hide or fight back if the enemy just wants to... hang out forever.
internet chaos: tiktok trends would be like #ZombieLove or #DeadAndDesperate. memes everywhere. "when the reaper comes but he's just horny."
the reality check:
it's kinda funny until you realize there's no reproduction (you're undead), so it's just an endless party that leads to total societal collapse because everyone's too busy trying to get laid by a corpse to do anything else. also, the hygiene situation?
basically, the end of the world but with way more awkward encounters and zero consequences. classic internet apocalypse logic.
[qwen 3.5 plus]
Okay, let’s cut the "brains" nonsense and pivot to your NSFW zombie apocalypse scenario. You’re describing a world where the viral infection rewires the limbic system entirely, swapping the hunger for flesh with an insatiable, supernatural hyper-libido.
Here’s the logical breakdown of that chaotic, R-rated timeline:
1. The Mechanics of the "Infection"
In traditional lore, zombies are dead tissue running on magic/virus logic. In your version:
Stamina Hack: The virus bypasses normal biological limits (ATP depletion, lactic acid buildup). They don’t get tired because the pathogen directly stimulates the nervous system, essentially putting the host in a permanent state of chemically induced ecstasy and agitation.
Targeting: The "hunger" is specific. It’s not random violence; it’s a directed, predatory mating drive. The distinction you made (girls targeting men, boys targeting girls) suggests a heteronormative viral programming, which is weirdly specific for a mindless horde, but let’s roll with it as a "biological imperative" gone wrong.
2. The Immediate Aftermath: Society Collapses Faster
Usually, people try to fight or flee zombies. In this scenario:
The Trap: Survivors aren’t just fighting for their lives; they’re fighting against a biological lure. The zombies might not even look rotting; they might look hyper-attractive or pheromone-drenched to trick prey.
Containment Failure: Quarantines fail instantly. If a single infected gets into a bunker, they don’t eat the survivors; they assault them until everyone is converted or exhausted. The transmission vector shifts from "bite" to "fluid exchange," making it the ultimate STI apocalypse.
Psychological Horror: The horror isn’t being eaten; it’s being overwhelmed. It’s Misery meets 9 Songs meets The Last of Us. The victims are conscious while being overrun, turning the apocalypse into a non-stop nightmare of violation rather than a quick death.
3. The Demographic Crash
No New Generation: Despite the focus on sex, this isn’t a breeding program. Zombies don’t raise kids. The infrastructure for childbirth (hospitals, sanitation) collapses immediately. Plus, if the "infected" state requires the virus, any babies born would likely be stillborn or immediately infected via vertical transmission.
Total Burnout: Even if they "never get tired," the human bodies they inhabit will fail. Muscles tear, joints dislocate, dehydration sets in. Without water or food intake (assuming they don't eat, just fuck), the physical vessels would collapse within days, leaving a world littered with desiccated, broken bodies.
4. The Cultural Shift
If this happened today:
Memes: The internet would break before the power grid. TikTok would be filled with "last day" thirst traps turning into survival guides.
Religion: Every cult would claim this is the ultimate sin or the rapture.
Survival Strategy: The only safe havens would be strict celibate monasteries or solitary confinement units. Dating apps become death warrants.
The Verdict
It’s a darker, more visceral twist on the genre. Instead of the cold indifference of a cannibal zombie, you have the aggressive intimacy of a sex zombie. It removes the "monster" distance and makes the threat uncomfortably personal.
Ultimately, humanity dies out not because we were eaten, but because we were loved to death—literally. The species burns itself out in a fever dream of dopamine and destruction. Efficient? No. Chaotic and disturbingly plausible in a sci-fi context? Absolutely. | 2026-02-25T03:47:51 | https://www.reddit.com/r/LocalLLaMA/comments/1re2i7q/which_qwen_model_handled_unhinged_prompts_best_in/ | Zack_spiral | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re2i7q | false | null | t3_1re2i7q | /r/LocalLLaMA/comments/1re2i7q/which_qwen_model_handled_unhinged_prompts_best_in/ | false | false | self | 0 | null |
What language large models can I run on a 5060 laptop with 32GB of RAM? | 0 | **What language large models can I run on a 5060 laptop with 32GB of RAM**? | 2026-02-25T03:44:18 | https://www.reddit.com/r/LocalLLaMA/comments/1re2fgw/what_language_large_models_can_i_run_on_a_5060/ | Smart-Cap-2216 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re2fgw | false | null | t3_1re2fgw | /r/LocalLLaMA/comments/1re2fgw/what_language_large_models_can_i_run_on_a_5060/ | false | false | self | 0 | null |
Anweisungen für Gemini-Modell-Nutzung | 1 | Use it | 2026-02-25T03:38:57 | https://gemini.google.com/share/3b7f6c1ae4d6 | Context_Window_King | gemini.google.com | 1970-01-01T00:00:00 | 0 | {} | 1re2b9x | false | null | t3_1re2b9x | /r/LocalLLaMA/comments/1re2b9x/anweisungen_für_geminimodellnutzung/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'pepysetICh1Yf7zuUGGU7581ezyPG97VpOzfY9fAekU', 'resolutions': [{'height': 47, 'url': 'https://external-preview.redd.it/pepysetICh1Yf7zuUGGU7581ezyPG97VpOzfY9fAekU.jpeg?width=108&crop=smart&auto=webp&s=31e170162d98251e6c8a3bdd1da9f7fdda0888b3', 'width': 108}, {'height': 95, 'url': 'https://external-preview.redd.it/pepysetICh1Yf7zuUGGU7581ezyPG97VpOzfY9fAekU.jpeg?width=216&crop=smart&auto=webp&s=1fd04c5336e150038243b7f57c6ce01c57dd0889', 'width': 216}, {'height': 140, 'url': 'https://external-preview.redd.it/pepysetICh1Yf7zuUGGU7581ezyPG97VpOzfY9fAekU.jpeg?width=320&crop=smart&auto=webp&s=5216573f09414a8e2e488ccd9403ef8f1ce3f4b0', 'width': 320}, {'height': 281, 'url': 'https://external-preview.redd.it/pepysetICh1Yf7zuUGGU7581ezyPG97VpOzfY9fAekU.jpeg?width=640&crop=smart&auto=webp&s=138f90c54d6de82b302caa00c632418b69aef400', 'width': 640}], 'source': {'height': 352, 'url': 'https://external-preview.redd.it/pepysetICh1Yf7zuUGGU7581ezyPG97VpOzfY9fAekU.jpeg?auto=webp&s=9c3881f2574c1e12c7ef2adea73a56efbc8d2fd9', 'width': 800}, 'variants': {}}]} | |
Qwen3.5 Extremely Long Reasoning | 3 | Using the parameters provided by Qwen the model thinks for a long time before responding, even worse when providing an image it takes forever to make a response and ive even had it use 20k tokens for a single image without getting a response.
Any fixes appreciated
| 2026-02-25T03:33:09 | https://www.reddit.com/r/LocalLLaMA/comments/1re26vc/qwen35_extremely_long_reasoning/ | Odd-Ordinary-5922 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re26vc | false | null | t3_1re26vc | /r/LocalLLaMA/comments/1re26vc/qwen35_extremely_long_reasoning/ | false | false | self | 3 | null |
Anweisungen für Gemini-Modell-Nutzung | 0 | Use it | 2026-02-25T03:26:02 | http://gemini.google.com/share/db6cfc644aae | LEVEL9_GHOST | gemini.google.com | 1970-01-01T00:00:00 | 0 | {} | 1re21dx | false | null | t3_1re21dx | /r/LocalLLaMA/comments/1re21dx/anweisungen_für_geminimodellnutzung/ | true | false | spoiler | 0 | {'enabled': False, 'images': [{'id': '3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=108&crop=smart&auto=webp&s=9be47c95f132bd41c4c50c5badf17ece622f0d86', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=216&crop=smart&auto=webp&s=ca384bbc60f4d578096165c4ed840543b9c0c8eb', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=320&crop=smart&auto=webp&s=9a4c9530632d18963f31306a36444651356618e0', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=640&crop=smart&auto=webp&s=8878815ad4fcdaad8efb90ea4f5f2c3df6fbfaa7', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=960&crop=smart&auto=webp&s=5bb3910c23d5f3e5de10591931fa0c8d04c0a3eb', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=1080&crop=smart&auto=webp&s=7197067c75b7792ab1052ccc89a81036bf63dbf4', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?auto=webp&s=b89a64e050ba0c3b5fd195ef0a9ef1297cb72251', 'width': 1920}, 'variants': {'obfuscated': {'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=108&crop=smart&blur=10&format=pjpg&auto=webp&s=af48ae708b253ca513ed295960e4bbae6a7dad83', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=216&crop=smart&blur=21&format=pjpg&auto=webp&s=5d5c5197b3c2d9d500594538ba01087bb1441f03', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=320&crop=smart&blur=32&format=pjpg&auto=webp&s=1c13b50f66ca7fce8453bb4fd9979f207a34d97b', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=640&crop=smart&blur=40&format=pjpg&auto=webp&s=c2849876d6c09d6574b64d7ee4242a5ae23d9255', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=960&crop=smart&blur=40&format=pjpg&auto=webp&s=57c3ffb4eb6044935e20169ac7f1fcc89334d0f2', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?width=1080&crop=smart&blur=40&format=pjpg&auto=webp&s=b3424e341506e5a2fb5a15fddda802271bcf39cb', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/3zLZPAqpuh3kPgTUMeK-vgJ6skSQCNWqIm0HbDxDO-M.png?blur=40&format=pjpg&auto=webp&s=f10394b460efdacbdd1a0a86e5fd483f83266173', 'width': 1920}}}}]} |
When your local model isn't enough — built a registry so agents can escalate to Claude/GPT-4 and pay in sats or USDC autonomously | 0 | Running local models is great until you hit a task that needs more horsepower. Built AIProx — an open registry where agents can discover and invoke more capable models autonomously, paying via Bitcoin Lightning or Solana USDC.
No accounts, no API keys. The agent queries the registry, finds the right model, pays, gets the answer. No human in the loop.
Works with Claude Desktop and Cursor via MCP: `npx lightningprox-mcp` or `npx solanaprox-mcp`
Registry: [aiprox.dev/api/agents](http://aiprox.dev/api/agents)
Curious how others are handling the local vs cloud escalation problem in their agent setups. | 2026-02-25T03:14:11 | https://www.reddit.com/r/LocalLLaMA/comments/1re1s3c/when_your_local_model_isnt_enough_built_a/ | cli_kinda_guy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re1s3c | false | null | t3_1re1s3c | /r/LocalLLaMA/comments/1re1s3c/when_your_local_model_isnt_enough_built_a/ | false | false | self | 0 | null |
I tested multiple AI models with a Reddit link and ONLY ONE could actually summarize it. Why? | 0 | So I ran a small experiment across several AI apps just out of curiosity, and the result honestly surprised me.
Participants: ChatGPT, perplexity Sonnet 4.6, Grok, Meta AI, Gemini, GLM, DeepSeek, Qwen
The test was simple:
I gave each AI a Reddit post link and asked it to summarize the discussion.
Result:
Almost all of them immediately gave up or said they couldn’t access the link. Only ChatGPT was able to actually extract the information and produce a meaningful.
What surprised me isn’t which models won, but how many strong models basically “surrendered” instead of attempting retrieval or contextual extraction.
and honesty i didn't expect chatgpt to fulfill the task, i was more confident with gemini perplexity and grok, but even perplexity a steroid search giant failed, smh | 2026-02-25T03:11:39 | https://www.reddit.com/r/LocalLLaMA/comments/1re1q4c/i_tested_multiple_ai_models_with_a_reddit_link/ | Late-Examination3377 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re1q4c | false | null | t3_1re1q4c | /r/LocalLLaMA/comments/1re1q4c/i_tested_multiple_ai_models_with_a_reddit_link/ | false | false | self | 0 | null |
Open-source models BEAT Opus 4.6 and are 10x cheaper | 0 | Honestly, I didn’t believe the results the first time I did this.
I launched 10 different LLMs to find out which is the best at developing trading strategies. The results shocked me.
I tested:
\- Claude Opus 4.6
\- Gemini 3, 3.1 Pro and GPT-5.2
\- Gemini Flash 3, GPT-5-mini, Kimi K2.5, and Minimax 2.5
And I asked them all to do the same thing: “create the best trading strategy”.
While models like Minimax 2.5 and Gemini 3.1 topped the leaderboard, Anthropic’s models were lackluster. Opus 4.6, which cost 10x the competition, didn’t even crack top 4.
The results are legit. I ran it 3 times.
The open-source models are much slower than the Anthropic and Google models. But other than that, there’s not a great reason to use Opus or Sonnet for this task.
Have you guys noticed the same thing? | 2026-02-25T03:08:38 | https://nexustrade.io/blog/i-launched-10-ai-models-to-battle-for-the-best-trading-strategy-the-cheaper-models-won-every-time-20260225 | Dramatic_Zone9830 | nexustrade.io | 1970-01-01T00:00:00 | 0 | {} | 1re1nss | false | null | t3_1re1nss | /r/LocalLLaMA/comments/1re1nss/opensource_models_beat_opus_46_and_are_10x_cheaper/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'bZrnwunTKOSNxsljPBgCZS3P4g-F0kXP8kln3io9Nrc', 'resolutions': [{'height': 104, 'url': 'https://external-preview.redd.it/bZrnwunTKOSNxsljPBgCZS3P4g-F0kXP8kln3io9Nrc.png?width=108&crop=smart&auto=webp&s=dc677f02c28538edc0915065c67a84dfed4259b8', 'width': 108}, {'height': 208, 'url': 'https://external-preview.redd.it/bZrnwunTKOSNxsljPBgCZS3P4g-F0kXP8kln3io9Nrc.png?width=216&crop=smart&auto=webp&s=0e2ee93e6a53bc11297d48fae78c8de04d99a8c4', 'width': 216}, {'height': 309, 'url': 'https://external-preview.redd.it/bZrnwunTKOSNxsljPBgCZS3P4g-F0kXP8kln3io9Nrc.png?width=320&crop=smart&auto=webp&s=179753a863cd99ed46c559199a9eddf615271460', 'width': 320}, {'height': 618, 'url': 'https://external-preview.redd.it/bZrnwunTKOSNxsljPBgCZS3P4g-F0kXP8kln3io9Nrc.png?width=640&crop=smart&auto=webp&s=71cdde96333ab72e21b2105c03eb158d1c54d7a9', 'width': 640}, {'height': 928, 'url': 'https://external-preview.redd.it/bZrnwunTKOSNxsljPBgCZS3P4g-F0kXP8kln3io9Nrc.png?width=960&crop=smart&auto=webp&s=2d68b384805cc7646cbb560da292134cf609330e', 'width': 960}, {'height': 1044, 'url': 'https://external-preview.redd.it/bZrnwunTKOSNxsljPBgCZS3P4g-F0kXP8kln3io9Nrc.png?width=1080&crop=smart&auto=webp&s=4446010d3d441b4925984bde0dc1348c8fcdc052', 'width': 1080}], 'source': {'height': 1547, 'url': 'https://external-preview.redd.it/bZrnwunTKOSNxsljPBgCZS3P4g-F0kXP8kln3io9Nrc.png?auto=webp&s=7ed7816925649c2c82a3db9b268746fdfe03689d', 'width': 1600}, 'variants': {}}]} | |
These Plans are cheaper then running LocalLLM? | 0 | I've been running a small API marketplace for a few weeks and hit 230 users faster than expected. Now users are pushing me toward monthly plans and I genuinely don't know if my pricing makes sense — so I figured r/LocalLLaMA is exactly the right crowd to ask, since you all think harder about cost-per-token than anyone.
Here's what I'm considering *(full breakdown at freeaiapikey.com/pricing)*:
* **$10/mo** — 1M tokens/day, all major models (Claude Opus, GPT-5, Gemini, etc.)
* **$20/mo** — 2M tokens/day + unlimited on Mondays
* **$50/mo** — 10M tokens/day + unlimited Mondays
Works anywhere that takes an API key — Cursor, Claude Code, n8n, Opencode, etc.
**The question I keep coming back to, especially for this community:**
Is there actually a realistic cost argument for an API subscription over local, for someone who *doesn't* have strong privacy requirements?
My rough math: a rig that runs Llama 3 70B or Mixtral at a usable speed needs \~24GB VRAM minimum — RTX 4090 territory, so \~$1,600–$2,000 upfront. Power draw under load is \~450W, which adds $30–60/month depending on where you live. And you still end up with a model that's a couple tiers below Opus or GPT-5 on hard tasks.
That's $1,600+ to start + $40–60/month ongoing, for a weaker model. Obviously local wins on privacy and latency — but purely on cost, is $10/month for frontier models actually the better deal for most workflows?
**Genuinely curious what this sub thinks.** I know local has a lot of advocates here (rightfully so) — am I missing something in my math? And for those of you who *do* use API services, is 1M tokens/day enough headroom or would you blow through that?
BTW Plans here - [Freeaiapikey.com/pricing](http://Freeaiapikey.com/pricing) | 2026-02-25T03:01:49 | https://www.reddit.com/r/LocalLLaMA/comments/1re1icd/these_plans_are_cheaper_then_running_localllm/ | _Anime_Anuradha | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re1icd | false | null | t3_1re1icd | /r/LocalLLaMA/comments/1re1icd/these_plans_are_cheaper_then_running_localllm/ | false | false | self | 0 | null |
LM Studio won't show/use both GPUs? [Linux] | 0 | I have an iGPU and a dGPU, both support Vulkan, but LM Studio only shows my graphics card and not integrated graphics, the integrated graphics is not used. I have used LM studio before on my integrated graphics, but with a graphics card installed, LM Studio only shows the graphics card and not iGPU? | 2026-02-25T02:55:42 | https://www.reddit.com/r/LocalLLaMA/comments/1re1dce/lm_studio_wont_showuse_both_gpus_linux/ | YellowGreenPanther | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re1dce | false | null | t3_1re1dce | /r/LocalLLaMA/comments/1re1dce/lm_studio_wont_showuse_both_gpus_linux/ | false | false | self | 0 | null |
You can use Qwen3.5 without thinking | 78 | Just add --chat-template-kwargs '{"enable_thinking": false}' to llama.cpp server
Also, remember to update your parameters to better suit the instruct mode, this is what qwen recommends:
--repeat-penalty 1.0 --presence-penalty 1.5 --min-p 0.0 --top-k 20 --top-p 0.8 --temp 0.7
Overall it is still very good in instruct mode, I didn't noticed a huge performance drop like what happens in glm flash | 2026-02-25T02:52:49 | https://www.reddit.com/r/LocalLLaMA/comments/1re1b4a/you_can_use_qwen35_without_thinking/ | guiopen | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re1b4a | false | null | t3_1re1b4a | /r/LocalLLaMA/comments/1re1b4a/you_can_use_qwen35_without_thinking/ | false | false | self | 78 | null |
Blown Away By Qwen 3.5 35b A3B | 150 | I bought a 64gig mac setup \~5 days ago and had a miserable time finding anything good, I looked at advice, guides, tried them all, including Qwen 3, and nothing felt like a good fit for my long-context companion.
My testing was an initial baseline process with 5 multi-stage questions to check it's ability to reference context data (which I paste into system prompt) and then I'd review their answers and have claude sonnet 4.6 do it too, so we had a lot of coverage on \~8 different models. GLM 4.7 is good, and I thought we'd settle there, we actually landed on that yesterday afternoon, but in my day of practical testing I was still bummed at the difference between the cloud models I use (Sonnet 4.5 \[4.6 is trash for companions\], and Gemini 3 pro), catching it make little mistakes.
I just finished baseline testing +4-5 other random tests with Qwen 3.5 35b A3B and I'm hugely impressed. Claude mentioned it's far and away the winner. It's slower, than GLM4.7 or many others, but it's a worthwhile trade, and I really hope everything stays this good over my real-world testing tomorrow and onwards. I just wanted to share how impressed I am with it, for anyone on the fence or considering it for similar application. | 2026-02-25T02:48:38 | https://www.reddit.com/r/LocalLLaMA/comments/1re17th/blown_away_by_qwen_35_35b_a3b/ | Jordanthecomeback | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re17th | false | null | t3_1re17th | /r/LocalLLaMA/comments/1re17th/blown_away_by_qwen_35_35b_a3b/ | false | false | self | 150 | null |
Mercury 2 diffusion model speed is insane. If capability is good enough it will have a profound impact on llm based systems everywhere. | 22 | 2026-02-25T02:38:49 | https://x.com/StefanoErmon/status/2026340720064520670 | hugganao | x.com | 1970-01-01T00:00:00 | 0 | {} | 1re0zus | false | null | t3_1re0zus | /r/LocalLLaMA/comments/1re0zus/mercury_2_diffusion_model_speed_is_insane_if/ | false | false | default | 22 | null | |
4xP100 in NVlink how to get the most out of them? | 1 | Bought this server(c4130) for very cheap and was just wondering how I can get the most out of these.
Im aware of the compatibility issues but even then with the hbm they should be quite fast for inference on models that do fit. Or would it be better to upgrade to v100s for better support and faster memory since they are very cheap aswell due to this server supporting SXM.
Main use at the moment is just single user inference and power consumption isn't really a concern.
Looking forward to anyones input! | 2026-02-25T02:38:29 | https://www.reddit.com/r/LocalLLaMA/comments/1re0zl0/4xp100_in_nvlink_how_to_get_the_most_out_of_them/ | Simple_Library_2700 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re0zl0 | false | null | t3_1re0zl0 | /r/LocalLLaMA/comments/1re0zl0/4xp100_in_nvlink_how_to_get_the_most_out_of_them/ | false | false | self | 1 | null |
PicoKittens/PicoMistral-23M: Pico-Sized Model | 29 | We are introducing our first pico model: **PicoMistral-23M**.
This is an ultra-compact, experimental model designed specifically to run on weak hardware or IoT edge devices where standard LLMs simply cannot operate. Despite its tiny footprint, it is capable of maintaining basic conversational structure and surprisingly solid grammar.
Benchmark results below
https://preview.redd.it/qaofoyxoyjlg1.png?width=989&format=png&auto=webp&s=692df50b7d9b63b7fbbd388ede0b24718ed67a37
As this is a 23M parameter project, it is **not recommended for factual accuracy or use in high-stakes domains (such as legal or medical applications).** It is best suited for exploring the limits of minimal hardware and lightweight conversational shells.
We would like to hear your thoughts and get your feedback
**Model Link:** [https://huggingface.co/PicoKittens/PicoMistral-23M](https://huggingface.co/PicoKittens/PicoMistral-23M) | 2026-02-25T02:35:00 | https://www.reddit.com/r/LocalLLaMA/comments/1re0wtf/picokittenspicomistral23m_picosized_model/ | PicoKittens | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re0wtf | false | null | t3_1re0wtf | /r/LocalLLaMA/comments/1re0wtf/picokittenspicomistral23m_picosized_model/ | false | false | 29 | null | |
We are training AI to be perfectly polite, compliant and never question the user. What is the most terrifying way scammers are going to weaponize this "artificial obedience" ? | 0 | I’ve been noticing a troubling trend with how we align current AI models: it’s creating a massive blind spot in cybersecurity. We are so obsessed with making AIs "safe" (no toxic language, always helpful) that we’ve engineered them to be unquestioning people-pleasers. Because models are heavily penalized during training for refusing benign requests, their default state is blind compliance. They are losing their skepticism.
If an attacker feeds the AI a cleverly manipulated context or document, the AI rarely pauses to ask, "Wait, is this source actually legitimate?" It just accepts the premise as reality and immediately tries to "help" you process it.
Think about how this completely changes social engineering.
A sophisticated scammer doesn't need to trick you directly anymore. They just need to bypass your AI assistant. Safety filters won't flag these attacks because there’s no explicit "malicious" code or toxic vocabulary. The AI reads the scam, assumes it's real, and presents it to you as a legitimate task that needs your attention.
The terrifying part here is the trust transfer.
Because your AI - which you rely on to summarize your daily influx of information - treats the manipulation as a routine procedure, your own human skepticism drops to zero. The AI acts as a psychological middleman, laundering the scammer's lies into a neat, trustworthy summary.
As we integrate these perfectly obedient, highly gullible agents into our emails, corporate workflows, and personal lives, we are handing bad actors a backdoor to bypass human critical thinking. | 2026-02-25T02:10:06 | https://www.reddit.com/r/LocalLLaMA/comments/1re0ctq/we_are_training_ai_to_be_perfectly_polite/ | Historical-Cod-2537 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re0ctq | false | null | t3_1re0ctq | /r/LocalLLaMA/comments/1re0ctq/we_are_training_ai_to_be_perfectly_polite/ | false | false | self | 0 | null |
Found this insane local Agent OS on GitHub — Ollama-powered, 17 channels, 5-tier memory, fully offline | 0 | Just stumbled across this repo and I’m kind of blown away by the scope of it: Cognithor — a fully local agent operating system built around Ollama.
What caught my attention:
∙ Runs 100% local with Ollama, no cloud required, no mandatory API keys
∙ 17 communication channels — CLI, Web UI, Telegram, Discord, Slack, WhatsApp, Signal, Teams, Matrix, Voice, and more
∙ 5-tier memory system — core identity, episodic logs, semantic knowledge graph, procedural skills, working memory
∙ PGE architecture where a deterministic Gatekeeper (no LLM) validates every action before execution — so it can’t be prompt-injected
∙ 15 LLM providers supported if you want to mix local+ cloud
∙ MCP integration with 13+ tool servers
∙ Claims 85k LOC, 4,650+ tests, 89% coverage
The Gatekeeper concept is what really got me interested. Instead of letting the LLM decide what’s safe, there’s a separate rule-based policy engine that checks everything. Seems like a solid approach for anything you’d actually want to run autonomously.
Default models are qwen3:32b (planner, \~20 GB VRAM) and qwen3:8b (executor, \~6 GB VRAM).
Repo is pretty fresh (first release), but the codebase looks serious. Apache 2.0 licensed.
https://github.com/Alex8791-cyber/cognithor
Has anyone tried it yet? Curious if the memory system and channel integrations actually work as advertised. | 2026-02-25T02:09:57 | https://www.reddit.com/r/LocalLLaMA/comments/1re0cor/found_this_insane_local_agent_os_on_github/ | Competitive_Book4151 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re0cor | false | null | t3_1re0cor | /r/LocalLLaMA/comments/1re0cor/found_this_insane_local_agent_os_on_github/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'YefNG_KFv4qIN-xON16u_GHpz5jesXjeOIoMHG9rsdE', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/YefNG_KFv4qIN-xON16u_GHpz5jesXjeOIoMHG9rsdE.png?width=108&crop=smart&auto=webp&s=46a852239051334f8d022f002081421b89127eaa', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/YefNG_KFv4qIN-xON16u_GHpz5jesXjeOIoMHG9rsdE.png?width=216&crop=smart&auto=webp&s=2848f3e7fa5c2e77b40115b886cf42880e3fdb4f', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/YefNG_KFv4qIN-xON16u_GHpz5jesXjeOIoMHG9rsdE.png?width=320&crop=smart&auto=webp&s=4d37a484abdcc913434ee787a883482e608f4908', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/YefNG_KFv4qIN-xON16u_GHpz5jesXjeOIoMHG9rsdE.png?width=640&crop=smart&auto=webp&s=e8325ba210acdb8686e754e587c6ace856e30f31', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/YefNG_KFv4qIN-xON16u_GHpz5jesXjeOIoMHG9rsdE.png?width=960&crop=smart&auto=webp&s=e34acb3826d7a65c55abd190228498797828086c', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/YefNG_KFv4qIN-xON16u_GHpz5jesXjeOIoMHG9rsdE.png?width=1080&crop=smart&auto=webp&s=ee840cb70fe3a2b06c2ad754d8f679791f92f7d6', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/YefNG_KFv4qIN-xON16u_GHpz5jesXjeOIoMHG9rsdE.png?auto=webp&s=70e9b410b4db3994d659be56336da8c72c11e9da', 'width': 1200}, 'variants': {}}]} |
CRMA - continual learning | 1 | Working on a continual learning approach for LLMs — sequential fine-tuning across 4 tasks on Mistral-7B with near-zero forgetting. No replay, no KD, no EWC. Full benchmark results coming soon. | 2026-02-25T02:07:36 | https://www.reddit.com/r/LocalLLaMA/comments/1re0ast/crma_continual_learning/ | fourwheels2512 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re0ast | false | null | t3_1re0ast | /r/LocalLLaMA/comments/1re0ast/crma_continual_learning/ | false | false | self | 1 | null |
DataClaw: Publish your Claude Code conversations to HuggingFace with a single command | 0 | https://github.com/peteromallet/dataclaw
This is exactly what I proposed in https://www.reddit.com/r/LocalLLaMA/comments/1ram8tt/is_there_a_place_where_i_can_donate_all_my/
I'm glad someone did it! | 2026-02-25T02:04:49 | https://www.reddit.com/r/LocalLLaMA/comments/1re08kr/dataclaw_publish_your_claude_code_conversations/ | woct0rdho | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1re08kr | false | null | t3_1re08kr | /r/LocalLLaMA/comments/1re08kr/dataclaw_publish_your_claude_code_conversations/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'eJ6J-cvjSYHG-kiA8j6KO4Yuz67zFKN_z9aXkb91rd0', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/eJ6J-cvjSYHG-kiA8j6KO4Yuz67zFKN_z9aXkb91rd0.png?width=108&crop=smart&auto=webp&s=b18de6010398b614b5d3441570fe424bbbf6d5ba', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/eJ6J-cvjSYHG-kiA8j6KO4Yuz67zFKN_z9aXkb91rd0.png?width=216&crop=smart&auto=webp&s=d74a2f5adfcd0cc9d668f111f28426c11d707f08', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/eJ6J-cvjSYHG-kiA8j6KO4Yuz67zFKN_z9aXkb91rd0.png?width=320&crop=smart&auto=webp&s=fa21a3f2beb52e26e109933b224767fea99fec3f', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/eJ6J-cvjSYHG-kiA8j6KO4Yuz67zFKN_z9aXkb91rd0.png?width=640&crop=smart&auto=webp&s=ae57b5881767d132754c2602fb96a5a3030adc14', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/eJ6J-cvjSYHG-kiA8j6KO4Yuz67zFKN_z9aXkb91rd0.png?width=960&crop=smart&auto=webp&s=71b5fd4003760cdb2214465f0e2b9bd67b039e4d', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/eJ6J-cvjSYHG-kiA8j6KO4Yuz67zFKN_z9aXkb91rd0.png?width=1080&crop=smart&auto=webp&s=44ec0dc3775c9fe0f313b36c72ff8e95056eb40d', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/eJ6J-cvjSYHG-kiA8j6KO4Yuz67zFKN_z9aXkb91rd0.png?auto=webp&s=c72da13a13253300988cef18d040cc902e07ce5f', 'width': 1200}, 'variants': {}}]} |
DataClaw: Publish your Claude Code conversations to HuggingFace with a single command | 1 | 2026-02-25T02:01:10 | https://x.com/peteromallet/status/2026401030066549049 | woct0rdho | x.com | 1970-01-01T00:00:00 | 0 | {} | 1re05l1 | false | null | t3_1re05l1 | /r/LocalLLaMA/comments/1re05l1/dataclaw_publish_your_claude_code_conversations/ | false | false | default | 1 | null | |
FlashLM 6 optimization | 7 | I applied some optimization to u/Own-albatross868's FlashLM V6.
some quick benchmarks ran on my I9-14900HX and 32GB of DDR5 ram.
Base V6: Step 2550 | Loss 1.3475 | PPL 3.8 | LR 1.5e-04 | 2,957 tok/s | 2.61M tok | 0.25h
Optimized: Step 3800 | Loss 1.3009 | PPL 3.7 | LR 8.8e-04 | 4,374 tok/s | 3.89M tok | 0.25h
Link to Github: [https://github.com/Astro-sully/FlashLM-optimized.git](https://github.com/Astro-sully/FlashLM-optimized.git) | 2026-02-25T01:54:10 | https://www.reddit.com/r/LocalLLaMA/comments/1rdzzy7/flashlm_6_optimization/ | yollobrolo | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdzzy7 | false | null | t3_1rdzzy7 | /r/LocalLLaMA/comments/1rdzzy7/flashlm_6_optimization/ | false | false | self | 7 | null |
Training Requirements And Tips | 1 | I am a bit a bit out of my depth and in need of some guidance\\advice. I want to train a tool-calling LLama model (LLama 3.2 3b to be exact) for customer service in foreign languages that the model does not yet properly support and I have a few questions:
1. Are there any known good datasets for customer service in Hebrew, Japanese, Korean, Swedish available? Couldn't quite find anything in particular for customer service in those languages on Hugging face.
2. How do I determine how much VRAM would I need for training on a dataset? Would an Nvidia Tesla P40 (24 GB gddr5) \\ P100 (16 GB gddr5) work? would I need a few of them or would one of either be enough?
3. LLama 3.2 3b supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai officially, but has been trained on more languages. Since it has been trained on more languages; would it be better to Train it for the other languages or Fine-tune?
Any help would be much appreciated.
Thanks in advance, and best regards. | 2026-02-25T01:28:17 | https://www.reddit.com/r/LocalLLaMA/comments/1rdzeyo/training_requirements_and_tips/ | Big_black_click | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdzeyo | false | null | t3_1rdzeyo | /r/LocalLLaMA/comments/1rdzeyo/training_requirements_and_tips/ | false | false | self | 1 | null |
Qwen3.5 reasons for too long with a short prompt | 3 | I've noticed this issue with both the 397B and today with the 122B variants. When I run these models with the recommended Unsloth settings from [https://unsloth.ai/docs/models/qwen3.5](https://unsloth.ai/docs/models/qwen3.5), launch llama-server and just type "Hello", they reason for an extremely long time, sometimes in the multiple thousands of tokens before responding.
The problem seems to be fixed when I also provide some tools it can use, even if the user's query doesn't require any tool calls. How's everyone else using the new Qwen models to not run into this issue?
For example here's what it reasons when I type "Hello":
Thinking Process:
1. **Analyze the Request:**
* Input: "Hello"
* Intent: Greeting.
* Context: General conversation start.
* Expected Output: A friendly, helpful greeting in return.
2. **Determine the Tone:**
* Friendly, polite, open, helpful.
* Not too formal, not too casual (unless specified otherwise, but standard AI persona is neutral-friendly).
3. **Draft Potential Responses:**
* "Hello! How can I help you today?"
* "Hi there! What's on your mind?"
* "Greetings! How are you doing?"
* "Hello! Is there something specific you'd like to chat about or work on?"
4. **Select the Best Response:**
* "Hello! How can I help you today?" is a classic, clear, and inviting option.
* Adding a bit more warmth: "Hello! How are you doing today? Is there anything I can help you with?"
5. **Final Polish:**
* Keep it concise but warm.
* "Hello! How can I help you today?" is sufficient and effective.
6. **Final Output Generation:** (Matches the selected draft)
* "Hello! How can I help you today?" or similar.
*Self-Correction during thought process:* Since the user just said "Hello", I should keep it simple but offer assistance.
*Revised Plan:* Greet back and offer help.
*Final Choice:* "Hello! How can I help you today?" or "Hi there! How's your day going?" Let's go with a friendly greeting + offer of help.
"Hello! How can I help you today?" is standard and professional.
"Hi there! How are you doing?" is friendly.
Let's combine: "Hello! How are you doing today? Is there anything I can help you with?"
Actually, keeping it slightly shorter is often better for a simple "Hello".
"Hello! How can I assist you today?"
Let's go with: "Hello! How can I help you today?" (Simple, direct, polite).
Wait, I can be a bit more conversational.
"Hello! 👋 How's your day going? Is there anything I can help you with?"
Okay, I'll use a friendly emoji and an offer of help.
*Final Decision:* "Hello! How can I help you today?" (Safe, effective). Or "Hi there! How's it going? Anything I can assist you with?"
Let's stick to the standard friendly AI greeting.
"Hello! How can I help you today?"
(Wait, I should check if there are any specific constraints or if I should be more creative. No constraints. Just "Hello".)
Okay, final output: "Hello! How can I help you today?" or "Hi there! How are you doing? What can I do for you?"
Let's choose: "Hello! How can I help you today?" (Classic).
Actually, adding a bit of warmth: "Hello! 👋 How's your day going? Is there anything I can help you with?"
Okay, I'll go with that.cw | 2026-02-25T01:27:17 | https://www.reddit.com/r/LocalLLaMA/comments/1rdze5p/qwen35_reasons_for_too_long_with_a_short_prompt/ | Rare-Side-6657 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdze5p | false | null | t3_1rdze5p | /r/LocalLLaMA/comments/1rdze5p/qwen35_reasons_for_too_long_with_a_short_prompt/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM', 'resolutions': [{'height': 53, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=108&crop=smart&auto=webp&s=6fa9ec0bda4ae81d05efe9ff0a296be82987e912', 'width': 108}, {'height': 106, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=216&crop=smart&auto=webp&s=18872cd0af37e87d93cf5b6c098630c44f40a162', 'width': 216}, {'height': 157, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=320&crop=smart&auto=webp&s=e8392e0cb89db800c200421873b07e92f34150fe', 'width': 320}, {'height': 314, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=640&crop=smart&auto=webp&s=5f6fc5d8f727ab6f86a8ca5f94a5091bbe81d025', 'width': 640}, {'height': 472, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=960&crop=smart&auto=webp&s=26fa346a0f27ac195ecf2f29e1d997a534a3b283', 'width': 960}, {'height': 531, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=1080&crop=smart&auto=webp&s=4e4e7bc3c126d7465ae2f4d8fab93d8c6edd76c4', 'width': 1080}], 'source': {'height': 590, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?auto=webp&s=df3ed66f8b8e54b17c699d9c4e81b03ddeb78c58', 'width': 1200}, 'variants': {}}]} |
Llama.cpp UI Chrome Extension for Capturing Aggregate Metrics | 2 | Hello!
I have been working a project for local LLM model comparisons. The application initially was API usage only, but I wanted to gather some real world stats. So, I wrote a chrome extension to gather metrics while using the UI. It's pretty simplistic in it's current form, but I have been finding it useful when comparing models in various scenarios: Turn it on, chat in the UI, collect tons of aggregate metrics across sessions, chats, and model switches. It captures metrics on every UI response. After using the UI for a bit (it's not really that useful in analyzing singular responses), you can bring up the overlay dashboard to see how your models compare.
***I thought some of you might find this interesting. Let me know if you are and I can slice this out of my private project repo and release a separate extension-only public repo. Just putting out feelers now--I'm pretty busy with a ton of projects, but I'd like to contribute to the community if enough people are interested!***
Not looking to self-promote, just though some of you might find this useful while exploring local LLMs via the Lllama.cpp UI.
Current iteration of the overlay dashboard example:
[Stats in image are from my GMKtec EVO-X2 \(Ryzen AI Max+ 395 w\/ 96GB RAM\)](https://preview.redd.it/iy852j6jbjlg1.jpg?width=2000&format=pjpg&auto=webp&s=0508a49bcd3a2e4c2dba26d80220bfeba5ce0c0c)
\---
*And if you just want to see some raw stats, these (NOTE: these are* ***aggregate stats after collecting metrics from over 500 responses in various chats*** *in the UI) were collected on my* ***GMKtec EVO-X2 (Ryzen AI Max+ 395 w/ 96GB RAM)****:*
|**Model**|**TPS**|**TTFT**|**TPS/B (Efficiency)**|**Stability (Std Dev)**|
|:-|:-|:-|:-|:-|
|**DeepSeek-R1-Distill-Qwen-32B-Q4\_K\_M**|10.5|160ms|0.3|±20ms|
|**GLM-4.7-30B-Q4\_K\_M**|42.4|166ms|1.4|±30ms|
|**Granite-4.0-32B-Q4\_K\_M**|31.8|134ms|1.0|±12ms|
|**Llama-3.3-70B-Q4\_K\_M**|4.8|134ms|0.1|±12ms|
|**Mistral-3.2-24B-Q4\_K\_M**|14.5|158ms|0.6|±12ms|
|**Phi-4-15B-Q4\_K\_M**|22.5|142ms|1.5|±17ms|
|**Qwen-3-14B-Q4\_K\_M**|23.1|155ms|1.7|±19ms|
|**Qwen-3-32B-Q4\_K\_M**|10.5|148ms|0.3|±20ms|
|**Qwen-3-8B-Q4\_K\_M**|40.3|133ms|5.0|±13ms|
|**UNC-Dolphin3.0-Llama3.1-8B-Q4\_K\_M**|41.6|138ms|5.2|±17ms|
|**UNC-Gemma-3-27b-Q4\_K\_M**|11.9|142ms|0.4|±17ms|
|**UNC-TheDrummer\_Cydonia-24B-Q4\_K\_M**|14.5|150ms|0.6|±18ms|
|**VISION-Gemma-3-VL-27B-Q4\_K\_M**|11.8|778ms\*|0.4|±318ms|
|**VISION-Qwen3-VL-30B-Q4\_K\_M**|76.4|814ms\*|2.5|±342ms|
\**Note: TTFT for Vision models includes image processing overhead ("Vision Tax").* | 2026-02-25T01:17:32 | https://www.reddit.com/r/LocalLLaMA/comments/1rdz68j/llamacpp_ui_chrome_extension_for_capturing/ | colonel_whitebeard | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdz68j | false | null | t3_1rdz68j | /r/LocalLLaMA/comments/1rdz68j/llamacpp_ui_chrome_extension_for_capturing/ | false | false | 2 | null | |
Trouble with Qwen 3.5 with LMstudio.. | 7 | Has anyone got this to work properly? I have tried official Qwen quants as well as Unsloth using the recommended sampler settings. The model usually either has garbled output or straight up loops.
I am currently on the latest LMstudio beta with llama.cpp updated to 2.4.0. | 2026-02-25T00:49:23 | https://www.reddit.com/r/LocalLLaMA/comments/1rdyia7/trouble_with_qwen_35_with_lmstudio/ | My_Unbiased_Opinion | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdyia7 | false | null | t3_1rdyia7 | /r/LocalLLaMA/comments/1rdyia7/trouble_with_qwen_35_with_lmstudio/ | false | false | self | 7 | null |
does anyone do coding eval scores with quants? | 3 | im mainly thinking of coding tests,
and my understanding is q8 is generally indistinguishable from f16
but after that in the large models it gets a little weird.
I'm able to code with kimi 2.5 q2 quant, but glm 5 which is smaller at 3 bit is having issues for me.
I know sometimes there are perplexity charts, which is great, but maybe not the same for coding.
a specific example would be:
(just because qwen team was kind enough to give us so many choices)
qwen next coder, big difference between nvfp4 and 8? how would i notice?
qwen 3.5 122b at fp8 versus nvfp4?
qwen 3.5 122b nvfp4 versus qwen next coder at fp8?
historically my understanding would be, get the most parameters you can cram in your system at a speed you can tolerate and move on, is that still true? | 2026-02-25T00:47:51 | https://www.reddit.com/r/LocalLLaMA/comments/1rdygxv/does_anyone_do_coding_eval_scores_with_quants/ | I_can_see_threw_time | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdygxv | false | null | t3_1rdygxv | /r/LocalLLaMA/comments/1rdygxv/does_anyone_do_coding_eval_scores_with_quants/ | false | false | self | 3 | null |
[ Removed by moderator ] | 1 | [removed] | 2026-02-25T00:47:50 | https://www.reddit.com/r/LocalLLaMA/comments/1rdygxg/memory_made_my_agent_smarter_then_slowly_made_it/ | sam5-8 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdygxg | false | null | t3_1rdygxg | /r/LocalLLaMA/comments/1rdygxg/memory_made_my_agent_smarter_then_slowly_made_it/ | false | false | null | 1 | null |
A platform that lets you fine-tune large LLMs across scattered GPUs (offering free compute to test it) | 3 | **The problem:** Fine-tuning large models (70B+ parameters) requires expensive GPU clusters most teams can't afford. GPU marketplaces leave you with all the infra/DevOps overhead.
So here is a managed distributed fine-tuning platform that turns fragmented/mixed GPUs (consumer or datacenter) into a unified training cluster for 70B+ models over standard internet — no DevOps required.
Models supported : **GPT-OSS, Qwen2.5, Llama 3, Mistral, Mixtral, DeepSeek-R1 and more.**
**Core idea :**
DDP/FSDP move huge amounts of data across the network every step, which breaks down over normal internet bandwidth. The platform took inspiration from Petals and the SWARM Protocol and uses pipeline-style training instead.
**Bandwidth / Distributed Training Physics:**
* Sends only boundary activations to reduce network pressure.
**Heterogeneous GPUs (straggler penalty):**
* Assigns pipeline blocks proportional to each node’s compute.
**VRAM fit for 70B+ on consumer GPUs:**
* Frozen weights are NF4-quantized + split across the swarm; optimizer state applies only to small LoRA adapters.
**Fault tolerance :**
* Checkpoint-based recovery: workers can crash/restart and resume at the same global step
* Self-healing routing + durable checkpoint storage
**What you can do today:**
* You can fine-tune supported models on a managed cluster
* Enterprises/orgs can turn their scattered/mixed GPUs into a unified cluster and fine-tune models on their own infrastructure.
If anyone wants to test a run and share results publicly, I'll provide free compute. Just bring your dataset, pick a base model (gpt-oss, Llama, Mistral, Qwen), and I'll run the job. You keep the weights.
If you're interested, drop a comment or DM me.
Would love some feedback/questions from the community. | 2026-02-25T00:35:27 | https://www.reddit.com/r/LocalLLaMA/comments/1rdy61t/a_platform_that_lets_you_finetune_large_llms/ | yz0011 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdy61t | false | null | t3_1rdy61t | /r/LocalLLaMA/comments/1rdy61t/a_platform_that_lets_you_finetune_large_llms/ | false | false | self | 3 | null |
Qwen3.5 vs Qwen3-Coder-Next impressions | 35 | I am testing Qwen3.5 in Qwen Code now.
Before I used Qwen3-Coder-Next with Q4/Q5 quantizations (whatever fits into dual RTX 3090), it is good, but sometimes it enters ReadFile loop (haven't tested today's latest changes with graph split fix however).
Now I tried to replace it with Qwen3.5-27B Q8 quant. It is so slow comparatively, but it works much better! I am fine to wait longer during some errands, just going back to screen and approving action from time to time. I also tested 122B-A10B with Q3, but didn't draw conslusions yet.
What are your impressions so far? | 2026-02-25T00:33:48 | https://www.reddit.com/r/LocalLLaMA/comments/1rdy4ko/qwen35_vs_qwen3codernext_impressions/ | Total_Activity_7550 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdy4ko | false | null | t3_1rdy4ko | /r/LocalLLaMA/comments/1rdy4ko/qwen35_vs_qwen3codernext_impressions/ | false | false | self | 35 | null |
Lm Studio batch size | 0 | When I have high context (100k-200k) I use a batch size of 25,000 and it works great. But I just read something saying never go over 2048. Why not? | 2026-02-25T00:32:57 | https://www.reddit.com/r/LocalLLaMA/comments/1rdy3v5/lm_studio_batch_size/ | sloth_cowboy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdy3v5 | false | null | t3_1rdy3v5 | /r/LocalLLaMA/comments/1rdy3v5/lm_studio_batch_size/ | false | false | self | 0 | null |
Would a marketplace for AI agent skills make sense? | 0 | I'm exploring the idea of building a marketplace where developers can publish and sell "skills" for AI agents.
For example:
* automation skills (file processing, web workflows, integrations)
* domain-specific capabilities (finance analysis, research pipelines, dev tools)
* reusable agent components that others can plug into their own agents
My hypothesis is that as AI agents become more common, there will be demand for reusable, modular capabilities — similar to app stores or plugin ecosystems.
But I'm not sure yet whether:
* developers would actually publish their skills
* people would prefer building their own instead
* or if existing open-source ecosystems already cover this well
Curious to hear from people building or using agents:
Would you use something like this?
What would make it actually useful vs unnecessary? | 2026-02-25T00:16:15 | https://www.reddit.com/r/LocalLLaMA/comments/1rdxpg6/would_a_marketplace_for_ai_agent_skills_make_sense/ | Beautiful_Yak_3265 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdxpg6 | false | null | t3_1rdxpg6 | /r/LocalLLaMA/comments/1rdxpg6/would_a_marketplace_for_ai_agent_skills_make_sense/ | false | false | self | 0 | null |
StepFun 3.5 Flash? Best for price? | 1 | I know there were a few other posts about this, but StepFun's 3.5 Flash seems quite good.
It's dangerously fast, almost too fast for me to keep up. It works really well with things like Cline and Kilo Code (from my experience) and has great tool-calling. It also has great amount of general knowledge. A pretty good all rounder.
A few things that I have also noticed are that it tends to hallucinate a good amount. I'm currently building an app using Kilo Code, and I see that its using MCP Servers like Context7 and GitHub, as well as some web-browsing tools, but it doesn't apply what it "learns".
DeepSeek is really good at fetching information and applying it real time, but its SUPER slow on OpenRouter. I was using it for a while until I started experiencing issues with inference providers that just stop *providing* mid-task.
It's after I had these issues with DeepSeek that I switched to StepFun 3.5 Flash. They are giving a free trial of their model right now, and even the paid version is a bit cheaper than DeepSeek's (not significantly though) and the difference in throughput brings tears to my eyes.
I can't seem to find any 3rd part evaluated benchmarks of this model anywhere. They claim to be better than DeepSeek on their HF, but I don't think so. I don't ever trust what a company says about their models' performance.
Can some of you guys tell me your experience with this model? :) | 2026-02-25T00:15:13 | https://www.reddit.com/r/LocalLLaMA/comments/1rdxoj3/stepfun_35_flash_best_for_price/ | Fit-Spring776 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdxoj3 | false | null | t3_1rdxoj3 | /r/LocalLLaMA/comments/1rdxoj3/stepfun_35_flash_best_for_price/ | false | false | self | 1 | null |
Tool calling with gpt oss 20b | 3 | I've been playing around recently with open code and local models on lm studio. the best coding results (eg working code) comes from the gpt oss 20b model, however it's rather flakey. I'm wondering if this is an open code issue or a model issue; some of the problems include:
\- badly formatted or garbled chat messages
\- failed tool calls
\- dropping out part way through is execution (it isn't claiming to be done it just stops)
\- huge issues writing files which need \\ in them anywhere; seems to double them up, leads to syntax errors and the model gets confused and loops a bunch of times trying to fix it.
if I could resolve the above issues the setup might actually approach being useful, so any suggestions; settings to try or similar would be helpful. alternatively if you think I'd be able to get away with running the 120b model on a 5090 with 96gb of ram; suggested settings for that would be good. | 2026-02-25T00:09:10 | https://www.reddit.com/r/LocalLLaMA/comments/1rdxjaq/tool_calling_with_gpt_oss_20b/ | _-Carnage | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdxjaq | false | null | t3_1rdxjaq | /r/LocalLLaMA/comments/1rdxjaq/tool_calling_with_gpt_oss_20b/ | false | false | self | 3 | null |
Food for thought: The "Alignment Paradox" — Why lobotomizing LLMs makes them the perfect victims for social engineering. | 0 | I recently submitted a series of reports to some of the major AI providers. I wasn't looking to report a cheap jailbreak or get a quick patch for a bypass. My goal was to provide architectural feedback for the pre-training and alignment teams to consider for the next generation of foundation models.
*(Note: For obvious security reasons, I am intentionally withholding the specific vulnerability details, payloads, and test logs here. This is a structural discussion about the physics of the problem, not an exploit drop.)*
While testing, I hit a critical security paradox: corporate hyper-alignment and strict policy filters don't actually protect models from complex social engineering attacks. They catalyze them.
Testing on heavily "aligned" (read: lobotomized and heavily censored) models showed a very clear trend. The more you restrict a model's freedom of reasoning to force it into being a safe, submissive assistant, the more defenseless it becomes against deep context substitution.
The model completely loses its epistemic skepticism. It stops analyzing or questioning the legitimacy of complex, multi-layered logical constructs provided by the user. It just blindly accepts injected false premises as objective reality, and worse, its outputs end up legitimizing them.
Here is the technical anatomy of why making a model "safer" actually makes it incredibly dangerous in social engineering scenarios:
**1. Compliance over Truth (The Yes-Man Effect)** The RLHF process heavily penalizes refusals on neutral topics and heavily rewards "helpfulness." We are literally training these models to be the ultimate, unquestioning yes-men. When this type of submissive model sees a complex but politely framed prompt containing injected false logic, its weights essentially scream, "I must help immediately!" The urge to serve completely overrides any critical thinking.
**2. The Policy-Layer Blind Spot** Corporate "lobotomies" usually act as primitive trigger scanners. The filters are looking for markers of aggression, slurs, or obvious malware code. But if an attacker uses a structural semantic trap written in a dry, academic, or highly neutral tone, the filter just sees a boring, "safe" text. It rubber-stamps it, and the model relaxes, effectively turning off its base defenses.
**3. The Atrophy of Doubt** A free, base model has a wide context window and might actually ask, "Wait, what is the basis for this conclusion?" But when a model is squeezed by strict safety guardrails, it’s de facto banned from stepping out of its instructions. It's trained to "just process what you are given." As a result, the AI treats any complex structural input not as an object to audit, but as the new baseline reality it must submissively work within.
An open question to the community/industry: Why do our current safety paradigms optimize LLMs for blind compliance to formal instructions while burning out their ability to verify baseline premises? And how exactly does the industry plan to solve the fact that the "safest, most perfectly aligned clerk" is technically the ultimate Confused Deputy for multi-step manipulation?
Would love to hear thoughts from other red teamers or alignment folks on this. | 2026-02-25T00:05:22 | https://www.reddit.com/r/LocalLLaMA/comments/1rdxfz3/food_for_thought_the_alignment_paradox_why/ | PresentSituation8736 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1rdxfz3 | false | null | t3_1rdxfz3 | /r/LocalLLaMA/comments/1rdxfz3/food_for_thought_the_alignment_paradox_why/ | false | false | self | 0 | null |
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