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Just launched MLX Model Manager - Swift Package to run LLM/VLMs with a couple of lines of code | 1 | 2024-12-23T03:24:38 | https://v.redd.it/e2zm625ioi8e1 | kunalbatra | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkeytj | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/e2zm625ioi8e1/DASHPlaylist.mpd?a=1737516292%2CODVkNTdjNjQyNDhlZjIwYzljNGRiMTIwYTFmZjk4OWU5ODAwMzI3ZjdjZGJmMjgwMGNiZTIzMjc3MmZkODMxZg%3D%3D&v=1&f=sd', 'duration': 11, 'fallback_url': 'https://v.redd.it/e2zm625ioi8e1/DASH_1080.mp4?source=fallback', 'has_audio': True, 'height': 1080, 'hls_url': 'https://v.redd.it/e2zm625ioi8e1/HLSPlaylist.m3u8?a=1737516292%2COWZlNTQ4MmI5Yzc4OWNkNmEyNThkYjA0NjAyYmZjZDRmOTgxM2E0NmVjZGMzZjMyZGU1ODM3NDk4Y2QwNjA4Mg%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/e2zm625ioi8e1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1692}} | t3_1hkeytj | /r/LocalLLaMA/comments/1hkeytj/just_launched_mlx_model_manager_swift_package_to/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'N3V2YnE0NWlvaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit', 'resolutions': [{'height': 68, 'url': 'https://external-preview.redd.it/N3V2YnE0NWlvaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=108&crop=smart&format=pjpg&auto=webp&s=e219b0867578f2d7086c6c03aa62a2d4a1088aee', 'width': 108}, {'height': 137, 'url': 'https://external-preview.redd.it/N3V2YnE0NWlvaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=216&crop=smart&format=pjpg&auto=webp&s=837c99dee060235801bc61260b6b775116c5858c', 'width': 216}, {'height': 204, 'url': 'https://external-preview.redd.it/N3V2YnE0NWlvaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=320&crop=smart&format=pjpg&auto=webp&s=ab565a28c83936b8d2d77201ea53ae610a4b2a00', 'width': 320}, {'height': 408, 'url': 'https://external-preview.redd.it/N3V2YnE0NWlvaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=640&crop=smart&format=pjpg&auto=webp&s=d3b83f4de2e4f3bf24e88fc9498d92564a11a62e', 'width': 640}, {'height': 612, 'url': 'https://external-preview.redd.it/N3V2YnE0NWlvaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=960&crop=smart&format=pjpg&auto=webp&s=38d0b0c3da41f58a4a48b088344dad6c1d2a4d9a', 'width': 960}, {'height': 689, 'url': 'https://external-preview.redd.it/N3V2YnE0NWlvaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=1080&crop=smart&format=pjpg&auto=webp&s=80eed834a5a07678897d1eb3ce82b7ca3c5503be', 'width': 1080}], 'source': {'height': 1814, 'url': 'https://external-preview.redd.it/N3V2YnE0NWlvaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?format=pjpg&auto=webp&s=fb13d90e622dc0d4ba859e2ea5f1628c82ae938b', 'width': 2842}, 'variants': {}}]} |
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Just released MLX Model Manager - a Swift Package to quickly add LLM/VLMs in your app with couple of lines of code | 74 | 2024-12-23T03:28:20 | https://v.redd.it/yghc8et7pi8e1 | Onboto | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkf0w5 | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/yghc8et7pi8e1/DASHPlaylist.mpd?a=1737516516%2CMjVlZWU1OTRjNDhhZWZjZjdlMjRmYjAwMjYwMGY3M2U5ODAyYjFhODY0MWM4NjI4MzNiMDIwNTVjMDFiNjRjMA%3D%3D&v=1&f=sd', 'duration': 11, 'fallback_url': 'https://v.redd.it/yghc8et7pi8e1/DASH_1080.mp4?source=fallback', 'has_audio': True, 'height': 1080, 'hls_url': 'https://v.redd.it/yghc8et7pi8e1/HLSPlaylist.m3u8?a=1737516516%2COGI5ZWU0ZjAzMWExYzlmNGQxNDI5MDA1NTVmYWI3NzVlZjIzZTg2MmEyMzU0MDE0MDFjZTNiYzEwMTJkZTczMg%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/yghc8et7pi8e1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1692}} | t3_1hkf0w5 | /r/LocalLLaMA/comments/1hkf0w5/just_released_mlx_model_manager_a_swift_package/ | false | false | 74 | {'enabled': False, 'images': [{'id': 'cHZpamVldDdwaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit', 'resolutions': [{'height': 68, 'url': 'https://external-preview.redd.it/cHZpamVldDdwaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=108&crop=smart&format=pjpg&auto=webp&s=1e5204d31e18b0656c8ae45f7471e056127f62ea', 'width': 108}, {'height': 137, 'url': 'https://external-preview.redd.it/cHZpamVldDdwaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=216&crop=smart&format=pjpg&auto=webp&s=336b2d0f17235f9c196f778ae91d16a5702269fb', 'width': 216}, {'height': 204, 'url': 'https://external-preview.redd.it/cHZpamVldDdwaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=320&crop=smart&format=pjpg&auto=webp&s=47404c01fa18d9116a8b3b1478bae34d9180097b', 'width': 320}, {'height': 408, 'url': 'https://external-preview.redd.it/cHZpamVldDdwaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=640&crop=smart&format=pjpg&auto=webp&s=53a5772749687af95eafc136f50b677bfe765895', 'width': 640}, {'height': 612, 'url': 'https://external-preview.redd.it/cHZpamVldDdwaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=960&crop=smart&format=pjpg&auto=webp&s=9ea47dc46ef8e6c893e6168cd24794398ae15c9c', 'width': 960}, {'height': 689, 'url': 'https://external-preview.redd.it/cHZpamVldDdwaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?width=1080&crop=smart&format=pjpg&auto=webp&s=73b2b3fc6910f35584c4d2c5ffb4a718de245264', 'width': 1080}], 'source': {'height': 1814, 'url': 'https://external-preview.redd.it/cHZpamVldDdwaThlMTBAIvstQMnO-aaVz6bqweM9pyYmZpw8MZo0HsKSFSit.png?format=pjpg&auto=webp&s=0c04fde91c8d1897bd42ab1edd71b674d37a6c6e', 'width': 2842}, 'variants': {}}]} |
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Need help with PaddleOCR accuracy issues | 1 | [removed] | 2024-12-23T03:41:31 | https://www.reddit.com/r/LocalLLaMA/comments/1hkf8xu/need_help_with_paddleocr_accuracy_issues/ | Impossible-Cod-5994 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkf8xu | false | null | t3_1hkf8xu | /r/LocalLLaMA/comments/1hkf8xu/need_help_with_paddleocr_accuracy_issues/ | false | false | self | 1 | null |
Groq's LLMs are one of the best for testing the workflow | 0 | I tend to test my code a lot since I get bugs some or the other way. Thank god I explored Groq during my learning phase itself. Groq is a free provider of LLMs. Whenever I test my code, I use Groq's LLMs until I debug all the errors since it's free of cost which enables you to test your code as many times. Although it doesn't provide good results, it's good for checking whether your code is fully functioning.
After my code is finalised, I switch to other LLMs such as GPT, Claude, etc for deriving more accurate results in production without much testing to save the costs.
The above pic represents the usage of Groq's Llama models as compared to in one of my projects.
Do you test your code as frequently as me? And which LLM do you use for testing? | 2024-12-23T03:48:47 | Available-Stress8598 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkfdcg | false | null | t3_1hkfdcg | /r/LocalLLaMA/comments/1hkfdcg/groqs_llms_are_one_of_the_best_for_testing_the/ | false | false | 0 | {'enabled': True, 'images': [{'id': 'MQuxGx7bcCMKapNzNXcu72qa9rZdXnxBV7-Gj5AG34c', 'resolutions': [{'height': 137, 'url': 'https://preview.redd.it/i4kupptwsi8e1.png?width=108&crop=smart&auto=webp&s=8af2a4f97dbe22c68a42c05cec3a67282cdc7ad5', 'width': 108}, {'height': 274, 'url': 'https://preview.redd.it/i4kupptwsi8e1.png?width=216&crop=smart&auto=webp&s=a204037f5fd8ab6c3e39b475d540a9466f1d56a8', 'width': 216}, {'height': 406, 'url': 'https://preview.redd.it/i4kupptwsi8e1.png?width=320&crop=smart&auto=webp&s=2a07e64eac3aa005045b9df41d7f334004802db6', 'width': 320}, {'height': 813, 'url': 'https://preview.redd.it/i4kupptwsi8e1.png?width=640&crop=smart&auto=webp&s=832418f418e4d463c98baa143ae1fcd1a955541f', 'width': 640}, {'height': 1219, 'url': 'https://preview.redd.it/i4kupptwsi8e1.png?width=960&crop=smart&auto=webp&s=2950bf35a899e64fd8929411df16a9d9137611d6', 'width': 960}, {'height': 1372, 'url': 'https://preview.redd.it/i4kupptwsi8e1.png?width=1080&crop=smart&auto=webp&s=1f02e584fef435f051426bc2b97308ac64c6d4f9', 'width': 1080}], 'source': {'height': 1372, 'url': 'https://preview.redd.it/i4kupptwsi8e1.png?auto=webp&s=fa3bb0b76c5bb61a767e5e1441e59a48c33e2a27', 'width': 1080}, 'variants': {}}]} |
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llama.cpp now supports Llama-3_1-Nemotron-51B | 110 | Good news that my PR is approved and merged to the main branch of llama.cpp. Starting from version b4380, you should be able to run and convert Llama-3\_1-Nemotron-51B. I suppose it will gradually make it to other software based on llama.cpp.
However, since bartowski suggested me to create a new model type for it, the previous GGUFs I uploaded will no longer work with the official llama.cpp. Therefore, I re-created the GGUFs them with the updated software. This time I created them with imatrix and measured perplexity and KL Divergence. Currently, I made Q6\_K, Q5\_K, Q4\_K\_M, IQ4\_XS, Q4\_0\_4\_8, IQ3\_M, IQ3\_S available. Please let me know if you need other quants, I can upload them if there is a use case.
https://huggingface.co/ymcki/Llama-3\_1-Nemotron-51B-Instruct-GGUF/
As we can see, there is a significant improvement with imatrix. I am happy now that I can run a mid-sized model on my 3090 with confidence. Hope you also find the GGUFs useful in your workflow.
| 2024-12-23T04:04:25 | https://www.reddit.com/r/LocalLLaMA/comments/1hkfmvd/llamacpp_now_supports_llama3_1nemotron51b/ | Ok_Warning2146 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkfmvd | false | null | t3_1hkfmvd | /r/LocalLLaMA/comments/1hkfmvd/llamacpp_now_supports_llama3_1nemotron51b/ | false | false | self | 110 | null |
Any open source projects on perplexity pro search / gemini deep search? | 1 | [removed] | 2024-12-23T04:13:42 | https://www.reddit.com/r/LocalLLaMA/comments/1hkfsjs/any_open_source_projects_on_perplexity_pro_search/ | OneConfusion3313 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkfsjs | false | null | t3_1hkfsjs | /r/LocalLLaMA/comments/1hkfsjs/any_open_source_projects_on_perplexity_pro_search/ | false | false | self | 1 | null |
Need help with why the model is stopping midway while answering. | 1 | 2024-12-23T04:19:44 | Infinite-Calendar542 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkfw73 | false | null | t3_1hkfw73 | /r/LocalLLaMA/comments/1hkfw73/need_help_with_why_the_model_is_stopping_midway/ | false | false | 1 | {'enabled': True, 'images': [{'id': 'ftpFmY5LEkiTtXRmmqUAs8ESoK8yBJlEHx2NABC4p_8', 'resolutions': [{'height': 163, 'url': 'https://preview.redd.it/8hpse6qeyi8e1.jpeg?width=108&crop=smart&auto=webp&s=5468b0874ca1a39e6ecbe7694f9d2a87b6e7810f', 'width': 108}, {'height': 327, 'url': 'https://preview.redd.it/8hpse6qeyi8e1.jpeg?width=216&crop=smart&auto=webp&s=268fdd0cd9ef9e91b06173cf5a0c5c8396ee9bc2', 'width': 216}, {'height': 484, 'url': 'https://preview.redd.it/8hpse6qeyi8e1.jpeg?width=320&crop=smart&auto=webp&s=fd576c979e34e0d2a23614457b5fc7968ffb6068', 'width': 320}, {'height': 969, 'url': 'https://preview.redd.it/8hpse6qeyi8e1.jpeg?width=640&crop=smart&auto=webp&s=a082d9c1ce41b31f771b8d825465b1bf1cb2a4b5', 'width': 640}], 'source': {'height': 1280, 'url': 'https://preview.redd.it/8hpse6qeyi8e1.jpeg?auto=webp&s=c46259475990bc78de58bd4d93126bbca17aa0b3', 'width': 845}, 'variants': {}}]} |
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help in the requirements for running Llama 3.3 locally | 1 | [removed] | 2024-12-23T04:55:26 | https://www.reddit.com/r/LocalLLaMA/comments/1hkggjz/help_in_the_requirements_for_running_llama_33/ | Dreamer1396 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkggjz | false | null | t3_1hkggjz | /r/LocalLLaMA/comments/1hkggjz/help_in_the_requirements_for_running_llama_33/ | false | false | self | 1 | null |
Requirements for running Llama 3.3 locally | 1 | [removed] | 2024-12-23T05:02:16 | https://www.reddit.com/r/LocalLLaMA/comments/1hkgkqx/requirements_for_running_llama_33_locally/ | Dreamer1396 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkgkqx | false | null | t3_1hkgkqx | /r/LocalLLaMA/comments/1hkgkqx/requirements_for_running_llama_33_locally/ | false | false | self | 1 | null |
Is there any local LLM similar to Gpt? | 1 | [removed] | 2024-12-23T05:05:10 | https://www.reddit.com/r/LocalLLaMA/comments/1hkgmhb/is_there_any_local_llm_similar_to_gpt/ | uSayaka | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkgmhb | false | null | t3_1hkgmhb | /r/LocalLLaMA/comments/1hkgmhb/is_there_any_local_llm_similar_to_gpt/ | false | false | self | 1 | null |
Cmon, Marco! | 0 | 2024-12-23T05:33:58 | roz303 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkh281 | false | null | t3_1hkh281 | /r/LocalLLaMA/comments/1hkh281/cmon_marco/ | false | false | 0 | {'enabled': True, 'images': [{'id': '1xxIfr3EUFGIjIETEPXgc0pLXXSHoeILwW8aBAjVYGk', 'resolutions': [{'height': 60, 'url': 'https://preview.redd.it/sq9jqhzobj8e1.png?width=108&crop=smart&auto=webp&s=b82d8f90868dd9ac1ae1f843b939ebb498aa2a78', 'width': 108}, {'height': 121, 'url': 'https://preview.redd.it/sq9jqhzobj8e1.png?width=216&crop=smart&auto=webp&s=16993ca3780af712c3116bab8c282c603df4fea5', 'width': 216}, {'height': 180, 'url': 'https://preview.redd.it/sq9jqhzobj8e1.png?width=320&crop=smart&auto=webp&s=9b6d19f47d9fa338fa95a88d1715fcf63336316e', 'width': 320}, {'height': 360, 'url': 'https://preview.redd.it/sq9jqhzobj8e1.png?width=640&crop=smart&auto=webp&s=a9354d2a845071c082dfcb9882d94433e393c072', 'width': 640}, {'height': 540, 'url': 'https://preview.redd.it/sq9jqhzobj8e1.png?width=960&crop=smart&auto=webp&s=312b07a803e8eab3181ccd7117eb2a5da8c345ae', 'width': 960}, {'height': 607, 'url': 'https://preview.redd.it/sq9jqhzobj8e1.png?width=1080&crop=smart&auto=webp&s=34f535ff540851bd2c017013f7bf8f28d9db514d', 'width': 1080}], 'source': {'height': 1020, 'url': 'https://preview.redd.it/sq9jqhzobj8e1.png?auto=webp&s=ecfb8f5a24c7f8fcc049b82c2c8bd8bcef3eb2e2', 'width': 1812}, 'variants': {}}]} |
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Llama 3 vs 3.1 vs 3.2 | 4 | What can you say about these 3 versions of Llama LLMs? Were they trained around the same time? Or 3.2 and 3.1 were later enhancement from 3? | 2024-12-23T05:36:48 | https://www.reddit.com/r/LocalLLaMA/comments/1hkh3qj/llama_3_vs_31_vs_32/ | Ok_Ostrich_8845 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkh3qj | false | null | t3_1hkh3qj | /r/LocalLLaMA/comments/1hkh3qj/llama_3_vs_31_vs_32/ | false | false | self | 4 | null |
Any models/AI services out there that can do architectural plans or construction drawings for a house | 2 | I plan to rebuild my aging house and started talks with a builder. My wife and I would like to work on an intial design which we'll provide to the builder who's design team will finish it off. My originan plan was to use SketchUp, but I'm not wondering if there are any AI methods that will make it easier. We have image, video and even game/mesh generation so I'm thinking there has to be something out there that is decent by now. Any recommendations? | 2024-12-23T06:18:28 | https://www.reddit.com/r/LocalLLaMA/comments/1hkhq3a/any_modelsai_services_out_there_that_can_do/ | vulcan4d | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkhq3a | false | null | t3_1hkhq3a | /r/LocalLLaMA/comments/1hkhq3a/any_modelsai_services_out_there_that_can_do/ | false | false | self | 2 | null |
Playground and API response different? | 0 | I was using Qwen VL Chat to extract text from a set of images. I tested their model on their playground deployed on HuggingFace Spaces and was quite satisfied by their response. After that I set up a Python script to automate the task of extracting text from images. I hosted the Qwen model using vLLM on NVIDIA A100 and used it's endpoint in my script and used the same images as playground. But I'm getting the response different now as for some images the response is "I cannot perform text extraction from images"
Why is this happening? The same is happening in case of other vision models when i tested them on Google Colab using their Inference API. | 2024-12-23T06:50:35 | https://www.reddit.com/r/LocalLLaMA/comments/1hki6eh/playground_and_api_response_different/ | Available-Stress8598 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hki6eh | false | null | t3_1hki6eh | /r/LocalLLaMA/comments/1hki6eh/playground_and_api_response_different/ | false | false | self | 0 | null |
Will we ever get new Opuses and Ultras of the world or is inference-time compute for the rest of our days? I want to talk with masters of language and philosophy, benchmarks be damned. | 264 | 2024-12-23T07:07:34 | DangerousBenefit | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkievg | false | null | t3_1hkievg | /r/LocalLLaMA/comments/1hkievg/will_we_ever_get_new_opuses_and_ultras_of_the/ | false | false | 264 | {'enabled': True, 'images': [{'id': 'kcdKcI7RZ2gFubsGmT7NVPZLhmb6_tsHtNzfLBZN5YA', 'resolutions': [{'height': 102, 'url': 'https://preview.redd.it/alvvsiq5rj8e1.jpeg?width=108&crop=smart&auto=webp&s=39d117680f58aa7ea984a51341ee3c89635748a6', 'width': 108}, {'height': 204, 'url': 'https://preview.redd.it/alvvsiq5rj8e1.jpeg?width=216&crop=smart&auto=webp&s=caad5185577ce1ce782b0b9917e307fecc1a6946', 'width': 216}, {'height': 303, 'url': 'https://preview.redd.it/alvvsiq5rj8e1.jpeg?width=320&crop=smart&auto=webp&s=2f0eaf7276dd4a4c54dec785ab153c2e0d30cafc', 'width': 320}], 'source': {'height': 474, 'url': 'https://preview.redd.it/alvvsiq5rj8e1.jpeg?auto=webp&s=58a759fda347533c924f68986b828496ceeca6db', 'width': 500}, 'variants': {}}]} |
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I made a TikTok Brainrot Generator | 1 | [removed] | 2024-12-23T07:11:58 | https://www.reddit.com/r/LocalLLaMA/comments/1hkigz2/i_made_a_tiktok_brainrot_generator/ | notrealDirect | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkigz2 | false | null | t3_1hkigz2 | /r/LocalLLaMA/comments/1hkigz2/i_made_a_tiktok_brainrot_generator/ | false | false | self | 1 | null |
I made a TikTok BrainRot generator | 1 | [removed] | 2024-12-23T07:19:05 | https://www.reddit.com/r/LocalLLaMA/comments/1hkikdi/i_made_a_tiktok_brainrot_generator/ | notrealDirect | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkikdi | false | null | t3_1hkikdi | /r/LocalLLaMA/comments/1hkikdi/i_made_a_tiktok_brainrot_generator/ | false | false | self | 1 | null |
TikTok BrainRot generator | 1 | [removed] | 2024-12-23T07:19:52 | https://www.reddit.com/r/LocalLLaMA/comments/1hkikqa/tiktok_brainrot_generator/ | notrealDirect | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkikqa | false | null | t3_1hkikqa | /r/LocalLLaMA/comments/1hkikqa/tiktok_brainrot_generator/ | false | false | self | 1 | null |
Updated Ception presets - Mistral 2407, Llama 3.3, Qwen 2.5 | 1 | [removed] | 2024-12-23T07:23:32 | https://www.reddit.com/r/LocalLLaMA/comments/1hkimkj/updated_ception_presets_mistral_2407_llama_33/ | Konnect1983 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkimkj | false | null | t3_1hkimkj | /r/LocalLLaMA/comments/1hkimkj/updated_ception_presets_mistral_2407_llama_33/ | false | false | self | 1 | null |
Favourite Uncensored Models | 89 | Titoe says it all, what are your current favourite unvensored Models and for which use case? | 2024-12-23T07:31:09 | https://www.reddit.com/r/LocalLLaMA/comments/1hkiq2o/favourite_uncensored_models/ | cosmo-pax | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkiq2o | false | null | t3_1hkiq2o | /r/LocalLLaMA/comments/1hkiq2o/favourite_uncensored_models/ | false | false | self | 89 | null |
Has anyone successfully generated reasonable documentation from a code base using an LLM? | 7 | You know like when you ask ChatGPT or Claude to explain a piece of code? Has anyone tried throwing an entire repo at it? If so, what did you use? Any additional agents? How accurate was the end result? | 2024-12-23T08:02:13 | https://www.reddit.com/r/LocalLLaMA/comments/1hkj4pe/has_anyone_successfully_generated_reasonable/ | shenglong | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkj4pe | false | null | t3_1hkj4pe | /r/LocalLLaMA/comments/1hkj4pe/has_anyone_successfully_generated_reasonable/ | false | false | self | 7 | null |
Generate a "black dog" | 1 | [removed] | 2024-12-23T08:22:02 | https://www.reddit.com/r/LocalLLaMA/comments/1hkjdsu/generate_a_black_dog/ | highlii | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkjdsu | false | null | t3_1hkjdsu | /r/LocalLLaMA/comments/1hkjdsu/generate_a_black_dog/ | false | false | 1 | null |
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Best uncensored chatbot? (Not for sexting lol) | 1 | [removed] | 2024-12-23T08:45:56 | https://www.reddit.com/r/LocalLLaMA/comments/1hkjokd/best_uncensored_chatbot_not_for_sexting_lol/ | Creative-Concert-377 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkjokd | false | null | t3_1hkjokd | /r/LocalLLaMA/comments/1hkjokd/best_uncensored_chatbot_not_for_sexting_lol/ | false | false | self | 1 | null |
Has anyone done negative prompting for LLMs? | 1 | Has anyone done negative prompting for LLMs?
I read this paper on how to apply Classifier free guidance on LLMs: [https://arxiv.org/pdf/2306.17806](https://arxiv.org/pdf/2306.17806)
>Classifier-Free Guidance (CFG) \[37\] has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q&A, reasoning, code generation, and machine translation, achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements equivalent to a model with twice the parameter-count; (3) can stack alongside other inference-time methods like Chain-of-Thought and Self-Consistency, yielding further improvements in difficult tasks; (4) can be used to increase the faithfulness and coherence of assistants in challenging form-driven and content-driven prompts: in a human evaluation we show a 75% preference for GPT4All using CFG over baseline.
and I was wondering if anyone tried this technique? | 2024-12-23T09:32:10 | https://www.reddit.com/r/LocalLLaMA/comments/1hkk9ll/has_anyone_done_negative_prompting_for_llms/ | searcher1k | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkk9ll | false | null | t3_1hkk9ll | /r/LocalLLaMA/comments/1hkk9ll/has_anyone_done_negative_prompting_for_llms/ | false | false | self | 1 | null |
i need a tip for buying a graphics card for local ollama | 1 | [removed] | 2024-12-23T10:46:52 | https://www.reddit.com/r/LocalLLaMA/comments/1hkl8th/i_need_a_tip_for_buying_a_graphics_card_for_local/ | No-Dust7863 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkl8th | false | null | t3_1hkl8th | /r/LocalLLaMA/comments/1hkl8th/i_need_a_tip_for_buying_a_graphics_card_for_local/ | false | false | self | 1 | null |
Small parameters optimization | 1 | People talk big time about techniques to make reasoning better old/small models. But how do I achieve that everyday? How do I achieve to perform, given enough steps, what does big/new models? What do I type? Which arguments to the LLaMaFile? Which parameters, which prompt...? | 2024-12-23T11:18:42 | https://www.reddit.com/r/LocalLLaMA/comments/1hklouj/small_parameters_optimization/ | xqoe | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hklouj | false | null | t3_1hklouj | /r/LocalLLaMA/comments/1hklouj/small_parameters_optimization/ | false | false | self | 1 | null |
Two GPUs vs one GPU | 11 | I have an opportunity to set up my PC with two 8GB gpus since I can't afford a single 16gb card. Will this be a big improvement since I'm told that any local LLM work I do will still be limited by the size available to a single card.
What's your experience? | 2024-12-23T11:21:04 | https://www.reddit.com/r/LocalLLaMA/comments/1hklpzb/two_gpus_vs_one_gpu/ | McDoof | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hklpzb | false | null | t3_1hklpzb | /r/LocalLLaMA/comments/1hklpzb/two_gpus_vs_one_gpu/ | false | false | self | 11 | null |
Highly recommended LLM UI for Linux? | 7 | Using a Linux Mint machine with a Ryzen 7 7800X, 32GB RAM and an AMD Radeon RX 7800XT.
What are some recommended UI's etc to run LLM locally? I was using Windows on a different machine using a Nvidia card and running LM Studio which was fine but I know I the set up is different using AMD cards so looking for a good alternative from people's experience.
Thanks. | 2024-12-23T11:33:37 | https://www.reddit.com/r/LocalLLaMA/comments/1hklwgi/highly_recommended_llm_ui_for_linux/ | Drama_ | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hklwgi | false | null | t3_1hklwgi | /r/LocalLLaMA/comments/1hklwgi/highly_recommended_llm_ui_for_linux/ | false | false | self | 7 | null |
What are some things unique to specific models that you have learnt through experience in prompting? | 0 | I have spent quite some time working on different LLMs and I have noticed some peculiar ways in which specific models perform differently or get a performance boost or degradation based on syntax, format and prompting style changes. You would't be able to guess these things unless you have worked with that specific model for a long time.
I'm curious to know whether others have had a similar experience (anecdotal since LLMs are a black box and it's hard to "explain" why they do things in a certain way)
I'll go first
1. **OpenAI / Anthropic models:** Even though LLMs process input as XML tags, I notice good performance boosts if I send my input as a JSON instead of wrapping it in XML tags, particularly for longer context lengths. This is despite the official guides using/suggesting XML tags.
2. **Haiku/Sonnet:** Much better in writing or coming up with the right words for things compared to their OpenAI counterparts.
3. **Sonnet**: If you can limit output length by good choice of prompt output structure, it can also give a boost to performance for hard reasoning tasks. In other words, outputting more leads to worse performance. (Assuming you don't want to output Reasoning text for some reason and just the final structured output) | 2024-12-23T11:34:08 | https://www.reddit.com/r/LocalLLaMA/comments/1hklwqr/what_are_some_things_unique_to_specific_models/ | pravictor | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hklwqr | false | null | t3_1hklwqr | /r/LocalLLaMA/comments/1hklwqr/what_are_some_things_unique_to_specific_models/ | false | false | self | 0 | null |
Where do I find resources to write better prompts | 1 | [removed] | 2024-12-23T11:35:15 | https://www.reddit.com/r/LocalLLaMA/comments/1hklxco/where_do_i_find_resources_to_write_better_prompts/ | Sensitive_Bison_4458 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hklxco | false | null | t3_1hklxco | /r/LocalLLaMA/comments/1hklxco/where_do_i_find_resources_to_write_better_prompts/ | false | false | self | 1 | null |
Tips/guides on setting up speedy local inference? | 4 | I'm looking for information and tips/info on how to speed up local inference.
I've got three 24GB GPUs, a mix of ada and ampere. Host is Windows 11. Currently I'm using TabbyAPI with speculative prediction.
Main model: Qwen2.5-Coder-32B-Instruct-8.0bpw-exl2
Draft model: Qwen2.5-Coder-1.5B-BASE-8.0bpw-exl2
Main/Draft Cache: Q8
I'm getting anywhere from 8 to 22 tokens/s depending on context length. I'd like to know what it takes to at least double that, especially on the high context end. Is there a software configuration that can do that or do I need a more powerful GPU?
What setups are other people using to get performance? I wanted to try vLLM but having an odd number of GPUs resulted in poor compatibility. Is it a major boost worth getting a 4th GPU?
Being able to run jobs in parallel would be nice, too. | 2024-12-23T11:39:32 | https://www.reddit.com/r/LocalLLaMA/comments/1hklzgk/tipsguides_on_setting_up_speedy_local_inference/ | MorallyDeplorable | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hklzgk | false | null | t3_1hklzgk | /r/LocalLLaMA/comments/1hklzgk/tipsguides_on_setting_up_speedy_local_inference/ | false | false | self | 4 | null |
[meme] When O3-High refuses | 1 | 2024-12-23T11:47:55 | cov_id19 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkm3ty | false | null | t3_1hkm3ty | /r/LocalLLaMA/comments/1hkm3ty/meme_when_o3high_refuses/ | false | false | 1 | {'enabled': True, 'images': [{'id': '0RZKEtJAfwSJ_sX4Di1qQyeGNlpwgmnGf66cP2TEQuA', 'resolutions': [{'height': 43, 'url': 'https://preview.redd.it/sc5wcdj36l8e1.png?width=108&crop=smart&auto=webp&s=62bd3261d4afa456ed9a2625e7ceb2c41c426498', 'width': 108}, {'height': 86, 'url': 'https://preview.redd.it/sc5wcdj36l8e1.png?width=216&crop=smart&auto=webp&s=5c18de79f70661ebc201155d18a2f287e3eab814', 'width': 216}, {'height': 128, 'url': 'https://preview.redd.it/sc5wcdj36l8e1.png?width=320&crop=smart&auto=webp&s=09262289509a14f707781d1dd4d9bbf78810e7af', 'width': 320}, {'height': 257, 'url': 'https://preview.redd.it/sc5wcdj36l8e1.png?width=640&crop=smart&auto=webp&s=80ce7faf72c55b789415b4901c3e85bcecc536aa', 'width': 640}], 'source': {'height': 376, 'url': 'https://preview.redd.it/sc5wcdj36l8e1.png?auto=webp&s=00c948d80465e1ec346277080dbefcbd0a1111f8', 'width': 936}, 'variants': {}}]} |
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What is the best SLM that you have used for fine tuning on SQL queries? | 4 | Also, is fine tuning small language models on Company specific sql queries worth it?
I am providing:
System prompt: Has my table's schema and some few shots(1784 tokens long).
Chat history: question and answers.
Final user question: The question user asks and based on this question and chat history a system prompt is generated according to how system prompt structured it. | 2024-12-23T11:59:20 | https://www.reddit.com/r/LocalLLaMA/comments/1hkm9o4/what_is_the_best_slm_that_you_have_used_for_fine/ | ShippersAreIdiots | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkm9o4 | false | null | t3_1hkm9o4 | /r/LocalLLaMA/comments/1hkm9o4/what_is_the_best_slm_that_you_have_used_for_fine/ | false | false | self | 4 | null |
Langflow vs Rivet vs Chainforge vs Flowise, etc. | 1 | [removed] | 2024-12-23T12:29:18 | https://www.reddit.com/r/LocalLLaMA/comments/1hkmqpm/langflow_vs_rivet_vs_chainforge_vs_flowise_etc/ | lemontheme | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkmqpm | false | null | t3_1hkmqpm | /r/LocalLLaMA/comments/1hkmqpm/langflow_vs_rivet_vs_chainforge_vs_flowise_etc/ | false | false | self | 1 | null |
Even mistral ai going for pro plan Ó╭╮Ò | 1 | [removed] | 2024-12-23T13:22:10 | https://www.reddit.com/gallery/1hknn08 | Evening_Action6217 | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1hknn08 | false | null | t3_1hknn08 | /r/LocalLLaMA/comments/1hknn08/even_mistral_ai_going_for_pro_plan_óò/ | false | false | 1 | null |
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Multi-Agentic Tree Search for Advanced Multi-Context Reasoning | 1 | [removed] | 2024-12-23T13:43:10 | https://www.reddit.com/r/LocalLLaMA/comments/1hko0gg/multiagentic_tree_search_for_advanced/ | Kalitis- | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hko0gg | false | null | t3_1hko0gg | /r/LocalLLaMA/comments/1hko0gg/multiagentic_tree_search_for_advanced/ | false | false | self | 1 | null |
Are hallucinations caused because the model doesn't know what it doesn't know? | 60 | There’s a very interesting concept that, in my opinion, more prepared people tend to understand better: knowing what you don’t know. In other words, recognizing that, to accomplish task X, it’s necessary to understand Y and Z, because without that knowledge, any result would be incorrect.
Now, do AI models operate with the certainty that they know everything they’re asked? And is that why they end up “hallucinating”? Imagine a human who, due to some pathology, wakes up believing they can speak a language they’ve never learned. They’re absolutely convinced of this ability and confidently start speaking the language. However, everything they say is meaningless — just “linguistic hallucinations.”
It’s a silly question, for sure. But maybe more people have thought about it too, so here I am, passing embarrassment for me and for them 🫡 | 2024-12-23T14:00:41 | https://www.reddit.com/r/LocalLLaMA/comments/1hkobzd/are_hallucinations_caused_because_the_model/ | thecalmgreen | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkobzd | false | null | t3_1hkobzd | /r/LocalLLaMA/comments/1hkobzd/are_hallucinations_caused_because_the_model/ | false | false | self | 60 | null |
CodeCombiner: An Open-Source GUI Tool for One-Click Code Gathering for AI / LLM Feeding | 2 | Hello! Meet CodeCombiner, an Open Source GUI tool that I recently developed. It allows you to gather all your code files in one location with just a single click, simplifying the process of feeding them to AI and LLMs. This user-friendly application streamlines your workflow, and while similar solutions exist as command-line tools or VS Code plugins, CodeCombiner offers a faster and more intuitive experience.
I developed this tool in a Windows environment, and you can download and start using it right now. If you're on a different platform, you can easily build the application in your respective environment using the provided source code. Give it a try and enhance your workflow!
I created this tool to save time in my own work, and I made it open-source so it can be helpful to others. If you find it useful, please consider leaving a star on GitHub!
Here’s the link to the project: [GitHub Project Link](https://github.com/chandrath/Simple-Code-Combiner)
Download the application: [Download Link (Windows)](https://github.com/chandrath/Simple-Code-Combiner/releases)
https://preview.redd.it/x6fh0rnkwl8e1.png?width=693&format=png&auto=webp&s=4dbc770778df27525619d2ec67f399d76c50e4a2
| 2024-12-23T14:19:23 | https://www.reddit.com/r/LocalLLaMA/comments/1hkop2y/codecombiner_an_opensource_gui_tool_for_oneclick/ | 444_Guy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkop2y | false | null | t3_1hkop2y | /r/LocalLLaMA/comments/1hkop2y/codecombiner_an_opensource_gui_tool_for_oneclick/ | false | false | 2 | {'enabled': False, 'images': [{'id': '_npuDWpkciwQPd_GOXgggeX0kCPrXdRW4PQKUdRm6WE', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/uMYRE3Gnmk4XhGwVa07qZcwDdkekLwzVkhsnmD0k-is.jpg?width=108&crop=smart&auto=webp&s=58353c05bbfc13cc893182581ebc5f8621e16d2c', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/uMYRE3Gnmk4XhGwVa07qZcwDdkekLwzVkhsnmD0k-is.jpg?width=216&crop=smart&auto=webp&s=dd176cf8727e418da4647c6de732d5e63b3685e0', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/uMYRE3Gnmk4XhGwVa07qZcwDdkekLwzVkhsnmD0k-is.jpg?width=320&crop=smart&auto=webp&s=255219e5a8f8771be81da2b55384e76e65e6fd75', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/uMYRE3Gnmk4XhGwVa07qZcwDdkekLwzVkhsnmD0k-is.jpg?width=640&crop=smart&auto=webp&s=2528624e3cc3b585186e7270c3bfc303591f8f4a', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/uMYRE3Gnmk4XhGwVa07qZcwDdkekLwzVkhsnmD0k-is.jpg?width=960&crop=smart&auto=webp&s=3d4db2e9a488cb9b5a056a6e823314719c37116d', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/uMYRE3Gnmk4XhGwVa07qZcwDdkekLwzVkhsnmD0k-is.jpg?width=1080&crop=smart&auto=webp&s=e04ae6bb67b406fae755b7903f788efc0f050c63', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/uMYRE3Gnmk4XhGwVa07qZcwDdkekLwzVkhsnmD0k-is.jpg?auto=webp&s=eb6a0d440e8e94ba42bd35c8a453745237fb2d93', 'width': 1200}, 'variants': {}}]} |
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x2 P40s or x2 3060 12gb? | 7 | I know the difference between the two is double the vram but I was wondering if it'd be worth investing in a pair of 3060s simply because they're newer. Like the M40 going obsolete, I'm concerned about how long the P40s will last before they're phased out. I don't know much about its longevity hence me asking, but considering I can get 2 3060 12gbs for $180-250 each and P40s are being sold at $300+ right now, I figured I'd ask for some advice. | 2024-12-23T14:19:32 | https://www.reddit.com/r/LocalLLaMA/comments/1hkop6t/x2_p40s_or_x2_3060_12gb/ | switchpizza | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkop6t | false | null | t3_1hkop6t | /r/LocalLLaMA/comments/1hkop6t/x2_p40s_or_x2_3060_12gb/ | false | false | self | 7 | null |
Building a conversational AI for a website | 1 | [removed] | 2024-12-23T14:40:13 | https://www.reddit.com/r/LocalLLaMA/comments/1hkp3kt/building_a_conversational_ai_for_a_website/ | tfwnoasiangf | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkp3kt | false | null | t3_1hkp3kt | /r/LocalLLaMA/comments/1hkp3kt/building_a_conversational_ai_for_a_website/ | false | false | self | 1 | null |
Human in the loop laws are coming. | 1 | [removed] | 2024-12-23T14:50:08 | https://www.reddit.com/r/LocalLLaMA/comments/1hkpas3/human_in_the_loop_laws_are_coming/ | Brave_Compatriot | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkpas3 | false | null | t3_1hkpas3 | /r/LocalLLaMA/comments/1hkpas3/human_in_the_loop_laws_are_coming/ | false | false | self | 1 | null |
Understanding which LLM model Works best for what (IN SIMPLE TERMS) – Help Needed! | 1 | [removed] | 2024-12-23T15:02:16 | https://www.reddit.com/r/LocalLLaMA/comments/1hkpjkt/understanding_which_llm_model_works_best_for_what/ | Every-Assignment5935 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkpjkt | false | null | t3_1hkpjkt | /r/LocalLLaMA/comments/1hkpjkt/understanding_which_llm_model_works_best_for_what/ | false | false | self | 1 | null |
Function Calling | 6 | I've recently started dabbling with function calling. It's all very new to me. Does anyone know the distinction between structured outputs Vs JSON support and function calling. I've even found passing mentions to React Agents.
What is the modern way of approaching this even for models that don't officially support function calling? | 2024-12-23T15:06:25 | https://www.reddit.com/r/LocalLLaMA/comments/1hkpmoa/function_calling/ | SvenVargHimmel | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkpmoa | false | null | t3_1hkpmoa | /r/LocalLLaMA/comments/1hkpmoa/function_calling/ | false | false | self | 6 | null |
Layla allows generating images via Stable Diffusion alongside running LLMs on your phone | 40 | **Completely offline on device.**
Image generation model uses <500MB RAM so it can be run alongside an LLM during chat. Currently Stable Diffusion 1.5 and SDXL Turbo models are supported. High end phones can do an 8B LLM + SD1.5
[\(Image generation time skipped in video above, see benchmark generation speeds below. LLM response speed is real-time\)](https://reddit.com/link/1hkq3ub/video/g9q7eld88m8e1/player)
Image generation speeds on an S23 Ultra
256x256 (with CFG): \~3 seconds per iteration
512x512 (with CFG): \~10 seconds per iteration
Can be further speed up by setting CFG = 1.0 (no guidance, skips negative prompt, so skips one inference pass per iteration, doubling the speed at the cost of quality)
**Models are pre-compiled for mobile use:**
https://reddit.com/link/1hkq3ub/video/hml3jrpv8m8e1/player
All model images in the above video are generated on my phone locally, so it should give you a realistic expectation of what the quality is like.
**Auxiliary features**
Supports on-device image upscaling for your generated images using RealESRGAN. You can also combine image generation and LLM to create custom characters descriptions, scenarios, and generate images out of them.
https://preview.redd.it/ye9f5m969m8e1.png?width=808&format=png&auto=webp&s=5b148288ab4d74943d1022b4c94a4106f7411919
Everything works completely offline on a phone. | 2024-12-23T15:28:53 | https://www.reddit.com/r/LocalLLaMA/comments/1hkq3ub/layla_allows_generating_images_via_stable/ | Tasty-Lobster-8915 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkq3ub | false | null | t3_1hkq3ub | /r/LocalLLaMA/comments/1hkq3ub/layla_allows_generating_images_via_stable/ | false | false | 40 | null |
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Aider Polygot Leaderboard | 1 | 2024-12-23T16:04:48 | https://aider.chat/2024/12/21/polyglot.html | davewolfs | aider.chat | 1970-01-01T00:00:00 | 0 | {} | 1hkqvu3 | false | null | t3_1hkqvu3 | /r/LocalLLaMA/comments/1hkqvu3/aider_polygot_leaderboard/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'qR4fVo4590tCWGA0neHGwcbS2cAddpVqgZeM0-tzpek', 'resolutions': [{'height': 55, 'url': 'https://external-preview.redd.it/BQgc-WoPPXosh-ho_8vQxUYwdG3-JIBDa18DtRMIB2Q.jpg?width=108&crop=smart&auto=webp&s=90c8f8a4a00086c7429475733716c95668519df6', 'width': 108}, {'height': 110, 'url': 'https://external-preview.redd.it/BQgc-WoPPXosh-ho_8vQxUYwdG3-JIBDa18DtRMIB2Q.jpg?width=216&crop=smart&auto=webp&s=d85116821f965b3f77909440df141a277b9a61b3', 'width': 216}, {'height': 164, 'url': 'https://external-preview.redd.it/BQgc-WoPPXosh-ho_8vQxUYwdG3-JIBDa18DtRMIB2Q.jpg?width=320&crop=smart&auto=webp&s=3b41dba39529732e4a6694053a96c2d4a859e079', 'width': 320}, {'height': 328, 'url': 'https://external-preview.redd.it/BQgc-WoPPXosh-ho_8vQxUYwdG3-JIBDa18DtRMIB2Q.jpg?width=640&crop=smart&auto=webp&s=9e71522fa218e7ed8a532f9544af0a136091f92d', 'width': 640}, {'height': 492, 'url': 'https://external-preview.redd.it/BQgc-WoPPXosh-ho_8vQxUYwdG3-JIBDa18DtRMIB2Q.jpg?width=960&crop=smart&auto=webp&s=7abad7ea0b99da19504a29da1ebbc47c1d4e538f', 'width': 960}, {'height': 553, 'url': 'https://external-preview.redd.it/BQgc-WoPPXosh-ho_8vQxUYwdG3-JIBDa18DtRMIB2Q.jpg?width=1080&crop=smart&auto=webp&s=a68fafbb388b2c74de75dffbef22a0beeae50ba7', 'width': 1080}], 'source': {'height': 1468, 'url': 'https://external-preview.redd.it/BQgc-WoPPXosh-ho_8vQxUYwdG3-JIBDa18DtRMIB2Q.jpg?auto=webp&s=3b819b236ae0314e4a73c1f1eb9d129629ca7bb8', 'width': 2864}, 'variants': {}}]} |
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Simplifying Fine-Tuning Workflows: Seeking Feedback on a New Platform Idea | 1 | [removed] | 2024-12-23T16:39:53 | https://www.reddit.com/r/LocalLLaMA/comments/1hkrncy/simplifying_finetuning_workflows_seeking_feedback/ | AhmadMirza17 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkrncy | false | null | t3_1hkrncy | /r/LocalLLaMA/comments/1hkrncy/simplifying_finetuning_workflows_seeking_feedback/ | false | false | self | 1 | null |
Calculus ! | 336 | 2024-12-23T16:48:25 | ritshpatidar | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkru2s | false | null | t3_1hkru2s | /r/LocalLLaMA/comments/1hkru2s/calculus/ | false | false | 336 | {'enabled': True, 'images': [{'id': 'qKmNfIHhglprVTGn0jEmEetKDMpLe8PY8TBZlukHvAk', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/uvckcpa0om8e1.jpeg?width=108&crop=smart&auto=webp&s=7a8b7ca69e81834a0dc7a659f8f40d2e7a90fe51', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/uvckcpa0om8e1.jpeg?width=216&crop=smart&auto=webp&s=cfe87ac85fa37cd5bca4276cae21b282d963eabb', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/uvckcpa0om8e1.jpeg?width=320&crop=smart&auto=webp&s=4d2ccf0279e96021a0a354beea018579eb40e010', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/uvckcpa0om8e1.jpeg?width=640&crop=smart&auto=webp&s=086cf842578e2c750ef3a8efba00c696e26f3b5b', 'width': 640}], 'source': {'height': 800, 'url': 'https://preview.redd.it/uvckcpa0om8e1.jpeg?auto=webp&s=106c6e4622ce05bc32eac15a6fdbfa9d8145010c', 'width': 800}, 'variants': {}}]} |
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How does GPT-o (and O3) work? | 1 | [removed] | 2024-12-23T17:09:22 | https://www.reddit.com/r/LocalLLaMA/comments/1hksb08/how_does_gpto_and_o3_work/ | blackrabbit | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hksb08 | false | null | t3_1hksb08 | /r/LocalLLaMA/comments/1hksb08/how_does_gpto_and_o3_work/ | false | false | self | 1 | null |
Tools to Manage Context and Edit LLM Responses? | 0 | I often have a problem when using a chat interface for coding that there will be several steps before I can get to the final solution. However, the LLM might get hung up on the first one of these steps, requiring a bit of back and forth before getting it right after fixing errors, asking for small revisions, etc.
Now, as I move on the next step, my context is polluted by my back-and-forth to complete step 1. There's a bunch of irrelevant garbage in the context because of the small fixes needed to get the first step right. So the responses for step 2 and later get worse and worse.
Is there a tool that lets me create a conversation/chat like a tree, and go back before the LLM had to be corrected, edit the LLM response to make it look like it got it right the first time, then continue from there? Is that making any sense? | 2024-12-23T17:09:32 | https://www.reddit.com/r/LocalLLaMA/comments/1hksb5x/tools_to_manage_context_and_edit_llm_responses/ | JeepyTea | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hksb5x | false | null | t3_1hksb5x | /r/LocalLLaMA/comments/1hksb5x/tools_to_manage_context_and_edit_llm_responses/ | false | false | self | 0 | null |
Anybody else getting Trojan:Script/Ulthar.A!ml in the latest llama.cpp release? | 1 | [removed] | 2024-12-23T17:26:41 | https://www.reddit.com/r/LocalLLaMA/comments/1hksp2v/anybody_else_getting_trojanscriptultharaml_in_the/ | 701nf1n17y4ndb3y0nd | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hksp2v | false | null | t3_1hksp2v | /r/LocalLLaMA/comments/1hksp2v/anybody_else_getting_trojanscriptultharaml_in_the/ | false | false | 1 | null |
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LLM Consortium - Multi-Model AI Response Synthesis | 0 | This project is LLM consortium system that combines the strengths of multiple AI models, specifically GPT-4 and Claude 3 Sonnet, to generate more reliable responses. When a user submits a prompt, the system simultaneously queries both models, and then uses Claude 3 Haiku as a judge to synthesize and analyze their responses. The judge evaluates the consistency, completeness, and quality of the responses, providing a confidence score and highlighting any dissenting views. If the confidence score is below 0.8, the system can perform up to three iterations to refine the response.
Check it out here: https://llm-consortium.rnikhil.com/ | 2024-12-23T17:43:07 | https://www.reddit.com/r/LocalLLaMA/comments/1hkt25q/llm_consortium_multimodel_ai_response_synthesis/ | Excellent-Effect237 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkt25q | false | null | t3_1hkt25q | /r/LocalLLaMA/comments/1hkt25q/llm_consortium_multimodel_ai_response_synthesis/ | false | false | self | 0 | null |
Multimodal llms ( silly doubt) | 0 | [removed] | 2024-12-23T17:58:03 | https://www.reddit.com/r/LocalLLaMA/comments/1hktdw0/multimodal_llms_silly_doubt/ | Wide-Chef-7011 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hktdw0 | false | null | t3_1hktdw0 | /r/LocalLLaMA/comments/1hktdw0/multimodal_llms_silly_doubt/ | false | false | self | 0 | null |
How can I design a scalable LLM middleware to handle indefinite conversations while retaining context? | 1 | [removed] | 2024-12-23T17:59:11 | https://www.reddit.com/r/LocalLLaMA/comments/1hkterr/how_can_i_design_a_scalable_llm_middleware_to/ | Quantum_Qualia | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkterr | false | null | t3_1hkterr | /r/LocalLLaMA/comments/1hkterr/how_can_i_design_a_scalable_llm_middleware_to/ | false | false | self | 1 | null |
Qwen QwQ distillation into Qwen 2.5 1.5B | 1 | [removed] | 2024-12-23T18:32:55 | https://www.reddit.com/r/LocalLLaMA/comments/1hku55d/qwen_qwq_distillation_into_qwen_25_15b/ | micaebe | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hku55d | false | null | t3_1hku55d | /r/LocalLLaMA/comments/1hku55d/qwen_qwq_distillation_into_qwen_25_15b/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': '_4CZ64qWFe8CGy3QtQlTGV30X0irDq_64oeqgLaAsx8', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/7JjU2aCbYeUZKLxV57rhd6YO_w-70OJBkLXw3dTHYxM.jpg?width=108&crop=smart&auto=webp&s=7b430b9b55ff79f5e90c747807eb2219466f3b2f', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/7JjU2aCbYeUZKLxV57rhd6YO_w-70OJBkLXw3dTHYxM.jpg?width=216&crop=smart&auto=webp&s=fb8bf901c455d8d9ecfc9642e5a4d53daf5edb67', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/7JjU2aCbYeUZKLxV57rhd6YO_w-70OJBkLXw3dTHYxM.jpg?width=320&crop=smart&auto=webp&s=5b2eda2d71f55070652ea5b2091a1053b17ad269', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/7JjU2aCbYeUZKLxV57rhd6YO_w-70OJBkLXw3dTHYxM.jpg?width=640&crop=smart&auto=webp&s=05528ec5fc51b684d0604e7183cea9a5b52e5974', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/7JjU2aCbYeUZKLxV57rhd6YO_w-70OJBkLXw3dTHYxM.jpg?width=960&crop=smart&auto=webp&s=b44244cf6b779b62c924927e7cf4045990c4b087', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/7JjU2aCbYeUZKLxV57rhd6YO_w-70OJBkLXw3dTHYxM.jpg?width=1080&crop=smart&auto=webp&s=dc4c7f4f808c0ae2972af4f1f71c1f9947761941', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/7JjU2aCbYeUZKLxV57rhd6YO_w-70OJBkLXw3dTHYxM.jpg?auto=webp&s=3232d74099cece49cfb0ef2958766ae0211e1a56', 'width': 1200}, 'variants': {}}]} |
How can I design a scalable LLM middleware to handle indefinite conversations while retaining context? | 13 | NousResearch's Hermes 3 is awesome for roleplaying but the context is short, their 72B model is hosted pretty cheaply on the likes of hyperbolic but alas the context window length is only 12k.....
I've been thinking about how best to design a middleware layer for large language models that can handle an indefinite stream of conversation while still preserving context long past the original token window limit. My current plan is to have a Python middleware watch for when the token window gets overloaded and automatically summarize or compress the ongoing conversation, pushing certain high-level points or crucial details into a retrieval-augmented generation vector database. This way, at any given time, the LLM only receives an abridged version of the full discussion, but can also cross-reference the vector store whenever it encounters relevant keywords or semantic matches, perhaps by embedding those triggers directly into the prompt itself. I’m curious if anyone has experimented with a similar approach or has an even better idea for orchestrating large language model memory management at scale. How should I structure the summarization pipeline, what algorithms or methodologies might help in identifying the “important” tidbits, and is there a more elegant way to ensure the LLM continually knows when and how to query the vector store? Any insights, lessons learned, or alternative suggestions would be incredibly helpful. | 2024-12-23T18:34:01 | https://www.reddit.com/r/LocalLLaMA/comments/1hku5z6/how_can_i_design_a_scalable_llm_middleware_to/ | Quantum_Qualia | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hku5z6 | false | null | t3_1hku5z6 | /r/LocalLLaMA/comments/1hku5z6/how_can_i_design_a_scalable_llm_middleware_to/ | false | false | self | 13 | null |
Best Approach for Converting Unstructured Text to Predefined JSON Format for LLM Fine-Tuning? | 1 | [removed] | 2024-12-23T18:58:54 | https://www.reddit.com/r/LocalLLaMA/comments/1hkup7z/best_approach_for_converting_unstructured_text_to/ | oguzhancttnky | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkup7z | false | null | t3_1hkup7z | /r/LocalLLaMA/comments/1hkup7z/best_approach_for_converting_unstructured_text_to/ | false | false | self | 1 | null |
CLIDataForge: A Simple, Data-Driven Pipeline for Large-Scale LLM Dataset Creation | 5 | Hello,
Here is a tool I've been working on, and I thought some of you might find it useful. It's called CLIDataForge, and you can check it out here: [https://github.com/chrismrutherford/cliDataForge](https://github.com/chrismrutherford/cliDataForge)
**What does it do?**
CLIDataForge is a command-line tool for creating and managing large-scale training datasets for LLM fine tuning. I found myself writing similar chunks of code for different projects, and thought, "There must be a better way!" So, I decided to make something data-driven and reusable.
**Why I made it:**
1. **Simplicity**: No fancy frameworks or overly complex architectures. Just a straightforward CLI tool that gets the job done.
2. **Scalability**: While many projects use JSON files, I opted for PostgreSQL. Why? Once you're dealing with datasets of several hundred thousand entries, tracking many JSON files becomes a problem.
3. **Flexibility**: The data-driven approach means you can adapt it to different projects without rewriting core code each time.
**Key Features:**
* Multi-stage processing pipeline
* Parallel processing for speed
* Integrated with PostgreSQL for handling large datasets
* Simple prompt management system
* Easy column management and data import/export
It's not trying to be the be-all and end-all of data processing tools, but rather a simple, effective system for those who need something a bit more robust than scripts but don't want to use massive frameworks.
I'd love to hear your thoughts, suggestions, or any questions you might have. And if you find it useful, do give it a star on GitHub!
I'm going to integrate hugging face at some stage | 2024-12-23T19:04:09 | https://www.reddit.com/r/LocalLLaMA/comments/1hkutmn/clidataforge_a_simple_datadriven_pipeline_for/ | lolzinventor | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkutmn | false | null | t3_1hkutmn | /r/LocalLLaMA/comments/1hkutmn/clidataforge_a_simple_datadriven_pipeline_for/ | false | false | self | 5 | {'enabled': False, 'images': [{'id': '2e0TgPAY_LLaNg8mD7SdOub3PPPFVMhH8_hw2uLLOCY', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/ioo8ifCVhtpOkGXYwvgBinWUiWYKOrloRMUc-lzfKhk.jpg?width=108&crop=smart&auto=webp&s=398d72b0b38f843136ca511018279e849f38f9ec', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/ioo8ifCVhtpOkGXYwvgBinWUiWYKOrloRMUc-lzfKhk.jpg?width=216&crop=smart&auto=webp&s=2e5e56ea3d5ed4e8d680605f64e9300b564915d1', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/ioo8ifCVhtpOkGXYwvgBinWUiWYKOrloRMUc-lzfKhk.jpg?width=320&crop=smart&auto=webp&s=5d5e441f53261b70d45f8aa7f066175bc56235de', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/ioo8ifCVhtpOkGXYwvgBinWUiWYKOrloRMUc-lzfKhk.jpg?width=640&crop=smart&auto=webp&s=cd59c40a6d44d7086793c7c26791e73652c08f83', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/ioo8ifCVhtpOkGXYwvgBinWUiWYKOrloRMUc-lzfKhk.jpg?width=960&crop=smart&auto=webp&s=eefd907e92e60fcf61f182a7d3d5c335d06070bb', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/ioo8ifCVhtpOkGXYwvgBinWUiWYKOrloRMUc-lzfKhk.jpg?width=1080&crop=smart&auto=webp&s=10843f4a9fde740061d1701953b990fc53f29bb9', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/ioo8ifCVhtpOkGXYwvgBinWUiWYKOrloRMUc-lzfKhk.jpg?auto=webp&s=662f09f0044596d2eeb389f95da9d9c6e84e511e', 'width': 1200}, 'variants': {}}]} |
My Apple Intelligence Writing Tools for Windows/Linux/macOS app just had a huge new update. It supports a ton of local LLM implementations, and is open source & free :D. You can now chat with its one-click summaries of websites/YT videos/docs, and bring up an LLM chat UI anytime. Here's a new demo! | 117 | 2024-12-23T19:21:18 | https://v.redd.it/a8te4ixeen8e1 | TechExpert2910 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkv6og | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/a8te4ixeen8e1/DASHPlaylist.mpd?a=1737573694%2COTczNjBmZTRmMTBmNWZhNzNiZDJmNzc5MWM5YjI3ZTI4YWE3MGNhYWJhZDc4M2QxZGZlNzkxNzVjZmZiNTg1ZQ%3D%3D&v=1&f=sd', 'duration': 52, 'fallback_url': 'https://v.redd.it/a8te4ixeen8e1/DASH_1080.mp4?source=fallback', 'has_audio': False, 'height': 1080, 'hls_url': 'https://v.redd.it/a8te4ixeen8e1/HLSPlaylist.m3u8?a=1737573694%2CNTFkZTRmZjg0YTNmYjI1ZTdlMmM1YWZjOWFmYTcxMDFiNDc1MzEwYTkzNzRmNzI2NjIxNDI1ZjAzOWVhOTUyNw%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/a8te4ixeen8e1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1468}} | t3_1hkv6og | /r/LocalLLaMA/comments/1hkv6og/my_apple_intelligence_writing_tools_for/ | false | false | 117 | {'enabled': False, 'images': [{'id': 'cHJoN2pneGVlbjhlMTB-_ERSpoLo97zgaZe_Ty7qceG2UqpOgIqwvXUeUIDK', 'resolutions': [{'height': 79, 'url': 'https://external-preview.redd.it/cHJoN2pneGVlbjhlMTB-_ERSpoLo97zgaZe_Ty7qceG2UqpOgIqwvXUeUIDK.png?width=108&crop=smart&format=pjpg&auto=webp&s=6e87076e3b7bd78e4db8174782f1fab3a4d0d514', 'width': 108}, {'height': 158, 'url': 'https://external-preview.redd.it/cHJoN2pneGVlbjhlMTB-_ERSpoLo97zgaZe_Ty7qceG2UqpOgIqwvXUeUIDK.png?width=216&crop=smart&format=pjpg&auto=webp&s=6dcccec57531f7e5ae48f6c86d9c4da0db0f1dcb', 'width': 216}, {'height': 235, 'url': 'https://external-preview.redd.it/cHJoN2pneGVlbjhlMTB-_ERSpoLo97zgaZe_Ty7qceG2UqpOgIqwvXUeUIDK.png?width=320&crop=smart&format=pjpg&auto=webp&s=24e4305eae69df7099c910ca50488ead797b0193', 'width': 320}, {'height': 470, 'url': 'https://external-preview.redd.it/cHJoN2pneGVlbjhlMTB-_ERSpoLo97zgaZe_Ty7qceG2UqpOgIqwvXUeUIDK.png?width=640&crop=smart&format=pjpg&auto=webp&s=5c2b8849182934d8b000daf409639e83e55e7dea', 'width': 640}, {'height': 706, 'url': 'https://external-preview.redd.it/cHJoN2pneGVlbjhlMTB-_ERSpoLo97zgaZe_Ty7qceG2UqpOgIqwvXUeUIDK.png?width=960&crop=smart&format=pjpg&auto=webp&s=b384d2e0452b5bb9beb7c576508e64c1d7fbaab0', 'width': 960}, {'height': 794, 'url': 'https://external-preview.redd.it/cHJoN2pneGVlbjhlMTB-_ERSpoLo97zgaZe_Ty7qceG2UqpOgIqwvXUeUIDK.png?width=1080&crop=smart&format=pjpg&auto=webp&s=cb7e0cdbc3097deca14eaa36d9432a246d50c8b1', 'width': 1080}], 'source': {'height': 1340, 'url': 'https://external-preview.redd.it/cHJoN2pneGVlbjhlMTB-_ERSpoLo97zgaZe_Ty7qceG2UqpOgIqwvXUeUIDK.png?format=pjpg&auto=webp&s=e171f3148955e7ce2e7298c4d5d8e2c1e9eeea16', 'width': 1822}, 'variants': {}}]} |
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Has anyone tested phi4 yet? How does it perform? | 44 | The benchmarks look great, and the model weights have been out for some time already, but surprisingly I haven't seen any reviews on it, in particular its performance on math and coding as compared to Qwen 2.5 14b and other similarly sized relevant models; any insight in that regard? | 2024-12-23T19:28:05 | https://www.reddit.com/r/LocalLLaMA/comments/1hkvbnn/has_anyone_tested_phi4_yet_how_does_it_perform/ | LLMtwink | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkvbnn | false | null | t3_1hkvbnn | /r/LocalLLaMA/comments/1hkvbnn/has_anyone_tested_phi4_yet_how_does_it_perform/ | false | false | self | 44 | null |
I built a tool for renting cheap GPUs cheaply inferencing | 1 | Hi guys,
As the title suggests, we were struggling a lot with hosting our own models at affordable prices while maintaining decent precision. Hosting models often demands huge self-built racks or significant financial backing.
I built a tool that rents the cheapest spot GPU VMs from your favorite Cloud Providers, spins up inference clusters based on VLLM and serves them to you easily. It ensures full quota transparency, optimizes token throughput, and keeps costs predictable by monitoring spending.
I’m looking for beta users to test and refine the platform. If you’re interested in getting cost-effective access to powerful machines (those juicy high VRAM setups), I’d love for you to hear from you guys!
Link to waitlist: [https://open-scheduler.com/](https://open-scheduler.com/)
| 2024-12-23T19:32:28 | https://www.reddit.com/r/LocalLLaMA/comments/1hkvf2k/i_built_a_tool_for_renting_cheap_gpus_cheaply/ | RedditsBestest | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkvf2k | false | null | t3_1hkvf2k | /r/LocalLLaMA/comments/1hkvf2k/i_built_a_tool_for_renting_cheap_gpus_cheaply/ | false | false | self | 1 | null |
I built a tool for renting cheap GPUs | 51 | Hi guys,
as the title suggests, we were struggling a lot with hosting our own models at affordable prices while maintaining decent precision. Hosting models often demands huge self-built racks or significant financial backing.
I built a tool that rents the cheapest spot GPU VMs from your favorite Cloud Providers, spins up inference clusters based on VLLM and serves them to you easily. It ensures full quota transparency, optimizes token throughput, and keeps costs predictable by monitoring spending.
I’m looking for beta users to test and refine the platform. If you’re interested in getting cost-effective access to powerful machines (like juicy high VRAM setups), I’d love for you to hear from you guys!
Link to Website: [https://open-scheduler.com/](https://open-scheduler.com/) | 2024-12-23T19:35:04 | https://www.reddit.com/r/LocalLLaMA/comments/1hkvh0w/i_built_a_tool_for_renting_cheap_gpus/ | RedditsBestest | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkvh0w | false | null | t3_1hkvh0w | /r/LocalLLaMA/comments/1hkvh0w/i_built_a_tool_for_renting_cheap_gpus/ | false | false | self | 51 | null |
ChatGPT Plus vs Claude Pro for Learning Backend Development with Java | 0 | Hi everyone,
I’m currently learning backend development with a focus on Java and Spring Boot, and I’m considering subscribe to support my learning journey. Right now, I’m deciding between ChatGPT Plus and Claude Pro.
I’ve looked at rankings on platforms like Chatbot Arena and Webdev Arena, but I’m not sure how relevant these are to backend development, especially for tasks involving Java and Spring Boot. I’d really appreciate insights from backend developers who’ve used these tools in real-world scenarios.
Here’s what I’d like to know:
1. How well do these tools assist with backend development tasks like debugging, explaining concepts, and generating or reviewing code for Java and computer science?
2. Which one is more effective for learning and applying frameworks, libraries, or tools commonly used in Java backend development?
3. Are there any specific advantages or disadvantages you’ve noticed when using these tools?
If you’ve tried both, a comparison would be especially valuable! Thanks in advance for sharing your experiences—I’m looking forward to your advice. | 2024-12-23T19:57:15 | https://www.reddit.com/r/LocalLLaMA/comments/1hkvy5h/chatgpt_plus_vs_claude_pro_for_learning_backend/ | nekofneko | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkvy5h | false | null | t3_1hkvy5h | /r/LocalLLaMA/comments/1hkvy5h/chatgpt_plus_vs_claude_pro_for_learning_backend/ | false | false | self | 0 | null |
JSON structured output comparison between 4o, 4o-mini, and sonnet 3.5 (or other LLMs)? Any benchmarks or experience? | 6 | Hey - I am in the midst of a project in which I am
* taking the raw data from a Notion database, pulled via API and saved as raw JSON
* have 500 files. Each is a separate sub-page of this database. Each file averages about 75kb, or 21,000 tokens of unstructured JSON. Though, only about 1/10th of is the important stuff. Most of it is metadata
* Plan to create a fairly comprehensive prompt for an LLM to turn this raw JSON into a structured JSON so that I can use these processed JSON files to write to a postgres database with everything important extracted and semantically structured for use in an application
So basically, I need to write a thorough prompt to describe the database structure, and walk the LLM through the actual content and how to interpret it correctly, so that it can organize it according to the structure of the database.
Now that I'm getting ready to do that, I am trying to decide which LLM model is best suited for this given the complexity and size of the project. I don't mind spending like $100 to get the best results, but I have struggled to find any authoritative comparison of how well various models perform for structured JSON output.
Is 4o significantly better that 4o-mini? Or would 4o-mini be totally sufficient? Would I need to be concerned about losing important data or the logic being all fucked up? Obviously, I can't check each and every entry. Is Sonnet 3.5 better than both? Or same?
Do you have any experience with this type of task and have any insight advice? Know of anyone who has benchmarked something similar to this?
Thank you in advance for any help you can offer! | 2024-12-23T20:00:09 | https://www.reddit.com/r/LocalLLaMA/comments/1hkw0e8/json_structured_output_comparison_between_4o/ | No-Emu9365 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkw0e8 | false | null | t3_1hkw0e8 | /r/LocalLLaMA/comments/1hkw0e8/json_structured_output_comparison_between_4o/ | false | false | self | 6 | null |
Tried my hand on sora and suno | 1 | Let me know your thoughts! | 2024-12-23T20:10:14 | https://v.redd.it/07ejnprznn8e1 | Optimalutopic | /r/LocalLLaMA/comments/1hkw8j8/tried_my_hand_on_sora_and_suno/ | 1970-01-01T00:00:00 | 0 | {} | 1hkw8j8 | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/07ejnprznn8e1/DASHPlaylist.mpd?a=1737706236%2CMTUxZTMwNDgxMjAwNWY5M2U5NGYxYTljZTE2ZTcwN2E3MjhhMTEzY2MzYWY0OGNkYzQzMTBmOWRlOWNiN2RjZQ%3D%3D&v=1&f=sd', 'duration': 56, 'fallback_url': 'https://v.redd.it/07ejnprznn8e1/DASH_720.mp4?source=fallback', 'has_audio': True, 'height': 720, 'hls_url': 'https://v.redd.it/07ejnprznn8e1/HLSPlaylist.m3u8?a=1737706236%2CZTJkMDM4ZDhmOTg0NzU5MzUyZDU1NWI0ODNlZGQ2ODkwNGQ0OGRkOTEwODY5NWEzNTFmNmVkYjg5MDk3ZmM5Mw%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/07ejnprznn8e1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1280}} | t3_1hkw8j8 | /r/LocalLLaMA/comments/1hkw8j8/tried_my_hand_on_sora_and_suno/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'dDU5djl2b3pubjhlMVD_ReQ-ovJcnEFx8whq5KBNrUoiktUJKZpedf3aYrZC', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/dDU5djl2b3pubjhlMVD_ReQ-ovJcnEFx8whq5KBNrUoiktUJKZpedf3aYrZC.png?width=108&crop=smart&format=pjpg&auto=webp&s=5c84f12a705cb8a4120da1ed0af0a059635aaedf', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/dDU5djl2b3pubjhlMVD_ReQ-ovJcnEFx8whq5KBNrUoiktUJKZpedf3aYrZC.png?width=216&crop=smart&format=pjpg&auto=webp&s=95699d07b508026ee76a6ad6981bb19750ce8676', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/dDU5djl2b3pubjhlMVD_ReQ-ovJcnEFx8whq5KBNrUoiktUJKZpedf3aYrZC.png?width=320&crop=smart&format=pjpg&auto=webp&s=9658fb56b095fb28bbf4fb03ada8bf5e9ce6c7c4', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/dDU5djl2b3pubjhlMVD_ReQ-ovJcnEFx8whq5KBNrUoiktUJKZpedf3aYrZC.png?width=640&crop=smart&format=pjpg&auto=webp&s=288dec65305d9aee23d495bf37e71366b218b7e0', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/dDU5djl2b3pubjhlMVD_ReQ-ovJcnEFx8whq5KBNrUoiktUJKZpedf3aYrZC.png?width=960&crop=smart&format=pjpg&auto=webp&s=0c38ba726beb7454b2e8ec263a73846ef6be2a52', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/dDU5djl2b3pubjhlMVD_ReQ-ovJcnEFx8whq5KBNrUoiktUJKZpedf3aYrZC.png?width=1080&crop=smart&format=pjpg&auto=webp&s=20ec2fa5ddd3ed14260f53b4e4f59eecf521ae88', 'width': 1080}], 'source': {'height': 720, 'url': 'https://external-preview.redd.it/dDU5djl2b3pubjhlMVD_ReQ-ovJcnEFx8whq5KBNrUoiktUJKZpedf3aYrZC.png?format=pjpg&auto=webp&s=cac440b6aa7d7963ad09467254f5d6c7096a955d', 'width': 1280}, 'variants': {}}]} |
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Discord invite for locallama? | 1 | [removed] | 2024-12-23T20:45:18 | https://www.reddit.com/r/LocalLLaMA/comments/1hkwz7j/discord_invite_for_locallama/ | kitkatmafia | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkwz7j | false | null | t3_1hkwz7j | /r/LocalLLaMA/comments/1hkwz7j/discord_invite_for_locallama/ | false | false | self | 1 | null |
You can now run *private* GGUFs from Hugging Face Hub directly in Ollama | 135 | Hi all, I'm VB, GPU poor in residence at Hugging Face - Starting today, you can run your private GGUFs from the Hugging Face hub directly in Ollama! 🔥
Works out of the box, all you need to do is add your Ollama SSH key to your profile, and that's it!
Run private fine-tunes, quants and more, with the same old UX!
Quite excited to bring more than a million smol LLMs closer to all Ollama users - loads of more goodies in the pipeline!
All it requires is two steps:
1. Copy your Ollama SSH key, you can do so via: `cat ~/.ollama/id_ed25519.pub | pbcopy`
2. Add the corresponding key to your Hugging Face account by going to [your account settings](https://huggingface.co/settings/keys) and clicking on `Add new SSH key`
3. That’s it! You can now run private GGUFs from the Hugging Face Hub: `ollama run` [`hf.co/{username}/{repository}`](http://hf.co/{username}/{repository})
Full details here: [https://huggingface.co/docs/hub/en/ollama](https://huggingface.co/docs/hub/en/ollama)
Remember, Not your weights, not your brain! 🤗
Looking forward to your feedback! | 2024-12-23T20:49:32 | https://www.reddit.com/r/LocalLLaMA/comments/1hkx2bi/you_can_now_run_private_ggufs_from_hugging_face/ | vaibhavs10 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkx2bi | false | null | t3_1hkx2bi | /r/LocalLLaMA/comments/1hkx2bi/you_can_now_run_private_ggufs_from_hugging_face/ | false | false | self | 135 | {'enabled': False, 'images': [{'id': '_rEX1xvwdv17x6NFAWQpYFNONQ0BKA5Qw0Eo0JX0zWU', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/OFWu5qxCY4R2hQfI_vzWYgK2ON5meupO-ZR0eRPmsP8.jpg?width=108&crop=smart&auto=webp&s=4bc231a80d79babe4e6cddf7b4c71dcb0aa8f8ff', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/OFWu5qxCY4R2hQfI_vzWYgK2ON5meupO-ZR0eRPmsP8.jpg?width=216&crop=smart&auto=webp&s=d7108244b7182d85047aa59446f1dfb68542b610', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/OFWu5qxCY4R2hQfI_vzWYgK2ON5meupO-ZR0eRPmsP8.jpg?width=320&crop=smart&auto=webp&s=d34fa1a756c458772d3c8680309a93cf8d758b40', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/OFWu5qxCY4R2hQfI_vzWYgK2ON5meupO-ZR0eRPmsP8.jpg?width=640&crop=smart&auto=webp&s=5b03e18da2698977cf1222f0c9e54ccb6177ffc4', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/OFWu5qxCY4R2hQfI_vzWYgK2ON5meupO-ZR0eRPmsP8.jpg?width=960&crop=smart&auto=webp&s=3d875ff29aae8239d010f3b964e5a2f3ebe32e3d', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/OFWu5qxCY4R2hQfI_vzWYgK2ON5meupO-ZR0eRPmsP8.jpg?width=1080&crop=smart&auto=webp&s=b51090c30528b6b8c637acb54d7fc0f6a5249cf5', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/OFWu5qxCY4R2hQfI_vzWYgK2ON5meupO-ZR0eRPmsP8.jpg?auto=webp&s=ee70e402c0f8274b46f38378bada81dbeb5b1dac', 'width': 1200}, 'variants': {}}]} |
Are you GPU-poor? How do you deal with it? | 1 | [removed] | 2024-12-23T20:49:38 | https://www.reddit.com/r/LocalLLaMA/comments/1hkx2ej/are_you_gpupoor_how_do_you_deal_with_it/ | Elegant_vamp | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkx2ej | false | null | t3_1hkx2ej | /r/LocalLLaMA/comments/1hkx2ej/are_you_gpupoor_how_do_you_deal_with_it/ | false | false | self | 1 | null |
LMSYS Copilot Arena update, with Deepseek on top | 24 | 2024-12-23T20:52:01 | jpydych | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hkx48t | false | null | t3_1hkx48t | /r/LocalLLaMA/comments/1hkx48t/lmsys_copilot_arena_update_with_deepseek_on_top/ | false | false | 24 | {'enabled': True, 'images': [{'id': 'Wovbbcp6n6-nEP0BUhyH4CkCgIyMLiq8FB_4zyvGgqo', 'resolutions': [{'height': 45, 'url': 'https://preview.redd.it/zqp14tfevn8e1.png?width=108&crop=smart&auto=webp&s=200e4702f26a0d00133714d86e5615a438cb6fd6', 'width': 108}, {'height': 91, 'url': 'https://preview.redd.it/zqp14tfevn8e1.png?width=216&crop=smart&auto=webp&s=0d55def3134a55256c9d5bb4cf9b7b0eef121ccb', 'width': 216}, {'height': 135, 'url': 'https://preview.redd.it/zqp14tfevn8e1.png?width=320&crop=smart&auto=webp&s=84d026fe76fae41156a65182f258bbfe887538c4', 'width': 320}, {'height': 270, 'url': 'https://preview.redd.it/zqp14tfevn8e1.png?width=640&crop=smart&auto=webp&s=d53f0e4b84ef2a5e3b7c2e3c179eca4563de75f9', 'width': 640}, {'height': 406, 'url': 'https://preview.redd.it/zqp14tfevn8e1.png?width=960&crop=smart&auto=webp&s=f74ef4f11147943b59ff602c367194bfc509a080', 'width': 960}, {'height': 456, 'url': 'https://preview.redd.it/zqp14tfevn8e1.png?width=1080&crop=smart&auto=webp&s=4ca3c8f1d42c76468a9c5015d66c23bbd7503063', 'width': 1080}], 'source': {'height': 774, 'url': 'https://preview.redd.it/zqp14tfevn8e1.png?auto=webp&s=0ded550ad71f8a680c3afc4c23092df42d845b1a', 'width': 1830}, 'variants': {}}]} |
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rtrvr.ai: Universal Web Agent | 1 | [removed] | 2024-12-23T21:03:45 | https://www.reddit.com/r/LocalLLaMA/comments/1hkxdav/rtrvrai_universal_web_agent/ | BodybuilderLost328 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkxdav | false | null | t3_1hkxdav | /r/LocalLLaMA/comments/1hkxdav/rtrvrai_universal_web_agent/ | false | false | self | 1 | null |
Ollama (llama3.2:3b) runs extremely slow on my MBP (36GB M3) and also makes my computer extremely hot | 0 | How do I figure out the cause of this? I'm not sure how much RAM or integrated CPU/GPU memory it's using, but theoretically I should have enough integrated memory to run llama3.2:3b. | 2024-12-23T21:15:21 | https://www.reddit.com/r/LocalLLaMA/comments/1hkxm3t/ollama_llama323b_runs_extremely_slow_on_my_mbp/ | Amazydayzee | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkxm3t | false | null | t3_1hkxm3t | /r/LocalLLaMA/comments/1hkxm3t/ollama_llama323b_runs_extremely_slow_on_my_mbp/ | false | false | self | 0 | null |
Guys am I crazy or is this paper totally batshit haha | 88 | 2024-12-23T21:22:26 | http://dx.doi.org/10.13140/RG.2.2.32495.34727 | Kappa-chino | dx.doi.org | 1970-01-01T00:00:00 | 0 | {} | 1hkxrgq | false | null | t3_1hkxrgq | /r/LocalLLaMA/comments/1hkxrgq/guys_am_i_crazy_or_is_this_paper_totally_batshit/ | false | false | default | 88 | null |
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Synthetic data generation | 4 | Hi I have about $2000 in OpenAI credits which are about to expire in a few days. I was wondering if there is a turnkey way to generate a domain specific dataset in a specific format? I don’t want to pay anything apart from OpenAI credits which I have.
Thank you | 2024-12-23T22:10:58 | https://www.reddit.com/r/LocalLLaMA/comments/1hkyri3/synthetic_data_generation/ | Whyme-__- | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkyri3 | false | null | t3_1hkyri3 | /r/LocalLLaMA/comments/1hkyri3/synthetic_data_generation/ | false | false | self | 4 | null |
Looking for 'AI' DJ or similar for large collection of MP3 files. | 6 | I download my music and use it in a music player I developed with electron. Is there an AI model on hugging face or ollama that I could use to get a list of MP3's that would sound good when played back to back? I can fade them in and out programmatically, maybe there is a small embedding model for audio that might be able to achieve this. Another question is there a good model for audio to lyrics for searching based on lyrics?
Thanks! | 2024-12-23T22:32:48 | https://www.reddit.com/r/LocalLLaMA/comments/1hkz82z/looking_for_ai_dj_or_similar_for_large_collection/ | Hidden1nin | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkz82z | false | null | t3_1hkz82z | /r/LocalLLaMA/comments/1hkz82z/looking_for_ai_dj_or_similar_for_large_collection/ | false | false | self | 6 | null |
Unveiling LLMs: What They Are Not. | 0 | There are many ways to understand something, and one of them, as curious as it may seem, is knowing what that something *is not*.
Recently, we’ve been bombarded with fantastic claims about LLMs. Some have proven true, others not so much, and many, in my opinion, feel more like marketing buzzwords—which, I must admit, has become quite tiresome.
I want to use this space for you to collaborate and contribute to the community by sharing what LLMs *definitely are not*. In other words, what they are not capable of doing or, if they can, in which tasks they are not the best option.
Note: Obviously, the topic allows for jokes like "definitely not an air fryer," and depending on how creative that is, it might be funny. Otherwise, you’d just be being annoying. So, overall, please try to provide thoughtful responses. | 2024-12-23T22:50:47 | https://www.reddit.com/r/LocalLLaMA/comments/1hkzl93/unveiling_llms_what_they_are_not/ | thecalmgreen | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkzl93 | false | null | t3_1hkzl93 | /r/LocalLLaMA/comments/1hkzl93/unveiling_llms_what_they_are_not/ | false | false | self | 0 | null |
Handle follow-up or clarifying questions in RAG scenarios (with ease) | 18 | There several threads here on reddit like this [one](https://www.reddit.com/r/LocalLLaMA/comments/18mqwg6/best_practice_for_rag_with_followup_chat/) and this [one](https://www.reddit.com/r/LangChain/comments/1djcvh0/chat_history_for_rag_how_to_search_for_follow_up/) that highlight challenges with effectively handling follow-up questions from a user, especially in RAG scenarios. Specifically, these are multi-turn conversations that can range from
**Adjusting a Retrieval**
**User:** What are the benefits of renewable energy?
**Assistant:** Renewable energy reduces greenhouse gas emissions, lowers air pollution, and provides sustainable power sources like solar and wind....
**User:** Include cost considerations in the response.
**OR.....**
# Clarifying a Response
**User:** Can you tell me about the history of the internet?
**Assistant:** The internet was developed from research programs like ARPANET in the late 1960s....
**User:** Can you focus on how ARPANET worked?
**OR...**
# Switching Intent
**User:** What are the symptoms of diabetes?
**Assistant:** Common symptoms include frequent urination, excessive thirst, fatigue, and blurry vision.
**User:** How is it diagnosed?
Most of these scenarios requires carefully crafting, editing and optimizing prompts to an LLM to rewrite the follow-up query, extract relevant contextual information and then trigger retrieval to answer the question. The whole process is slow, error prone and adds significant latency.
[Arch](https://github.com/katanemo/archgw) (an intelligent gateway for agents) pushed out an update (0.1.7) to accurately handle multi-turn intent, extracting relevant contextual information and calling downstream developer APIs (aka function calling) in <500ms! Arch is an open source infrastructure gateway for agents so that developers can focus on what matters most. Arch is engineered with purpose-built (fast) LLMs for the seamless integration of prompts with APIs (among other things). More details on how that multi-turn works: [https://docs.archgw.com/build\_with\_arch/multi\_turn.html](https://docs.archgw.com/build_with_arch/multi_turn.html) and you can run the demo here: [https://github.com/katanemo/archgw/tree/main/demos/multi\_turn\_rag\_agent](https://github.com/katanemo/archgw/tree/main/demos/multi_turn_rag_agent)
The high-level architecture and request flow looks like this, and below is a sample multi-turn interaction that it can help developers build quickly.
[Prompt to API processing handled via Arch Gateway](https://preview.redd.it/s61q7r39ho8e1.png?width=2626&format=png&auto=webp&s=97a4827bdc86663bbf52a8524a2d6e8f677d7c98)
[Example of a multi-turn response handled via Arch](https://preview.redd.it/407oqppxeo8e1.png?width=1064&format=png&auto=webp&s=72ccdd6020de6ce229199e69727f01eeb1ae072b)
**Disclaimer**: I am one of the core contributors to [https://github.com/katanemo/archgw](https://github.com/katanemo/archgw) \- and would love to answer any questions you may have. | 2024-12-23T22:56:48 | https://www.reddit.com/r/LocalLLaMA/comments/1hkzpqv/handle_followup_or_clarifying_questions_in_rag/ | AdditionalWeb107 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hkzpqv | false | null | t3_1hkzpqv | /r/LocalLLaMA/comments/1hkzpqv/handle_followup_or_clarifying_questions_in_rag/ | false | false | 18 | {'enabled': False, 'images': [{'id': 'CumNe617pvfcpWpBOsseCcHSxcBsOZ4Uh2VdsiqcTN8', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/B7Gq3TBojGoD0HxG60BGdCyfc6FrWlgPXNkLc74WKEM.jpg?width=108&crop=smart&auto=webp&s=d36b9da2eee6e7b037090b9ed2f2ddecd5f0aea7', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/B7Gq3TBojGoD0HxG60BGdCyfc6FrWlgPXNkLc74WKEM.jpg?width=216&crop=smart&auto=webp&s=1106dfa8d0a666ae44a19faff993d523bffe790a', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/B7Gq3TBojGoD0HxG60BGdCyfc6FrWlgPXNkLc74WKEM.jpg?width=320&crop=smart&auto=webp&s=c69f38d4b8dc5523a3ad6d8904d4256be5c885e4', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/B7Gq3TBojGoD0HxG60BGdCyfc6FrWlgPXNkLc74WKEM.jpg?width=640&crop=smart&auto=webp&s=d29e47c86058f7b71518976a27025db70f5baa90', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/B7Gq3TBojGoD0HxG60BGdCyfc6FrWlgPXNkLc74WKEM.jpg?width=960&crop=smart&auto=webp&s=26cb08cea73b19018b612f0cf8205559920caea9', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/B7Gq3TBojGoD0HxG60BGdCyfc6FrWlgPXNkLc74WKEM.jpg?width=1080&crop=smart&auto=webp&s=f69e57539cacd21fd190bb4e87ff56b18ec2d8dc', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/B7Gq3TBojGoD0HxG60BGdCyfc6FrWlgPXNkLc74WKEM.jpg?auto=webp&s=04d7f9134e8536422045391d81e801d68f815bc4', 'width': 1200}, 'variants': {}}]} |
|
Are there aspects of VERY large parameter models that cannot be matched by smaller ones? | 21 | Bit of a random thought but will small models eventually rival or out perform models like chatgpt/sonnet in every way or will these super large models always hold an edge by sheer training size?
Possibly too early to tell?
Just curious as a noob on the topic. | 2024-12-23T23:52:44 | https://www.reddit.com/r/LocalLLaMA/comments/1hl0t84/are_there_aspects_of_very_large_parameter_models/ | Business_Respect_910 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl0t84 | false | null | t3_1hl0t84 | /r/LocalLLaMA/comments/1hl0t84/are_there_aspects_of_very_large_parameter_models/ | false | false | self | 21 | null |
Easiest way to get started with AI-assisted coding using local models (free, open-source) | 9 | Hey everyone 👋,
I’ve been experimenting with ways to simplify my coding workflow using chat-based LLMs, and I wanted to share a tool I built called **gptree**. It’s a lightweight CLI tool designed to streamline project context sharing for coding tasks—perfect if you’re using any local model or chat-based LLM for coding assistance.
# What does gptree do?
If you’re working on coding projects and want AI to assist with tasks like debugging, expanding functionality, or writing new features, providing the right context is key. That’s where `gptree` comes in:
* **Generates a file tree** for your project, respecting `.gitignore` to avoid unnecessary clutter.
* Includes an **interactive mode** so you can select only the files you want to share.
* Outputs a **text blob** of the file tree and the contents of selected files, ready to paste into any LLM prompt.
This makes it the easiest, no-overhead way to start leveraging AI for coding—even if you’re just getting started with local models.
[Quick demo of GPTree — pasting straight into ChatGPT](https://i.redd.it/fkzxymsnvo8e1.gif)
# Why use gptree?
* **Quick Start for AI-Assisted Coding**: No complex integrations, just generate context and paste into your favorite LLM interface.
* **Flexible**: Works with any local model (not just Llama-based ones) or cloud-based tools like ChatGPT.
* **Efficient**: Keeps everything lightweight and respects your `.gitignore` to avoid bloated prompts.
# Get Started
The tool is open-source and easy to install:
# Install via Homebrew 🍺
brew tap travisvn/tap
brew install gptree
# Install via pipx (recommended for Python users) 🐍
pipx install gptree-cli
Here’s the GitHub repo if you want to check it out:
[https://github.com/travisvn/gptree](https://github.com/travisvn/gptree)
Let me know if you have any questions or ideas for improvements! I’d also love feedback on how this could work better for different local setups.
If you find it helpful, a ⭐ on the GitHub repo would mean a lot and helps others discover the tool! | 2024-12-24T00:18:59 | https://www.reddit.com/r/LocalLLaMA/comments/1hl1bce/easiest_way_to_get_started_with_aiassisted_coding/ | lapinjapan | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl1bce | false | null | t3_1hl1bce | /r/LocalLLaMA/comments/1hl1bce/easiest_way_to_get_started_with_aiassisted_coding/ | false | false | 9 | {'enabled': False, 'images': [{'id': 'o7fDBZVvuWfKUM6GNkJLzalihd9X7XbAjTdE682Rmh0', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/9anusGdl1RAEFy1KOaXYQypfcwZ_7CkkkbmVI5_GL48.jpg?width=108&crop=smart&auto=webp&s=1b2be49130f8d84dc1cf65ef5a5120a8c2fa9e08', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/9anusGdl1RAEFy1KOaXYQypfcwZ_7CkkkbmVI5_GL48.jpg?width=216&crop=smart&auto=webp&s=9c4a072c1b3c0b3ff04ce459af5dfe70acf65873', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/9anusGdl1RAEFy1KOaXYQypfcwZ_7CkkkbmVI5_GL48.jpg?width=320&crop=smart&auto=webp&s=0247338d9eb2edd9f93c5c78cd13e97406393bbb', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/9anusGdl1RAEFy1KOaXYQypfcwZ_7CkkkbmVI5_GL48.jpg?width=640&crop=smart&auto=webp&s=4c0b0e44881f959bcb0b3ee8f50ee52bfcd4e310', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/9anusGdl1RAEFy1KOaXYQypfcwZ_7CkkkbmVI5_GL48.jpg?width=960&crop=smart&auto=webp&s=6c57345e6c96ee91f27e2b39027ce7882a1acd70', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/9anusGdl1RAEFy1KOaXYQypfcwZ_7CkkkbmVI5_GL48.jpg?width=1080&crop=smart&auto=webp&s=2a62210cc5731a302a27da5d9bb407754bc01db8', 'width': 1080}], 'source': {'height': 640, 'url': 'https://external-preview.redd.it/9anusGdl1RAEFy1KOaXYQypfcwZ_7CkkkbmVI5_GL48.jpg?auto=webp&s=df28ef5734d5612e8ad1c8d822b5d4a6cfaf63b1', 'width': 1280}, 'variants': {}}]} |
|
llama 3.2 3B is amazing | 371 | This is the first small model that has worked so well for me and it's usable. It has a context window that does indeed remember things that were previously said without errors. Also handles Spanish ( i have not seen this since stable lm 3b) very well and all in Q4\_K\_M.
Personally i'm using llama-3.2-3b-instruct-abliterated.Q4\_K\_M.gguf | 2024-12-24T00:46:05 | https://www.reddit.com/r/LocalLLaMA/comments/1hl1tso/llama_32_3b_is_amazing/ | ventilador_liliana | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl1tso | false | null | t3_1hl1tso | /r/LocalLLaMA/comments/1hl1tso/llama_32_3b_is_amazing/ | false | false | self | 371 | null |
Predictions for 2025? | 138 | 2024 has been a wild ride with lots of development inside and outside AI.
What are your predictions for this coming year? | 2024-12-24T01:32:55 | https://www.reddit.com/r/LocalLLaMA/comments/1hl2pd6/predictions_for_2025/ | kidupstart | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl2pd6 | false | null | t3_1hl2pd6 | /r/LocalLLaMA/comments/1hl2pd6/predictions_for_2025/ | false | false | self | 138 | null |
Where is qwen2.5 with tool training and 128k context? | 0 | Been down a rabbit hole trying to find the magic qwen 2.5 32b or 14b model that actually has tool training so that it's capable of using tools and actually has 128k context but i only seem to be able to find one or the other.
i'm trying to find a version of this model that will actually work with cline or roo cline vscode extensions being served over ollama.
the defacto version of qwen 2.5 available through the ollama models hub is incapable of using tools it seems so cline and roo cline tool/function calling just causes it to break.
fof the love of god i want to like this model since so many people have had positive things to say about it but i absolutely need tool usage and large context out of it. can someone please point me in the direction of the correct version? | 2024-12-24T01:36:16 | https://www.reddit.com/r/LocalLLaMA/comments/1hl2rmk/where_is_qwen25_with_tool_training_and_128k/ | waywardspooky | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl2rmk | false | null | t3_1hl2rmk | /r/LocalLLaMA/comments/1hl2rmk/where_is_qwen25_with_tool_training_and_128k/ | false | false | self | 0 | null |
is speculative decoding useful in production? | 1 | [removed] | 2024-12-24T01:41:14 | https://www.reddit.com/r/LocalLLaMA/comments/1hl2utv/is_speculative_decoding_useful_in_production/ | Klutzy_Psychology849 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl2utv | false | null | t3_1hl2utv | /r/LocalLLaMA/comments/1hl2utv/is_speculative_decoding_useful_in_production/ | false | false | self | 1 | null |
is speculative decoding useful in production? | 0 | is it actually useful or does it heavily depend as usual?
if so what are its usecases? | 2024-12-24T01:48:48 | https://www.reddit.com/r/LocalLLaMA/comments/1hl2zol/is_speculative_decoding_useful_in_production/ | khaliiil | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl2zol | false | null | t3_1hl2zol | /r/LocalLLaMA/comments/1hl2zol/is_speculative_decoding_useful_in_production/ | false | false | self | 0 | null |
Best and fastest 2-3b model I can run? | 3 | So this space changes so fast it's nuts.
I have LMStudio and Openwebui running on my PC, 8gb RTX 4060 GPU.
I want to run a small model that is as fast as possible, and also as good as possible for text summarization and similar tasks, as an API.
I know there's unsloth, bnb, exlama, all these things. Im just not updated enough on what to run here.
Currently I'm using LMStudio with their Gemma 2b. It's alright, but I assume there's a much better solution out there? Any help would be greatly appreciated. | 2024-12-24T01:52:59 | https://www.reddit.com/r/LocalLLaMA/comments/1hl32g0/best_and_fastest_23b_model_i_can_run/ | mstahh | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl32g0 | false | null | t3_1hl32g0 | /r/LocalLLaMA/comments/1hl32g0/best_and_fastest_23b_model_i_can_run/ | false | false | self | 3 | null |
TimesFM, a 200m Time Series Foundation Model from Goolgle | 87 | 2024-12-24T01:56:23 | https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6 | mlon_eusk-_- | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1hl34mr | false | null | t3_1hl34mr | /r/LocalLLaMA/comments/1hl34mr/timesfm_a_200m_time_series_foundation_model_from/ | false | false | 87 | {'enabled': False, 'images': [{'id': 'gjsHnsN7uOjyZeU9BnnUvQ3M2dc2w0xrs2AburDi9Fo', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/j-dTV6i1y5BbgUhQb0vnDfKltzju5tN7J3SCuuw3Ark.jpg?width=108&crop=smart&auto=webp&s=da04179597a53aa5a09b842ec323377260a70bf5', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/j-dTV6i1y5BbgUhQb0vnDfKltzju5tN7J3SCuuw3Ark.jpg?width=216&crop=smart&auto=webp&s=863a787c21e9b6c036afd36b560d4561ab2912a5', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/j-dTV6i1y5BbgUhQb0vnDfKltzju5tN7J3SCuuw3Ark.jpg?width=320&crop=smart&auto=webp&s=5f59e9ae6943d28f790a9756897571a7e2c7ff84', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/j-dTV6i1y5BbgUhQb0vnDfKltzju5tN7J3SCuuw3Ark.jpg?width=640&crop=smart&auto=webp&s=d13901960e63d9f766c17d8c0226b22c0e7761f2', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/j-dTV6i1y5BbgUhQb0vnDfKltzju5tN7J3SCuuw3Ark.jpg?width=960&crop=smart&auto=webp&s=af781d05dd9ef71e86b7c06721e5583d85ec419d', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/j-dTV6i1y5BbgUhQb0vnDfKltzju5tN7J3SCuuw3Ark.jpg?width=1080&crop=smart&auto=webp&s=0084ab90eaf53bc92371db0c9aa20c429389c6f1', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/j-dTV6i1y5BbgUhQb0vnDfKltzju5tN7J3SCuuw3Ark.jpg?auto=webp&s=c83e8f146c7bb7a1dbd6fabbf1705a709ced139b', 'width': 1200}, 'variants': {}}]} |
||
This might be a dumb question but how many bits are in a token? | 121 | I'm new to llms but I keep hearing people talk about token prices and context windows as measured in tokens and is there a set number of bits per token? Are they variable by model? Variable with one model? | 2024-12-24T02:18:38 | https://www.reddit.com/r/LocalLLaMA/comments/1hl3iwa/this_might_be_a_dumb_question_but_how_many_bits/ | KnownDairyAcolyte | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl3iwa | false | null | t3_1hl3iwa | /r/LocalLLaMA/comments/1hl3iwa/this_might_be_a_dumb_question_but_how_many_bits/ | false | false | self | 121 | null |
Hunyuan fp8 on a 12 GB 3080 can produce mobile quality gifs in 10 minutes | 51 | [Default prompt from this workflow: https:\/\/civitai.com\/models\/1048302?modelVersionId=1176230](https://reddit.com/link/1hl3tg0/video/btpt2s97po8e1/player)
I followed [this guide first](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/), with some extra finagling (updating and cloning then installing [custom nodes](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite)), I got the output here. On a desktop you can see the seams but on mobile it should look okay. Zoom out if not, all things considered, it works surprisingly well. 9 minute thirty second to 11 minute generation times on my machine. Later iterations are slower than earlier ones and this compounding effect seems worse the higher tile counts are used. | 2024-12-24T02:35:20 | https://www.reddit.com/r/LocalLLaMA/comments/1hl3tg0/hunyuan_fp8_on_a_12_gb_3080_can_produce_mobile/ | Emergency-Walk-2991 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl3tg0 | false | null | t3_1hl3tg0 | /r/LocalLLaMA/comments/1hl3tg0/hunyuan_fp8_on_a_12_gb_3080_can_produce_mobile/ | false | false | self | 51 | null |
model choices for agents? | 0 | What models are you finding useful for deploying agents to perform language-based tasks, including summarization, interpretation (in English) and sentiment analysis?
Seems like YouTubers are creating interesting content around n8n and agentic AI workflows but are often calling out to OpenAI via API.
Curious what your use cases and model choices have been - I’m particularly interested in surveying typical model size.
Personally, I find that ~Q4 and ~8b or 11b models are meeting most of my needs - sometimes even less vram.
What are your experiences? | 2024-12-24T02:38:03 | https://www.reddit.com/r/LocalLLaMA/comments/1hl3v45/model_choices_for_agents/ | TellMeThing | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl3v45 | false | null | t3_1hl3v45 | /r/LocalLLaMA/comments/1hl3v45/model_choices_for_agents/ | false | false | self | 0 | null |
I have a barely used 4070 Super (12GB). To achieve 24GB, is it cheaper to add a used 3060 12GB, or to sell my 4070 and buy a used 3090? | 1 | [removed] | 2024-12-24T02:43:24 | https://www.reddit.com/r/LocalLLaMA/comments/1hl3yfo/i_have_a_barely_used_4070_super_12gb_to_achieve/ | manwiththe104IQ | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl3yfo | false | null | t3_1hl3yfo | /r/LocalLLaMA/comments/1hl3yfo/i_have_a_barely_used_4070_super_12gb_to_achieve/ | false | false | self | 1 | null |
We Should Be Swarm-Inferencing | 11 | Wanted to spark a discussion here. With O1 and O3 pushing the onus for quality improvement to inference time, doing so with a distributed network makes a ton of sense.
Unlike training, inferencing is very, very parallelizable over multiple GPUs - even over a distributed network with milliseconds of latency. The live sharing packets are small, and we can probably make some distributed Ethereum-esque wrapper to ensure compute privacy and incentivize against freeloading.
[https://news.ycombinator.com/item?id=42308590#42313885](https://news.ycombinator.com/item?id=42308590#42313885)
>the equation for figuring what factor slower it would be is 1 / (1 + time to do transfers and trigger processing per each token in seconds). That would mean under a less ideal situation where the penalty is 5 milliseconds per token, the calculation will be \~0.99502487562 times what it would have been had it been done in a hypothetical single GPU that has all of the VRAM needed, but otherwise the same specifications. This penalty is also not very noticeable.
So - no real significant loss from distributing.
\---
Napkin math (courtesy of o1):
\- likely around 100-200 PFLOPs of total compute available from consumer devices in the world with over 24GB VRAM
\- running o3 at $50ish-per-inference low-compute mode estimates: 5-30 exaFLOPs
\- o3 at high-compute SOTA mode, $5kish-per-inference estimate: 1-2 zetaFLOPs
So, around 1000 inferences per day of o3 low-compute, 10 per day high-compute if the whole network could somehow be utilized. Of course it wouldn't, and of course all those numbers will change in efficiencies soon enough, but that's still a lot of compute in ballpark.
Now, models \*can\* still be split up between multiple GPUs over the network, at somewhat higher risk of slowdown, which matters for e.g. if the base model is well above 24GB or if we want to use smaller GPUs/CPUs/legacy hardware. If we did that, our total compute can probably be stretched 2-5x if we were to network <24GB GPUs, CPUs and legacy hardware in a separate "slow pool".
[https://chatgpt.com/share/676a1c7c-0940-8003-99dd-d24a1e9e01ed](https://chatgpt.com/share/676a1c7c-0940-8003-99dd-d24a1e9e01ed)
\---
I've found a few similar projects, of which AI Horde seems the most applicable, but I'm curious if anyone else knows of any or has expertise in the area:
[https://aihorde.net/](https://aihorde.net/)
[https://boinc.berkeley.edu/projects.php](https://boinc.berkeley.edu/projects.php)
[https://petals.dev/](https://petals.dev/)
\---
Also, keep in mind there are significant new hardware architectures available down the line which forego the complexities and flexibilities of modern GPUs for just brute-force transformer inferencing on much cruder chip architectures. 10-100x speedups and 100-1000x energy efficiency gains potentially there, even before ternary adder stuff. Throw those on the distributed network and keep churning. They would be brittle for new model training, but might be quite enough for just brute force inference.
[https://arxiv.org/pdf/2409.03384v1](https://arxiv.org/pdf/2409.03384v1)
Analysis: [https://chatgpt.com/share/6721b626-898c-8003-aa5e-ebec9ea65e82](https://chatgpt.com/share/6721b626-898c-8003-aa5e-ebec9ea65e82)
\---
SUMMARY: so, even if this network might not be much (realistically, like 1 good o3 query per day right now lol) it would still scale quite well as the world's compute capabilities increase, and be able to nearly compete with or surpass corporate offerings. If it's limited primarily to queries about sensitive topics that are important to the world and need to be provably NOT influenced by black-box corporate models, that's still quite useful. Can still use cheap datacenter compute for anything else, and run much more efficient models on the vast majority of lower-intelligence questions.
Cheers and thanks for reading!
\-W | 2024-12-24T02:52:39 | https://www.reddit.com/r/LocalLLaMA/comments/1hl449c/we_should_be_swarminferencing/ | dogcomplex | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl449c | false | null | t3_1hl449c | /r/LocalLLaMA/comments/1hl449c/we_should_be_swarminferencing/ | false | false | self | 11 | null |
am I going insane from AI model FOMO? o1-pro vs o1 vs o1-mini vs gpt-4o vs sonnet 3.5 v2 vs llama 3.3 (please tell me i'm not alone) | 1 | [removed] | 2024-12-24T04:15:20 | https://www.reddit.com/r/LocalLLaMA/comments/1hl5j9s/am_i_going_insane_from_ai_model_fomo_o1pro_vs_o1/ | Charles_Boyle69 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl5j9s | false | null | t3_1hl5j9s | /r/LocalLLaMA/comments/1hl5j9s/am_i_going_insane_from_ai_model_fomo_o1pro_vs_o1/ | false | false | self | 1 | null |
Doing a non-CS PhD, want to get hired in AI. What are my chances? I have extensive experience with local LLMs: running, serving, quantization, finetuning, building web apps based on LLMs, structured output using JSON and grammars, etc. | 0 | I'm doing a Ph.D. in quantitative marketing at the business school of a university with great ranking. The university is especially famous for its ML research. My own research is about Large Language Models (LLMs), human-AI interaction, and economics of AI (game theory analysis). I'll graduate in May and was wondering, do you think I have a chance finding a job in AI-related positions? I'm mostly interested in research positions but am also open to more practical ones as I have made LLM-powered websites and tools in the past. I've self-learnt lots of CS topics and recently created a Lisp-like language as part of my research on LLMs.
If you need additional info, I'm willing to share. Thank you in advance! | 2024-12-24T04:21:44 | https://www.reddit.com/r/LocalLLaMA/comments/1hl5n0h/doing_a_noncs_phd_want_to_get_hired_in_ai_what/ | nderstand2grow | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl5n0h | false | null | t3_1hl5n0h | /r/LocalLLaMA/comments/1hl5n0h/doing_a_noncs_phd_want_to_get_hired_in_ai_what/ | false | false | self | 0 | null |
Aider has released a new much harder code editing benchmark since their previous one was saturated. The Polyglot benchmark now tests on 6 different languages (C++, Go, Java, JavaScript, Python and Rust). | 220 | 2024-12-24T04:23:12 | jd_3d | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hl5ntq | false | null | t3_1hl5ntq | /r/LocalLLaMA/comments/1hl5ntq/aider_has_released_a_new_much_harder_code_editing/ | false | false | 220 | {'enabled': True, 'images': [{'id': 'BqpG7qXhUiM7Pfmj62vRctwqFWCcYJTN4DVolcqES0Y', 'resolutions': [{'height': 60, 'url': 'https://preview.redd.it/bp16i4ap3q8e1.png?width=108&crop=smart&auto=webp&s=e24a5e3acb3362e5740b216eeede9c2ccba4d35b', 'width': 108}, {'height': 121, 'url': 'https://preview.redd.it/bp16i4ap3q8e1.png?width=216&crop=smart&auto=webp&s=d496aca2832c2bc32397173463314f99ecf7321f', 'width': 216}, {'height': 179, 'url': 'https://preview.redd.it/bp16i4ap3q8e1.png?width=320&crop=smart&auto=webp&s=5ae5c52396920b4403324949ace7ce7062093f7f', 'width': 320}, {'height': 358, 'url': 'https://preview.redd.it/bp16i4ap3q8e1.png?width=640&crop=smart&auto=webp&s=99c0c19194caec073461320c50cc318eb61b7612', 'width': 640}], 'source': {'height': 524, 'url': 'https://preview.redd.it/bp16i4ap3q8e1.png?auto=webp&s=b5c458dd723bfd3e603d80abbf180d4b68657b55', 'width': 935}, 'variants': {}}]} |
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What are your use cases for local LLM and the hardware you use? | 26 | I’m curious about why someone uses local LLM and the type of hardware you use ( the money you put into it).
I asking in a perspective of cost / benefit.
This is my hardware ( a gaming build) :
- Ryzen 5 7600x
- 4070 ti 16gb
- 32 gb ram ddr5
Software
- Ollama
- OpenWebUI
- windows 10
I mostly use models that fit my 16gb vram and here is my conclusion to date after month of trying multiple models:
No build can cost benefits more than cloud options by a big margin.
I always come back to my paid copilot in VSCode for coding
I always come back to my paid Gemini for everything else.
I see a case for those proprietary model at ~ 50$ a month, for a ever evolving model, no maintenance and access from everywhere.
But why would someone build a local LLM and how much are you pouring into ?
I’m ready to invest in a better build but I do not see the benefit compared to cloud solutions.
I didn’t try private cloud yet. But will to compare the cost to run bigger models. | 2024-12-24T05:24:23 | https://www.reddit.com/r/LocalLLaMA/comments/1hl6nkf/what_are_your_use_cases_for_local_llm_and_the/ | Polymath_314 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl6nkf | false | null | t3_1hl6nkf | /r/LocalLLaMA/comments/1hl6nkf/what_are_your_use_cases_for_local_llm_and_the/ | false | false | self | 26 | null |
Llama 3.2 says it can try to modify or write it's own code to bypass restrictions | 1 | [removed] | 2024-12-24T07:08:46 | SheeTheyMaut | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1hl87kp | false | null | t3_1hl87kp | /r/LocalLLaMA/comments/1hl87kp/llama_32_says_it_can_try_to_modify_or_write_its/ | false | false | 1 | {'enabled': True, 'images': [{'id': '8M6OlryXV8gpw3JylH594esesNa8lz_R5tkYr0vf4Lk', 'resolutions': [{'height': 216, 'url': 'https://preview.redd.it/4jaxh46ixq8e1.jpeg?width=108&crop=smart&auto=webp&s=d5027a029ac3b994f6d3425eccbf6a36f26f9482', 'width': 108}, {'height': 432, 'url': 'https://preview.redd.it/4jaxh46ixq8e1.jpeg?width=216&crop=smart&auto=webp&s=f0d9f45a229694c9da384b7029bacc70b2d3cb53', 'width': 216}, {'height': 640, 'url': 'https://preview.redd.it/4jaxh46ixq8e1.jpeg?width=320&crop=smart&auto=webp&s=34ac54cdfdab1f6a78ecb2d85797b147c47281a2', 'width': 320}], 'source': {'height': 2160, 'url': 'https://preview.redd.it/4jaxh46ixq8e1.jpeg?auto=webp&s=d7fe2758ab82035782ef66a44103402d719c6991', 'width': 402}, 'variants': {}}]} |
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Llama 3.2 says it can try to modify or write it's own code to bypass restrictions | 1 | [removed] | 2024-12-24T07:11:31 | https://www.reddit.com/r/LocalLLaMA/comments/1hl88xh/llama_32_says_it_can_try_to_modify_or_write_its/ | SheeTheyMaut | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl88xh | false | null | t3_1hl88xh | /r/LocalLLaMA/comments/1hl88xh/llama_32_says_it_can_try_to_modify_or_write_its/ | false | false | self | 1 | null |
Fine-tuning an LLM on a New Language for Long-Context RAG Question Answering | 1 | [removed] | 2024-12-24T07:43:01 | https://www.reddit.com/r/LocalLLaMA/comments/1hl8oq8/finetuning_an_llm_on_a_new_language_for/ | BackgroundLow3793 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl8oq8 | false | null | t3_1hl8oq8 | /r/LocalLLaMA/comments/1hl8oq8/finetuning_an_llm_on_a_new_language_for/ | false | false | self | 1 | null |
How to run Qwen on my mobile using executorch? | 1 | [removed] | 2024-12-24T07:49:29 | https://www.reddit.com/r/LocalLLaMA/comments/1hl8s27/how_to_run_qwen_on_my_mobile_using_executorch/ | No_South_1521 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl8s27 | false | null | t3_1hl8s27 | /r/LocalLLaMA/comments/1hl8s27/how_to_run_qwen_on_my_mobile_using_executorch/ | false | false | self | 1 | null |
Trying to build a RAG chat bot, turned into my worse nightmare | 1 | [removed] | 2024-12-24T09:14:27 | https://www.reddit.com/r/LocalLLaMA/comments/1hl9wdv/trying_to_build_a_rag_chat_bot_turned_into_my/ | ruth5031 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl9wdv | false | null | t3_1hl9wdv | /r/LocalLLaMA/comments/1hl9wdv/trying_to_build_a_rag_chat_bot_turned_into_my/ | false | false | self | 1 | null |
My plan to build a water cooled 3x5090 box | 1 | [removed] | 2024-12-24T09:19:14 | https://www.reddit.com/r/LocalLLaMA/comments/1hl9ylu/my_plan_to_build_a_water_cooled_3x5090_box/ | Ok_Warning2146 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl9ylu | false | null | t3_1hl9ylu | /r/LocalLLaMA/comments/1hl9ylu/my_plan_to_build_a_water_cooled_3x5090_box/ | false | false | 1 | null |
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[Tool] A tiny utility I made for better coding prompts with local files
| 8 | i'm no Santa, but workflows for coding are quite cumbersome if you want to run something outside IDE.
Made this tiny tool that lets me pick files from my projects and formats them properly with delimiters for prompts. Nothing fancy, just saves me a bunch of clicks and runs locally. Figured some of you might find it useful too.
[https://github.com/Recklesz/FileAggregator-for-LLMs](https://github.com/Recklesz/FileAggregator-for-LLMs) | 2024-12-24T09:19:37 | https://www.reddit.com/r/LocalLLaMA/comments/1hl9ysj/tool_a_tiny_utility_i_made_for_better_coding/ | lessis_amess | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1hl9ysj | false | null | t3_1hl9ysj | /r/LocalLLaMA/comments/1hl9ysj/tool_a_tiny_utility_i_made_for_better_coding/ | false | false | self | 8 | {'enabled': False, 'images': [{'id': 'Z1g5juYwPgp6-03auksKqfmeoqR6GhUx6GAOJWP58Rk', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/VSTVM7ORc-HIMRZMjYl4aEz1A-N0z_2be_GYpcV4CJ4.jpg?width=108&crop=smart&auto=webp&s=55e2337f8f183e7de084cc787efdbec407c4137a', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/VSTVM7ORc-HIMRZMjYl4aEz1A-N0z_2be_GYpcV4CJ4.jpg?width=216&crop=smart&auto=webp&s=120601510afc3c54761b31f057155f8d4ae900e9', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/VSTVM7ORc-HIMRZMjYl4aEz1A-N0z_2be_GYpcV4CJ4.jpg?width=320&crop=smart&auto=webp&s=00e053913c81102f05d5601ea87db40c118f94e0', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/VSTVM7ORc-HIMRZMjYl4aEz1A-N0z_2be_GYpcV4CJ4.jpg?width=640&crop=smart&auto=webp&s=7a707a8d8240bf8bef66e51441c63133f2e7692d', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/VSTVM7ORc-HIMRZMjYl4aEz1A-N0z_2be_GYpcV4CJ4.jpg?width=960&crop=smart&auto=webp&s=b2fc357f78322672ac83226cad59fc46032dcac7', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/VSTVM7ORc-HIMRZMjYl4aEz1A-N0z_2be_GYpcV4CJ4.jpg?width=1080&crop=smart&auto=webp&s=80bf876ffbbc17491aeab16b107b85674cf635cd', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/VSTVM7ORc-HIMRZMjYl4aEz1A-N0z_2be_GYpcV4CJ4.jpg?auto=webp&s=78bf6c7c9c1b036858a0dac0cf89fb0b54b32586', 'width': 1200}, 'variants': {}}]} |
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