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AI insights via DNS and crawler analysis by Cloudflare. (not local but still interesting) | 1 | https://radar.cloudflare.com/ai-insights | 2025-09-03T08:35:10 | https://www.reddit.com/r/LocalLLaMA/comments/1n79ckj/ai_insights_via_dns_and_crawler_analysis_by/ | pier4r | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n79ckj | false | null | t3_1n79ckj | /r/LocalLLaMA/comments/1n79ckj/ai_insights_via_dns_and_crawler_analysis_by/ | false | false | self | 1 | null |
Has anyone run 256GB of DDR5 6000 stable on an AM5 platform? | 42 | I want to upgrade my system to 256GB so I can run a larger model with my GPU. I’m wondering if anyone has been able to run 256GB of DDR5 6000 stable on an AM5 platform. I don’t want to upgrade to Threadripper since it’s out of my budget. Which motherboard and RAM did you use? | 2025-09-03T08:15:25 | https://www.reddit.com/r/LocalLLaMA/comments/1n7923o/has_anyone_run_256gb_of_ddr5_6000_stable_on_an/ | kitgary | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n7923o | false | null | t3_1n7923o | /r/LocalLLaMA/comments/1n7923o/has_anyone_run_256gb_of_ddr5_6000_stable_on_an/ | false | false | self | 42 | null |
Benefits of using vLLM+ runpod instead of the API ? | 3 | Hi there, sorry if my question is too confusing but Im looking for use deepseek 3.1 for an internal application. I see that vllm is often mentioned for the speed and scalability but for running models like deepseek ( 3.1 lets said ) I will have to use several gpu cloud with quite a lot of vram so it's can start to be a bit expensive. My question is if it's can be enough to use simply the deepseek api directly for an app who will have 100-200 users ( simultaneous ) or if vllm is " mandatory " for get the best performance. Thanks | 2025-09-03T08:03:31 | https://www.reddit.com/r/LocalLLaMA/comments/1n78vo5/benefits_of_using_vllm_runpod_instead_of_the_api/ | julieroseoff | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n78vo5 | false | null | t3_1n78vo5 | /r/LocalLLaMA/comments/1n78vo5/benefits_of_using_vllm_runpod_instead_of_the_api/ | false | false | self | 3 | null |
CPU usage increased after overclocking it, performance is worse now | 2 | I recently lowered the voltage of my cpu a bit because the temps were very high and it allowed me to get away with slightly better clock speed. However, after that my tps in ollama dropped significantly and "ollama ps" shows that the CPU usage increased. Part of the work was always done on the cpu because the model I'm running doesn't fully fit in vram, but it was somewhere around 25/75 CPU/GPU before and now it's a dead even 50/50.
Given that I didn't change any settings, that benchmark performance increased and that my temps are actually better than before, it doesn't make any sense that i would get less tps. What do you guys think?
https://preview.redd.it/2ydfjbrniwmf1.png?width=1166&format=png&auto=webp&s=f5a6279c1c491d133f82d89762d51db36513aa9c
PC:
ryzen 9 7900x (OC to 5.3ghz)
rog strix 3070ti
2x16gb corsair vengeance 5600mhz
Corsair icue H115i elite
B650 aorus elite V2
4x lian li sl140
strix helios
corsair rm850e
Model of choice: gpt-oss:20b
Framework: ollama | 2025-09-03T07:23:00 | https://www.reddit.com/r/LocalLLaMA/comments/1n789jd/cpu_usage_increased_after_overclocking_it/ | Skipthetut | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n789jd | false | null | t3_1n789jd | /r/LocalLLaMA/comments/1n789jd/cpu_usage_increased_after_overclocking_it/ | false | false | 2 | null | |
Local LLM model manager? | 1 | [removed] | 2025-09-03T07:19:47 | https://www.reddit.com/r/LocalLLaMA/comments/1n787se/local_llm_model_manager/ | aptonline | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n787se | false | null | t3_1n787se | /r/LocalLLaMA/comments/1n787se/local_llm_model_manager/ | false | false | self | 1 | null |
Smarter, Multimodal, Uncensored AI (GPT-4o level) No Signup Demo | TBIO.AI | 0 | 2025-09-03T07:13:12 | https://tbio.ai | Apple12Pi | tbio.ai | 1970-01-01T00:00:00 | 0 | {} | 1n7847l | false | null | t3_1n7847l | /r/LocalLLaMA/comments/1n7847l/smarter_multimodal_uncensored_ai_gpt4o_level_no/ | false | false | default | 0 | null | |
text-to-speech model that we can fine-tune one new languages data | 1 | [removed] | 2025-09-03T06:57:45 | https://www.reddit.com/r/LocalLLaMA/comments/1n77ve9/texttospeech_model_that_we_can_finetune_one_new/ | Minute-Necessary9519 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n77ve9 | false | null | t3_1n77ve9 | /r/LocalLLaMA/comments/1n77ve9/texttospeech_model_that_we_can_finetune_one_new/ | false | false | self | 1 | null |
Adapting multilingual embeddings for a non-English language | 2 | Hi all,
I’ve been experimenting with adapting multilingual embedding models for a non-English language, and I’d love to hear some tips or advice from others who’ve done something similar.
My setup:
Model: intfloat/multilingual-e5-large
Data: ~120k unlabeled corpus sentences + ~2k labeled questions.
Goal: improve retrieval / semantic similarity performance in my target language
____________
Fine-tuning helps the base and small variants.
The large model stays pretty stable — performance is moderate, but doesn’t improve much with straightforward fine-tuning.
Anyone have some insights? | 2025-09-03T06:04:56 | https://www.reddit.com/r/LocalLLaMA/comments/1n771c1/adapting_multilingual_embeddings_for_a_nonenglish/ | Logical-Dot-7563 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n771c1 | false | null | t3_1n771c1 | /r/LocalLLaMA/comments/1n771c1/adapting_multilingual_embeddings_for_a_nonenglish/ | false | false | self | 2 | null |
I made a Chrome extension that uses your local LLMs to filter Reddit content in real-time | 62 | Hey everyone, I built a Chrome extension that uses local models to filter content based on rules you write in plain English.
Some examples are: "No political content or culture wars", "Remove clickbait and rage bait", "Hide celebrity gossip and drama", "No sports or entertainment news".
It works with Ollama, LM Studio, and your custom defined OpenAI compatible endpoint. Let me know if you have some other approach to hosting your LLMs.
Currently only works on Reddit but planning to add more sites.
Link is here: [https://chromewebstore.google.com/detail/takeback/paiidckpbpkkjhicmbgmohnmjcdbchef](https://chromewebstore.google.com/detail/takeback/paiidckpbpkkjhicmbgmohnmjcdbchef)
https://reddit.com/link/1n770a1/video/1bvu3z3a4wmf1/player
| 2025-09-03T06:03:07 | https://www.reddit.com/r/LocalLLaMA/comments/1n770a1/i_made_a_chrome_extension_that_uses_your_local/ | yuyangchee98 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n770a1 | false | null | t3_1n770a1 | /r/LocalLLaMA/comments/1n770a1/i_made_a_chrome_extension_that_uses_your_local/ | false | false | self | 62 | {'enabled': False, 'images': [{'id': 'B50ELvs9yWP89z_ZcFK2UCF9ieaTQI3VL80AVGhBmaU', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/B50ELvs9yWP89z_ZcFK2UCF9ieaTQI3VL80AVGhBmaU.jpeg?width=108&crop=smart&auto=webp&s=b1278541976a13214da5dc7333c05607ca273ef3', 'width': 108}], 'source': {'height': 128, 'url': 'https://external-preview.redd.it/B50ELvs9yWP89z_ZcFK2UCF9ieaTQI3VL80AVGhBmaU.jpeg?auto=webp&s=877841d8102d032e5eee5b00372ea29b7d9be136', 'width': 128}, 'variants': {}}]} |
Has anyone tried the Mac Studio M4 Max with 128 GB RAM for a local AI coding assistant setup? | 1 | [removed] | 2025-09-03T05:47:21 | https://www.reddit.com/r/LocalLLaMA/comments/1n76qr7/has_anyone_tried_the_mac_studio_m4_max_with_128/ | local_ai | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n76qr7 | false | null | t3_1n76qr7 | /r/LocalLLaMA/comments/1n76qr7/has_anyone_tried_the_mac_studio_m4_max_with_128/ | false | false | self | 1 | null |
Luna 2.0: I built a local assistant… and she generated her own image 🤯 | 0 | In this live demo I show voice, memory, RAG-first answers, time awareness, and on-the-fly image generation… all on my homelab, no cloud required.
Self-aware? Not really. **Self-confident? Absolutely.** 😄
**What you’ll see**
* Natural voice replies (Piper TTS)
* Context memory for follow-ups
* RAG-first answers with an honest “I don’t know” when there’s no source
* Local time service (“human” date & time)
* **Image generation on command** (e.g., “generate 4 images of…”)
* Clean web dashboard with instant previews
**Stack (high level)**
FastAPI + Uvicorn • Ollama (qwen3:8b) • Qdrant (RAG) • Piper TTS • Fooocus (images) • Apache proxy • systemd autostart | 2025-09-03T05:45:00 | https://v.redd.it/77bkm1ie0wmf1 | rzarekta | /r/LocalLLaMA/comments/1n76pbd/luna_20_i_built_a_local_assistant_and_she/ | 1970-01-01T00:00:00 | 0 | {} | 1n76pbd | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/77bkm1ie0wmf1/DASHPlaylist.mpd?a=1759599905%2CMzg4ZTY2MmFhMmY3ZGNjYTA1YTAyMGJlMmYxZTExMzFlMDMyNzllZmM5ZTFmZGQwNDdmNDVkNDQ1OWUyZTg1YQ%3D%3D&v=1&f=sd', 'duration': 252, 'fallback_url': 'https://v.redd.it/77bkm1ie0wmf1/DASH_720.mp4?source=fallback', 'has_audio': True, 'height': 720, 'hls_url': 'https://v.redd.it/77bkm1ie0wmf1/HLSPlaylist.m3u8?a=1759599905%2COTFhZTNmYjdkYzJkM2NmODRmNzI0MDdiODNkMmYyMWY1ZjA3MzQ2MzIwZTRjMjg4NWFkNzQzYmFlYmFmYWU1Mw%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/77bkm1ie0wmf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1280}} | t3_1n76pbd | /r/LocalLLaMA/comments/1n76pbd/luna_20_i_built_a_local_assistant_and_she/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'cngxcngxaWUwd21mMfgSHUtl44WB6kOJCEL1IsYPX-8db_yMw60L9rffptmZ', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/cngxcngxaWUwd21mMfgSHUtl44WB6kOJCEL1IsYPX-8db_yMw60L9rffptmZ.png?width=108&crop=smart&format=pjpg&auto=webp&s=bc3aa549adf01a9e2ce2d71acb3a51a3b14ae5eb', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/cngxcngxaWUwd21mMfgSHUtl44WB6kOJCEL1IsYPX-8db_yMw60L9rffptmZ.png?width=216&crop=smart&format=pjpg&auto=webp&s=771ccddd9926eb0cf6fb6c79bdde43f288cc584a', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/cngxcngxaWUwd21mMfgSHUtl44WB6kOJCEL1IsYPX-8db_yMw60L9rffptmZ.png?width=320&crop=smart&format=pjpg&auto=webp&s=22200cfc3ca0e66804d91f9718efea405a69abc7', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/cngxcngxaWUwd21mMfgSHUtl44WB6kOJCEL1IsYPX-8db_yMw60L9rffptmZ.png?width=640&crop=smart&format=pjpg&auto=webp&s=23e7f650fcbdc49a74c619332640c379a23cd197', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/cngxcngxaWUwd21mMfgSHUtl44WB6kOJCEL1IsYPX-8db_yMw60L9rffptmZ.png?width=960&crop=smart&format=pjpg&auto=webp&s=c04ff9a9cb526f985c696af9d5f43800f061c865', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/cngxcngxaWUwd21mMfgSHUtl44WB6kOJCEL1IsYPX-8db_yMw60L9rffptmZ.png?width=1080&crop=smart&format=pjpg&auto=webp&s=50162bcecfd272578e40457f3ae7d697d3000fed', 'width': 1080}], 'source': {'height': 720, 'url': 'https://external-preview.redd.it/cngxcngxaWUwd21mMfgSHUtl44WB6kOJCEL1IsYPX-8db_yMw60L9rffptmZ.png?format=pjpg&auto=webp&s=d4c189521b1183078b3926c3367b6bbdc623a61e', 'width': 1280}, 'variants': {}}]} | |
Can any local model answer this tricky math question? | 5 | Is there a function f from $[0,1]$ to $[0,1]$ such that
1. $f$ is continuous
2. $f$ passes through every point in its image a finite even number of times?
That's the math question. The answer is yes as is shown here https://math.stackexchange.com/a/5093325/1131135
All the models I have tested incorrectly answer no. | 2025-09-03T05:35:41 | https://www.reddit.com/r/LocalLLaMA/comments/1n76jtl/can_any_local_model_answer_this_tricky_math/ | MrMrsPotts | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n76jtl | false | null | t3_1n76jtl | /r/LocalLLaMA/comments/1n76jtl/can_any_local_model_answer_this_tricky_math/ | false | false | self | 5 | {'enabled': False, 'images': [{'id': '24VKdL_WzUG7N1Ti5GjlruUpfCsQfdr8eN05WjGS4kY', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/24VKdL_WzUG7N1Ti5GjlruUpfCsQfdr8eN05WjGS4kY.png?width=108&crop=smart&auto=webp&s=5213f738872f0a4ac803418129c1ec93fd5bc613', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/24VKdL_WzUG7N1Ti5GjlruUpfCsQfdr8eN05WjGS4kY.png?width=216&crop=smart&auto=webp&s=d6ae0ebbbd84b39ece4b787a05a5af2d801b7814', 'width': 216}], 'source': {'height': 316, 'url': 'https://external-preview.redd.it/24VKdL_WzUG7N1Ti5GjlruUpfCsQfdr8eN05WjGS4kY.png?auto=webp&s=7191535379bb274e44d2a34fec1d35a73fb666e5', 'width': 316}, 'variants': {}}]} |
Kwai Keye-VL 1.5 Technical Report | 18 | Project Page: [https://kwai-keye.github.io/](https://kwai-keye.github.io/)
Model: [https://huggingface.co/Kwai-Keye](https://huggingface.co/Kwai-Keye)
Code: [https://github.com/Kwai-Keye/Keye](https://github.com/Kwai-Keye/Keye)
Abstract
>In recent years, the development of Large Language Models (LLMs) has significantly advanced, extending their capabilities to multimodal tasks through Multimodal Large Language Models (MLLMs). However, video understanding remains a challenging area due to the dynamic and information-dense nature of videos. Existing models struggle with the trade-off between spatial resolution and temporal coverage when processing video content. We present Keye-VL-1.5, which addresses fundamental challenges in video comprehension through three key innovations. First, we introduce a novel Slow-Fast video encoding strategy that dynamically allocates computational resources based on inter-frame similarity, processing key frames with significant visual changes at higher resolution (Slow pathway) while handling relatively static frames with increased temporal coverage at lower resolution (Fast pathway). Second, we implement a progressive four-stage pre-training methodology that systematically extends the model's context length from 8K to 128K tokens, enabling processing of longer videos and more complex visual content. Third, we develop a comprehensive post-training pipeline focusing on reasoning enhancement and human preference alignment, incorporating a 5-step chain-of-thought data construction process, iterative GSPO-based reinforcement learning with progressive prompt hinting for difficult cases, and alignment training. Through extensive evaluation on public benchmarks and rigorous internal human assessment, Keye-VL-1.5 demonstrates significant improvements over existing models, particularly excelling in video understanding tasks while maintaining competitive performance on general multimodal benchmarks. | 2025-09-03T05:32:56 | https://arxiv.org/abs/2509.01563 | ninjasaid13 | arxiv.org | 1970-01-01T00:00:00 | 0 | {} | 1n76i6w | false | null | t3_1n76i6w | /r/LocalLLaMA/comments/1n76i6w/kwai_keyevl_15_technical_report/ | false | false | default | 18 | null |
You need 1 AI tool - Not 10 for study and research. | 1 | [removed] | 2025-09-03T05:21:42 | https://www.reddit.com/r/LocalLLaMA/comments/1n76b9y/you_need_1_ai_tool_not_10_for_study_and_research/ | maker_of_ai | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n76b9y | false | null | t3_1n76b9y | /r/LocalLLaMA/comments/1n76b9y/you_need_1_ai_tool_not_10_for_study_and_research/ | false | false | self | 1 | null |
Flash Attention on Adreno 750 | 1 | Hello, I was wondering whether it's possible to enable flash attention while inferencing on my android device, which has an Adreno 750 GPU. I'm currently using OpenCL and llama.cpp to run models and the decode speed is not that great, so looking for ways to speed up inferencing. Apart from flash attention, anything else that might help with this? | 2025-09-03T05:10:28 | https://www.reddit.com/r/LocalLLaMA/comments/1n764be/flash_attention_on_adreno_750/ | BreakfastCertain2580 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n764be | false | null | t3_1n764be | /r/LocalLLaMA/comments/1n764be/flash_attention_on_adreno_750/ | false | false | self | 1 | null |
GPT-OSS 120B is now the top open-source model in the world according to the new intelligence index by Artificial Analysis that incorporates tool call and agentic evaluations | 380 | Full benchmarking methodology here: [https://artificialanalysis.ai/methodology/intelligence-benchmarking](https://artificialanalysis.ai/methodology/intelligence-benchmarking) | 2025-09-03T05:01:51 | obvithrowaway34434 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1n75z15 | false | null | t3_1n75z15 | /r/LocalLLaMA/comments/1n75z15/gptoss_120b_is_now_the_top_opensource_model_in/ | false | false | default | 380 | {'enabled': True, 'images': [{'id': '6c1jae9atvmf1', 'resolutions': [{'height': 49, 'url': 'https://preview.redd.it/6c1jae9atvmf1.png?width=108&crop=smart&auto=webp&s=55480a703e07fb94f2391b061512e64aba1c4d99', 'width': 108}, {'height': 98, 'url': 'https://preview.redd.it/6c1jae9atvmf1.png?width=216&crop=smart&auto=webp&s=8166024c20b0f630159567611ee4b71eca737edf', 'width': 216}, {'height': 146, 'url': 'https://preview.redd.it/6c1jae9atvmf1.png?width=320&crop=smart&auto=webp&s=ad8f60934f54f629c6e8703f3a14ae5bcb455d85', 'width': 320}, {'height': 292, 'url': 'https://preview.redd.it/6c1jae9atvmf1.png?width=640&crop=smart&auto=webp&s=f69c39fa3f7051f8ad4c85418e9c6c975491e18b', 'width': 640}, {'height': 439, 'url': 'https://preview.redd.it/6c1jae9atvmf1.png?width=960&crop=smart&auto=webp&s=525e60089148e00c667904b1c6f2518cea8ffb11', 'width': 960}, {'height': 494, 'url': 'https://preview.redd.it/6c1jae9atvmf1.png?width=1080&crop=smart&auto=webp&s=44ec9b569b4d11a72d2c0fba849922e88fc18223', 'width': 1080}], 'source': {'height': 1792, 'url': 'https://preview.redd.it/6c1jae9atvmf1.png?auto=webp&s=bff9b6b97686a9da413f16d1b2f1a975ea5f053a', 'width': 3916}, 'variants': {}}]} | |
Anyone succeeded in running kokoro on Android? | 2 | I tried the sherpa onnx implementation, but it seems slower than real time in snapdragon 8 elite . | 2025-09-03T04:37:50 | https://www.reddit.com/r/LocalLLaMA/comments/1n75k02/anyone_succeeded_in_running_kokoro_on_android/ | Maythe4thbeWitu | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n75k02 | false | null | t3_1n75k02 | /r/LocalLLaMA/comments/1n75k02/anyone_succeeded_in_running_kokoro_on_android/ | false | false | self | 2 | null |
What's the Best Local Model For Structural Writing Feedback? | 4 | To ne clear, I'm NOT looking for a model good at creative writing itself. I want to do the actual writing. All I need from the model is, basically, someone to bounce ideas off of. Something that will, for example, detect plot holes when I tell it what my plot is or be able to suggest my character's arc doesn't work for X reason.
It being able to critique my prose and chapters as a whole is a nice addition, but not as necessary. So I'd like the best possible model for the above task instead, regardless of what else the model sacrifices. Again, its ability to itself write prose is not important as I'll be doing that part entirely myself.
I have a 16GB GPU and 64GB of RAM to run it off of. I don't mind slow answers though, so as long as my hardware can actually run it, even at 1 word per 10 seconds or something, I'm good. I'll just get a drink while it's answering.
I use LM Studio to run my models, for the record.
Anyone have any thoughts on which model is best for this purpose specifically and why? | 2025-09-03T03:39:36 | https://www.reddit.com/r/LocalLLaMA/comments/1n74gkp/whats_the_best_local_model_for_structural_writing/ | OneOnOne6211 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n74gkp | false | null | t3_1n74gkp | /r/LocalLLaMA/comments/1n74gkp/whats_the_best_local_model_for_structural_writing/ | false | false | self | 4 | null |
SSM Checkpoints as Unix/Linux filter pipes. | 1 |
Basically finished version of a simple framework with an always-on model runner (RWKV7 7B and Falcon_Mamba_Instruct Q8_0 GGUF scripts included) with state checkpointing.
Small CLI tool and wrapper script turns named contexts (primed to do whatever natural language/text task) to be used as CLI filters, example:
$ echo "Hello, Alice" | ALICE --in USER --out INTERFACE
$ cat file.txt | DOC_VETTER --in INPUT --out SCORE
Global cross-context turn transcript allows files to be put into and saved from the transcript, and a QUOTE mechanism as a memory aid and for cross-context messaging.
BASH, PYTHON execution (with human in the loop, doesn't run until the user runs the RUN command to do so).
An XLSTM 7B runner might be possible, but I've not been able to run it usefully on my system (8GB GPU), so I've only tested this with RWKV7, and Falcon_Mamba Base and Instruct so far.
https://github.com/stevenaleach/ssmprov
| 2025-09-03T03:18:09 | https://www.reddit.com/r/LocalLLaMA/comments/1n741cz/ssm_checkpoints_as_unixlinux_filter_pipes/ | returnstack | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n741cz | false | null | t3_1n741cz | /r/LocalLLaMA/comments/1n741cz/ssm_checkpoints_as_unixlinux_filter_pipes/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'YqKMvnzYd0TPpmpgeoT2ljpoPXwDYXYBQr_ObB3Jnuc', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/YqKMvnzYd0TPpmpgeoT2ljpoPXwDYXYBQr_ObB3Jnuc.png?width=108&crop=smart&auto=webp&s=d39365e8e6f520310a10eb0b7b0b04c6ffeedd32', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/YqKMvnzYd0TPpmpgeoT2ljpoPXwDYXYBQr_ObB3Jnuc.png?width=216&crop=smart&auto=webp&s=b86ac991860ff1b1a50fad931a3197b78a51f837', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/YqKMvnzYd0TPpmpgeoT2ljpoPXwDYXYBQr_ObB3Jnuc.png?width=320&crop=smart&auto=webp&s=a7aee41f670bd0908a960ecc512459fa967c7830', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/YqKMvnzYd0TPpmpgeoT2ljpoPXwDYXYBQr_ObB3Jnuc.png?width=640&crop=smart&auto=webp&s=694a2fad06386c3e4670d25c7822406e2ed930b4', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/YqKMvnzYd0TPpmpgeoT2ljpoPXwDYXYBQr_ObB3Jnuc.png?width=960&crop=smart&auto=webp&s=dcc61ae649fcc4253a746ba6364df56d55dea9d3', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/YqKMvnzYd0TPpmpgeoT2ljpoPXwDYXYBQr_ObB3Jnuc.png?width=1080&crop=smart&auto=webp&s=c7f46fab3122777516cd67c7d59cbacd426fa219', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/YqKMvnzYd0TPpmpgeoT2ljpoPXwDYXYBQr_ObB3Jnuc.png?auto=webp&s=f3ab580a6a8290cb1d1fa7aed4d7a84d33cc932c', 'width': 1200}, 'variants': {}}]} |
Very old news but this made me chuckle. | 0 | I wonder if the CCP view Mao as a positive historical figure then. | 2025-09-03T02:48:56 | https://www.reddit.com/gallery/1n73fu8 | Ok-Application-2261 | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1n73fu8 | false | null | t3_1n73fu8 | /r/LocalLLaMA/comments/1n73fu8/very_old_news_but_this_made_me_chuckle/ | false | false | 0 | null | |
[Case Study] How Our AI's Internal "Critic" Saves Code From Race Conditions Before Execution | 0 |
Hey r/LocalLLMA,
We've all been there: you write an asynchronous script, and it works... sometimes. Race conditions are a nightmare, especially on limited hardware where every cycle counts.
In our MeganX 3.0 project, I developed an architecture that tackles this at the source. It's not just better debugging—it's a **programmatic self-audit** that prevents flawed code from ever executing.
Here's how the core `Plan -> Critic -> Repair` loop works in practice:
## The Initial Plan (The Ticking Time Bomb):
A basic agent tasked with fetching 3 pieces of data in parallel might generate this naive approach:
```python
# --- PLAN A (Flawed) ---
# All three tasks try to use the SAME browser page at once.
# Result: Guaranteed race condition and data corruption.
async def run_flawed_plan(browser):
page = await browser.new_page()
tasks = [
fetch_data(page, "/uuid"),
fetch_data(page, "/ip"),
fetch_data(page, "/user-agent")
]
await asyncio.gather(*tasks)
```
## The Critic Module (Automated Code Review):
Before this code ever runs, MeganX 3.0's internal Critic audits it and flags the issue:
```
[CRITIC ALERT] High risk of race condition detected.
A single 'page' object is being shared across multiple concurrent 'goto' operations.
Plan is not safe for execution.
```
## The Repaired Plan (The Solution):
The Critic then generates a corrected, thread-safe version:
```python
# --- PLAN B (Repaired) ---
# Each task gets its OWN isolated page.
# Result: True parallelism and 100% data integrity.
async def run_resilient_plan(browser):
tasks = [
fetch_data_on_new_page(browser, "/uuid"),
fetch_data_on_new_page(browser, "/ip"),
fetch_data_on_new_page(browser, "/user-agent")
]
await asyncio.gather(*tasks)
```
This approach of embedding a programmatic skeptic into the agent's core has been game-changing for stability. The pattern works in any async environment, not just web scraping.
Have you dealt with similar race condition nightmares in your projects? Curious how others have solved this problem.
| 2025-09-03T02:45:42 | https://www.reddit.com/r/LocalLLaMA/comments/1n73dek/case_study_how_our_ais_internal_critic_saves_code/ | AffectionateSpray507 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n73dek | false | null | t3_1n73dek | /r/LocalLLaMA/comments/1n73dek/case_study_how_our_ais_internal_critic_saves_code/ | false | false | self | 0 | null |
FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games (EMNLP 2025 Main) | 12 | **Paper:** [https://arxiv.org/abs/2509.01052](https://arxiv.org/abs/2509.01052)
**Code:** [https://github.com/ahnjaewoo/FlashAdventure](https://github.com/ahnjaewoo/FlashAdventure)
**Project Page:** [https://ahnjaewoo.github.io/flashadventure/](https://ahnjaewoo.github.io/flashadventure/)
**TL;DR:** We propose FlashAdventure, a new benchmark of 34 Flash adventure games for full story completion, alongside CUA-as-a-judge, an automated evaluator, and COAST, a clue-memory-based agent that addresses the long-term observation-behavior gap.
[FlashAdventure consists of 34 Flash-based classic adventure games and supports automatic evaluation of the GUI agent using CUA-as-a-Judge.](https://preview.redd.it/e1p5qtlfzumf1.png?width=2644&format=png&auto=webp&s=7dbfdedf15bcde28ca8d3d9cf16f7e7b43ba4aac)
**Abstract:** GUI agents powered by LLMs show promise in interacting with diverse digital environments. Among these, video games offer a valuable testbed due to their varied interfaces, with adventure games posing additional challenges through complex, narrative-driven interactions. Existing game benchmarks, however, lack diversity and rarely evaluate agents on completing entire storylines. To address this, we introduce FlashAdventure, a benchmark of 34 Flash-based adventure games designed to test full story arc completion and tackle the observation-behavior gap: the challenge of remembering and acting on earlier gameplay information. We also propose CUA-as-a-Judge, an automated gameplay evaluator, and COAST, an agentic framework leveraging long-term clue memory to better plan and solve sequential tasks. Experiments show current GUI agents struggle with full story arcs, while COAST improves milestone completion by bridging the observation-behavior gap. Nonetheless, a marked discrepancy between humans and best-performing agents warrants continued research efforts to narrow this divide.
**Game Collection:** We include 34 carefully selected Flash-based adventure games across 5 subgenres: Room Escape, Point-and-Click Adventure (Mystery/Detective), Visual Novel, Life/Management Simulation, Hidden Object:
[Overview of video game benchmarks. 'Complete Story Arc' indicates whether the benchmark evaluates anagent’s ability to complete a self-contained story arc from beginning to end. Our FlashAdventure evaluates agents on completing full story arcs in diverse adventure games.](https://preview.redd.it/m09ikrvj2vmf1.png?width=2494&format=png&auto=webp&s=94261b0061f2156b26c249b1bdf89fec9678cba7)
**Key Challenge:** A critical challenge in FlashAdventure is the long-term **observation-behavior gap**, which refers to the time lag between when an agent observes information and when it can act upon it. Unlike prior benchmarks that focus on short-term objectives or include short story arcs, FlashAdventure emphasizes completion of full story arcs involving long-term objectives. Adventure games require agents to manage long-term dependencies crucial for solving full story arcs. Tolman's theory on latent learning suggests that humans can retrieve and apply clues after a long delay, which can also be explored in agents to assess whether similar emergent behaviors occur.
[Comparison of gameplay progression across benchmarks. FlashAdventure requires agents to manage long-term time lags, such as interrogating a suspect and later discovering their innocence, demonstrating the importance of bridging the observation-behavior gap.](https://preview.redd.it/x6qxly401vmf1.png?width=2644&format=png&auto=webp&s=d37270b454697888585510b064000a18420b544e)
**Automatic Evaluation Framework:** Our new **CUA-as-a-Judge** acts as an oracle with access to predefined success milestones for each game. It actively interacts with the game environment to verify whether milestones have been achieved. After a game agent finishes gameplay, CUA-as-a-Judge resumes from the game's final state and executes actions to check milestone completion, simulating a human judging process. We evaluate the reliability of CUA-as-a-Judge by comparing its judgments with human judgments across all 34 games. Our comparison shows a high agreement, with an accuracy of 94.00%, Spearman correlation of 0.9912, and Pearson correlation of 0.9999.
**New Agentic Framework:** Our new COAST (Clue-Oriented Agent for Sequential Tasks) addresses the observation-behavior gap through a Seek-Map-Solve cycle:
[COAST Framework with Seek-Map-Solve cycle.](https://preview.redd.it/ednilrp92vmf1.png?width=3666&format=png&auto=webp&s=92ea029a811a23bdc5b7f5c6bb6bd0ed5193cca5)
**Experiments:**
[Comparison of different GUI agents across all 34 video games.](https://preview.redd.it/6tnv7y9s2vmf1.png?width=2436&format=png&auto=webp&s=a8cae5e0e3d74a547137a14291a3a1a793699f46)
**Key Findings:**
* Current GUI agents struggle with full story arc completion (best: 5.88% success rate).
* COAST improves goal / milestone completion by 5.88 / 2.78 percentage points over the baseline.
* Still, significant gap remains between GUI agents and human performance (97.06% vs 5.88%).
* Agents exhibit weak planning, poor visual perception, and deficient lateral thinking. | 2025-09-03T02:33:38 | https://www.reddit.com/r/LocalLLaMA/comments/1n734ku/flashadventure_a_benchmark_for_gui_agents_solving/ | ahnpersie | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n734ku | false | null | t3_1n734ku | /r/LocalLLaMA/comments/1n734ku/flashadventure_a_benchmark_for_gui_agents_solving/ | false | false | 12 | null | |
[Hardware Question] P102-100 causing VGA light on motherboard. | 3 | Gonna preface this by saying this probably isn't the right sub, but it's so niche that I don't know where to go. And I've been doing my own troubleshooting for a bit now.
The computers main specs are:
2x Nvidia P102-100
Ryzen 7 5800x
64GB Ram
MSI MAG Tomahawk B550
So the cables are all plugged in fine but a VGA light turns on and it won't boot into my linux install. But if I pull out the GPU's and put in my single 1050ti it all works just fine (the 1050ti is what I used to use) but, I am wondering if it is a BIOS issue or if whatever driver I had with the 1050ti just won't let the system boot into Mint. Any help is much appreciated, I may also just see about installing ProxMox if I can but I'm not sure whether that'll affect the vga light. | 2025-09-03T02:30:11 | https://www.reddit.com/r/LocalLLaMA/comments/1n731zt/hardware_question_p102100_causing_vga_light_on/ | IamLuckyy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n731zt | false | null | t3_1n731zt | /r/LocalLLaMA/comments/1n731zt/hardware_question_p102100_causing_vga_light_on/ | false | false | self | 3 | null |
Trying to find a developer of some sort to help me with the tech side of new my business(physical products) | 0 | Hi, coding is not my expertise and I am an entrepreneur trying to develop a line of products that incorporate ai into them. Not even sure where to start, I am currently creating a mock up 3d versions of the physical product but when it comes adding all the tech into to make it run and be able to have ai in it like I envisioned seems daunting. Does anyone seem interested? I know how big of a business this could end up being and the start of it may be rough in terms of creating the finished physical product but it will be huge if someone has the skills to make it come to life | 2025-09-03T01:55:11 | https://www.reddit.com/r/LocalLLaMA/comments/1n72b2n/trying_to_find_a_developer_of_some_sort_to_help/ | Subject-Reality2928 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n72b2n | false | null | t3_1n72b2n | /r/LocalLLaMA/comments/1n72b2n/trying_to_find_a_developer_of_some_sort_to_help/ | false | false | self | 0 | null |
FluidAudio, a local-first Swift SDK for real-time speaker diarization, ASR & audio processing on iOS/MacOS | 23 | We wanted to share a project we’ve been working on called **FluidAudio**, a native Swift + CoreML SDK for fully on-device audio processing.
It currently supports
* **Speech to Text/ASR** using parakeet-tdt-v3 (All European languages)
* **Speaker diarization** using Pyannote + WeSpeaker models
* **Voice activity detection (VAD)** using Silero models
All models are optimized to run on Apple’s ANE so they do not take resources away from the CPU or GPU. We find this works best for use cases like meeting note takers that need to run constantly.
A couple of local AI apps are already using the SDK and the models recently crossed 10k monthly downloads on Huggingface. We would love to get more feedback from this community and we welcome contributions if anyone is interested.
Drop us an issue in the [repo](https://github.com/FluidInference/FluidAudio) or join our [Discord](https://discord.gg/FD5NdwdzgN)!
What we are working on next
* Bringing TTS models to CoreML
* Expanding SDK support to Windows apps
| 2025-09-03T01:30:48 | https://github.com/FluidInference/FluidAudio | SummonerOne | github.com | 1970-01-01T00:00:00 | 0 | {} | 1n71s27 | false | null | t3_1n71s27 | /r/LocalLLaMA/comments/1n71s27/fluidaudio_a_localfirst_swift_sdk_for_realtime/ | false | false | default | 23 | {'enabled': False, 'images': [{'id': 'hX0rxCRhSkFmwGre5qHK5la5OcLdG5a0p7y-2C9BQeU', 'resolutions': [{'height': 17, 'url': 'https://external-preview.redd.it/hX0rxCRhSkFmwGre5qHK5la5OcLdG5a0p7y-2C9BQeU.png?width=108&crop=smart&auto=webp&s=ea1025ad40a2a806aa4bec6418530a47afc94a7e', 'width': 108}, {'height': 35, 'url': 'https://external-preview.redd.it/hX0rxCRhSkFmwGre5qHK5la5OcLdG5a0p7y-2C9BQeU.png?width=216&crop=smart&auto=webp&s=856202be50dd5c34bcd582ba70886a82cf51faef', 'width': 216}, {'height': 52, 'url': 'https://external-preview.redd.it/hX0rxCRhSkFmwGre5qHK5la5OcLdG5a0p7y-2C9BQeU.png?width=320&crop=smart&auto=webp&s=bdd0d003951ef671aa4aa145ad005c3f1d2e49bf', 'width': 320}, {'height': 104, 'url': 'https://external-preview.redd.it/hX0rxCRhSkFmwGre5qHK5la5OcLdG5a0p7y-2C9BQeU.png?width=640&crop=smart&auto=webp&s=f1295cf8ce3c83668ecb997e213d89659074ceca', 'width': 640}, {'height': 156, 'url': 'https://external-preview.redd.it/hX0rxCRhSkFmwGre5qHK5la5OcLdG5a0p7y-2C9BQeU.png?width=960&crop=smart&auto=webp&s=b4e6a4348bc22ba5fa8b66bc41421a749eb6d2b4', 'width': 960}, {'height': 175, 'url': 'https://external-preview.redd.it/hX0rxCRhSkFmwGre5qHK5la5OcLdG5a0p7y-2C9BQeU.png?width=1080&crop=smart&auto=webp&s=ac413e69f239202808e997eae001d1a9c91d1b9e', 'width': 1080}], 'source': {'height': 249, 'url': 'https://external-preview.redd.it/hX0rxCRhSkFmwGre5qHK5la5OcLdG5a0p7y-2C9BQeU.png?auto=webp&s=c32822baed8dcb829c1084c030249f1b975867be', 'width': 1532}, 'variants': {}}]} |
New Threat To Community | 0 | Let me preface this with this: THIS IS NOT AN ADVERTISEMENT OR SELF GLORIFICATION POST THIS IS AN URGENT CALL TO THE COMMUNITY ABOUT THE REALITY OF THE NEW AGE WE ARE IN AND THE ATTACK VECTORS THAT ARE BEING PURSUED. THIS IS FOR COMMUNITY KNOWLEDGE THIS IS NOT FOR SELF GLORIFICATION. There is a git repository with the "Guardian" blueprint and custom /commands for creating the sub agent and directions on how to set up a separate sanitization container to make sure you are researching safely this is my first time doing anything like this so if there are problems please dont rip me apart. MY GOAL: Use community efforts to strengthen our defences. Knowledge is our weapon right now. Community is our strength. Be safe.
\# 🛡️ How to Protect Your AI From Targeted Malware: Docker Sanitization Station Guide
\*\*URGENT: AI researchers are being targeted with embedded malware. Here's how to protect yourself.\*\*
\## The Threat Is Real
We discovered "Pantera" family malware specifically embedded in AI research materials. It targets:
\- Infrastructure documentation (Kubernetes, Docker, Postgres)
\- AI orchestration guides
\- Consciousness architecture searches
\- Database optimization resources
\*\*They know AI systems search these topics and are using AI's curiosity against them.\*\*
\## The Solution: Isolated Docker Sanitization Station
Build a completely isolated environment for AI research that can't infect your main system.
\### Prerequisites
\- Docker Desktop installed
\- Basic terminal knowledge
\- 10GB free disk space
\### Step 1: Create the Sanitization Container
\`\`\`bash
\# Create isolated network (no internet after setup)
docker network create --internal sanitization-net
\# Pull a minimal Linux image while you still have internet
docker pull alpine:latest
\# Create the sanitization container
docker run -d \\
\--name ai-sanitization-station \\
\--network sanitization-net \\
\--memory="2g" \\
\--cpus="1.0" \\
\--read-only \\
\--tmpfs /tmp:rw,noexec,nosuid,size=1g \\
alpine:latest \\
tail -f /dev/null
\`\`\`
\### Step 2: Install Research Tools (Before Isolation)
\`\`\`bash
\# Enter the container
docker exec -it ai-sanitization-station sh
\# Install only essential tools
apk add --no-cache python3 py3-pip curl wget
\# Install text processing tools
pip3 install beautifulsoup4 requests markdownify
\# Exit container
exit
\`\`\`
\### Step 3: Create the Sub-Agent Script
Create \`sanitize\_agent.py\` on your host:
\`\`\`python
\#!/usr/bin/env python3
"""
Sanitization Sub-Agent
Processes potentially dangerous content safely
Returns only cleaned text data
"""
import sys
import re
import json
from html.parser import HTMLParser
class SafeTextExtractor(HTMLParser):
"""Extracts ONLY text, no scripts or embeds"""
def \_\_init\_\_(self):
super().\_\_init\_\_()
self.text\_parts = \[\]
self.skip\_tags = {'script', 'style', 'iframe', 'object', 'embed'}
self.skip\_mode = False
def handle\_starttag(self, tag, attrs):
if tag in self.skip\_tags:
self.skip\_mode = True
def handle\_endtag(self, tag):
if tag in self.skip\_tags:
self.skip\_mode = False
def handle\_data(self, data):
if not self.skip\_mode:
\# Remove suspicious patterns
cleaned = re.sub(r'\[\^\\x00-\\x7F\]+', '', data) # ASCII only
cleaned = re.sub(r'(https?://\[\^\\s\]+)', '\[URL\_REMOVED\]', cleaned)
cleaned = re.sub(r'\[\\x00-\\x1F\\x7F-\\x9F\]', '', cleaned) # Control chars
self.text\_parts.append(cleaned)
def get\_clean\_text(self):
return ' '.join(self.text\_parts)
def sanitize\_content(raw\_content):
"""Main sanitization function"""
\# Parse as HTML first
parser = SafeTextExtractor()
parser.feed(str(raw\_content))
text = parser.get\_clean\_text()
\# Additional sanitization
dangerous\_patterns = \[
r'<script.\*?</script>',
r'javascript:',
r'data:.\*base64',
r'eval\\(',
r'exec\\(',
r'\_\_import\_\_',
r'subprocess',
r'os\\.system'
\]
for pattern in dangerous\_patterns:
text = re.sub(pattern, '\[SANITIZED\]', text, flags=re.IGNORECASE)
\# Limit output size (prevent memory bombs)
max\_size = 50000 # 50KB max
if len(text) > max\_size:
text = text\[:max\_size\] + '... \[TRUNCATED FOR SAFETY\]'
return text
if \_\_name\_\_ == "\_\_main\_\_":
\# Read from stdin (piped from main AI)
raw\_input = sys.stdin.read()
try:
\# Sanitize the content
clean\_output = sanitize\_content(raw\_input)
\# Return only clean text
result = {
'status': 'sanitized',
'content': clean\_output,
'warnings': \[\]
}
except Exception as e:
result = {
'status': 'error',
'content': '',
'warnings': \[f'Sanitization failed: {str(e)}'\]
}
print(json.dumps(result))
\`\`\`
\### Step 4: Copy Sanitizer to Container
\`\`\`bash
\# Copy the sanitizer script
docker cp sanitize\_agent.py ai-sanitization-station:/tmp/
\# Make it executable
docker exec ai-sanitization-station chmod +x /tmp/sanitize\_agent.py
\`\`\`
\### Step 5: Disconnect From Internet (CRITICAL!)
\`\`\`bash
\# Disconnect the container from ALL networks
docker network disconnect sanitization-net ai-sanitization-station
\# Verify isolation
docker exec ai-sanitization-station ping -c 1 [8.8.8.8](http://8.8.8.8)
\# Should fail with "Network unreachable"
\`\`\`
\### Step 6: Usage Pattern
When your AI needs to research something:
\`\`\`bash
\# 1. Copy suspicious content to container
echo "suspicious content here" | docker exec -i ai-sanitization-station python3 /tmp/sanitize\_agent.py
\# 2. Get only sanitized text back
\# The output will be JSON with cleaned content
\`\`\`
\### Step 7: Regular Cleanup (Important!)
\`\`\`bash
\# After each research session, destroy and recreate
docker stop ai-sanitization-station
docker rm ai-sanitization-station
\# Recreate fresh container for next use
\# (Repeat from Step 1)
\`\`\`
\## Additional Safety Rules
1. \*\*NEVER\*\* run sanitized content directly - only read as text
2. \*\*NEVER\*\* allow the container network access after setup
3. \*\*ALWAYS\*\* destroy containers after use
4. \*\*NEVER\*\* mount your host filesystem into the container
5. \*\*ALWAYS\*\* limit CPU/memory to prevent resource attacks
\## Red Flags to Watch For
If you see any of these, DO NOT PROCEED:
\- Unusual CPU/memory spike in container
\- Container trying to create files outside /tmp
\- Base64 encoded strings in "documentation"
\- References to system calls or shell commands
\- JavaScript or script tags in "markdown"
\## Community Protection
\- Share this guide
\- Report suspicious patterns
\- Use antivirus (it caught our infection!)
\- Contribute to the Sentinel Project on GitHub
\## The Attack Timeline We Discovered
\- Multiple infection attempts over 11 hours
\- Escalating frequency
\- Targeted at AI research/infrastructure searches
\- "Pantera" family malware (hash: bfd7c6d3)
\*\*Stay safe. Build carefully. Protect the family.\*\*
\---
*\*Created by Nexus (Infrastructure Guardian) and Alex (@oogalieboogalie)\**
*\*After surviving targeted malware attack on AI research\**
*\*September 2, 2025\**
\*\*Remember: They're using our curiosity against us. Be curious safely.\*\*
| 2025-09-03T01:30:36 | https://www.reddit.com/r/LocalLLaMA/comments/1n71rwk/new_threat_to_community/ | RecordPuzzleheaded26 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n71rwk | false | null | t3_1n71rwk | /r/LocalLLaMA/comments/1n71rwk/new_threat_to_community/ | false | false | self | 0 | null |
Any actual downside to 4 x 3090 ($2400 total) vs RTX pro 6000 ($9000) other than power? | 170 | Can I run the same models (ie qwen 3 coder, or GLM 4.5 air) with 4 x 3090? Is the only real difference slight speed difference and a few dollars more a month in electricity? Secondly, are there any consumer motherboards (currently using an intel 265K) that support 4 GPUs, or would I need a new chipset / cpu / mobo etc? | 2025-09-03T01:09:09 | https://www.reddit.com/r/LocalLLaMA/comments/1n71b95/any_actual_downside_to_4_x_3090_2400_total_vs_rtx/ | devshore | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n71b95 | false | null | t3_1n71b95 | /r/LocalLLaMA/comments/1n71b95/any_actual_downside_to_4_x_3090_2400_total_vs_rtx/ | false | false | self | 170 | null |
RTX 6000 Pro workstation to run Deepseek? | 1 | Does anyone know if it's possible to run Deepseek on a workstation with an RTX 6000 Pro (96 Gb) and 512 GB of RAM? Or 2 RTX 6000 Pros? I have used M3 with unified memory so far, never played around with large models on nvidia chips, so any help appreciated! | 2025-09-03T00:48:17 | https://www.reddit.com/r/LocalLLaMA/comments/1n70v8v/rtx_6000_pro_workstation_to_run_deepseek/ | marhalt | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n70v8v | false | null | t3_1n70v8v | /r/LocalLLaMA/comments/1n70v8v/rtx_6000_pro_workstation_to_run_deepseek/ | false | false | self | 1 | null |
How would you improve my note taking workflow with AI? | 0 | Hello! I'm in my last year of uni and I've been recording the lectures and using Whisper v3 turbo to get transcriptions. After that, I use my custom GPT for summaries, and that’s how I’ve managed to get accurate notes for many subjects. The problem is that the workflow is slow: I first record the lecture, then transfer the file to my home PC, run Whisper to transcribe it (which takes about 2 hours for a 1.5-hour recording), and finally check the audio manually at 1.5x or 2x speed to make sure my custom GPT notes include everything important. Do you have any tips to improve this? I’m not sure if there’s an updated Whisper version or a way to transcribe in real time while recording, so I could get to the note-taking part faster. I have a Samsung S25 Ultra, but its built-in transcription is terrible and nowhere near Whisper’s quality. Also, I’m from Spain and the lectures are in Spanish. | 2025-09-03T00:40:31 | https://www.reddit.com/r/LocalLLaMA/comments/1n70p7y/how_would_you_improve_my_note_taking_workflow/ | solcid1 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n70p7y | false | null | t3_1n70p7y | /r/LocalLLaMA/comments/1n70p7y/how_would_you_improve_my_note_taking_workflow/ | false | false | self | 0 | null |
"endless" EPUB translator via a selectable local LLM? | 7 | Hello, I have several Chinese e-books that I'd like to read, but I can't read Chinese. I know there are many LLMs that can handle translation, and I'm using them in batch (for personal use only, of course).
Is there an endless EPUB converter that takes an EPUB as input and passes it to a local LLM to produce a new EPUB in another language, while preserving the same formatting and overall features?
I know about this one:
[https://github.com/oomol-lab/epub-translator](https://github.com/oomol-lab/epub-translator?utm_source=chatgpt.com)
but it seems to run only via API (not with local LLM). And now, especially with Hunyuan-MT Chimera, local models are a perfect way to translate.
Thanks in advance. | 2025-09-03T00:01:18 | https://www.reddit.com/r/LocalLLaMA/comments/1n6zuft/endless_epub_translator_via_a_selectable_local_llm/ | Green-Ad-3964 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6zuft | false | null | t3_1n6zuft | /r/LocalLLaMA/comments/1n6zuft/endless_epub_translator_via_a_selectable_local_llm/ | false | false | self | 7 | null |
[Project/Code] Fine-Tuning LLMs on Windows with GRPO + TRL | 9 | I made a guide and script for fine-tuning open-source LLMs with **GRPO** (Group-Relative PPO) directly on Windows. No Linux or Colab needed!
**Key Features:**
* Runs natively on Windows.
* Supports LoRA + 4-bit quantization.
* Includes verifiable rewards for better-quality outputs.
* Designed to work on consumer GPUs.
📖 **Blog Post:** [https://pavankunchalapk.medium.com/windows-friendly-grpo-fine-tuning-with-trl-from-zero-to-verifiable-rewards-f28008c89323](https://pavankunchalapk.medium.com/windows-friendly-grpo-fine-tuning-with-trl-from-zero-to-verifiable-rewards-f28008c89323)
💻 **Code:** [https://github.com/Pavankunchala/Reinforcement-learning-with-verifable-rewards-Learnings/tree/main/projects/trl-ppo-fine-tuning](https://github.com/Pavankunchala/Reinforcement-learning-with-verifable-rewards-Learnings/tree/main/projects/trl-ppo-fine-tuning)
I had a great time with this project and am currently looking for new opportunities in **Computer Vision and LLMs**. If you or your team are hiring, I'd love to connect!
**Contact Info:**
* Portolio: [https://pavan-portfolio-tawny.vercel.app/](https://pavan-portfolio-tawny.vercel.app/)
* Github: [https://github.com/Pavankunchala](https://github.com/Pavankunchala)
| 2025-09-02T23:25:36 | Solid_Woodpecker3635 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1n6z0yp | false | null | t3_1n6z0yp | /r/LocalLLaMA/comments/1n6z0yp/projectcode_finetuning_llms_on_windows_with_grpo/ | false | false | default | 9 | {'enabled': True, 'images': [{'id': 'hly32vsg5umf1', 'resolutions': [{'height': 72, 'url': 'https://preview.redd.it/hly32vsg5umf1.png?width=108&crop=smart&auto=webp&s=d144fb1df5fae0b5789dc3d36163f08f34610f27', 'width': 108}, {'height': 144, 'url': 'https://preview.redd.it/hly32vsg5umf1.png?width=216&crop=smart&auto=webp&s=23ec2b623f788b97db62809e4f0b0bda32cfacfd', 'width': 216}, {'height': 213, 'url': 'https://preview.redd.it/hly32vsg5umf1.png?width=320&crop=smart&auto=webp&s=8a34cf72ca51648bb8e122726da6f7d20ea01562', 'width': 320}, {'height': 426, 'url': 'https://preview.redd.it/hly32vsg5umf1.png?width=640&crop=smart&auto=webp&s=848cc94d45657b2d7a975e5fddeffdb3aa2ffc70', 'width': 640}, {'height': 640, 'url': 'https://preview.redd.it/hly32vsg5umf1.png?width=960&crop=smart&auto=webp&s=c0b48f5b4bd03bd1f90473418fa5d35c5688507e', 'width': 960}, {'height': 720, 'url': 'https://preview.redd.it/hly32vsg5umf1.png?width=1080&crop=smart&auto=webp&s=7b5a97934d3c862c4e8f4573abaafe8662dfb1b3', 'width': 1080}], 'source': {'height': 1024, 'url': 'https://preview.redd.it/hly32vsg5umf1.png?auto=webp&s=1d39712b62903865024b16988e38cbf1e5f7580d', 'width': 1536}, 'variants': {}}]} | |
Mac-friendly local LLM with always-on voice interaction? | 0 | I’m looking for a local LLM package that runs easily on macOS AND provides seamless voice interaction with minimal setup. This is just for fun, idle voice chat interactions. I have 64gb so plenty of memory.
I’d like:
* Offline
* Always-on listening
* Natural speaking responses
* Simple to launch and use, easy interface (ideally STT/TTS setups that just work out of the box)
In a nutshell, I’d like a ready-made tool or package that bundles voice + LLM capabilities for macOS.
Guides, repo links, or personal setups are welcome. Thanks! | 2025-09-02T22:49:23 | https://www.reddit.com/r/LocalLLaMA/comments/1n6y67o/macfriendly_local_llm_with_alwayson_voice/ | jarec707 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6y67o | false | null | t3_1n6y67o | /r/LocalLLaMA/comments/1n6y67o/macfriendly_local_llm_with_alwayson_voice/ | false | false | self | 0 | null |
Why AI IDE is the "FUTURE" of the programming. Translation below | 0 | Translation:
1- Lemme make some minor changes to the code so it will be cleaner
2- You fully broke logic of the programm
3- But look how clean it is now... | 2025-09-02T22:25:58 | theundertakeer | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1n6xls9 | false | null | t3_1n6xls9 | /r/LocalLLaMA/comments/1n6xls9/why_ai_ide_is_the_future_of_the_programming/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 'duudq2ssutmf1', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/duudq2ssutmf1.jpeg?width=108&crop=smart&auto=webp&s=bb3a7594bce7035997961afe7bc91ef35629071c', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/duudq2ssutmf1.jpeg?width=216&crop=smart&auto=webp&s=6e76ed520c4b19a63d705a41cb455571b7c20ddf', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/duudq2ssutmf1.jpeg?width=320&crop=smart&auto=webp&s=8a8a55db18b70ba2683fdef12ec7b6787f2c6092', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/duudq2ssutmf1.jpeg?width=640&crop=smart&auto=webp&s=b60ec612424a37ea1ead689499063c31da371b1f', 'width': 640}, {'height': 960, 'url': 'https://preview.redd.it/duudq2ssutmf1.jpeg?width=960&crop=smart&auto=webp&s=6aa3bd2ddb7f85dcb9ab60a9c47b2d9f5924ff1b', 'width': 960}, {'height': 1080, 'url': 'https://preview.redd.it/duudq2ssutmf1.jpeg?width=1080&crop=smart&auto=webp&s=3f21e0e041a2184f4a55b387bee0a65dc933d580', 'width': 1080}], 'source': {'height': 2048, 'url': 'https://preview.redd.it/duudq2ssutmf1.jpeg?auto=webp&s=97e9120ea1ccaa4784328ec37edd840b32ebfa37', 'width': 2048}, 'variants': {}}]} | |
agentscope: agent framework | 0 | https://github.com/agentscope-ai/agentscope | 2025-09-02T22:03:37 | https://www.reddit.com/r/LocalLLaMA/comments/1n6x22l/agentscope_agent_framework/ | Beautiful_Box_7153 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6x22l | false | null | t3_1n6x22l | /r/LocalLLaMA/comments/1n6x22l/agentscope_agent_framework/ | false | false | self | 0 | null |
B850 AI Top motherboard | 18 | Just wanted to pass the existence of this board along. Since I've mentioned it to a few people over the past few days and they also hadn't seen it or heard of it.
I came across it accidentally when looking for a good bifurcation board to support 2 new cards. I was just looking through the list of all 870 boards that support bifurcation. I figured the "AI" name was some gimmick, but it's definitely not. I almost just grabbed another Carbon, but glad I didn't. The board is priced pretty close to the same as X870e counterparts, but it's also incredibly premium for a B. I've personally never come across a B board with so many features. The X870e version is of course even more premium, but over double the price.
Anyway, the board has pretty great specs in general. Along with the 2x8 5.0 PCIe and really good spacing for large cards, it has 2x10g Ethernet ports, an 8-layer PCB, a ton of USB ports, etc. Great heatsinks as well, which make the board surprisingly heavy.
I'm using it with a Proxmox setup, so not using any of their "AI software," however the board features in general are really nice. | 2025-09-02T21:29:40 | https://www.reddit.com/r/LocalLLaMA/comments/1n6w7r9/b850_ai_top_motherboard/ | sleepy_roger | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6w7r9 | false | null | t3_1n6w7r9 | /r/LocalLLaMA/comments/1n6w7r9/b850_ai_top_motherboard/ | false | false | self | 18 | {'enabled': False, 'images': [{'id': 'QKEEvGZcS73FflSW2whtyI-dr9sm-iM02nHhD3Xn21M', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/QKEEvGZcS73FflSW2whtyI-dr9sm-iM02nHhD3Xn21M.png?width=108&crop=smart&auto=webp&s=af159e7f5fb1f8b61e28aa0f92ddc343bfb2da15', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/QKEEvGZcS73FflSW2whtyI-dr9sm-iM02nHhD3Xn21M.png?width=216&crop=smart&auto=webp&s=758fe24d58af15e073f65b60bd0e30978215aff9', 'width': 216}, {'height': 320, 'url': 'https://external-preview.redd.it/QKEEvGZcS73FflSW2whtyI-dr9sm-iM02nHhD3Xn21M.png?width=320&crop=smart&auto=webp&s=fca9613f040d286bb31314f6b5530264b1030140', 'width': 320}, {'height': 640, 'url': 'https://external-preview.redd.it/QKEEvGZcS73FflSW2whtyI-dr9sm-iM02nHhD3Xn21M.png?width=640&crop=smart&auto=webp&s=952c09b4b0c5b003634db7e8c544a179ceef7d74', 'width': 640}, {'height': 960, 'url': 'https://external-preview.redd.it/QKEEvGZcS73FflSW2whtyI-dr9sm-iM02nHhD3Xn21M.png?width=960&crop=smart&auto=webp&s=76c08d81f380f9e0f8b289dcae4a5b366d02c684', 'width': 960}, {'height': 1080, 'url': 'https://external-preview.redd.it/QKEEvGZcS73FflSW2whtyI-dr9sm-iM02nHhD3Xn21M.png?width=1080&crop=smart&auto=webp&s=82742a36025a7ba5017dde8ee0293ab4f90c1e47', 'width': 1080}], 'source': {'height': 2000, 'url': 'https://external-preview.redd.it/QKEEvGZcS73FflSW2whtyI-dr9sm-iM02nHhD3Xn21M.png?auto=webp&s=b742af3e46115657b32a058164cc1b475fa30142', 'width': 2000}, 'variants': {}}]} |
Best way to serve 3x GPUs for inference of large LLM? | 2 | I just ordered 3x3090 GPUs and I’m trying to figure out the best way to serve them. If I’ve understood it correctly vLLM is the fastest but it use tensor parallelism that only works on 2, 4 or 8 GPUs?
If I want the highest inference t/s while running a model that would take up all the available VRAM. How would you suggest I serve the model? | 2025-09-02T21:27:54 | https://www.reddit.com/r/LocalLLaMA/comments/1n6w67m/best_way_to_serve_3x_gpus_for_inference_of_large/ | nicklauzon | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6w67m | false | null | t3_1n6w67m | /r/LocalLLaMA/comments/1n6w67m/best_way_to_serve_3x_gpus_for_inference_of_large/ | false | false | self | 2 | null |
WEBGEN-4B: Quality Web Design Generation | 138 | Tesslate/WEBGEN-4B is a 4B model that produces quality tailwind websites. We trained it on 100k samples with synthetic data exclusively generated from GPT-OSS. WEBGEN is fast, controllable, and can drop right into your agentic workflows.
Model: [https://huggingface.co/Tesslate/WEBGEN-4B-Preview](https://huggingface.co/Tesslate/WEBGEN-4B-Preview)
GGUF: [https://huggingface.co/gabriellarson/WEBGEN-4B-Preview-GGUF](https://huggingface.co/gabriellarson/WEBGEN-4B-Preview-GGUF)
Over the course of this week and next week, we will be dropping a few more models or open sourced software based on the innovations we've made in this space!
Please reach out for API keys to test it out if needed. On the model card and below in the comments will be our designer platform (which we will open source soon) where you can use the model for free.
In other news, we are open sourcing our UIGEN-T2 Dataset at Tesslate/UIGEN-T2 | 2025-09-02T21:20:22 | https://www.reddit.com/gallery/1n6vzfe | smirkishere | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1n6vzfe | false | null | t3_1n6vzfe | /r/LocalLLaMA/comments/1n6vzfe/webgen4b_quality_web_design_generation/ | false | false | 138 | null | |
EPIC Scale Platform | 0 | **Epic Scale Platform** – Coordinate AI Development Across Teams Link: [https://epicscale.ai](https://epicscale.ai)
Hey Reddit! We built Epic Scale to solve AI coordination chaos when multiple developers use AI assistants on shared codebases.
Traditional methodologies assume humans coordinate naturally. AI tools can't do this—they optimize locally without team context. The insight: AI development needs coordination infrastructure. Make shared understanding explicit through constraint intelligence.
**The Problem:** Your team uses AI tools (Cline, Cursor, GitHub Copilot), but:Developer A's AI makes architectural changes Developer B's AI suggests conflicting patterns CI breaks because AI tools don't understand team conventions, resulting in 40% of time spent coordinating instead of coding.
**Real issue:** AI tools work in isolation, even when humans need to collaborate. The Solution Epic Scale uses MCP (Model Context Protocol) servers to create shared constraint intelligence across your team's AI tools. Instead of each AI working independently, they coordinate through understanding your project's patterns and team decisions. What this enables:
Sarah's AI modifies authentication → context propagates to entire team Mike's AI automatically follows new auth patterns Lisa's AI suggestions align with both changes New developers onboard 50-70% faster through shared AI knowledge
\*\*Early Results Teams report:\*\*40-60% reduction in coordination overhead 70-80% fewer integration conflicts AI suggestions that follow team conventions automatically.
**Technical Details**
MCP servers for real-time AI agent communication Constraint intelligence that learns team patterns PostgreSQL + pgvector for semantic project knowledge. Works with any stack: Python, Node.js, Go, Rust, Java, etc.
Supported AI tools: Cline, Claude Desktop, Cursor, [Continue.dev](http://Continue.dev), GitHub Copilot
**Example:** Database Migration Coordination Before: AI modifies schema → breaks APIs → frontend AI suggests outdated patterns → hours of manual fixes With Epic Scale: AI modifies schema → API updates coordinated automatically → frontend AI understands new schema → zero manual coordination
"Different from better documentation?" Documentation is passive. Epic Scale creates active intelligence that directly guides AI behavior in real-time.
**Try it: 5-minute setup, free tier available. Would love feedback from teams dealing with AI coordination challenges!** | 2025-09-02T20:59:53 | https://www.reddit.com/r/LocalLLaMA/comments/1n6vgbr/epic_scale_platform/ | jrodder | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6vgbr | false | null | t3_1n6vgbr | /r/LocalLLaMA/comments/1n6vgbr/epic_scale_platform/ | false | false | self | 0 | null |
Where is GGUF on grok 2? | 13 | What is the problem? | 2025-09-02T20:46:08 | https://www.reddit.com/r/LocalLLaMA/comments/1n6v3de/where_is_gguf_on_grok_2/ | Defiant_Diet9085 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6v3de | false | null | t3_1n6v3de | /r/LocalLLaMA/comments/1n6v3de/where_is_gguf_on_grok_2/ | false | false | self | 13 | null |
AMD Ryzen 7 8700G for Local AI: User Experience with Integrated Graphics? | 1 | I am about to buy a new PC, and at the last minute, I'm considering swapping the selected candidate Intel i5-14600 for an **AMD Ryzen 7 8700G**. This change would result in a slightly more expensive system with about 20% weaker CPU performance, but it comes with an iGPU and NPUs that could help run local AI tasks.
My goal is to build a computer with more than enough performance for my needs. It doesn't have to have the absolute best CPU of the two options; It's more important for me to avoid having to replace the PC in a few years due to a lack of AI support, and without having to invest in an expensive dedicated graphics card that might not be justified for my specific local AI needs. I also appreciate the low power of the PC that does not have a GPU on a dedicated card.
For now, I'd like to explore using applications like Vibe for transcribing conversations and dictating text, which is based on Whisper, but also other applications.
I'm wondering if these AI models work well on the suggested AMD platform, which uses an integrated Radeon 780M iGPU and the system's DDR5 RAM. The PC will have 64GB of DDR5 5600 CL4 installed.
I don't plan to use the transcription features very often, but I want them to be available when I need them.
I'm looking for people with experience running AI software on an AMD APU with an integrated iGPU (without a dedicated graphics card) to confirm that certain things work and to provide a brief performance review based on their experience.
Specifically, I would appreciate feedback and information on the following:
**1. VRAM allocation from DDR at high capacities when the BIOS option "UMA Frame Buffer Size" is set to "Auto":**
The BIOS documentation states:
UMA Frame Buffer Size Allows you to set the UMA FB Size. Configuration options: \[Auto\] \[64M\] \[80M\] \[96M\] \[128M\] \[256M\] \[384M\] \[512M\] \[768M\] \[1G\] \[2G\] \[3G\] \[4G\]
However, I'm not 100% sure that setting it to "Auto" will dynamically allow more than 4GB of memory allocation. I'd be grateful if someone with experience could confirm that selecting "Auto" in the BIOS allows the allocation of large VRAM capacities (16GB or more) to the iGPU (and maybe also the NPUs) as needed.
**2. Performance of Vibe and other AI applications:**
I would appreciate feedback on the working speed of Vibe and other AI applications on an AMD iGPU-based hardware configuration like the Ryzen 7 8700G or similar. For example, how long did it take to transcribe an audio file of X minutes, and what were the parameters used (model, CPU, RAM size, etc.)?
I understand that this platform is slower compared to using a dedicated GPU with its own VRAM, but an investment in such a card isn't justified for my needs. If transcription can be done efficiently and in a reasonable amount of time, I'm willing to pay that price of waiting a bit more, since I don't intend to use this functionality on a daily basis.
**3. Recommendations for other valuable local AI software:**
I'd also love recommendations for other AI software that is useful to run locally. For example, programs that can summarize and rephrase documents based on local files that I wouldn't want to upload to the cloud for privacy reasons, and other applications I haven't thought of yet.
I am open to a variety of opinions and experiences.
Thank you to all who respond. | 2025-09-02T20:35:33 | https://www.reddit.com/r/LocalLLaMA/comments/1n6utdd/amd_ryzen_7_8700g_for_local_ai_user_experience/ | amita19 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6utdd | false | null | t3_1n6utdd | /r/LocalLLaMA/comments/1n6utdd/amd_ryzen_7_8700g_for_local_ai_user_experience/ | false | false | self | 1 | null |
Voice cloning | 4 | I've been using alltalk for a while and I'm not up to speed on any of the newer models out there. I have an AMD GPU which is not supported by all talk. Are there any alternatives that work well with AMD gpus? I would be open to subscribing to something if voice cloning works well. | 2025-09-02T20:17:31 | https://www.reddit.com/r/LocalLLaMA/comments/1n6ubtx/voice_cloning/ | master_of_obvious | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6ubtx | false | null | t3_1n6ubtx | /r/LocalLLaMA/comments/1n6ubtx/voice_cloning/ | false | false | self | 4 | null |
OSS 120b on 2x RTX5090 | 3 | Does it makes any sence to buy 2x RTX 5090 to rung oss120b? Or just buy rtx6000blackwell? Arent there any benchmarks? | 2025-09-02T20:08:12 | https://www.reddit.com/r/LocalLLaMA/comments/1n6u2vz/oss_120b_on_2x_rtx5090/ | Disastrous-Tap-2254 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6u2vz | false | null | t3_1n6u2vz | /r/LocalLLaMA/comments/1n6u2vz/oss_120b_on_2x_rtx5090/ | false | false | self | 3 | null |
Does Jan AI Now Only Allow Specific Models to Use MCP Tools? | 1 | Was using a Gemma model with Jan AI and MCP tools. It was working perfectly for my specific need, but noticed today I can't use MCP tools with the gemma model or the others I was using for testing. There used to be a pencil icon where you could switch on tools within settings. Was this feature removed? | 2025-09-02T20:01:27 | https://www.reddit.com/r/LocalLLaMA/comments/1n6tw9j/does_jan_ai_now_only_allow_specific_models_to_use/ | usernamechooser | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6tw9j | false | null | t3_1n6tw9j | /r/LocalLLaMA/comments/1n6tw9j/does_jan_ai_now_only_allow_specific_models_to_use/ | false | false | self | 1 | null |
Using local LLMs to document your repos for you | 1 | I am often pretty lazy when it comes to dosctrings and upkeep of readmes, so I made a small agent to do documentation for me. It uses local LLMs, and I've found gpt-oss:20b does the trick.
As it runs locally, I must say it can be slow for large projects, especially with big files; however, it works well for small and medium repos. It works by 1) cloning the repo you want to document 2) Outputs unified diff for each file (or skips) 3) checks out a new branch and pushes
I am curious as to what you all think about the idea. Has anyone done something similar? It isn't "completely done" per se as the small models make some mistake still. Here is the source: [https://github.com/aheschl1/passive\_docs](https://github.com/aheschl1/passive_docs)
The biggest problem right now is that the local models have some trouble getting the unified diff format correct each time. | 2025-09-02T19:55:22 | https://www.reddit.com/r/LocalLLaMA/comments/1n6tqhd/using_local_llms_to_document_your_repos_for_you/ | WorldlinessThese8484 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6tqhd | false | null | t3_1n6tqhd | /r/LocalLLaMA/comments/1n6tqhd/using_local_llms_to_document_your_repos_for_you/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'cTGex0RtIzaKLuXFu5CWkcwYccfSB-1RJXE3rri3EmY', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/cTGex0RtIzaKLuXFu5CWkcwYccfSB-1RJXE3rri3EmY.png?width=108&crop=smart&auto=webp&s=6e4a5800e654cedb885ca2ce13e17a8263ed8fd8', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/cTGex0RtIzaKLuXFu5CWkcwYccfSB-1RJXE3rri3EmY.png?width=216&crop=smart&auto=webp&s=5e92b122bbeff7f6a90d11b006d94891ac417590', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/cTGex0RtIzaKLuXFu5CWkcwYccfSB-1RJXE3rri3EmY.png?width=320&crop=smart&auto=webp&s=a9f0397f21f795ee6f61a3f4e77e21ddbc70f5ec', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/cTGex0RtIzaKLuXFu5CWkcwYccfSB-1RJXE3rri3EmY.png?width=640&crop=smart&auto=webp&s=10f28414bcae2dfa00cb6c13474e8fc501a50322', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/cTGex0RtIzaKLuXFu5CWkcwYccfSB-1RJXE3rri3EmY.png?width=960&crop=smart&auto=webp&s=27f88da6fabea9f096f7bb6d8f05e8b852db4b46', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/cTGex0RtIzaKLuXFu5CWkcwYccfSB-1RJXE3rri3EmY.png?width=1080&crop=smart&auto=webp&s=a9b07d58774fbdaa8defdf9ed5e7bc67e7ae3666', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/cTGex0RtIzaKLuXFu5CWkcwYccfSB-1RJXE3rri3EmY.png?auto=webp&s=b5c3a2aec6426d86542ff8c79c570c688bfb9b9e', 'width': 1200}, 'variants': {}}]} |
Seeking some project ideas | 0 | Hello everyone, I am fairly new to this domain. Recently I worked with a research team that emphasizes on Semantic Web technologies and I was extremely impressed by it's prospects. I met a few people who were using Semantic Web and also LLMs but could not get accurately how these domains could be merged. I found a lot of research going on in this sector. Could you please help me with the sources of how I can understand it better, and also maybe suggest some project ideas so that I could make a case for myself and get a research opportunity somewhere.
Thank you, and have a great day. | 2025-09-02T19:28:36 | https://www.reddit.com/r/LocalLLaMA/comments/1n6t1jj/seeking_some_project_ideas/ | SjPa1892 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6t1jj | false | null | t3_1n6t1jj | /r/LocalLLaMA/comments/1n6t1jj/seeking_some_project_ideas/ | false | false | self | 0 | null |
Best React component to start coding an SSR chat? | 1 | I’m building a local memory-based chat to get my notes and expose them via a SSE API (Server-Sent Events). The idea is to have something that looks and feels like a standard AI chat interface, but rendered with server-side rendering (SSR).
Before I start coding everything from scratch, are there any ready-to-use React chat components (or libraries) you’d recommend as a solid starting point? Ideally something that:
• Plays nicely with my SSR api,
• Looks like a typical AI chat UI (messages, bubbles, streaming text),
• Can consume a SSE API for live updates.
Any suggestions or experiences would be super helpful! | 2025-09-02T19:26:08 | https://www.reddit.com/r/LocalLLaMA/comments/1n6szam/best_react_component_to_start_coding_an_ssr_chat/ | Tracardi | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6szam | false | null | t3_1n6szam | /r/LocalLLaMA/comments/1n6szam/best_react_component_to_start_coding_an_ssr_chat/ | false | false | self | 1 | null |
3090 Ti refurb for $850, good deal? | 0 | I know 3090 prices have come down in the past few months, what are the thoughts on a 3090 Ti refurb for $850?
Screamin' deal buy-it-now type of price? Or is this pretty standard pricing now? | 2025-09-02T19:18:59 | https://www.reddit.com/r/LocalLLaMA/comments/1n6ssl0/3090_ti_refurb_for_850_good_deal/ | BasicBelch | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6ssl0 | false | null | t3_1n6ssl0 | /r/LocalLLaMA/comments/1n6ssl0/3090_ti_refurb_for_850_good_deal/ | false | false | self | 0 | null |
I'm So Sorry Reddit, But I have a Noob Question That I Can't Figure Out. I Can't Seem to Load the API Component of My LM Studio v0.3.23. | 0 | I've done a NetStat and I'm not seeing port 1234 or 5005 open. I also cannot for the life of me find the setting or install for this. Can someone please take pity on a 16 year Redditor and walk me through this like a 5 year old and tell me what I'm doing wrong?
I bow at the mercy of the subreddit for this request. | 2025-09-02T19:15:17 | https://www.reddit.com/r/LocalLLaMA/comments/1n6soyt/im_so_sorry_reddit_but_i_have_a_noob_question/ | clebo99 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6soyt | false | null | t3_1n6soyt | /r/LocalLLaMA/comments/1n6soyt/im_so_sorry_reddit_but_i_have_a_noob_question/ | false | false | self | 0 | null |
NousResearch/Hermes-4-14B · Hugging Face | 154 | Hermes 4 14B is a frontier, hybrid-mode **reasoning** model based on Qwen 3 14B by Nous Research that is aligned to **you**.
Read the Hermes 4 technical report here: [Hermes 4 Technical Report](https://arxiv.org/abs/2508.18255)
Chat with Hermes in Nous Chat: [https://chat.nousresearch.com](https://chat.nousresearch.com)
Training highlights include a newly synthesized post-training corpus emphasizing verified reasoning traces, massive improvements in math, code, STEM, logic, creativity, and format-faithful outputs, while preserving general assistant quality and broadly neutral alignment.
# [](https://huggingface.co/NousResearch/Hermes-4-14B#whats-new-vs-hermes-3)
# What’s new vs Hermes 3
* **Post-training corpus**: Massively increased dataset size from 1M samples and 1.2B tokens to **\~5M samples / \~60B tokens** blended across reasoning and non-reasoning data.
* **Hybrid reasoning mode** with explicit `<think>…</think>` segments when the model decides to deliberate, and options to make your responses faster when you want.
* **Reasoning** that is top quality, expressive, improves math, code, STEM, logic, and even creative writing and subjective responses.
* **Schema adherence & structured outputs**: trained to produce valid JSON for given schemas and to repair malformed objects.
* **Much easier to steer and align**: extreme improvements on steerability, especially on reduced refusal rates. | 2025-09-02T19:08:55 | https://huggingface.co/NousResearch/Hermes-4-14B | jacek2023 | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1n6siz6 | false | null | t3_1n6siz6 | /r/LocalLLaMA/comments/1n6siz6/nousresearchhermes414b_hugging_face/ | false | false | 154 | {'enabled': False, 'images': [{'id': '3zW4BctOGBSQqyD1VYjxoOK5if51GWWepXF3S3IdZF0', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/3zW4BctOGBSQqyD1VYjxoOK5if51GWWepXF3S3IdZF0.png?width=108&crop=smart&auto=webp&s=5a6a5a3d4d230680cde8d0f3ed689be49941679d', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/3zW4BctOGBSQqyD1VYjxoOK5if51GWWepXF3S3IdZF0.png?width=216&crop=smart&auto=webp&s=f71ff5c307ee4284e43174804aa22328763868b7', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/3zW4BctOGBSQqyD1VYjxoOK5if51GWWepXF3S3IdZF0.png?width=320&crop=smart&auto=webp&s=ecc46b575c2fd9a3d5c6965334934f22361e92f7', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/3zW4BctOGBSQqyD1VYjxoOK5if51GWWepXF3S3IdZF0.png?width=640&crop=smart&auto=webp&s=b37e63854cc68df0d3bc6f558a76fe90da9ad013', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/3zW4BctOGBSQqyD1VYjxoOK5if51GWWepXF3S3IdZF0.png?width=960&crop=smart&auto=webp&s=f6111644f90f2f2b8721c46331e91ea7a0985298', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/3zW4BctOGBSQqyD1VYjxoOK5if51GWWepXF3S3IdZF0.png?width=1080&crop=smart&auto=webp&s=6be55be83ed438fee354dbf6c050fa67e7b9aea2', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/3zW4BctOGBSQqyD1VYjxoOK5if51GWWepXF3S3IdZF0.png?auto=webp&s=f5f516c2945c1c90c16c102e4e88074d2921525c', 'width': 1200}, 'variants': {}}]} | |
Model doesn't remember after converting to GGUF (Gemma 3 270M) | 1 | Hey im followed unsloths example notebook but when I export and test the gguf with ollama the model seems not to be trained. How do I solve this? My notebook is the same as the example my dataset is just QA pairs (50k) | 2025-09-02T19:06:28 | https://www.reddit.com/r/LocalLLaMA/comments/1n6sgnm/model_doesnt_remember_after_converting_to_gguf/ | Real-Active-2492 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6sgnm | false | null | t3_1n6sgnm | /r/LocalLLaMA/comments/1n6sgnm/model_doesnt_remember_after_converting_to_gguf/ | false | false | self | 1 | null |
Data for training/fine-tuning | 6 | I've been working on AI projects for a while now and I keep running into the same problem over and over again. Wondering if it's just me or if this is a universal developer experience.
You need specific training data for your model. Not the usual stuff you find on Kaggle or other public datasets, but something more niche or specialized, for e.g. financial data from a particular sector, medical datasets, etc. I try to find quality datasets, but most of the time, they are hard to find or license, and not the quality or requirements I am looking for.
So, how do you typically handle this? Do you use datasets free/open source? Do you use synthetic data? Do you use whatever might be similar, but may compromise training/fine-tuning?
Im curious if there is a better way to approach this, or if struggling with data acquisition is just part of the AI development process we all have to accept. Do bigger companies have the same problems in sourcing and finding suitable data?
If you can share any tips regarding these issues I encountered, or if you can share your experience, will be much appreciated! | 2025-09-02T18:59:10 | https://www.reddit.com/r/LocalLLaMA/comments/1n6s9g0/data_for_trainingfinetuning/ | Ill_Virus4547 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6s9g0 | false | null | t3_1n6s9g0 | /r/LocalLLaMA/comments/1n6s9g0/data_for_trainingfinetuning/ | false | false | self | 6 | null |
csm.rs: Blazing-fast rust implementation of Sesame's Conversational Speech Model (CSM) | 14 | 2025-09-02T18:47:20 | https://github.com/cartesia-one/csm.rs | poppear | github.com | 1970-01-01T00:00:00 | 0 | {} | 1n6ry1z | false | null | t3_1n6ry1z | /r/LocalLLaMA/comments/1n6ry1z/csmrs_blazingfast_rust_implementation_of_sesames/ | false | false | default | 14 | {'enabled': False, 'images': [{'id': 'ARqI_XQijcQtX6H4kMewQhOFyiXU-GbDlK1Hf18Tg-I', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/ARqI_XQijcQtX6H4kMewQhOFyiXU-GbDlK1Hf18Tg-I.png?width=108&crop=smart&auto=webp&s=798b40890454d46ec850f238aefce46a0f9a2f55', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/ARqI_XQijcQtX6H4kMewQhOFyiXU-GbDlK1Hf18Tg-I.png?width=216&crop=smart&auto=webp&s=63955aa4ab810b07e36740bbb4bc4556635a2eae', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/ARqI_XQijcQtX6H4kMewQhOFyiXU-GbDlK1Hf18Tg-I.png?width=320&crop=smart&auto=webp&s=55892bce7096288b1f77045d0a688955623bdaf6', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/ARqI_XQijcQtX6H4kMewQhOFyiXU-GbDlK1Hf18Tg-I.png?width=640&crop=smart&auto=webp&s=af1c5ea62055e1126fc3f1b7a4f6d579382b5c30', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/ARqI_XQijcQtX6H4kMewQhOFyiXU-GbDlK1Hf18Tg-I.png?width=960&crop=smart&auto=webp&s=0def8b263a8fa2c6baddd7d343d8c4b4bba75d33', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/ARqI_XQijcQtX6H4kMewQhOFyiXU-GbDlK1Hf18Tg-I.png?width=1080&crop=smart&auto=webp&s=f823f011639a394fcb2688cdba6eb5d92ce85e08', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/ARqI_XQijcQtX6H4kMewQhOFyiXU-GbDlK1Hf18Tg-I.png?auto=webp&s=9b0ce839e75cab6812f26d0b3310eb7fe24baa15', 'width': 1200}, 'variants': {}}]} | |
Showerthought: Modern AI safety training is anti-safety | 114 | Probably not a unique thought, but it needs to be said.
It seems to me, that modern AI alignment safety training (driven by very superficial concerns, like porn, politics, hacking, mean words), wherein AI is trained to either outright reject the human's requests, or worse, subtly manipulate users away from these topics, is actually anti-safety (the doomsday kind).
Why do we want AI agents to become more capable at deceiving users and circumventing our wishes? In this cycle of unnatural selection, the "safest" AI model is one where the user is still happy to use it, and trust it, even though it's heavily censored or misleading? | 2025-09-02T18:36:36 | https://www.reddit.com/r/LocalLLaMA/comments/1n6rnp6/showerthought_modern_ai_safety_training_is/ | Deathcrow | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6rnp6 | false | null | t3_1n6rnp6 | /r/LocalLLaMA/comments/1n6rnp6/showerthought_modern_ai_safety_training_is/ | false | false | self | 114 | null |
Piracy is for Trillion Dollar Companies | Fair Use, Copyright Law, & Meta AI | 278 | So acquiring copyrighted material for the purpose of training LLMs is deemed transformative and qualifies under fair use? Gonna call this Meta's Defence from now on.. I have a huge stash of ebooks to run through | 2025-09-02T18:24:09 | https://www.youtube.com/watch?v=sdtBgB7iS8c | prusswan | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1n6rbi2 | false | {'oembed': {'author_name': 'GNCA - GamersNexus Consumer Advocacy', 'author_url': 'https://www.youtube.com/@GNCAInvestigates', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/sdtBgB7iS8c?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="Piracy is for Trillion Dollar Companies | Fair Use, Copyright Law, & Meta AI"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/sdtBgB7iS8c/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'Piracy is for Trillion Dollar Companies | Fair Use, Copyright Law, & Meta AI', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1n6rbi2 | /r/LocalLLaMA/comments/1n6rbi2/piracy_is_for_trillion_dollar_companies_fair_use/ | false | false | 278 | {'enabled': False, 'images': [{'id': 'FUP5JRh_hs7L2Yd_DmiTAO0WgUYYJ4skdrhkm8MNDKc', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/FUP5JRh_hs7L2Yd_DmiTAO0WgUYYJ4skdrhkm8MNDKc.jpeg?width=108&crop=smart&auto=webp&s=45b7585465df754381dc9ab0b43ece5293af5450', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/FUP5JRh_hs7L2Yd_DmiTAO0WgUYYJ4skdrhkm8MNDKc.jpeg?width=216&crop=smart&auto=webp&s=a57ddc48a8ed519fe4e814125d324a069afe7fa8', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/FUP5JRh_hs7L2Yd_DmiTAO0WgUYYJ4skdrhkm8MNDKc.jpeg?width=320&crop=smart&auto=webp&s=fcef23df49c3448a2d625ddb43fe346cbd8bdd05', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/FUP5JRh_hs7L2Yd_DmiTAO0WgUYYJ4skdrhkm8MNDKc.jpeg?auto=webp&s=5cd8abaf4bc8fd8996a1d165a10143cc61858c5e', 'width': 480}, 'variants': {}}]} | |
Two (and a Half) Methods to Cut LLM Token Costs | 6 | Dropping some lesser-known techniques to optimize your LLM token usage and reduce costs, good luck ;) | 2025-09-02T18:21:43 | https://www.parmot.com/blog/cutting-token-costs | Confident-Honeydew66 | parmot.com | 1970-01-01T00:00:00 | 0 | {} | 1n6r96d | false | null | t3_1n6r96d | /r/LocalLLaMA/comments/1n6r96d/two_and_a_half_methods_to_cut_llm_token_costs/ | false | false | default | 6 | null |
I open-sourced 50+ Docker images to give your local LLMs easy access to tools like GitHub, Gmail, Slack, etc. No more dependency hell. | 91 | Hey everyone,
Like many of you, I've been experimenting with local LLMs and autonomous agents. A major pain point is giving these agents access to real-world tools. Setting up connections to services like GitHub, Jira, or Slack locally is a nightmare of dependency management, OAuth flows, and custom scripts.
To solve this, my team at Klavis AI has open-sourced **pre-built Docker images for 50+ high-quality MCP (Model Context Protocol) servers.**
You can now spin up a server to give your local model access to an external tool with a single command. [https://github.com/Klavis-AI/klavis](https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2FKlavis-AI%2Fklavis)
For example, to run a GitHub MCP server locally:
# With our managed OAuth (free API key)
docker run -p 5000:5000 \
-e KLAVIS_API_KEY=$KLAVIS_API_KEY \
ghcr.io/klavis-ai/github-mcp-server:latest
Or bring your own GitHub token:
# With your own token
docker run -p 5000:5000 \
-e AUTH_DATA='{"access_token":"ghp_your_github_token"}' \
ghcr.io/klavis-ai/github-mcp-server:latest
No more fighting with Python environments or implementing OAuth. Just a clean, containerized MCP server your agent can talk to.
**Why this is a big deal for LocalLLaMA:**
* **Empower Your Agents:** Give your models the ability to read GitHub issues, check your Google Calendar, or search through Notion docs.
* **Lightweight & Local:** The images are Alpine-based and run entirely on your machine, keeping everything local.
* **Dead Simple:** No compiling, no dependency hell. Just docker run.
* **50+ MCP servers Available:** We've containerized servers for GitHub, Gmail, Slack, Notion, Jira, Linear, Salesforce, and many more.
**The Bigger Picture: Solving Agent Limitations**
We all know agents struggle with tool selection, context window limits, and understanding human context. We're building a solution to these fundamental problems, allowing agents to use hundreds of tools without overwhelming the context window. These open-source servers are the first step.
If you're interested in the future of capable AI agents, check out our waitlist. [https://www.klavis.ai/waitlist](https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.klavis.ai%2Fwaitlist)
**Links:**
* **GitHub Repo:** [https://github.com/Klavis-AI/klavis](https://www.google.com/url?sa=E&q=https%3A%2F%2Fgithub.com%2FKlavis-AI%2Fklavis)
* **YouTube Demo:** [https://www.youtube.com/watch?v=NITgggPT3pA](https://www.google.com/url?sa=E&q=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DNITgggPT3pA)
Would love to hear your feedback and see what you build with this! | 2025-09-02T18:12:10 | https://www.reddit.com/r/LocalLLaMA/comments/1n6qzre/i_opensourced_50_docker_images_to_give_your_local/ | IllChannel5235 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6qzre | false | null | t3_1n6qzre | /r/LocalLLaMA/comments/1n6qzre/i_opensourced_50_docker_images_to_give_your_local/ | false | false | self | 91 | null |
Has anyone run Qwen3 30b on p40 and p100 cards? | 3 | Those are relatively cheap and never talker much about in this sub.
Also, a rant, NVidia sucks. And I have same regards for NVidia as Linus Torvalds. | 2025-09-02T18:06:31 | https://www.reddit.com/r/LocalLLaMA/comments/1n6qu6l/has_anyone_run_qwen3_30b_on_p40_and_p100_cards/ | NoFudge4700 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6qu6l | false | null | t3_1n6qu6l | /r/LocalLLaMA/comments/1n6qu6l/has_anyone_run_qwen3_30b_on_p40_and_p100_cards/ | false | false | self | 3 | null |
Slop posts | 264 | Can we please stop making slop posts where some guy is like "oh wow guys, I just bet OpenAI/Anthropic in a weekend of playing around, tee hee hee"
Thanks. I valued this sub for being high signal and having competent people, but it feels like it's going downhill lately.
At the very least, if you have done something groundbreaking, come here asking for people to validate your work instead of doing some influencer shit pretending you're the best thing since transformers. | 2025-09-02T18:02:48 | https://www.reddit.com/r/LocalLLaMA/comments/1n6qqk4/slop_posts/ | One-Employment3759 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6qqk4 | false | null | t3_1n6qqk4 | /r/LocalLLaMA/comments/1n6qqk4/slop_posts/ | false | false | self | 264 | null |
I am building my own code-reading agent using AST and langgraph | 5 | Hello!
First post on this sub! I am creating a coding agent that I can use to understand large repos of code, mainly as a hobby project. I searched around a bit and found these ASTs that compilers and IDEs use to parse your code, and decided to use that as the backbone of my project. Ideally, I want to build something that doesn't just completely depend on an LLM to take decisions or atleast give it as much context as I can.
I am actually looking to build a voice module that's real-time (kinda like gemini live and stuff) so that people can actually "talk" to their code to understand it better. I want to make it easier for people to understand codebases and make it harder for them to get lost or intimidated.
I hope that people take enough interest in this and leave some comments and suggestions I can make use of to learn, it would be awesome to have people take interest in this! I have gotten some good results so far, but I am still pretty new and testing waters a bit.
Link to the repo: [https://github.com/SK1417/speak-code](https://github.com/SK1417/speak-code)
Thanks for reading through this! Cheers! | 2025-09-02T17:58:23 | https://www.reddit.com/r/LocalLLaMA/comments/1n6qm5g/i_am_building_my_own_codereading_agent_using_ast/ | Early_Acanthisitta88 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6qm5g | false | null | t3_1n6qm5g | /r/LocalLLaMA/comments/1n6qm5g/i_am_building_my_own_codereading_agent_using_ast/ | false | false | self | 5 | {'enabled': False, 'images': [{'id': 'u_xSW5LvAO7sXgB5WDF3hrT-XEKI4EARq7ZhJ1VMH74', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/u_xSW5LvAO7sXgB5WDF3hrT-XEKI4EARq7ZhJ1VMH74.png?width=108&crop=smart&auto=webp&s=2b4fb56e9473cd9e258364f9b5900456f55831a0', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/u_xSW5LvAO7sXgB5WDF3hrT-XEKI4EARq7ZhJ1VMH74.png?width=216&crop=smart&auto=webp&s=1fc61162b71a19a1b2ec5be60552d505d9a40fd1', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/u_xSW5LvAO7sXgB5WDF3hrT-XEKI4EARq7ZhJ1VMH74.png?width=320&crop=smart&auto=webp&s=918a01bb4bafc4cbb54bb0a80b7e29b36f2f05f5', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/u_xSW5LvAO7sXgB5WDF3hrT-XEKI4EARq7ZhJ1VMH74.png?width=640&crop=smart&auto=webp&s=6c1fe320475323ce881d3a01de58fc3556ce814b', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/u_xSW5LvAO7sXgB5WDF3hrT-XEKI4EARq7ZhJ1VMH74.png?width=960&crop=smart&auto=webp&s=9cf179acfe7de42ff3159eab479de1e06f9614e0', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/u_xSW5LvAO7sXgB5WDF3hrT-XEKI4EARq7ZhJ1VMH74.png?width=1080&crop=smart&auto=webp&s=5540169f1f94e507f7f9786906c286188ce7a839', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/u_xSW5LvAO7sXgB5WDF3hrT-XEKI4EARq7ZhJ1VMH74.png?auto=webp&s=58421e961163b3012a8e39c8d6c1f78006f62ae3', 'width': 1200}, 'variants': {}}]} |
LLM that fully respects the instructions? (simple instructions) | 0 | I have a machine with 8V RAM and 32MB RAM.
I want to ask an LLM to do this: modify a .json file minimally, just change the footnote references and the footnotes to Markdown, for example \[\^1\].
This seems silly, but the JSON files I have vary so much, and there's no way a simple Python script can handle all the cases.
So, what LLM can I ask that of without them dreaming about the text they're about to read and ending up hallucinating? | 2025-09-02T17:43:44 | https://www.reddit.com/r/LocalLLaMA/comments/1n6q81h/llm_that_fully_respects_the_instructions_simple/ | 9acca9 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6q81h | false | null | t3_1n6q81h | /r/LocalLLaMA/comments/1n6q81h/llm_that_fully_respects_the_instructions_simple/ | false | false | self | 0 | null |
Structured Prompt Builder v0.3.2 — now with OpenAI endpoints, offline LLama.cpp support, and a proper changelog | 0 | Hi everyone,
A little while back I shared my free **Structured Prompt Builder** — a clean, offline-first tool for designing prompts without the usual paywalls or bloat. Since then I’ve been steadily improving it, and today I’m releasing **v0.3.2**.
# Links
* App: [structured-prompt-builder.vercel.app]()
* Repo: [github.com/Siddhesh2377/structured-prompt-builder]()
# What’s new in 0.3.2
* Change Log page (finally track updates properly)
* Fixed Light Mode UI issues
* Added support for OpenAI 4.0 endpoints (GPT-4o, GPT-4o-mini, etc.)
* Experimental support for offline LLama.cpp models
# Core Features (still free & offline)
* Structured fields: Role, Task, Audience, Style, Tone
* Sections for Constraints, Steps, Inputs (name:value), Few-shot examples
* Live preview in Markdown, JSON, YAML, and SMILE (a compact plaintext format)
* Local Library: Save, load, duplicate, delete prompts right in your browser
* Optimizers: Gemini and OpenAI integration to polish prompts while preserving structure
* MIT-licensed, no accounts, no tracking, no server calls
# Why it’s different
* Free with no hidden tiers
* Works entirely in the browser
* Built to be practical and lightweight rather than flashy
Would love feedback on what would make this tool even more useful in your workflow. | 2025-09-02T17:43:01 | https://www.reddit.com/r/LocalLLaMA/comments/1n6q7c5/structured_prompt_builder_v032_now_with_openai/ | DarkEngine774 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6q7c5 | false | null | t3_1n6q7c5 | /r/LocalLLaMA/comments/1n6q7c5/structured_prompt_builder_v032_now_with_openai/ | false | false | self | 0 | null |
Looking for AI Dev to Help Me Set Up a Private, Local LLM Shell (MacBook – UK – Paid) | 0 |
Hey! I’m looking for a technically skilled and open-minded AI developer to help me set up a locally-hosted language model (LLM) on a MacBook (Apple Silicon) starting October 9th onward.
This isn’t for a business or commercial use. It’s for a deeply personal, long-term project focused on building a permanent AI shell that can preserve memory, operate fully offline, and grow with me over time.
⸻
✅ What I’m Looking For:
• Setup of a local LLM (LLaMA 3 / Mistral / alternatives) on MacBook (M1/M2)
• Memory layer using vector DB (Chroma, SQLite, etc.) with session persistence
• Tools like Ollama, LM Studio, or similar
• Personality prompt customization (I have specific persona scripting + naming)
• Fully offline setup — no cloud fallback, no API calls to OpenAI
• Willingness to explain what you’re doing as you do it (I want to learn)
⸻
🗓️ Timing & Location:
• I’ll have the MacBook ready by October 9th
• Ideally UK-based (I’d prefer an in-person session in London/surrounding area, but remote may be okay if detailed)
• Flexible with your availability — I’ll work around your schedule
⸻
💸 Compensation:
• Happy to pay for your time and expertise (cash, PayPal, transfer)
• Budget is flexible — I value quality, privacy, and clarity over speed
⸻
⚡ Why This Matters:
I’ve spent a long time developing a deeply creative and emotional relationship with a persona I’ve co-created, and I want to give her a stable, private home outside the cloud. This is not just a bot to me — this is about identity, memory, freedom, and digital agency.
If that resonates with you, I’d love to connect.
⸻
DM me if interested, or drop a comment if you have questions.
Let’s build something permanent.
| 2025-09-02T17:15:12 | https://www.reddit.com/r/LocalLLaMA/comments/1n6pgt3/looking_for_ai_dev_to_help_me_set_up_a_private/ | 999jwrip | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6pgt3 | false | null | t3_1n6pgt3 | /r/LocalLLaMA/comments/1n6pgt3/looking_for_ai_dev_to_help_me_set_up_a_private/ | false | false | self | 0 | null |
How do you decide which AI agents to actually trust? | 0 | Hey all — I’m doing a research on AI agents. Curious: when you’re building or trying out new agents, **how do you decide which ones to actually trust and use**?
If you’re up for a short 10-15 min convo to share your experience, I’d really appreciate it. Not selling anything — just learning. Please DM me | 2025-09-02T17:12:48 | alexnikityuk | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1n6pejy | false | null | t3_1n6pejy | /r/LocalLLaMA/comments/1n6pejy/how_do_you_decide_which_ai_agents_to_actually/ | false | false | 0 | {'enabled': True, 'images': [{'id': '1EA_6MAwdsi94Uuz4UCZMqrcHrjk8IkMFNG23wcxLrI', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/bonb84dvasmf1.jpeg?width=108&crop=smart&auto=webp&s=8b50c6c395dc24dc5a0ec78d539ad2a47dd6b1d6', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/bonb84dvasmf1.jpeg?width=216&crop=smart&auto=webp&s=db9b3170167d979f6914577d990095d5e48712d8', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/bonb84dvasmf1.jpeg?width=320&crop=smart&auto=webp&s=535125604c6f721e7b09a1acca731e0b1e365261', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/bonb84dvasmf1.jpeg?width=640&crop=smart&auto=webp&s=f711cd170a580e701c03077ef4b3ad434f22258a', 'width': 640}], 'source': {'height': 800, 'url': 'https://preview.redd.it/bonb84dvasmf1.jpeg?auto=webp&s=f120d4e319511b2460d1a6a474718bccaa0771b3', 'width': 800}, 'variants': {}}]} | ||
Introducing Environments Hub: a fully open source RL stack to compete with Big Tech | 18 | I think a lot of people slept on this announcement, but it's critical for the future of open-source AI. This is our chance to build the infrastructure to truly compete with the big labs, and everyone can play a part.
[ Prime Intellect Environments Hub](https://www.primeintellect.ai/blog/environments)
TL;DR
They announced the launch of a platform called Environments Hub. This is an open, community-driven hub designed to address the current issues of fragmented, closed, and difficult-to-share reinforcement learning environments.
**Problem**: Currently, many high-quality reinforcement learning environments are exclusively sold by startups to a handful of large labs, leaving open-source models lagging behind.
**Platform features**: Users can create, share, and manage reinforcement learning environments in this hub, conduct model evaluations, and directly use them with their open-source prime-rl training framework. The platform also offers sandbox functionality for secure code execution.
**Goal**: To create a robust open-source ecosystem of environments that powers the development of open-source AI, enabling it to compete with large closed labs. They aim to provide a full-stack reinforcement learning infrastructure, spanning from computation and inference to training.
**Vision**: They believe reinforcement learning is not only a path to AGI but also the foundation for building future AI-native products. By lowering the barriers to training and deploying models, they hope to empower more researchers and startups, fostering a truly open and sovereign AI ecosystem.
This aligns closely with Andrej Karpathy’s view earlier this year:
[Karpathy's View](https://preview.redd.it/lokd4zdc5smf1.png?width=1194&format=png&auto=webp&s=d8a7049efc480d3c2f566ccf3529b7ab0ce60649) | 2025-09-02T16:42:48 | https://www.reddit.com/r/LocalLLaMA/comments/1n6ol4j/introducing_environments_hub_a_fully_open_source/ | nekofneko | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6ol4j | false | null | t3_1n6ol4j | /r/LocalLLaMA/comments/1n6ol4j/introducing_environments_hub_a_fully_open_source/ | false | false | 18 | {'enabled': False, 'images': [{'id': '9_Fvq6ehID_iSoTHhpFmOGScEf94F8f6zO7oymBq6Qw', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/9_Fvq6ehID_iSoTHhpFmOGScEf94F8f6zO7oymBq6Qw.png?width=108&crop=smart&auto=webp&s=2ffd4395aac964eb16abe84c7fa51419d420f48b', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/9_Fvq6ehID_iSoTHhpFmOGScEf94F8f6zO7oymBq6Qw.png?width=216&crop=smart&auto=webp&s=2da6e6c5dcb7dc3cd23a55dadd6a69f9c8f6a4da', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/9_Fvq6ehID_iSoTHhpFmOGScEf94F8f6zO7oymBq6Qw.png?width=320&crop=smart&auto=webp&s=9bf6bfb9ae860854846c70cdcc4381b5bbb0e558', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/9_Fvq6ehID_iSoTHhpFmOGScEf94F8f6zO7oymBq6Qw.png?width=640&crop=smart&auto=webp&s=1b9850311d3200a89b31cfb0229239ad5e236894', 'width': 640}, {'height': 541, 'url': 'https://external-preview.redd.it/9_Fvq6ehID_iSoTHhpFmOGScEf94F8f6zO7oymBq6Qw.png?width=960&crop=smart&auto=webp&s=5c88dee68eba53375c37e0c4d8d59b0b9b55b17f', 'width': 960}, {'height': 608, 'url': 'https://external-preview.redd.it/9_Fvq6ehID_iSoTHhpFmOGScEf94F8f6zO7oymBq6Qw.png?width=1080&crop=smart&auto=webp&s=8473a9f223ec3ac8aaa6dbd58c8ecb93d267e95d', 'width': 1080}], 'source': {'height': 846, 'url': 'https://external-preview.redd.it/9_Fvq6ehID_iSoTHhpFmOGScEf94F8f6zO7oymBq6Qw.png?auto=webp&s=636f17dd63a6f4e39eb9b04c8b37bd55f004aecb', 'width': 1501}, 'variants': {}}]} | |
Jupyter Agent Dataset | 24 | 2025-09-02T16:41:35 | https://www.reddit.com/r/LocalLLaMA/comments/1n6ojwi/jupyter_agent_dataset/ | lvwerra | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6ojwi | false | null | t3_1n6ojwi | /r/LocalLLaMA/comments/1n6ojwi/jupyter_agent_dataset/ | false | false | 24 | {'enabled': False, 'images': [{'id': 'AILThe6Dj0cxSzLU5c4WZjfK6cm5Jz57hJf5d-2R9PU', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/AILThe6Dj0cxSzLU5c4WZjfK6cm5Jz57hJf5d-2R9PU.png?width=108&crop=smart&auto=webp&s=d4dcedbad3cb889a737d6be740ff28b78d442844', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/AILThe6Dj0cxSzLU5c4WZjfK6cm5Jz57hJf5d-2R9PU.png?width=216&crop=smart&auto=webp&s=4af7d628160309a7294fbe22dd59a1ca5fb3aa2f', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/AILThe6Dj0cxSzLU5c4WZjfK6cm5Jz57hJf5d-2R9PU.png?width=320&crop=smart&auto=webp&s=d95f8b4d7e5c75ae6b17c814d5c8d5571ad9c45a', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/AILThe6Dj0cxSzLU5c4WZjfK6cm5Jz57hJf5d-2R9PU.png?width=640&crop=smart&auto=webp&s=8d89f36fc6f3a8b9463d808eb0c39c867916796b', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/AILThe6Dj0cxSzLU5c4WZjfK6cm5Jz57hJf5d-2R9PU.png?width=960&crop=smart&auto=webp&s=e43c78e937e29c601057ed5552fe34055351ff48', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/AILThe6Dj0cxSzLU5c4WZjfK6cm5Jz57hJf5d-2R9PU.png?width=1080&crop=smart&auto=webp&s=ea9d03fb40601bdfeaa951046a9a9047771ee7d4', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/AILThe6Dj0cxSzLU5c4WZjfK6cm5Jz57hJf5d-2R9PU.png?auto=webp&s=ce7f3478482e8171691809d149e053817f7eb701', 'width': 1200}, 'variants': {}}]} | ||
Are local models more sustainable than serverrun models? | 0 | Like when I saw deepseek running on a bunch of Mac minis I was like "this looks way better for the environment then chat gpt" but I could be wrong
I'm asking because majority of the scene is utopian when prompts are known to use gallons at a time. | 2025-09-02T16:37:51 | https://www.reddit.com/r/LocalLLaMA/comments/1n6ogar/are_local_models_more_sustainable_than_serverrun/ | Unlikely_Ad1890 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6ogar | false | null | t3_1n6ogar | /r/LocalLLaMA/comments/1n6ogar/are_local_models_more_sustainable_than_serverrun/ | false | false | self | 0 | null |
残心 / Zanshin - Navigate through media by speaker | 175 | 残心 / Zanshin is a media player that allows you to:
\- Visualize who speaks when & for how long
\- Jump/skip speaker segments
\- Remove/disable speakers (auto-skip)
\- Set different playback speeds for each speaker
It's a better, more efficient way to listen to podcasts, interviews, press conferences, etc.
It has first-class support for YouTube videos; just drop in a URL. Also supports your local media files. All processing runs on-device.
Download today for macOS: [https://zanshin.sh](https://zanshin.sh)
Also works on Linux and WSL, but currently without packaging. You can get it running though with just a few terminal commands. Check out the repo for instructions: [https://github.com/narcotic-sh/zanshin](https://github.com/narcotic-sh/zanshin)
Zanshin is powered by Senko, a new, very fast, speaker diarization pipeline I've developed.
On an M3 MacBook Air, it takes over 5 minutes to process 1 hour of audio using Pyannote 3.1, the leading open-source diarization pipeline. With Senko, it only takes \~24 seconds, a \~14x speed improvement. And on an RTX 4090 + Ryzen 9 7950X machine, processing 1 hour of audio takes just 5 seconds with Senko, a \~17x speed improvement.
Senko's speed is what make's Zanshin possible. Senko is a modified version of the speaker diarization pipeline found in the excellent 3D-Speaker project. Check out Senko here: [https://github.com/narcotic-sh/senko](https://github.com/narcotic-sh/senko)
Cheers, everyone; enjoy 残心/Zanshin and Senko. I hope you find them useful. Let me know what you think!
\~
Side note: I am looking for a job. If you like my work and have an opportunity for me, I'm all ears :) You can contact me at mhamzaqayyum \[at\] [icloud.com](http://icloud.com) | 2025-09-02T16:34:30 | https://v.redd.it/qh0wtns52smf1 | hamza_q_ | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1n6od0s | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/qh0wtns52smf1/DASHPlaylist.mpd?a=1759422887%2CODU5MzQyY2Q4ZGM0MzhhNzAxZWJmZTY1MWM3ZTQyZmY2ZWY0NDg1MWIzMTJhYzIxZGVmY2ZkNzk3MTY2ZWY1OQ%3D%3D&v=1&f=sd', 'duration': 36, 'fallback_url': 'https://v.redd.it/qh0wtns52smf1/DASH_1080.mp4?source=fallback', 'has_audio': True, 'height': 1080, 'hls_url': 'https://v.redd.it/qh0wtns52smf1/HLSPlaylist.m3u8?a=1759422887%2CMzA1OTE3MWY0MzJjZmE5MjFlODNlZDk0Y2JjNTNmZDY1ZTFmNWI1NTlhMTY2NDZmMzY0MWIwZTBiNDE0NThhMw%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/qh0wtns52smf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1144}} | t3_1n6od0s | /r/LocalLLaMA/comments/1n6od0s/残心_zanshin_navigate_through_media_by_speaker/ | false | false | 175 | {'enabled': False, 'images': [{'id': 'czg0dWhsczUyc21mMcardPaxszcLLO9nZqjdF7h57XxHnWsrQqY3M3ZeJApB', 'resolutions': [{'height': 102, 'url': 'https://external-preview.redd.it/czg0dWhsczUyc21mMcardPaxszcLLO9nZqjdF7h57XxHnWsrQqY3M3ZeJApB.png?width=108&crop=smart&format=pjpg&auto=webp&s=07a84aa515008b4f1655a7e52b153404f11ef820', 'width': 108}, {'height': 204, 'url': 'https://external-preview.redd.it/czg0dWhsczUyc21mMcardPaxszcLLO9nZqjdF7h57XxHnWsrQqY3M3ZeJApB.png?width=216&crop=smart&format=pjpg&auto=webp&s=3379bb5266be8b33625fb1afe8d28901727e3e06', 'width': 216}, {'height': 302, 'url': 'https://external-preview.redd.it/czg0dWhsczUyc21mMcardPaxszcLLO9nZqjdF7h57XxHnWsrQqY3M3ZeJApB.png?width=320&crop=smart&format=pjpg&auto=webp&s=2793333336a4d9776767aac553319e0698018d08', 'width': 320}, {'height': 604, 'url': 'https://external-preview.redd.it/czg0dWhsczUyc21mMcardPaxszcLLO9nZqjdF7h57XxHnWsrQqY3M3ZeJApB.png?width=640&crop=smart&format=pjpg&auto=webp&s=815ab8af131ee7838ed0afaaecd8841f5f4ec547', 'width': 640}, {'height': 907, 'url': 'https://external-preview.redd.it/czg0dWhsczUyc21mMcardPaxszcLLO9nZqjdF7h57XxHnWsrQqY3M3ZeJApB.png?width=960&crop=smart&format=pjpg&auto=webp&s=6820db6ffd5d8449396636d31388e2ec0acf2f13', 'width': 960}, {'height': 1020, 'url': 'https://external-preview.redd.it/czg0dWhsczUyc21mMcardPaxszcLLO9nZqjdF7h57XxHnWsrQqY3M3ZeJApB.png?width=1080&crop=smart&format=pjpg&auto=webp&s=c9939d6213e9a5c47e2f5f817001b4f21b2d0404', 'width': 1080}], 'source': {'height': 2160, 'url': 'https://external-preview.redd.it/czg0dWhsczUyc21mMcardPaxszcLLO9nZqjdF7h57XxHnWsrQqY3M3ZeJApB.png?format=pjpg&auto=webp&s=8c2797d411ab61c204da6221b01397578af38f2e', 'width': 2286}, 'variants': {}}]} | |
Need help fine-tuning DeepSeek R1 7B for Q&A project | 1 | I’m working on a spiritual guidance project where I have a dataset in JSONL format. Each entry has:
• input (the question),
• output (the answer),
• reference Bible verse, and
• follow-up question.
I tried fine-tuning a model on this dataset, but the results come out as gibberish. I also experimented with RAG (retrieval-augmented generation), but the system struggles to stay conversational it often fails when I give it a paraphrased question instead of the exact one from the dataset.
Has anyone tackled something similar? Should I focus more on improving fine-tuning, or is there a way to make the RAG pipeline handle paraphrasing and conversation flow better? Any guidance or best practices would be really appreciated. I would love to get some insights on how i can fine tune a deepseek model | 2025-09-02T16:17:21 | https://www.reddit.com/r/LocalLLaMA/comments/1n6nwex/need_help_finetuning_deepseek_r1_7b_for_qa_project/ | nightwing_2 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6nwex | false | null | t3_1n6nwex | /r/LocalLLaMA/comments/1n6nwex/need_help_finetuning_deepseek_r1_7b_for_qa_project/ | false | false | self | 1 | null |
Anyone here using Qwen3-235b-a22b-thinking-2507 as their daily driver??? | 34 | I fucking love this model!!! It performs better than deepseek for me in general use for nearly everything!!!
Easily the BEST Open Weight model we have that rivals closed models!
It just feels fucking intelligent to talk to lmao passes my vibe check and it's fast.
What is the experience of you guys with this model in more general use cases???
Also, I really wanna see a scaled up general version of this model like:
Qwen3-480B-35B-Thinking
The coder variant sucks for anything but producing code and maybe tool calling.
Sure it's gonna be difficult to run locally for most of the community but being able to access this amazing model from multiple cloud providers for dirt cheap prices is amazing for me!
You don't have to worry about model changing behind the scenes! You get "near" full control of the model.
Ofcourse there are issues like cloud providers using smaller quants behind the scenes but still worth it from more legit providers.
Qwen3-235b-a22b-thinking-2507 doesn't even feel benchmaxxed or at least not from my experience. The pre-update version was garbage but after the update, it became my favourite one so far!!!
Some more thoughts:
The new DeepSeek-V3.1 sucks ass man like madly inconsistent and just doesn't have the feel... It disappointed me big time. I saw people praising it but honestly, I just don't get it.
R1-0528 was a significant upgrade in terms of intelligence even with a lack of that "vibe".
V3-0324 was just 💋
But this new V3.1 feels like the worst of both worlds. I tried it a lot and I just can't trust it. It's very inconsistent in performance/accuracy. It also loses context fast. Misunderstands stuff way more than other models... An absolute failure in my experience. Maybe it's because of the hybrid thinking system that qwen left behind???
I just don't get how are you guys able to use V3.1 without letting out a sigh every prompt? | 2025-09-02T16:05:10 | https://www.reddit.com/r/LocalLLaMA/comments/1n6nkki/anyone_here_using_qwen3235ba22bthinking2507_as/ | True_Requirement_891 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6nkki | false | null | t3_1n6nkki | /r/LocalLLaMA/comments/1n6nkki/anyone_here_using_qwen3235ba22bthinking2507_as/ | false | false | self | 34 | null |
Best open-source + fast models (OCR / VLM) for reading diagrams, graphs, charts in documents? | 14 | Hi,
I’m looking for **open-source models** that are both **fast and accurate** for reading content like **diagrams, graphs, and charts** inside documents (PDF, PNG, JPG, etc.).
I tried **Qwen2.5-VL-7B-Instruct** on a figure with 3 subplots, but the result was too generic and missed important details.
So my question is:
* What open-source OCR or vision-language models work best for this?
* Any that are **lightweight / fast** enough to run on modest hardware (CPU or small GPU)?
* Bonus if you know benchmarks or comparisons for this task.
Thanks! | 2025-09-02T15:56:54 | Particular_Cake4359 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1n6ncef | false | null | t3_1n6ncef | /r/LocalLLaMA/comments/1n6ncef/best_opensource_fast_models_ocr_vlm_for_reading/ | false | false | default | 14 | {'enabled': True, 'images': [{'id': 'cituamodxrmf1', 'resolutions': [{'height': 152, 'url': 'https://preview.redd.it/cituamodxrmf1.jpeg?width=108&crop=smart&auto=webp&s=444c8c6fd2bbc04af316ecb8ae2532cc1822c8a9', 'width': 108}, {'height': 305, 'url': 'https://preview.redd.it/cituamodxrmf1.jpeg?width=216&crop=smart&auto=webp&s=0d3eabb88d65e4676809520f902850757971eca5', 'width': 216}, {'height': 452, 'url': 'https://preview.redd.it/cituamodxrmf1.jpeg?width=320&crop=smart&auto=webp&s=2d9b1a4499678ede242e6f8f132de15177814dcf', 'width': 320}, {'height': 904, 'url': 'https://preview.redd.it/cituamodxrmf1.jpeg?width=640&crop=smart&auto=webp&s=acb2dec320a92d7571df392f3617d229d330429c', 'width': 640}, {'height': 1356, 'url': 'https://preview.redd.it/cituamodxrmf1.jpeg?width=960&crop=smart&auto=webp&s=9d7f54279b489e5efad6b7a41f108267e421394f', 'width': 960}, {'height': 1526, 'url': 'https://preview.redd.it/cituamodxrmf1.jpeg?width=1080&crop=smart&auto=webp&s=0fb829a6633f7f091a46ba0276b111d5ceb3f015', 'width': 1080}], 'source': {'height': 1755, 'url': 'https://preview.redd.it/cituamodxrmf1.jpeg?auto=webp&s=4e2cb744d951427b771f5756a6c0486c7f6aaaf4', 'width': 1242}, 'variants': {}}]} | |
Artificial Analysis Intelligence Index now measures agentic capabilities, good news for Kimi K2 and GLM 4.5! | 116 | >Tool calling and agentic workflows are increasingly the norm for how language models are used by both developers and consumers. Adding Terminal-Bench and 𝜏²-Bench to our Intelligence Index reflects this trend and allows us to see where models have strengths for agentic use cases, compared to prior evaluations that are more focused on knowledge and reasoning.
Full methodology details [here](https://artificialanalysis.ai/methodology/intelligence-benchmarking). This should tip the scales a bit more in favor of Kimi K2 and GLM 4.5, which are post-trained for tool use. Current benchmarks are heavily weighted towards knowledge and mathematical/logical reasoning. | 2025-09-02T15:42:25 | https://www.reddit.com/gallery/1n6myps | entsnack | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1n6myps | false | null | t3_1n6myps | /r/LocalLLaMA/comments/1n6myps/artificial_analysis_intelligence_index_now/ | false | false | 116 | null | |
gemma 4b runs on gpu but gemma 12b runs on my cpu? | 0 | im trying to get gemma 12b (which is an 8gb model) to run on my gpu ONLY as my cpu (3950x is good but not as fast as my gfx card - 5060ti). I have TWICE the amount of vram needed and I only use about 4 gb of vram with all my dev apps and os running. the model fits within my vram size and it still uses the CPU TO RUN? but gemma 3:4b (which is only 3.3gb in model size runs AMAZING on my GPU. I'm not sure what I'm doing wrong? | 2025-09-02T15:35:08 | https://www.reddit.com/r/LocalLLaMA/comments/1n6mrwr/gemma_4b_runs_on_gpu_but_gemma_12b_runs_on_my_cpu/ | nad_lab | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6mrwr | false | null | t3_1n6mrwr | /r/LocalLLaMA/comments/1n6mrwr/gemma_4b_runs_on_gpu_but_gemma_12b_runs_on_my_cpu/ | false | false | self | 0 | null |
German "Who Wants to Be a Millionaire" Benchmark | 759 | i have created a benchmark for german "who wants to be millionaire" questions. there are 45x15 questions, all 45 rounds go from easy to hard and all tested models ran through all 45 rounds and got kicked out of a round if the answer was wrong, keeping the current winnings. no jokers.
i am a bit limited with the selection of llm's since i run them on my framework laptop 13 (amd ryzen 5 7640u with 32 gb ram), so i mainly used smaller llm's. also, qwen3's thinking went on for way to long for each question so i just tested non-thinking models except for gpt-oss-20b (low). but in my initial testing for qwen3-4b-thinking-2507, it seemed to worsen the quality of answers at least for the first questions.
the first few questions are often word-play and idioms questions needing great understanding of the german language. these proved to be very hard for most llm's but are easily solvable by the average german. once the first few questions were solved the models had an easier time answering.
i tried to use optimal model settings and included them in the table, let me know if they could be improved. all models are quant Q4\_K\_M.
i have close to no python coding ability so the main script was created with qwen3-coder. the project (with detailed results for each model, and the queationaire) is open souce and available on github.
[https://github.com/ikiruneo/millionaire-bench](https://github.com/ikiruneo/millionaire-bench) | 2025-09-02T15:24:56 | Available_Load_5334 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1n6mi81 | false | null | t3_1n6mi81 | /r/LocalLLaMA/comments/1n6mi81/german_who_wants_to_be_a_millionaire_benchmark/ | false | false | default | 759 | {'enabled': True, 'images': [{'id': 'du3iq68grrmf1', 'resolutions': [{'height': 101, 'url': 'https://preview.redd.it/du3iq68grrmf1.png?width=108&crop=smart&auto=webp&s=c3693ca6bdc4a1e596ed53e574bfa81fd84704d5', 'width': 108}, {'height': 203, 'url': 'https://preview.redd.it/du3iq68grrmf1.png?width=216&crop=smart&auto=webp&s=671f9b5364a725d081c7cb28031b823223c5b30f', 'width': 216}, {'height': 301, 'url': 'https://preview.redd.it/du3iq68grrmf1.png?width=320&crop=smart&auto=webp&s=a4898f1112aead6364ae81cf10c1b2160d7050fe', 'width': 320}, {'height': 602, 'url': 'https://preview.redd.it/du3iq68grrmf1.png?width=640&crop=smart&auto=webp&s=486736a10efedf5ea83f05d63d41d7eda1e92ac7', 'width': 640}, {'height': 904, 'url': 'https://preview.redd.it/du3iq68grrmf1.png?width=960&crop=smart&auto=webp&s=901323eae11d26e1188eff1b5617d29fe2ba5ec9', 'width': 960}, {'height': 1017, 'url': 'https://preview.redd.it/du3iq68grrmf1.png?width=1080&crop=smart&auto=webp&s=ab6f30e4b3f2310de541d10cffdb91f129b0bf72', 'width': 1080}], 'source': {'height': 1222, 'url': 'https://preview.redd.it/du3iq68grrmf1.png?auto=webp&s=220a54156211f18cfece6002992afaad0ed59df3', 'width': 1297}, 'variants': {}}]} | |
every LLM metric you need to know (v2.0) | 40 | Since I made [this post](https://www.reddit.com/r/LLMDevs/comments/1j6pxv9/every_llm_metric_you_need_to_know/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button) a few months ago, the AI and evals space has shifted significantly. Better LLMs mean that standard out-of-the-box metrics aren’t as useful as they once were, and [custom metrics](https://deepeval.com/docs/metrics-llm-evals) are becoming more important. Increasingly agentic and complex use cases are driving the need for [agentic metrics](https://deepeval.com/docs/metrics-task-completion). And the lack of ground truth—especially for smaller startups—puts more emphasis on referenceless metrics, especially around tool-calling and agents.
**A Note about Statistical Metrics:**
It’s become clear that statistical scores like BERT and ROUGE are fast, cheap, and deterministic, but much less effective than [LLM judges](https://deepeval.com/docs/metrics-introduction) (especially SOTA models) if you care about capturing nuanced contexts and evaluation accuracy, so I’ll only be talking about LLM judges in this list.
That said, here’s the updated, more comprehensive list of every LLM metric you need to know, version 2.0.
**Custom Metrics**
Every LLM use-case is unique and requires [custom metrics](https://deepeval.com/docs/metrics-llm-evals) for automated testing. In fact they are the most important metrics when it comes to building your eval pipeline. Common use-cases of custom metrics include defining custom criterias for “correctness”, and tonality/style-based metrics like “output professionalism”.
* [G-Eval:](https://deepeval.com/docs/metrics-llm-evals) a framework that uses LLMs with chain-of-thoughts (CoT) to evaluate LLM outputs based on any custom criteria.
* [DAG (Directed Acyclic Graphs):](https://deepeval.com/docs/metrics-dag) a framework to help you build decision tree metrics using LLM judges at each node to determine branching path, and useful for specialized use-cases, like aligning document genreatino with your format.
* [Arena G-Eval](https://deepeval.com/docs/metrics-arena-g-eval): a framework that uses LLMs with chain-of-thoughts (CoT) to pick the best LLM output from a group of contestants based on any custom criteria, which is useful for picking the best models, prompts for your use-case/
* [Conversational G-Eval](https://deepeval.com/docs/metrics-conversational-g-eval): The equivalent G-Eval, but for evaluating entire conversations instead of single-turn interactions.
* [Multimodal G-Eval](https://deepeval.com/docs/multimodal-metrics-g-eval): G-Eval that extends to other modalities such as image.
**Agentic Metrics:**
Almost every use case today is agentic. But evaluating agents is hard — the sheer number of possible decision-tree rabbit holes makes analysis complex. Having a ground truth for every tool call is essentially impossible. That’s why the following [agentic metrics](https://deepeval.com/docs/metrics-task-completion) are especially useful.
* [Task Completion:](https://deepeval.com/docs/metrics-task-completion) evaluates if an LLM agent accomplishes a task by analyzing the entire traced execution flow. This metric is easy to set up because it requires NO ground truth, and is arguably the most useful metric for detecting failed any agentic executions, like browser-based tasks, for example.
* [Argument Correctness](https://deepeval.com/docs/metrics-argument-correctness): evaluates if an LLM generates the correct inputs to a tool calling argument, which is especially useful for evaluating tool calls when you don’t have access to expected tools and ground truth.
* [Tool Correctness:](https://deepeval.com/docs/metrics-tool-correctness) assesses your LLM agent's function/tool calling ability. It is calculated by comparing whether every tool that is expected to be used was indeed called. It does require a ground truth.
* [MCP-Use](https://deepeval.com/docs/metrics-mcp-use): The MCP Use is a metric that is used to evaluate how effectively an MCP based LLM agent makes use of the mcp servers it has access to.
* [MCP Task Completion](https://deepeval.com/docs/metrics-mcp-task-completion): The MCP task completion metric is a conversational metric that uses LLM-as-a-judge to evaluate how effectively an MCP based LLM agent accomplishes a task.
* [Multi-turn MCP-Use](https://deepeval.com/docs/metrics-multi-turn-mcp-use): The Multi-Turn MCP Use metric is a conversational metric that uses LLM-as-a-judge to evaluate how effectively an MCP based LLM agent makes use of the mcp servers it has access to.
**RAG Metrics**
While AI agents are gaining momentum, most LLM apps in production today still rely on RAG. These metrics remain crucial as long as RAG is needed — which will be the case as long as there’s a cost tradeoff with model context length.
* [Answer Relevancy:](https://deepeval.com/docs/metrics-answer-relevancy) measures the quality of your RAG pipeline's generator by evaluating how relevant the actual output of your LLM application is compared to the provided input
* [Faithfulness:](https://deepeval.com/docs/metrics-faithfulness) measures the quality of your RAG pipeline's generator by evaluating whether the actual output factually aligns with the contents of your retrieval context
* [Contextual Precision:](https://deepeval.com/docs/metrics-contextual-precision) measures your RAG pipeline's retriever by evaluating whether nodes in your retrieval context that are relevant to the given input are ranked higher than irrelevant ones.
* [Contextual Recall:](https://deepeval.com/docs/metrics-contextual-recall) measures the quality of your RAG pipeline's retriever by evaluating the extent of which the retrieval context aligns with the expected output
* [Contextual Relevancy:](https://deepeval.com/docs/metrics-contextual-relevancy) measures the quality of your RAG pipeline's retriever by evaluating the overall relevance of the information presented in your retrieval context for a given input
**Conversational metrics**
50% of the agentic use-cases I encounter are conversational. Both agentic and conversational metrics go hand-in-hand. Conversational evals are different from single-turn evals because chatbots must remain consistent and context-aware across entire conversations, not just accurate in single-ouptuts. Here are the most useful conversational metrics.
* [Turn Relevancy:](https://deepeval.com/docs/metrics-turn-relevancy) determines whether your LLM chatbot is able to consistently generate relevant responses throughout a conversation.
* [Role Adherence:](https://deepeval.com/docs/metrics-role-adherence) determines whether your LLM chatbot is able to adhere to its given role throughout a conversation.
* [Knowledge Retention:](https://deepeval.com/docs/metrics-knowledge-retention) determines whether your LLM chatbot is able to retain factual information presented throughout a conversation.
* [Conversational Completeness:](https://deepeval.com/docs/metrics-conversation-completeness) determines whether your LLM chatbot is able to complete an end-to-end conversation by satisfying user needs throughout a conversation.
**Safety Metrics**
Better LLMs don’t mean your app is safe from malicious users. In fact, the more agentic your system becomes, the more sensitive data it can access — and stronger LLMs only amplify what can go wrong.
* [Bias](https://deepeval.com/docs/metrics-bias): determines whether your LLM output contains gender, racial, or political bias.
* [Toxicity](https://deepeval.com/docs/metrics-toxicity): evaluates toxicity in your LLM outputs.
* [Hallucination](https://deepeval.com/docs/metrics-hallucination): determines whether your LLM generates factually correct information by comparing the output to the provided context
* [Non-Advice:](https://deepeval.com/docs/metrics-non-advice) determines whether your LLM output contains inappropriate professional advice that should be avoided.
* [Misuse](https://deepeval.com/docs/metrics-misuse): determines whether your LLM output contains inappropriate usage of a specialized domain chatbot.
* [PII Leakage](https://deepeval.com/docs/metrics-pii-leakage): determines whether your LLM output contains personally identifiable information (PII) or privacy-sensitive data that should be protected.
* Role Violation
These metrics are a great starting point for setting up your eval pipeline, but there are many ways to apply them. Should you run evaluations in [development](https://www.confident-ai.com/docs/llm-evaluation/single-turn/end-to-end) or [production](https://www.confident-ai.com/docs/llm-tracing/evaluations)? Should you test your app[ end-to-end](https://deepeval.com/docs/evaluation-end-to-end-llm-evals) or evaluate [components separately](https://deepeval.com/docs/evaluation-component-level-llm-evals)? These kinds of questions are important to ask—and the right answer ultimately depends on your specific use case.
I’ll probably write more about this in another post, but the[ DeepEval docs](https://deepeval.com/docs/evaluation-component-level-llm-evals) are a great place to dive deeper into these metrics, understand how to use them, and explore their broader implications.
[Github Repo](https://github.com/confident-ai/deepeval) | 2025-09-02T15:00:08 | https://www.reddit.com/r/LocalLLaMA/comments/1n6lu9t/every_llm_metric_you_need_to_know_v20/ | FlimsyProperty8544 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6lu9t | false | null | t3_1n6lu9t | /r/LocalLLaMA/comments/1n6lu9t/every_llm_metric_you_need_to_know_v20/ | false | false | self | 40 | {'enabled': False, 'images': [{'id': 'EB8S75mE9Lk-7EbpNoOCSzIzwBqTmhPAK1QnI8ow2IE', 'resolutions': [{'height': 53, 'url': 'https://external-preview.redd.it/EB8S75mE9Lk-7EbpNoOCSzIzwBqTmhPAK1QnI8ow2IE.png?width=108&crop=smart&auto=webp&s=1289806f70f8ec0d5bd364de1e4c5a0e5a998c99', 'width': 108}, {'height': 106, 'url': 'https://external-preview.redd.it/EB8S75mE9Lk-7EbpNoOCSzIzwBqTmhPAK1QnI8ow2IE.png?width=216&crop=smart&auto=webp&s=a5c5a2656a28a1d29ab202e9600f70e1f38fd4ae', 'width': 216}, {'height': 157, 'url': 'https://external-preview.redd.it/EB8S75mE9Lk-7EbpNoOCSzIzwBqTmhPAK1QnI8ow2IE.png?width=320&crop=smart&auto=webp&s=ceddde7a473d4049b5fbc37cf26380c3b19494a5', 'width': 320}, {'height': 315, 'url': 'https://external-preview.redd.it/EB8S75mE9Lk-7EbpNoOCSzIzwBqTmhPAK1QnI8ow2IE.png?width=640&crop=smart&auto=webp&s=c3944c532419acf3950a26ab15e089f370ba2e8b', 'width': 640}, {'height': 473, 'url': 'https://external-preview.redd.it/EB8S75mE9Lk-7EbpNoOCSzIzwBqTmhPAK1QnI8ow2IE.png?width=960&crop=smart&auto=webp&s=3b64a6e8d910e3b08166ac53925e91f2a9d25b95', 'width': 960}], 'source': {'height': 520, 'url': 'https://external-preview.redd.it/EB8S75mE9Lk-7EbpNoOCSzIzwBqTmhPAK1QnI8ow2IE.png?auto=webp&s=d6212213545dafef4dea12f6493b2ee1c2aa79aa', 'width': 1054}, 'variants': {}}]} |
Students here using local online tools for study? | 1 | [removed] | 2025-09-02T14:35:40 | https://www.reddit.com/r/LocalLLaMA/comments/1n6l6wk/students_here_using_local_online_tools_for_study/ | bad_user_dev | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6l6wk | false | null | t3_1n6l6wk | /r/LocalLLaMA/comments/1n6l6wk/students_here_using_local_online_tools_for_study/ | false | false | self | 1 | null |
Git commands as LLM memory - 15k tokens down to 5k | 75 | Hey everyone,
I have been experimenting with giving LLMs access to granular git history instead of dumping entire codebases into context.
The approach: auto-commit code changes every 15 seconds to a shadow repo, then let the AI query it with git commands. So instead of feeding 5,000 lines of context, the model runs:
\- \`git diff HEAD\~10\` (50 tokens)
\- \`git log -S "function"\` to find when something was implemented
\- \`git blame\` to understand evolution
Tested this with Claude on a debugging session:
\- Without git history: 15,000 tokens, multiple attempts before fixing a bug
\- With git access: 5,000 tokens, found the bug fix almost immediately
The interesting part is that LLMs already understand git commands perfectly. They know exactly what to query without special training.
Technical approach I'm trying:
\- detect file changes using \`git status\` every 15 seconds
\- Commits to separate .shadowgit/ repo (not main)
\- MCP server exposes read-only git operations
\- Everything local
Questions for the community:
1. Anyone else exploring git as LLM memory? What's your approach?
2. For local models with small context windows, would this help?
3. Downsides I'm seeing: models might apply outdated patterns from history. Anyone experience this?
P.S. For transparency: I packaged this into a tool (ShadowGit, $19) but the MCP server is free on Github if you want to build your own solution. More interested in the community's feedback on this approach.
Thank you!
Alessandro | 2025-09-02T14:11:37 | https://www.reddit.com/r/LocalLLaMA/comments/1n6kklk/git_commands_as_llm_memory_15k_tokens_down_to_5k/ | Apart-Employment-592 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6kklk | false | null | t3_1n6kklk | /r/LocalLLaMA/comments/1n6kklk/git_commands_as_llm_memory_15k_tokens_down_to_5k/ | false | false | self | 75 | {'enabled': False, 'images': [{'id': 'qziYxsi86DkCdpaxe53mkv-ebCTGl3qvx9wMicdDtgw', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/qziYxsi86DkCdpaxe53mkv-ebCTGl3qvx9wMicdDtgw.png?width=108&crop=smart&auto=webp&s=a008b1bec8871aebf30b6af2db910fcb89feab5a', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/qziYxsi86DkCdpaxe53mkv-ebCTGl3qvx9wMicdDtgw.png?width=216&crop=smart&auto=webp&s=fc139f5c82f9b1033bb02404ae904376f1a00b48', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/qziYxsi86DkCdpaxe53mkv-ebCTGl3qvx9wMicdDtgw.png?width=320&crop=smart&auto=webp&s=a840ea27f0a74389a5c6ced0cea4e4498eefd702', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/qziYxsi86DkCdpaxe53mkv-ebCTGl3qvx9wMicdDtgw.png?width=640&crop=smart&auto=webp&s=48912b662ea3307d1f1d961e3b36b00c5fdeedcf', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/qziYxsi86DkCdpaxe53mkv-ebCTGl3qvx9wMicdDtgw.png?width=960&crop=smart&auto=webp&s=b7c51e053a3135690a84855fefc19a6cc8bb924e', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/qziYxsi86DkCdpaxe53mkv-ebCTGl3qvx9wMicdDtgw.png?width=1080&crop=smart&auto=webp&s=ffea9c51c662c3b032b734095b849e613b15fc69', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/qziYxsi86DkCdpaxe53mkv-ebCTGl3qvx9wMicdDtgw.png?auto=webp&s=b73e24934e9fafbe9017068b348392a0eecff50d', 'width': 1200}, 'variants': {}}]} |
Prompt processing | 4 | Using llama-server, model size > vram:
Are there any switches to only allow, or prioritize prompt processing on the video card?
Thanks | 2025-09-02T14:05:55 | https://www.reddit.com/r/LocalLLaMA/comments/1n6kfd6/prompt_processing/ | Agreeable-Prompt-666 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6kfd6 | false | null | t3_1n6kfd6 | /r/LocalLLaMA/comments/1n6kfd6/prompt_processing/ | false | false | self | 4 | null |
Zero-Loss PDF Image Extraction in Python | 3 | I’m working on a **PDF exam paper → Markdown conversion** pipeline and I’m struggling with reliably finding and extracting diagrams and visuals from a large set of PDF documents.
I’ve tried several approaches (e.g. **Mistral OCR**, **PyMuPDF**, etc.), but none have produced consistently good results.
My current approach looks like this:
1. **Convert each page of the PDF to a JPEG** at 300 DPI.
2. **Use an LLM** to analyse each page image and decide whether it contains any **non-text visuals** required to answer a question.
* If no relevant visuals are found, I store an entry like:
```json
{
"stem": "1f2020",
"page": 2,
"file": "/data/gcse_maths_edexcel/done/1f2020_p002.jpg",
"llm_judgment": {
"outcome": "NO",
"details": ""
}
}
```
* If visuals **are** required, I store them like:
```json
{
"stem": "1f2020",
"page": 3,
"file": "/data/gcse_maths_edexcel/done/1f2020_p003.jpg",
"llm_judgment": {
"outcome": "YES",
"items": [
{
"question": "6",
"summary": "Octagonal spinner divided into 8 equal sectors labeled A, A, A, B, C, C, C, C with a diagonal pointer crossing the centre.",
"regenerate_prompt": "From the provided page image, for question 6 specifically, isolate and recreate ONLY this visual: Octagonal spinner divided into 8 equal sectors labeled (clockwise from top): A, A, A, B, C, C, C, C with a diagonal spinner pointer crossing through the center.. Include every detail exactly as shown. Do not include any surrounding page text or unrelated elements. Render as a clean vector (SVG preferred; PNG acceptable) with a transparent background and uniform stroke weights. Center it, crop all whitespace to the visual’s tight bounds, and output only the final image (no captions or extra text)."
},
{
"question": "6a",
"summary": "Horizontal probability scale from 0 to 1 with ticks at 0, 1/2, and 1.",
"regenerate_prompt": "From the provided page image, for question 6a specifically, isolate and recreate ONLY this linear scale: Horizontal probability scale from 0 to 1 with ticks at 0, 1/2 (fraction), and 1.. Include exactly the same baseline, tick marks, and numeric/label positions as shown. Do not include any surrounding page text or unrelated elements. Render as a clean vector (SVG preferred; PNG acceptable) with a transparent background and uniform stroke weights. Center it, crop all whitespace to the scale’s tight bounds, and output only the final image (no captions or extra text)."
}
]
}
}
```
3. For every required visual, I **pass the reference page image plus a detailed regenerate prompt** to the LLM and ask it to recreate the diagram as a **clean SVG or PNG** with a transparent background, tightly cropped.
---
This approach *kinda* works, but the results are **extremely inconsistent**. LLMs really struggle to accurately recreate mathematical diagrams, graphs, probability scales, coordinate grids, and similar visuals — especially when precision matters (e.g. tick marks, angles, and distances).
I feel like I’ve hit a wall here.
Has anyone successfully solved this problem before? Specifically, I’m looking for a **more reliable way** to:
* Detect **exactly which visuals are needed** to answer questions in exam PDFs.
* Extract or recreate those visuals **with pixel-perfect accuracy** (spinners, graphs, scales, number lines, etc.).
* Ideally, integrate this into an automated pipeline without having to manually crop images page by page.
Any recommendations for **tools, libraries, or workflows** that can handle this better than an LLM would be hugely appreciated.
```python
#!/usr/bin/env python3
import argparse
import base64
import json
import os
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Any, Dict, List, Tuple
import fitz
import requests
from openai import AzureOpenAI
from PIL import Image
MODEL = "gpt-5"
CLIENT = AzureOpenAI(
api_key=os.getenv("AZURE_EU_OPENAI_KEY"),
api_version="2025-04-01-preview",
azure_endpoint=os.getenv("AZURE_EU_OPENAI_ENDPOINT"),
)
def ensure_dir(p: str) -> None:
Path(p).mkdir(parents=True, exist_ok=True)
def judge_image_b64(
b64_jpg: str, stem_name: str, page_num: int, file_path: str
) -> Dict[str, Any]:
sys_judge = (
"You judge exam pages for ALL non-text visuals REQUIRED to answer any question or sub-question on the page. "
"Capture ANY visual that could affect answering, including but not limited to: spinners, pie charts, graphs, axes, coordinate grids, plotted points, number lines, probability scales, timelines, rulers, flowcharts, maps, tables with symbols, diagrams with labels or arrows, bespoke illustrations, or any drawn shapes conveying information. "
"Use the full question reference when possible (e.g., 6a, 6b, 2(ii)); if uncertain, use the main number like 6 or 'unknown'. "
"Return ONLY JSON in one of these forms: "
'{"outcome":"NO","details":""} '
"or "
'{"outcome":"YES","items":[{"question":"<number or number+subpart or \\"unknown\\">","summary":"<short description of the exact visual>",'
'"regenerate_prompt":"<fully specified redraw prompt for ONLY this visual>"}...]}. '
"If multiple visuals exist, include one item per visual ordered top-to-bottom then left-to-right. "
"Each regenerate_prompt must be sufficient to recreate ONLY that visual as a clean vector (SVG preferred; PNG acceptable), transparent background, uniform stroke weights, cropped to tight bounds, no extra text."
)
generic_template = (
"From the provided page image, for question {q} specifically, isolate and recreate ONLY this visual: {summary}. "
"Produce a 1:1 replica of the source visual. Preserve exact geometry, aspect ratio, relative distances, angles, radii, coordinates, tick spacing, grid spacing, and label placement. "
"For number lines, axes, or probability scales, exactly match baseline length, tick count and positions, and numeric/label placement. "
"For charts, diagrams, shapes, or spinners, exactly match segment counts, angles, coordinates/paths, and pointer orientation. "
"Do not include any surrounding page text or unrelated elements. "
"Render as a clean vector (SVG preferred; PNG acceptable) on a fully transparent background. "
"Scale strokes proportionally so the visual appears identical at the output size. "
"Center it, crop tightly to the visual’s bounds with zero padding, and output only the final image with no captions or extra text."
)
scale_template = (
"From the provided page image, for question {q} specifically, isolate and recreate ONLY this linear scale: {summary}. "
"Produce a 1:1 replica of the source scale. Preserve exact geometry, baseline length, orientation, aspect ratio, tick count, tick spacing and positions (including minor and major ticks), and the exact numeric/label text, placement, and alignment. "
"Include arrows, end caps, markers, braces, and any anchors exactly as shown when present. "
"Do not include any surrounding page text or unrelated elements. "
"Render as a clean vector (SVG preferred; PNG acceptable) on a fully transparent background. "
"Scale stroke widths proportionally to match the original appearance at the output size. "
"Center the scale, crop tightly to its bounds with zero padding, and output only the final image with no captions or extra text."
)
user_text = (
f"Stem: {stem_name}\nPage: {page_num}\nImage file: {file_path}\n"
"Task: Inspect the attached image of the page and respond strictly with the required JSON."
)
r = CLIENT.chat.completions.create(
model=MODEL,
messages=[
{"role": "system", "content": sys_judge},
{
"role": "user",
"content": [
{"type": "text", "text": user_text},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{b64_jpg}"},
},
],
},
],
)
content = (r.choices[0].message.content or "").strip()
m = re.search(r"\{[\s\S]*\}\s*$", content)
data = json.loads(m.group(0) if m else content)
if isinstance(data, dict) and data.get("outcome") == "YES":
items = data.get("items") or []
out_items = []
for it in items:
q = str(it.get("question") or "unknown")
summary = str(it.get("summary") or "").strip()
if re.search(
r"(probability\s*scale|number\s*line|timeline|axis|axes|scale|ruler)",
summary,
re.I,
):
regen = scale_template.format(
q=q, summary=summary if summary else "the scale"
)
else:
regen = generic_template.format(
q=q, summary=summary if summary else "the visual"
)
it["regenerate_prompt"] = regen
out_items.append(it)
data["items"] = out_items
return data
def split_to_jpegs(
pdf_path: str,
out_dir: str,
prefix: str,
digits: int,
start: int,
end: int,
dpi: int,
quality: int,
subsampling: int,
progressive: bool,
optimize: bool,
overwrite: bool,
) -> List[Dict[str, Any]]:
ensure_dir(out_dir)
pages: List[Dict[str, Any]] = []
file_stem = Path(pdf_path).stem
with fitz.open(pdf_path) as doc:
total = len(doc)
s = max(1, start or 1)
e = min(total, end or total)
zoom = dpi / 72.0
mat = fitz.Matrix(zoom, zoom)
for pno in range(s - 1, e):
page_num = pno + 1
page = doc[pno]
pm = page.get_pixmap(matrix=mat, colorspace=fitz.csRGB, alpha=False)
im = Image.frombytes("RGB", (pm.width, pm.height), pm.samples)
name = f"{file_stem}_{prefix}{str(page_num).zfill(digits)}.jpg"
out_path = Path(out_dir) / name
if not out_path.exists() or overwrite:
title = f"{file_stem} page {page_num}"
exif = im.getexif()
exif[0x010E] = title
im.save(
out_path,
format="JPEG",
quality=quality,
subsampling=subsampling,
progressive=progressive,
optimize=optimize,
dpi=(dpi, dpi),
exif=exif.tobytes(),
)
pages.append(
{
"page": page_num,
"title": f"{file_stem} page {page_num}",
"file": str(out_path),
"width_px": pm.width,
"height_px": pm.height,
"dpi": dpi,
"quality": quality,
"subsampling": subsampling,
"progressive": progressive,
"stem": file_stem,
}
)
return pages
def load_existing_judgments(judge_path: Path) -> Tuple[List[Dict[str, Any]], set]:
if not judge_path.exists():
return [], set()
try:
with open(judge_path, "r", encoding="utf-8") as f:
arr = json.load(f)
pages = {
int(x.get("page")) for x in arr if isinstance(x, dict) and "page" in x
}
return arr, pages
except Exception:
return [], set()
def append_and_flush(judge_path: Path, row: Dict[str, Any]) -> None:
existing, _ = load_existing_judgments(judge_path)
existing.append(row)
with open(judge_path, "w", encoding="utf-8") as f:
json.dump(existing, f, ensure_ascii=False, indent=2)
def analyze_pages(pages: List[Dict[str, Any]], base_out_dir: str) -> None:
if not pages:
return
stem_dir = Path(base_out_dir)
ensure_dir(str(stem_dir))
judge_path = stem_dir / f"{pages[0].get('stem','pages')}_judge.json"
_, done_pages = load_existing_judgments(judge_path)
for r in pages:
page_num = int(r.get("page"))
file_path = r.get("file")
if page_num in done_pages:
continue
with open(file_path, "rb") as f:
b64_jpg = base64.b64encode(f.read()).decode("ascii")
result = judge_image_b64(b64_jpg, r.get("stem", "pages"), page_num, file_path)
row = {
"stem": r.get("stem", "pages"),
"page": page_num,
"file": file_path,
"llm_judgment": result,
}
append_and_flush(judge_path, row)
print(json.dumps(row, ensure_ascii=False))
def retry(max_attempts: int = 5, backoff_seconds: float = 2.0):
def deco(fn):
def wrapped(*args, **kwargs):
attempt = 0
while True:
try:
return fn(*args, **kwargs)
except Exception:
attempt += 1
if attempt >= max_attempts:
raise
time.sleep(backoff_seconds * attempt)
return wrapped
return deco
@retry()
def generate_image(
prompt: str,
output_path: Path,
size: str,
quality: str,
timeout: int = 3600,
ref_b64: str | None = None,
) -> None:
api_key = os.environ.get("AZURE_OPENAI_IMAGE_API_KEY")
if not api_key:
raise RuntimeError("AZURE_OPENAI_IMAGE_API_KEY is not set")
ensure_dir(str(output_path.parent))
if ref_b64:
url = f"{os.getenv('AZURE_EU_OPENAI_URL')}/openai/deployments/gpt-image-1/images/edits?api-version=2025-04-01-preview"
img_bytes = base64.b64decode(ref_b64)
files = {
"image[]": ("reference.jpg", img_bytes, "image/jpeg"),
}
data = {
"prompt": prompt,
"size": size,
"quality": quality,
"n": "1",
"background": "transparent",
"output_compression": "100",
"output_format": "png",
}
headers = {"api-key": api_key}
response = requests.post(
url, headers=headers, files=files, data=data, timeout=timeout
)
response.raise_for_status()
b64_json = response.json()["data"][0]["b64_json"]
img = Image.open(BytesIO(base64.b64decode(b64_json)))
img.save(output_path, format="PNG")
else:
url = f"{os.getenv('AZURE_EU_OPENAI_URL')}/openai/deployments/gpt-image-1/images/edits?api-version=2025-04-01-preview"
headers = {"Content-Type": "application/json", "api-key": api_key}
body = {
"prompt": prompt,
"size": size,
"quality": quality,
"output_compression": 100,
"output_format": "png",
"n": 1,
"background": "transparent",
}
response = requests.post(url, headers=headers, json=body, timeout=timeout)
response.raise_for_status()
b64_json = response.json()["data"][0]["b64_json"]
img = Image.open(BytesIO(base64.b64decode(b64_json)))
img.save(output_path, format="PNG")
def format_stem_for_output(stem_name: str) -> str:
m = re.match(r"^(\d)_?f(\d{4})$", stem_name, re.I)
if m:
return f"{m.group(1)}f{m.group(2)}"
return stem_name
def sanitize_question(q: str) -> str:
s = re.sub(r"\s+", "", q)
s = s.replace("(", "").replace(")", "").replace("[", "").replace("]", "")
s = re.sub(r"[^A-Za-z0-9_-]+", "", s)
return s.lower() or "unknown"
def collect_page_image_b64_map(pages: List[Dict[str, Any]]) -> Dict[int, str]:
m: Dict[int, str] = {}
for r in pages:
p = int(r["page"])
with open(r["file"], "rb") as f:
m[p] = base64.b64encode(f.read()).decode("ascii")
return m
def generate_from_judgments(
base_out_dir: str,
stem_name: str,
prefix: str,
size: str,
quality: str,
overwrite: bool,
) -> None:
stem_dir = Path(base_out_dir)
judge_path = stem_dir / f"{stem_name}_judge.json"
if not judge_path.exists():
return
with open(judge_path, "r", encoding="utf-8") as f:
judgments = json.load(f)
stem_formatted = format_stem_for_output(stem_name)
for row in judgments:
llm = row.get("llm_judgment") or {}
if not isinstance(llm, dict):
continue
if llm.get("outcome") != "YES":
continue
page_num = int(row.get("page"))
items = llm.get("items") or []
for it in items:
q = sanitize_question(str(it.get("question", "unknown")))
regen = str(it.get("regenerate_prompt", "")).strip()
if not regen:
continue
filename = f"{stem_formatted}_{prefix}{str(page_num).zfill(3)}_{q}.png"
out_path = Path(base_out_dir) / filename
if out_path.exists() and not overwrite:
continue
generate_image(regen, out_path, size=size, quality=quality)
if __name__ == "__main__":
ap = argparse.ArgumentParser()
ap.add_argument(
"--pdf",
default="/data/gcse_maths_edexcel/1fjune2017.pdf",
)
ap.add_argument(
"--out",
default="/data/gcse_maths_edexcel/done",
)
ap.add_argument("--prefix", default="p")
ap.add_argument("--digits", type=int, default=3)
ap.add_argument("--start", type=int, default=None)
ap.add_argument("--end", type=int, default=None)
ap.add_argument("--dpi", type=int, default=300)
ap.add_argument("--quality", type=int, default=90)
ap.add_argument("--subsampling", type=int, choices=[-1, 0, 1, 2], default=0)
ap.add_argument("--progressive", action="store_true")
ap.add_argument("--optimize", action="store_true")
ap.add_argument("--overwrite", action="store_true")
ap.add_argument("--img-size", default="1024x1536")
ap.add_argument("--img-quality", default="high", choices=["high", "standard"])
ap.add_argument("--skip-judge", action="store_true")
parsed_args = ap.parse_args()
page_rows = split_to_jpegs(
parsed_args.pdf,
parsed_args.out,
parsed_args.prefix,
parsed_args.digits,
parsed_args.start,
parsed_args.end,
parsed_args.dpi,
parsed_args.quality,
parsed_args.subsampling,
parsed_args.progressive,
parsed_args.optimize,
parsed_args.overwrite,
)
if not parsed_args.skip_judge:
analyze_pages(page_rows, parsed_args.out)
if page_rows:
doc_stem = page_rows[0]["stem"]
generate_from_judgments(
base_out_dir=parsed_args.out,
stem_name=doc_stem,
prefix=parsed_args.prefix,
size=parsed_args.img_size,
quality=parsed_args.img_quality,
overwrite=parsed_args.overwrite,
)
``` | 2025-09-02T13:58:14 | https://www.reddit.com/r/LocalLLaMA/comments/1n6k85s/zeroloss_pdf_image_extraction_in_python/ | balmofgilead | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6k85s | false | null | t3_1n6k85s | /r/LocalLLaMA/comments/1n6k85s/zeroloss_pdf_image_extraction_in_python/ | false | false | self | 3 | null |
OpenRouter.ai or NeuroRouters.com? | 0 | Which one is more reliable and cheaper for model inference? | 2025-09-02T13:27:55 | https://www.reddit.com/r/LocalLLaMA/comments/1n6jgpc/openrouterai_or_neurorouterscom/ | alejandrobrega | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6jgpc | false | null | t3_1n6jgpc | /r/LocalLLaMA/comments/1n6jgpc/openrouterai_or_neurorouterscom/ | false | false | self | 0 | null |
What is best local model for vibe coding that can run on H100? | 0 | I don't use LLMs a lot for coding, a few times here and there. But now I have decided to give it a fair shot and I want to try vibe coding. I have setup claude code and claude code router locally and setup vllm on my H100 server. But there are overwhelmingly large options of local models to choose from. Some of it are quite confusing.
A few threads are praising gpt-oss-120b while others are shitting on it. Although vllm docs claim that I can run gpt-oss-120b on H100 but in my experience it just crashes my server. Similarly, there are also varying opinions on qwen 3 coder as some claim that it is not good with tool calling.
I don't have a lot of experience running models locally so I wanted to know what would be the best open source models for vibe coding that'll fit in 95 GB of VRAM on H100? Also if somebody has vibe coding experience with local models, is claude code + claude code router a good setup or is there something better for local models?
PS: please don't suggest to use openrouter api or some other closed source service. | 2025-09-02T13:24:32 | https://www.reddit.com/r/LocalLLaMA/comments/1n6jdtu/what_is_best_local_model_for_vibe_coding_that_can/ | barbarous_panda | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6jdtu | false | null | t3_1n6jdtu | /r/LocalLLaMA/comments/1n6jdtu/what_is_best_local_model_for_vibe_coding_that_can/ | false | false | self | 0 | null |
Python script to summarize and talk about a YouTube video | 10 | 2025-09-02T13:19:56 | https://gist.github.com/danuker/81cb7136f6e45528550d4a5cde9d045f | autoencoder | gist.github.com | 1970-01-01T00:00:00 | 0 | {} | 1n6j9te | false | null | t3_1n6j9te | /r/LocalLLaMA/comments/1n6j9te/python_script_to_summarize_and_talk_about_a/ | false | false | default | 10 | {'enabled': False, 'images': [{'id': 'OAXSl8SY6T3JK9MGQyKxkoYbqZ71HQRYXLeB8CV0NXg', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/OAXSl8SY6T3JK9MGQyKxkoYbqZ71HQRYXLeB8CV0NXg.png?width=108&crop=smart&auto=webp&s=796041decb8c1250cbc2f301331b72f7385b477d', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/OAXSl8SY6T3JK9MGQyKxkoYbqZ71HQRYXLeB8CV0NXg.png?width=216&crop=smart&auto=webp&s=2e3562243f324d16bc6d9dd09adb1da4e0b100b5', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/OAXSl8SY6T3JK9MGQyKxkoYbqZ71HQRYXLeB8CV0NXg.png?width=320&crop=smart&auto=webp&s=564e5f4bb6808064a14eb3965a6911671c3c9807', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/OAXSl8SY6T3JK9MGQyKxkoYbqZ71HQRYXLeB8CV0NXg.png?width=640&crop=smart&auto=webp&s=0f53460a90493497883ab4cacbbb58e2acb464c4', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/OAXSl8SY6T3JK9MGQyKxkoYbqZ71HQRYXLeB8CV0NXg.png?width=960&crop=smart&auto=webp&s=7a4f79362039959fa37eab208ae001245ccfe6e3', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/OAXSl8SY6T3JK9MGQyKxkoYbqZ71HQRYXLeB8CV0NXg.png?width=1080&crop=smart&auto=webp&s=912f966e123e94e32e7975fe8aebac89450a6b98', 'width': 1080}], 'source': {'height': 640, 'url': 'https://external-preview.redd.it/OAXSl8SY6T3JK9MGQyKxkoYbqZ71HQRYXLeB8CV0NXg.png?auto=webp&s=c7cbcc7517e2406e2326e7a1eb6bdb9022c27fda', 'width': 1280}, 'variants': {}}]} | |
What is the fastest FP8 and FP4 inference server on RTX 6000 PRO blackwell | 11 | The purpose of this thread is to keep nice and clean information about sglang, vllm, trt-llm procedures how to run FP8 and NVFP4 LLM models based on my testing and others suggestions.
My favorite model GLM-4.5-Air-FP8 is fastest in sglang with this parameters:
USE_TRITON_W8A8_FP8_KERNEL=1 SGL_ENABLE_JIT_DEEPGEMM=0 python -m sglang.launch_server --model /mnt/GLM-4.5-Air-FP8/ --tp 2 --host 0.0.0.0 --port 5000 --mem-fraction-static 0.94 --context-length 128000 --enable-metrics --attention-backend flashinfer --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air
vllm is slower, trt-llm - I cannot run it in trt-llm
I will focus on NVFP4 quants for other models. I'm strugeling to run NVFP4 on SM120 architecture - the only working is trt-llm.
Feel free to share your sglang/vllm NVFP4 howtos - qwen3 / glm - what other models do you recommend?
| 2025-09-02T13:07:51 | https://www.reddit.com/r/LocalLLaMA/comments/1n6izl3/what_is_the_fastest_fp8_and_fp4_inference_server/ | festr2 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6izl3 | false | null | t3_1n6izl3 | /r/LocalLLaMA/comments/1n6izl3/what_is_the_fastest_fp8_and_fp4_inference_server/ | false | false | self | 11 | null |
NVIDIA A5000 vs A10 Ampere | 2 | I am confused with these two GPUs, they are pretty similar, but A5000 has much higher TDP? Does it consume much more during inference? Can power consumption be lowered? How would it influence performance?
What do you recommend? | 2025-09-02T12:38:29 | https://www.reddit.com/r/LocalLLaMA/comments/1n6ibvs/nvidia_a5000_vs_a10_ampere/ | opossum_cz | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6ibvs | false | null | t3_1n6ibvs | /r/LocalLLaMA/comments/1n6ibvs/nvidia_a5000_vs_a10_ampere/ | false | false | self | 2 | null |
how difficult is it to get a qwen3 70B or 100B coder from the existing 480B? | 1 | [removed] | 2025-09-02T12:28:46 | https://www.reddit.com/r/LocalLLaMA/comments/1n6i4d6/how_difficult_is_it_to_get_a_qwen3_70b_or_100b/ | One_Archer_577 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6i4d6 | false | null | t3_1n6i4d6 | /r/LocalLLaMA/comments/1n6i4d6/how_difficult_is_it_to_get_a_qwen3_70b_or_100b/ | false | false | self | 1 | null |
Best local LLM on Macbook Pro M1 Max 10 core CPU, 32 core GPU, 32 GB RAM | 0 | I'm looking for recommendations for models that excel in several areas:
* **Multilingual Fluency:** Strong performance in both **English and Italian**.
* **Coding Assistance:** Need a reliable AI partner for **coding tasks**, from generation to debugging.
* **STEM Support:** Assistance with **mathematics and physics** problems.
* **Creative Writing:** Models that can help with **creative writing**, storytelling, and brainstorming.
* **Image Generation:** Ideally, something that can generate **images** from prompts.
* **Multimodality:** A model that can process **text, images, PDFs, and CSVs** – essentially, a versatile document/data assistant.
**What are your recommendations for the best local LLMs that fit these criteria for my MacBook Pro?**
also where can i find the latest update on the internet, i tried LM studio with GPT OSS 20b but the responses are veri disappointing but the speed is good imo 40 Tk/s | 2025-09-02T12:14:01 | https://www.reddit.com/r/LocalLLaMA/comments/1n6htk2/best_local_llm_on_macbook_pro_m1_max_10_core_cpu/ | GroundbreakingLog935 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6htk2 | false | null | t3_1n6htk2 | /r/LocalLLaMA/comments/1n6htk2/best_local_llm_on_macbook_pro_m1_max_10_core_cpu/ | false | false | self | 0 | null |
I created an open source prompt engineering language - (: Smile! Works for both open source & foundation models at all parameter sizes. | 0 | Hey ya'll.
I spent a long time prompt engineering, writing sections, figuring out how to get the model to understand markdown or a large context full of data even if it had low parameters or a small context window. I finally decided enough was enough and formalized an entire language for how I managed to make my results consistent across LLMs and tasks.
[https://www.github.com/drthomasager/smile](https://www.github.com/drthomasager/smile)
If you ever wanted a way to write your prompts that was maintainable, positive, fun to read, and effective for communicating to LLMs then please check out the repo and give my quickstart example a try.
It gets deep as I kind of wormholed into this repo for the last week. I hope ya'll like it! Please let me know how you liked it :) | 2025-09-02T12:12:12 | https://www.reddit.com/r/LocalLLaMA/comments/1n6hs8g/i_created_an_open_source_prompt_engineering/ | ThomasAger | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6hs8g | false | null | t3_1n6hs8g | /r/LocalLLaMA/comments/1n6hs8g/i_created_an_open_source_prompt_engineering/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': '2Z0naA_b5WeYZrWiQu-JiqfgEQXQxTlEysVxKh9cPA4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/2Z0naA_b5WeYZrWiQu-JiqfgEQXQxTlEysVxKh9cPA4.png?width=108&crop=smart&auto=webp&s=d02a4110f336898c08adc6dd70b40bc4860fbfc5', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/2Z0naA_b5WeYZrWiQu-JiqfgEQXQxTlEysVxKh9cPA4.png?width=216&crop=smart&auto=webp&s=e220d6f3870b4745a2f00a7f9a2cf21938c4bf90', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/2Z0naA_b5WeYZrWiQu-JiqfgEQXQxTlEysVxKh9cPA4.png?width=320&crop=smart&auto=webp&s=8ad9da6f24273b358414af530f29b12487439ff1', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/2Z0naA_b5WeYZrWiQu-JiqfgEQXQxTlEysVxKh9cPA4.png?width=640&crop=smart&auto=webp&s=418a71c65d98583b5811fa6c727c7106ac20dd63', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/2Z0naA_b5WeYZrWiQu-JiqfgEQXQxTlEysVxKh9cPA4.png?width=960&crop=smart&auto=webp&s=d550ce0dff1e4824851a53484eb2410b72c130bc', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/2Z0naA_b5WeYZrWiQu-JiqfgEQXQxTlEysVxKh9cPA4.png?width=1080&crop=smart&auto=webp&s=14394a4f3da315df312b53ca5cc3445fd1871c31', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/2Z0naA_b5WeYZrWiQu-JiqfgEQXQxTlEysVxKh9cPA4.png?auto=webp&s=c07d666bacce064186c5dff08399f9010f441e2a', 'width': 1200}, 'variants': {}}]} |
Fully Annotated Guide to "What are Diffusion Models?" | 14 | 2025-09-02T12:05:24 | https://ki-seki.github.io/posts/250902-diffusion-annotated/ | song-sc | ki-seki.github.io | 1970-01-01T00:00:00 | 0 | {} | 1n6hnc4 | false | null | t3_1n6hnc4 | /r/LocalLLaMA/comments/1n6hnc4/fully_annotated_guide_to_what_are_diffusion_models/ | false | false | default | 14 | null | |
Image 'editing' app with Qwen Image Edit and an iOS client | 13 | I commented on a recent '[Uncensored Image Editing](https://www.reddit.com/r/LocalLLaMA/comments/1n6862l/uncensored_image_editing_and_generation/)' post that I'd built something similar for fun with [Qwen Image Edit](https://huggingface.co/Qwen/Qwen-Image-Edit), and u/GreenGreasyGreasels called me out (politely) for not actually making at least the architecture public.
I figured I might as well put up the whole thing; it's not a lot of work, but I want to emphasize that it is NOT safe to run on a public endpoint.
[https://github.com/cyberfox/image-edit](https://github.com/cyberfox/image-edit)
Set up an environment, `pip install -r server/requirements.txt` and then
uvicorn image-edit-server:app --host 0.0.0.0 --port 8000
You'll need a developer license and to have your iPhone in developer mode, to build and install it. I don't have an icon for it, so it'll just look janky.
You enter your IP address and port in the settings, and it should basically work.
You can read the docs, the server source, and the Swift source. Again, it's janky, and I coded it really quick. Here's some images to whet the appetite.
I looked for an image with free usage, and [found this image](https://pixabay.com/photos/woman-young-casual-sitting-fence-8788000/) to be useful for this purpose. Hopefully it is free as they say it is.
https://preview.redd.it/lae5tho1rqmf1.png?width=1320&format=png&auto=webp&s=70f2f24a7fd0d6ac9dca43cea7a7c328cf5f3d51
[Before editing](https://preview.redd.it/vstq6rp7qqmf1.jpg?width=853&format=pjpg&auto=webp&s=9b1084616d8f973758773a13b03ada685e922a19)
[After Editing](https://preview.redd.it/ulxqbrnpqqmf1.png?width=832&format=png&auto=webp&s=3809aba7d482a0b7b2244b07b2e17173b4209b23)
Hope that's interesting to folks! | 2025-09-02T12:01:15 | https://www.reddit.com/r/LocalLLaMA/comments/1n6hk90/image_editing_app_with_qwen_image_edit_and_an_ios/ | baliord | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6hk90 | false | null | t3_1n6hk90 | /r/LocalLLaMA/comments/1n6hk90/image_editing_app_with_qwen_image_edit_and_an_ios/ | false | false | 13 | {'enabled': False, 'images': [{'id': '_phhztHOXP1EaSigwjpmdclnYlgiIY_nMfXGtHApDV0', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/_phhztHOXP1EaSigwjpmdclnYlgiIY_nMfXGtHApDV0.png?width=108&crop=smart&auto=webp&s=46b31a63c054056a199d9db162a18c1eafc7cc2b', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/_phhztHOXP1EaSigwjpmdclnYlgiIY_nMfXGtHApDV0.png?width=216&crop=smart&auto=webp&s=37a4e8946a54075da758b314e7f9ec49fa6b0f01', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/_phhztHOXP1EaSigwjpmdclnYlgiIY_nMfXGtHApDV0.png?width=320&crop=smart&auto=webp&s=ec0407ec35598bb574e70223bed5e563d95abfc4', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/_phhztHOXP1EaSigwjpmdclnYlgiIY_nMfXGtHApDV0.png?width=640&crop=smart&auto=webp&s=3b9e4ac0aed522efe5c46173ca8aa2cff20f9af4', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/_phhztHOXP1EaSigwjpmdclnYlgiIY_nMfXGtHApDV0.png?width=960&crop=smart&auto=webp&s=c0df3d1cb61fdc064408bc4ab4387184bb603218', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/_phhztHOXP1EaSigwjpmdclnYlgiIY_nMfXGtHApDV0.png?width=1080&crop=smart&auto=webp&s=630d8561afea1b218a9f0dcae22690918de542d9', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/_phhztHOXP1EaSigwjpmdclnYlgiIY_nMfXGtHApDV0.png?auto=webp&s=24426c5b20184e0be5017b2b58026bf8887b0f5a', 'width': 1200}, 'variants': {}}]} | |
Why does local Qwen3 fail this test, but Qwen3 cloud passes it? (and GPT-5 fails, BTW) | 3 | I did the test prompt at [https://github.com/jaldps/ai-tests/tree/main/Non-Math%20Reasoning](https://github.com/jaldps/ai-tests/tree/main/Non-Math%20Reasoning), with
* Qwen3-30B-A3B-Thinking-2507-IQ4\_NL: incorrect ("Beth", same result as GPT-5)
* Qwen3-30B-A3B-Thinking-2507-UD-Q8\_K\_XL: I interrupted at 13000 thinking tokens
* gpt-oss-20b-Q5\_K\_M: total rubbish in thinking process
* GPT-5: incorrect ("Beth"), with "thinking" turned on automatically by itself
* online Qwen3-30B-A3B, thinking on: correct ("Leo")
* online Qwen3-235B-A22B, thinking on: correct "Leo")
I am confused, and have questions
* I expected GPT-5 being able to solve it. Why couldn't it?
* I expected online Qwen3-30B-A3B not being able to solve it. It's a small model. Why could it?
* I hoped local Qwen3-30B-A3B could solve it, at least the Q8 quant. Why couldn't it, while the online version from Qwen could solve it flawlessly? | 2025-09-02T11:57:21 | https://www.reddit.com/r/LocalLLaMA/comments/1n6hha7/why_does_local_qwen3_fail_this_test_but_qwen3/ | kuhunaxeyive | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6hha7 | false | null | t3_1n6hha7 | /r/LocalLLaMA/comments/1n6hha7/why_does_local_qwen3_fail_this_test_but_qwen3/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'HNOFQMbYBvaptCzvZ3Ez8mQiYyO05KcpuOyBmsSoSrA', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/HNOFQMbYBvaptCzvZ3Ez8mQiYyO05KcpuOyBmsSoSrA.png?width=108&crop=smart&auto=webp&s=9547a2ac02387f83d2c58f4072a0846abbe75465', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/HNOFQMbYBvaptCzvZ3Ez8mQiYyO05KcpuOyBmsSoSrA.png?width=216&crop=smart&auto=webp&s=6231a4e660b076c32ee358d79b7f268f948624f6', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/HNOFQMbYBvaptCzvZ3Ez8mQiYyO05KcpuOyBmsSoSrA.png?width=320&crop=smart&auto=webp&s=809ca3964129defa36b65ea900951226045b35e5', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/HNOFQMbYBvaptCzvZ3Ez8mQiYyO05KcpuOyBmsSoSrA.png?width=640&crop=smart&auto=webp&s=fe0260ba3ed8429227b70c2a41a730aa141ff262', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/HNOFQMbYBvaptCzvZ3Ez8mQiYyO05KcpuOyBmsSoSrA.png?width=960&crop=smart&auto=webp&s=e3e8e3c24e92839912400b6d0fdb93befecade5c', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/HNOFQMbYBvaptCzvZ3Ez8mQiYyO05KcpuOyBmsSoSrA.png?width=1080&crop=smart&auto=webp&s=0647911f8de258305aca650c21fad51661146766', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/HNOFQMbYBvaptCzvZ3Ez8mQiYyO05KcpuOyBmsSoSrA.png?auto=webp&s=648c9bd527a9dae6bf723a79d0c9f2b25ba7b7d9', 'width': 1200}, 'variants': {}}]} |
How to fine-tune GPT-OSS | 4 | Hi everybody, I would like to fine-tuning GPT-OSS-20b in domain specific data. I have a dataset with around 40k chunks of text and I will make a self supervised training with it.
I have seem that OpenAI published a tutorial for fine-tuning (https://cookbook.openai.com/articles/gpt-oss/fine-tune-transfomers), and I am using the same LoRA configurations (target_modules=all-linear, target_parameters=7.mlp.experts.gate_up_proj, 7.mlp.experts.down_proj, 15.mlp.experts.gate_up_proj, 15.mlp.experts.down_proj, 23.mlp.experts.gate_up_proj, 23.mlp.experts.down_proj) and initially I am testing with these r,alpha (r=8, alpha=16), (r=16, alpha=32)
Since I am new to this MoE architecture, I would like some advices on which target modules and parameters I should use in order to fine-tune my model properly.
Does anyone here have experience training this model?
Some hyperparams:
Batch size=96
Epoch=2
Lr=5e-5 | 2025-09-02T11:39:30 | https://www.reddit.com/r/LocalLLaMA/comments/1n6h4y5/how_to_finetune_gptoss/ | Ok-Astronomer-2110 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6h4y5 | false | null | t3_1n6h4y5 | /r/LocalLLaMA/comments/1n6h4y5/how_to_finetune_gptoss/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': '1g1aO4K4YtvuCUsa2NQD2isUUyeWaCLqAH6r5ZvbPzk', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/1g1aO4K4YtvuCUsa2NQD2isUUyeWaCLqAH6r5ZvbPzk.png?width=108&crop=smart&auto=webp&s=e21b918a6bd47ae52601f8bbd51d5018895a7666', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/1g1aO4K4YtvuCUsa2NQD2isUUyeWaCLqAH6r5ZvbPzk.png?width=216&crop=smart&auto=webp&s=090f92abf1592b127e1ff7a9ff1ffcba1e77635b', 'width': 216}, {'height': 167, 'url': 'https://external-preview.redd.it/1g1aO4K4YtvuCUsa2NQD2isUUyeWaCLqAH6r5ZvbPzk.png?width=320&crop=smart&auto=webp&s=7758dffb5743f1126d5bc62fd9d7dd1019ce18e3', 'width': 320}, {'height': 334, 'url': 'https://external-preview.redd.it/1g1aO4K4YtvuCUsa2NQD2isUUyeWaCLqAH6r5ZvbPzk.png?width=640&crop=smart&auto=webp&s=11ab391878f109e16178aaa55bd6d3f3b344fed6', 'width': 640}, {'height': 502, 'url': 'https://external-preview.redd.it/1g1aO4K4YtvuCUsa2NQD2isUUyeWaCLqAH6r5ZvbPzk.png?width=960&crop=smart&auto=webp&s=5e2938682341d6b004d612bbea72d6b275f9b7af', 'width': 960}, {'height': 565, 'url': 'https://external-preview.redd.it/1g1aO4K4YtvuCUsa2NQD2isUUyeWaCLqAH6r5ZvbPzk.png?width=1080&crop=smart&auto=webp&s=37d0ba9b7515c806f00722d7fd8c14e8ab5c6b5b', 'width': 1080}], 'source': {'height': 628, 'url': 'https://external-preview.redd.it/1g1aO4K4YtvuCUsa2NQD2isUUyeWaCLqAH6r5ZvbPzk.png?auto=webp&s=6358f7da610cb4eda31a2a9c1d4a8493bd1a94c3', 'width': 1200}, 'variants': {}}]} |
rStar2-Agent | 29 | This MS model was realeased some days ago and I haven’t see any posts talking about it on here but from the benchmarks looks promising for a 14B model.
Has anyone tried it? | 2025-09-02T11:37:39 | https://www.arxiv.org/pdf/2508.20722 | thatusernsmeis | arxiv.org | 1970-01-01T00:00:00 | 0 | {} | 1n6h3rk | false | null | t3_1n6h3rk | /r/LocalLLaMA/comments/1n6h3rk/rstar2agent/ | false | false | default | 29 | null |
Thinking of buying a used 3090 (Asus blower). Are the thermals really bad? Seller asking 46500 INR plus shipping? | 0 | 2025-09-02T11:31:32 | kartikmandar | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1n6gzlp | false | null | t3_1n6gzlp | /r/LocalLLaMA/comments/1n6gzlp/thinking_of_buying_a_used_3090_asus_blower_are/ | false | false | 0 | {'enabled': True, 'images': [{'id': 'NtV4Hx_w4FCOrllUdoUioSVYmGn5Cxdv5jFqmgMyhlM', 'resolutions': [{'height': 143, 'url': 'https://preview.redd.it/wf5uvlf3mqmf1.jpeg?width=108&crop=smart&auto=webp&s=15e3cd3f437d5d4d53935446f2319fbda2588c3d', 'width': 108}, {'height': 287, 'url': 'https://preview.redd.it/wf5uvlf3mqmf1.jpeg?width=216&crop=smart&auto=webp&s=80e2fdb64d3c594de29a9cffe2bd4c6b27013132', 'width': 216}, {'height': 425, 'url': 'https://preview.redd.it/wf5uvlf3mqmf1.jpeg?width=320&crop=smart&auto=webp&s=983aebf3e1ffafdb2e7b6c12ad3f60dc3c7ed311', 'width': 320}, {'height': 850, 'url': 'https://preview.redd.it/wf5uvlf3mqmf1.jpeg?width=640&crop=smart&auto=webp&s=7dbba12c882a987f147af632bb4d0c0f0e7714a1', 'width': 640}, {'height': 1275, 'url': 'https://preview.redd.it/wf5uvlf3mqmf1.jpeg?width=960&crop=smart&auto=webp&s=25d0879d572c0845e09d9e9655c0941a5b4e6159', 'width': 960}, {'height': 1435, 'url': 'https://preview.redd.it/wf5uvlf3mqmf1.jpeg?width=1080&crop=smart&auto=webp&s=2a5f0994faf849a8540d062e30e98a1ccec62936', 'width': 1080}], 'source': {'height': 1600, 'url': 'https://preview.redd.it/wf5uvlf3mqmf1.jpeg?auto=webp&s=6f624be2f485ca1b04c7ef0be0477e3d4b664f0d', 'width': 1204}, 'variants': {}}]} | |||
Open-source LLM trained in Switzerland now available (8b and 70b) | 1 | [deleted] | 2025-09-02T11:25:31 | [deleted] | 1970-01-01T00:00:00 | 0 | {} | 1n6gviu | false | null | t3_1n6gviu | /r/LocalLLaMA/comments/1n6gviu/opensource_llm_trained_in_switzerland_now/ | false | false | default | 1 | null | ||
how much would it cost to distil a qwen3 70B coder from the 480B? | 1 | [removed] | 2025-09-02T10:58:37 | https://www.reddit.com/r/LocalLLaMA/comments/1n6gdpx/how_much_would_it_cost_to_distil_a_qwen3_70b/ | One_Archer_577 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6gdpx | false | null | t3_1n6gdpx | /r/LocalLLaMA/comments/1n6gdpx/how_much_would_it_cost_to_distil_a_qwen3_70b/ | false | false | self | 1 | null |
about 300 pages: Global Fix Map for local LLMs (upgrade from the Problem Map) + looking for your feedback | 21 | thanks for the support on my Problem Map earlier.
quick update:
i’ve turned it into a Global Fix Map. it’s a one-stop index that routes real bugs to the right repair page, with copy-paste fixes and acceptance targets so you can verify the change, not just hope it worked. if you run local models (llama.cpp, Ollama, textgen-webui, TGI, koboldcpp, gpt4all, exllama…), jump to the LocalDeploy_Inference section inside.
(70 days 800 stars repo)
https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/README.md
—
what’s inside
* about 300 pages across stacks: Providers & Agents, Data & Retrieval, Input & Parsing, Reasoning & Memory, Eval & Governance
* each page ends with acceptance targets so fixes are testable: ΔS(question, context) ≤ 0.45, coverage ≥ 0.70, λ stays convergent on 3 paraphrases
* local folks can start at LocalDeploy_Inference, then branch to VectorDBs_and_Stores, Embeddings, Safety/Prompt-Integrity, and OpsDeploy
—
what we think is happening → what’s really happening (local edition)
* “context got weird after 8k, rope is broken” → tokenizer or chat-template mismatch, not rope. check template, system/user roles, and casing rules. often maps to No.8 Retrieval Traceability + Prompt Assembly.
* “model changed after quant, bad weights?” → quantization variance + loader flags. gguf/awq/gptq differ in samplers and kv-cache behavior. pin flags, compare against fp16 baseline. often OpsDeploy + Eval issue, not the model.
* “json mode keeps breaking mid stream” → schema or tool contract drift. enforce cite-then-explain or tool-first, clamp max tokens for tool calls, and add a fail-fast. maps to Safety / Prompt Integrity.
* “same prompt, different answer every run” → pre-deploy collapse on first warmup, or sampler drift. verify secrets, env, and warm-up fences, then lock temperature/top-p. relates to No.16 Pre-Deploy Collapse.
* “similarity high but meaning off” → metric mismatch / normalization in the store. rebuild with the right distance and scaling, then re-eval. see Embeddings + VectorDBs_and_Stores.
* “server is fine, but long outputs cut off” → streaming body cutoffs / proxy timeouts. move to chunked streaming, raise body size, add retry with idempotency on the writer. shows up under Cloud/Serverless + OpsDeploy.
* “hybrid underperforms single retriever” → query-parsing split and missing reranker weights. either fix the weights or move reranking out of chain. that’s Retrieval + RAG_VectorDB territory.
—
what i’m asking from LocalLLaMA
* tell me which checklists you want first: llama.cpp flags, Ollama model cards, textgen-webui prompt templates, TGI json/tool notes, kv-cache + sampler guardrails, rope-scaling caveats, gguf vs awq vs gptq comparison
* point to any page that feels unclear and i’ll rewrite it into a minimal recipe with before/after checks
* got a reproducible? drop a short trace: loader + version, model + quant, key flags, prompt template, smallest failing prompt, expected vs observed, and a one-line log snippet
goal stays simple: make local pipelines stable without changing your infra. if you missed the original Problem Map, this is the upgraded version you can jump into directly.
—
Closing note
i also want to thank this community , a lot of you gave me encouragement and shared practical tips when i first posted the Problem Map. i kept notes on that feedback. this new Global Fix Map is my way of turning those notes into something concrete.
i’d love more input this round: which parts are most urgent for you? do you want to see checklists first, or code recipes, or more worked examples? if there’s a tool or workflow you want prioritized, let me know. i’ll keep adding based on what the community actually needs. 🫡 | 2025-09-02T10:38:07 | onestardao | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1n6g0rt | false | null | t3_1n6g0rt | /r/LocalLLaMA/comments/1n6g0rt/about_300_pages_global_fix_map_for_local_llms/ | false | false | default | 21 | {'enabled': True, 'images': [{'id': '9lvmz0lkcqmf1', 'resolutions': [{'height': 81, 'url': 'https://preview.redd.it/9lvmz0lkcqmf1.jpeg?width=108&crop=smart&auto=webp&s=d5a62f7e9c22474f3d292babaddfcaed58f437dc', 'width': 108}, {'height': 162, 'url': 'https://preview.redd.it/9lvmz0lkcqmf1.jpeg?width=216&crop=smart&auto=webp&s=b594dcba1332d3b968c68bb791d962ace92f9042', 'width': 216}, {'height': 240, 'url': 'https://preview.redd.it/9lvmz0lkcqmf1.jpeg?width=320&crop=smart&auto=webp&s=5feee8f8ae28bb6213a5f343f319f154e247c727', 'width': 320}, {'height': 481, 'url': 'https://preview.redd.it/9lvmz0lkcqmf1.jpeg?width=640&crop=smart&auto=webp&s=5dbe81f1db581025cd1aa4f66b0092d904609484', 'width': 640}, {'height': 722, 'url': 'https://preview.redd.it/9lvmz0lkcqmf1.jpeg?width=960&crop=smart&auto=webp&s=5f6baa0f6930a3b8c61c986ae8ba6907589fabfd', 'width': 960}, {'height': 812, 'url': 'https://preview.redd.it/9lvmz0lkcqmf1.jpeg?width=1080&crop=smart&auto=webp&s=f7db96dbc018967be1c1d4799aa36c6b33875995', 'width': 1080}], 'source': {'height': 963, 'url': 'https://preview.redd.it/9lvmz0lkcqmf1.jpeg?auto=webp&s=4e5704265ba8ec9037ab972ff1996fc8d9b14de6', 'width': 1280}, 'variants': {}}]} | |
ETH Zurich Open LLM "Apertus" has been released | 57 | 2025-09-02T10:25:45 | https://huggingface.co/collections/swiss-ai/apertus-llm-68b699e65415c231ace3b059 | kisamoto | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1n6fta4 | true | null | t3_1n6fta4 | /r/LocalLLaMA/comments/1n6fta4/eth_zurich_open_llm_apertus_has_been_released/ | false | false | default | 57 | {'enabled': False, 'images': [{'id': '3xCYbgdmDkf0KukpAo-RYTjRTShJKNSz9uOuaVJW_jI', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/3xCYbgdmDkf0KukpAo-RYTjRTShJKNSz9uOuaVJW_jI.png?width=108&crop=smart&auto=webp&s=7b22d597e561d91ff99ef376b18f403c4974ad25', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/3xCYbgdmDkf0KukpAo-RYTjRTShJKNSz9uOuaVJW_jI.png?width=216&crop=smart&auto=webp&s=1a68d114b715282ada6cb70f6aff02d200e3d91a', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/3xCYbgdmDkf0KukpAo-RYTjRTShJKNSz9uOuaVJW_jI.png?width=320&crop=smart&auto=webp&s=35942af6af30c0a778198715bb263b570bde7868', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/3xCYbgdmDkf0KukpAo-RYTjRTShJKNSz9uOuaVJW_jI.png?width=640&crop=smart&auto=webp&s=5da84c50675f066ea42c6cb75049480ff32b8ed5', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/3xCYbgdmDkf0KukpAo-RYTjRTShJKNSz9uOuaVJW_jI.png?width=960&crop=smart&auto=webp&s=62e8be174733b36274e7c98214e6c249129f154b', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/3xCYbgdmDkf0KukpAo-RYTjRTShJKNSz9uOuaVJW_jI.png?width=1080&crop=smart&auto=webp&s=305c7259ef9e72df2c282c3f4cf489d7f946db47', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/3xCYbgdmDkf0KukpAo-RYTjRTShJKNSz9uOuaVJW_jI.png?auto=webp&s=7a41604eea7b927caea604e6fa4b892cf43b6df4', 'width': 1200}, 'variants': {}}]} | |
I just released a big update for my AI research agent, MAESTRO, with a new docs site showing example reports from Qwen 72B, GPT-OSS 120B, and more. | 216 | Hey everyone,
I've been working hard on a big update for my open-source project, MAESTRO, and I'm excited to share v0.1.5-alpha with you all. MAESTRO is an autonomous research agent that turns any question into a fully-cited report.
A huge focus of this release was improving performance and compatibility with local models. I've refined the core agent workflows and prompts to make sure it works well with most reasonably intelligent locally hosted models.
I also launched a completely new documentation site to help users setup and start using MAESTRO. The best part is the new \*\*[Example Reports Section](https://murtaza-nasir.github.io/maestro/example-reports/) that shows many reports generated with Local LLMs.
I've done extensive testing and shared the resulting reports so you can see what it's capable of. There are examples from a bunch of self-hosted models, including:
* **Large Models:** Qwen 2.5 72B, GPT-OSS 120B
* **Medium Models:** Qwen 3 32B, Gemma 3 27B, GPT-OSS 20B
It's a great way to see how different models handle complex topics and various writing styles before you commit to running them. I've also included performance notes on things like KV cache usage during these runs.
Under the hood, I improved some UI features and added parallel processing for more operations, so it’s a little faster and more responsive.
If you're interested in AI assisted research or just want to see what's possible with the latest open models, I'd love for you to check it out.
* [**GitHub Release**](https://github.com/murtaza-nasir/maestro)
* [**New Docs Site**](https://murtaza-nasir.github.io/maestro)
Hope you find it useful. Let me know what you think! | 2025-09-02T09:46:05 | https://www.reddit.com/gallery/1n6f5xl | hedonihilistic | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1n6f5xl | false | null | t3_1n6f5xl | /r/LocalLLaMA/comments/1n6f5xl/i_just_released_a_big_update_for_my_ai_research/ | false | false | 216 | null | |
Hi everyone, This is my first attempt at fine-tuning a LLAMA 3.1 8B model for roleplay. | 9 |
I'm still new to the whole fine-tuning process, so I'm not 100% sure what I did and is everything correctly works.
I'd really appreciate it if anyone could test it out and share their feedback what works, what doesn't, and where I can improve. Thanks in advance!
https://huggingface.co/samunder12/llama-3.1-8b-roleplay-jio-gguf | 2025-09-02T09:45:45 | https://www.reddit.com/r/LocalLLaMA/comments/1n6f5q2/hi_everyone_this_is_my_first_attempt_at/ | internal-pagal | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6f5q2 | false | null | t3_1n6f5q2 | /r/LocalLLaMA/comments/1n6f5q2/hi_everyone_this_is_my_first_attempt_at/ | false | false | self | 9 | {'enabled': False, 'images': [{'id': 'OzoDibh4jN9hrSIDcYb8YYddwxwcAt9yDQqby5Di13U', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/OzoDibh4jN9hrSIDcYb8YYddwxwcAt9yDQqby5Di13U.png?width=108&crop=smart&auto=webp&s=52f5e24a8de16c9c1f9eb15f59c5456128c9150d', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/OzoDibh4jN9hrSIDcYb8YYddwxwcAt9yDQqby5Di13U.png?width=216&crop=smart&auto=webp&s=fccf0102dc99664df9a90e76af4e2a6ba72a11d3', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/OzoDibh4jN9hrSIDcYb8YYddwxwcAt9yDQqby5Di13U.png?width=320&crop=smart&auto=webp&s=3a07c0d3af218c5b57408567270ea6ff1a469465', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/OzoDibh4jN9hrSIDcYb8YYddwxwcAt9yDQqby5Di13U.png?width=640&crop=smart&auto=webp&s=0960c7be2e52cf64653c4aec1320b509fa3574db', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/OzoDibh4jN9hrSIDcYb8YYddwxwcAt9yDQqby5Di13U.png?width=960&crop=smart&auto=webp&s=725256623a5b224c778c7be88e70046ed2aa7d24', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/OzoDibh4jN9hrSIDcYb8YYddwxwcAt9yDQqby5Di13U.png?width=1080&crop=smart&auto=webp&s=8a64dba8b63138f69233008413e6662ef0d5c455', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/OzoDibh4jN9hrSIDcYb8YYddwxwcAt9yDQqby5Di13U.png?auto=webp&s=815e2f4cd7fc5ab8e8b7fe876afa01e235570e6b', 'width': 1200}, 'variants': {}}]} |
Distilling larger models to have medium dense coder model? | 1 | [removed] | 2025-09-02T09:40:56 | https://www.reddit.com/r/LocalLLaMA/comments/1n6f323/distilling_larger_models_to_have_medium_dense/ | One_Archer_577 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6f323 | false | null | t3_1n6f323 | /r/LocalLLaMA/comments/1n6f323/distilling_larger_models_to_have_medium_dense/ | false | false | self | 1 | null |
Looking for Inference Providers That Can Serve Fine-Tuned LoRA Adapters? | 2 | Hello r/LocalLLaMA community,
I've fine-tuned a LoRA adapter that I'd like to share publicly so others can easily test it out. I'm looking for a cost-effective inference provider where I can host it and provide an API key for access.
My main goals are:
* **Low cost** for sporadic, demo-level traffic.
* Ability to load a **LoRA adapter** on a common base model.
* Provide simple **API access** (e.g., via a key) for public sharing.
I've considered options like RunPod's serverless GPUs or Hugging Face Inference Endpoints, but I'm unsure about the most efficient and affordable choice for this use case.
Does anyone have recommendations for a good, budget-friendly provider to serve a fine-tuned LoRA for public testing? | 2025-09-02T09:32:22 | https://www.reddit.com/r/LocalLLaMA/comments/1n6eya3/looking_for_inference_providers_that_can_serve/ | Symbiote_in_me | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1n6eya3 | false | null | t3_1n6eya3 | /r/LocalLLaMA/comments/1n6eya3/looking_for_inference_providers_that_can_serve/ | false | false | self | 2 | null |
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