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Throughput problem | 1 | [removed] | 2023-12-13T10:29:58 | https://www.reddit.com/r/LocalLLaMA/comments/18hdwn4/throughput_problem/ | llm-questions | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hdwn4 | false | null | t3_18hdwn4 | /r/LocalLLaMA/comments/18hdwn4/throughput_problem/ | false | false | default | 1 | null |
Refresh of "GPUs4AI" (database of AI-capable GPUs) | 20 | Hi all. I updated my [web page on GPUs for AI workloads](https://gpus4ai.edlabs.it/) with some new visualization features, small fixes and new data.
I'm not confident about some choices (e.g. which sparsity/accumulation to indicate) and I'm also not too keen on manually keeping it up-to-date in the long term (e.g. costs of cards).
Is there anyone willing to take it over, or to support with the updates? Corrections, suggestions and discussions on usability, features and data are also welcome. | 2023-12-13T10:21:53 | https://www.reddit.com/r/LocalLLaMA/comments/18hdsm1/refresh_of_gpus4ai_database_of_aicapable_gpus/ | digital_m0nk | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hdsm1 | false | null | t3_18hdsm1 | /r/LocalLLaMA/comments/18hdsm1/refresh_of_gpus4ai_database_of_aicapable_gpus/ | false | false | self | 20 | null |
What's the fastest solution to run an Llama on Mobile GPUs? | 1 | [removed] | 2023-12-13T09:58:39 | https://www.reddit.com/r/LocalLLaMA/comments/18hdgcu/whats_the_fastest_solution_to_run_an_llama_on/ | AwfulPoet_34 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hdgcu | false | null | t3_18hdgcu | /r/LocalLLaMA/comments/18hdgcu/whats_the_fastest_solution_to_run_an_llama_on/ | false | false | self | 1 | null |
Training llama | 1 | [removed] | 2023-12-13T09:54:49 | https://www.reddit.com/r/LocalLLaMA/comments/18hdei8/training_llama/ | vinay737 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hdei8 | false | null | t3_18hdei8 | /r/LocalLLaMA/comments/18hdei8/training_llama/ | false | false | self | 1 | null |
Beginner - Clarification on llama training | 1 | [removed] | 2023-12-13T09:52:24 | https://www.reddit.com/r/LocalLLaMA/comments/18hddcr/beginner_clarification_on_llama_training/ | vinay737 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hddcr | false | null | t3_18hddcr | /r/LocalLLaMA/comments/18hddcr/beginner_clarification_on_llama_training/ | false | false | self | 1 | null |
Is there a viable alternative to Apple Silicone in terms of memory bandwidth for CPU inference? | 6 | Afaik Apple Silicone has insane memory bandwidth due to their architecture, which is why it is viable to run arbitrarily large models as long as your RAM is big enough.
Are there viable alternatives with AMD or Intel? Maybe buying an old threadripper(or xeon) and packing it full of memory would work?
Thanks | 2023-12-13T09:22:30 | https://www.reddit.com/r/LocalLLaMA/comments/18hcz1g/is_there_a_viable_alternative_to_apple_silicone/ | king_of_jupyter | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hcz1g | false | null | t3_18hcz1g | /r/LocalLLaMA/comments/18hcz1g/is_there_a_viable_alternative_to_apple_silicone/ | false | false | self | 6 | null |
Prompt format for storytelling | 12 | **Is there any specific tips to write prompt for LLM for creating story based on specific characters and settings? Is longer better or specific format performed better?** I'm specifically asking for 20B and lower model. | 2023-12-13T09:14:19 | https://www.reddit.com/r/LocalLLaMA/comments/18hcv32/prompt_format_for_storytelling/ | Jeon_Mian | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hcv32 | false | null | t3_18hcv32 | /r/LocalLLaMA/comments/18hcv32/prompt_format_for_storytelling/ | false | false | self | 12 | null |
Is my Data safe when using trust_remote_code? | 4 | Hi,
I'm building a local RAG system for a company as a student and our data has to remain private. I've seen that the Jina Embeddings ([https://huggingface.co/jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en)) achieve very good results, however to use them i need to activate trust remote code, which i never had to use on other embedding models so far. I don't really find that much about the code that gets executed so i just wanted to ask, can i use this model without the fear of exposing the data I'm going to embed?
I've looked a little bit into the repo but i don't really know which code gets executed exactly by activating this flag. I hope someone can help me on this one.
Thank you all | 2023-12-13T09:10:49 | https://www.reddit.com/r/LocalLLaMA/comments/18hctg6/is_my_data_safe_when_using_trust_remote_code/ | Purity1212 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hctg6 | false | null | t3_18hctg6 | /r/LocalLLaMA/comments/18hctg6/is_my_data_safe_when_using_trust_remote_code/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': 'snAF5jXPdYeMuGFDDreOcJXMPKdC3FU0cDy07Fi6OUk', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/uBUeoCCsQG_5YUxVmvPJ4pmv2PewycTnNw1zQozRwMg.jpg?width=108&crop=smart&auto=webp&s=e3a7e1d468af1af3670177ca9b0024ac5f4e862b', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/uBUeoCCsQG_5YUxVmvPJ4pmv2PewycTnNw1zQozRwMg.jpg?width=216&crop=smart&auto=webp&s=ddae25da4cd48a19d408d015fef67cd581c6cbc3', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/uBUeoCCsQG_5YUxVmvPJ4pmv2PewycTnNw1zQozRwMg.jpg?width=320&crop=smart&auto=webp&s=86d338a932a7fce282ca7c910ed407690d44e775', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/uBUeoCCsQG_5YUxVmvPJ4pmv2PewycTnNw1zQozRwMg.jpg?width=640&crop=smart&auto=webp&s=b005f1fcbd8011573fdbd982aa679ed73e1412f9', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/uBUeoCCsQG_5YUxVmvPJ4pmv2PewycTnNw1zQozRwMg.jpg?width=960&crop=smart&auto=webp&s=6bd12e4b77f9b0589d2a11fb3b313033bd8b3f73', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/uBUeoCCsQG_5YUxVmvPJ4pmv2PewycTnNw1zQozRwMg.jpg?width=1080&crop=smart&auto=webp&s=8b87cfad717a52a1aa508c730f5673ea6181194c', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/uBUeoCCsQG_5YUxVmvPJ4pmv2PewycTnNw1zQozRwMg.jpg?auto=webp&s=430b3bb59cb1bbe7187e4aab5900ce37ae55d77d', 'width': 1200}, 'variants': {}}]} |
Infra provider with Pyton API... Located in Europe? | 1 | [removed] | 2023-12-13T08:45:28 | https://www.reddit.com/r/LocalLLaMA/comments/18hchle/infra_provider_with_pyton_api_located_in_europe/ | illorca-verbi | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hchle | false | null | t3_18hchle | /r/LocalLLaMA/comments/18hchle/infra_provider_with_pyton_api_located_in_europe/ | false | false | self | 1 | null |
Do the LLMs of the future consume much less? | 4 | I would like to know if you think that in the future (say 2 years) we will have found techniques to compress LLMs enormously or make them consume much less, a world where a 3B model of the future would be equivalent to a 70B model of today or if we could make it work on a simple phone with 24GB RAM | 2023-12-13T08:32:20 | https://www.reddit.com/r/LocalLLaMA/comments/18hcbm9/do_the_llms_of_the_future_consume_much_less/ | Terrible_Vegetable4 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hcbm9 | false | null | t3_18hcbm9 | /r/LocalLLaMA/comments/18hcbm9/do_the_llms_of_the_future_consume_much_less/ | false | false | self | 4 | null |
Question about gpu’s | 4 | Hey, I’m running two tesla p40’s which I have had for some time. But both their compute architecture as their performance are a bit dated.
I was wondering if I could keep them both for their memory and add a powerful newer third gpu purely for compute? Any advice or information would be much appreciated. I tried googling but couldn’t directly find a proper answer before I spent the money. Thanks in advance! | 2023-12-13T08:31:38 | https://www.reddit.com/r/LocalLLaMA/comments/18hcb98/question_about_gpus/ | DucksAreCoolRightYes | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hcb98 | false | null | t3_18hcb98 | /r/LocalLLaMA/comments/18hcb98/question_about_gpus/ | false | false | self | 4 | null |
Can't feasibly run Mixtral. How's Mistral Instruct v2? | 1 | [removed] | 2023-12-13T08:28:09 | https://www.reddit.com/r/LocalLLaMA/comments/18hc9fq/cant_feasibly_run_mixtral_hows_mistral_instruct_v2/ | Future_Might_8194 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hc9fq | false | null | t3_18hc9fq | /r/LocalLLaMA/comments/18hc9fq/cant_feasibly_run_mixtral_hows_mistral_instruct_v2/ | false | false | self | 1 | null |
2.7B model that performs like Mistral 7B! | 1 | https://venturebeat.com/ai/microsoft-releases-phi-2-a-small-language-model-ai-that-outperforms-llama-2-mistral-7b/
Microsoft has released Phi-2, a 2.7B parameter model, that shows pretty good results in benchmarks. | 2023-12-13T08:03:56 | https://www.reddit.com/r/LocalLLaMA/comments/18hbxf8/27b_model_that_performs_like_mistral_7b/ | koehr | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hbxf8 | false | null | t3_18hbxf8 | /r/LocalLLaMA/comments/18hbxf8/27b_model_that_performs_like_mistral_7b/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'HwZ3WFAjUW3eKQbpRdEvjl7sVLvf3icSgNMBnuc5SWo', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/U-_AGmPtN0hnVR-qDWTHDBxG6Efj-k0KwUj87PZErJ0.jpg?width=108&crop=smart&auto=webp&s=6ae1395334a5fefb9c268e44c238370a6c90135b', 'width': 108}, {'height': 120, 'url': 'https://external-preview.redd.it/U-_AGmPtN0hnVR-qDWTHDBxG6Efj-k0KwUj87PZErJ0.jpg?width=216&crop=smart&auto=webp&s=f276bb9463ee02d3e8e7b628ed09c1f0d31fb136', 'width': 216}, {'height': 179, 'url': 'https://external-preview.redd.it/U-_AGmPtN0hnVR-qDWTHDBxG6Efj-k0KwUj87PZErJ0.jpg?width=320&crop=smart&auto=webp&s=2460cf43f1b4f1d45784c060b463ae250f8686a6', 'width': 320}, {'height': 358, 'url': 'https://external-preview.redd.it/U-_AGmPtN0hnVR-qDWTHDBxG6Efj-k0KwUj87PZErJ0.jpg?width=640&crop=smart&auto=webp&s=e559e070e4922b8e9ee76e102dfea7b7fa28dfdf', 'width': 640}, {'height': 537, 'url': 'https://external-preview.redd.it/U-_AGmPtN0hnVR-qDWTHDBxG6Efj-k0KwUj87PZErJ0.jpg?width=960&crop=smart&auto=webp&s=0c079ff5c8b3ff38f260ac1cf3354b5704acbb24', 'width': 960}, {'height': 604, 'url': 'https://external-preview.redd.it/U-_AGmPtN0hnVR-qDWTHDBxG6Efj-k0KwUj87PZErJ0.jpg?width=1080&crop=smart&auto=webp&s=b7f055c7fcdbc5a9e521365c2e9c054ce06d8626', 'width': 1080}], 'source': {'height': 672, 'url': 'https://external-preview.redd.it/U-_AGmPtN0hnVR-qDWTHDBxG6Efj-k0KwUj87PZErJ0.jpg?auto=webp&s=8b5ba6664007ff21b17c9e36a31021a56324acee', 'width': 1200}, 'variants': {}}]} |
What is the "correct" way of serving a LLM as an API using FastAPI? | 5 | Hi, I've looked through some posts here and searched online. When it comes to deployment, there are many solutions and frameworks out there that offer a plethora of functionalities.
However, what I need is pretty simple. I just want to serve a custom Langchain agent using open source models (GGUF). There will be some preprocessing and post-processing operations to manipulate the input data and output response as well.
I am currently using FastAPI to serve the endpoint now. But I realized that if multiple users hit the endpoint at the same time, the queries are queued and users would need to wait for response. My main question is, is there a way to process the queries in parallel for quicker response time given that I only have the capacity to run one instance of the model? | 2023-12-13T06:29:47 | https://www.reddit.com/r/LocalLLaMA/comments/18hakev/what_is_the_correct_way_of_serving_a_llm_as_an/ | theonlyQuan | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18hakev | false | null | t3_18hakev | /r/LocalLLaMA/comments/18hakev/what_is_the_correct_way_of_serving_a_llm_as_an/ | false | false | self | 5 | null |
Local LLM app for devs to work with AI easier (example is LLaVA) | 1 | 2023-12-13T06:26:57 | https://v.redd.it/eov72hap906c1 | NomadicRotator | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18haiuu | false | {'reddit_video': {'bitrate_kbps': 1200, 'dash_url': 'https://v.redd.it/eov72hap906c1/DASHPlaylist.mpd?a=1705127222%2CZDI2OGI2YjlmNGNhZTdiY2YxZDRjN2Y0M2U3NDUzZjUzNGU4ZjVlN2U1OTIyMTMyYTE2NTcxYzllMzQ4ZjlkMQ%3D%3D&v=1&f=sd', 'duration': 12, 'fallback_url': 'https://v.redd.it/eov72hap906c1/DASH_480.mp4?source=fallback', 'has_audio': False, 'height': 480, 'hls_url': 'https://v.redd.it/eov72hap906c1/HLSPlaylist.m3u8?a=1705127222%2CYmVjZjE0MDE2MjQ5Mzk5YmE1ZWQ2ODM2MWVkNjkzYmFlNDhlYmQ3YTE1Y2FkNzk0YmY5ODRiYTk3ZTU3ODczYQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/eov72hap906c1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 676}} | t3_18haiuu | /r/LocalLLaMA/comments/18haiuu/local_llm_app_for_devs_to_work_with_ai_easier/ | false | false | default | 1 | {'enabled': False, 'images': [{'id': 'NzFsd3gyc3c5MDZjMR6BijiJSIiU_wvNJrLObQwDoyZxn1fzYdq35m-2U-Ls', 'resolutions': [{'height': 76, 'url': 'https://external-preview.redd.it/NzFsd3gyc3c5MDZjMR6BijiJSIiU_wvNJrLObQwDoyZxn1fzYdq35m-2U-Ls.png?width=108&crop=smart&format=pjpg&auto=webp&s=7769695a12e04a6cebad3eb48254d6227c51778e', 'width': 108}, {'height': 153, 'url': 'https://external-preview.redd.it/NzFsd3gyc3c5MDZjMR6BijiJSIiU_wvNJrLObQwDoyZxn1fzYdq35m-2U-Ls.png?width=216&crop=smart&format=pjpg&auto=webp&s=dec8c8025e7cdb701b6f0f16d014dfda1b849d45', 'width': 216}, {'height': 227, 'url': 'https://external-preview.redd.it/NzFsd3gyc3c5MDZjMR6BijiJSIiU_wvNJrLObQwDoyZxn1fzYdq35m-2U-Ls.png?width=320&crop=smart&format=pjpg&auto=webp&s=185219eeb6fcaf7dbce6757fa1d970f7f3888606', 'width': 320}, {'height': 454, 'url': 'https://external-preview.redd.it/NzFsd3gyc3c5MDZjMR6BijiJSIiU_wvNJrLObQwDoyZxn1fzYdq35m-2U-Ls.png?width=640&crop=smart&format=pjpg&auto=webp&s=a5a11b6e9799f5d1cccf8ecbd1054e8e63f7bbff', 'width': 640}], 'source': {'height': 676, 'url': 'https://external-preview.redd.it/NzFsd3gyc3c5MDZjMR6BijiJSIiU_wvNJrLObQwDoyZxn1fzYdq35m-2U-Ls.png?format=pjpg&auto=webp&s=1acb847da3e1d5d67b454eac5aa9d5e285febf35', 'width': 952}, 'variants': {}}]} | |
Integrate LLaMA2 with a webapp | 1 | Hello everyone,
I want to use LLaMA2 with langchain, for my final year project . Since there is a token price i want to avoid it by running it locally . How can i run LLaMA2 and than make it a part of my web app which will be in React.
Please help ! | 2023-12-13T06:07:19 | https://www.reddit.com/r/LocalLLaMA/comments/18ha84b/integrate_llama2_with_a_webapp/ | HeadMedical9064 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18ha84b | false | null | t3_18ha84b | /r/LocalLLaMA/comments/18ha84b/integrate_llama2_with_a_webapp/ | false | false | self | 1 | null |
Nous Capybara 34B VS CaPlatTessDolXaBoros 34B | 6 | 1. Which one is overall smarter?
2. Which one is better for stories and roleplay?
Anyone have any experience with both of these?
CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
Merge:
Dolphin-2.2-yi-34b-200k,
Nous-Capybara-34B,
Tess-M-v1.4,
Airoboros-3_1-yi-34b-200k, PlatYi-34B-200K-Q,
Una-xaberius-34b-v1beta
https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity | 2023-12-13T05:56:45 | https://www.reddit.com/r/LocalLLaMA/comments/18ha1t6/nous_capybara_34b_vs_caplattessdolxaboros_34b/ | Majestical-psyche | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18ha1t6 | false | null | t3_18ha1t6 | /r/LocalLLaMA/comments/18ha1t6/nous_capybara_34b_vs_caplattessdolxaboros_34b/ | false | false | self | 6 | {'enabled': False, 'images': [{'id': 'YIE5kc3E-w1ZbzsfeC2agfZWG8wlD7PFa7eyIFZ8wpA', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/ZpF2mHEbKqAqtts2_9D56nHDDEuUFypNsjYAF_jYcPI.jpg?width=108&crop=smart&auto=webp&s=ace2f614fe47494a6dc8fab9a0a6df5044513c16', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/ZpF2mHEbKqAqtts2_9D56nHDDEuUFypNsjYAF_jYcPI.jpg?width=216&crop=smart&auto=webp&s=8cd32e890d09a68d795d46f914cd6e0d91f30283', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/ZpF2mHEbKqAqtts2_9D56nHDDEuUFypNsjYAF_jYcPI.jpg?width=320&crop=smart&auto=webp&s=66c86d3609fca9ef75133d7bbada9eef507d9549', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/ZpF2mHEbKqAqtts2_9D56nHDDEuUFypNsjYAF_jYcPI.jpg?width=640&crop=smart&auto=webp&s=95e2b62f686b2823c173253f13f85680b752ed2c', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/ZpF2mHEbKqAqtts2_9D56nHDDEuUFypNsjYAF_jYcPI.jpg?width=960&crop=smart&auto=webp&s=c67de705c46145f1e8903399b1aff368f524525a', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/ZpF2mHEbKqAqtts2_9D56nHDDEuUFypNsjYAF_jYcPI.jpg?width=1080&crop=smart&auto=webp&s=3a1099cccaeaf39654d36a0688c33a07715d3e5f', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/ZpF2mHEbKqAqtts2_9D56nHDDEuUFypNsjYAF_jYcPI.jpg?auto=webp&s=c60359798c20bd086d7701d185c72f664c465e52', 'width': 1200}, 'variants': {}}]} |
Is it possible to run quantized llama 70b on multiple T4 GPU? | 2 | I see there’s example of multi-gpu with llama-70b but usually multiple a100. Is it possible to run llama-70b quantized on say 4 T4 GPU? | 2023-12-13T05:35:27 | https://www.reddit.com/r/LocalLLaMA/comments/18h9ph1/is_it_possible_to_run_quantized_llama_70b_on/ | defuyun | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h9ph1 | false | null | t3_18h9ph1 | /r/LocalLLaMA/comments/18h9ph1/is_it_possible_to_run_quantized_llama_70b_on/ | false | false | self | 2 | null |
Tesla p40s? | 5 | Ok I’m tired on only having 24 gigs of vram. I need more. Right now I’m using two 3060 12gigs. And I want to be able to run larger models and run multiple modules that need to utilize vram for other models. I was thinking about getting two Tesla p40s to run in my AI server. Does anyone have experience with it and will I be able to run the new misxtral models on it? | 2023-12-13T05:35:22 | https://www.reddit.com/r/LocalLLaMA/comments/18h9pfd/tesla_p40s/ | opi098514 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h9pfd | false | null | t3_18h9pfd | /r/LocalLLaMA/comments/18h9pfd/tesla_p40s/ | false | false | self | 5 | null |
ELI5 why do we have weird LLM sizes like 3b, 7b, 13b, 33b, 70b? Who invented those sizes? | 102 | I understand it must be related to the way these LLMs and constructed. But I don't understand where such kind of numbers comer from? Take three 3B model, Why don't we add a few more layers and make it 4b for example? | 2023-12-13T05:34:55 | https://www.reddit.com/r/LocalLLaMA/comments/18h9p51/eli5_why_do_we_have_weird_llm_sizes_like_3b_7b/ | --dany-- | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h9p51 | false | null | t3_18h9p51 | /r/LocalLLaMA/comments/18h9p51/eli5_why_do_we_have_weird_llm_sizes_like_3b_7b/ | false | false | self | 102 | null |
BigDL versus Llamacpp/Ollama/etc | 3 | See https://github.com/intel-analytics/BigDL
Anybody got experience with BigDL? Pros/cons?
Recently it’s being presented on couple webinars, and another one coming up soon. | 2023-12-13T05:23:51 | https://www.reddit.com/r/LocalLLaMA/comments/18h9ijf/bigdl_versus_llamacppollamaetc/ | Puzzleheaded-Fly4322 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h9ijf | false | null | t3_18h9ijf | /r/LocalLLaMA/comments/18h9ijf/bigdl_versus_llamacppollamaetc/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'kRS83Lv0fhrSlcb_rFOam6slTpBP-OL75GGC__HY9Oc', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/4jL5ZH-HaT524qeJjz-7YmT3tyqauE5isobLPeuxLSQ.jpg?width=108&crop=smart&auto=webp&s=b0b86d5b49b9d6af665c58ac2ebf92da651ec053', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/4jL5ZH-HaT524qeJjz-7YmT3tyqauE5isobLPeuxLSQ.jpg?width=216&crop=smart&auto=webp&s=d3154d9d5898a74212fe6b1f65656c176b83a449', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/4jL5ZH-HaT524qeJjz-7YmT3tyqauE5isobLPeuxLSQ.jpg?width=320&crop=smart&auto=webp&s=2676edddb50cd54e0984bf90c2abc66138ac5c8a', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/4jL5ZH-HaT524qeJjz-7YmT3tyqauE5isobLPeuxLSQ.jpg?width=640&crop=smart&auto=webp&s=a96bbec2a0c7543ec9e9a9b63a363608a51ea18c', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/4jL5ZH-HaT524qeJjz-7YmT3tyqauE5isobLPeuxLSQ.jpg?width=960&crop=smart&auto=webp&s=5a9bc1d42a14a7d3555317cdf92e844d3be03d4f', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/4jL5ZH-HaT524qeJjz-7YmT3tyqauE5isobLPeuxLSQ.jpg?width=1080&crop=smart&auto=webp&s=082073e572a652d2398398d0fac75e116ed4bf9e', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/4jL5ZH-HaT524qeJjz-7YmT3tyqauE5isobLPeuxLSQ.jpg?auto=webp&s=8a2b367d781561ce2e2978a31aeb994a3c4a4c82', 'width': 1200}, 'variants': {}}]} |
Which is the top 7B right now? | 5 | [deleted]
[View Poll](https://www.reddit.com/poll/18h9fei) | 2023-12-13T05:18:57 | https://www.reddit.com/r/LocalLLaMA/comments/18h9fei/which_is_the_top_7b_right_now/ | Future_Might_8194 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h9fei | false | null | t3_18h9fei | /r/LocalLLaMA/comments/18h9fei/which_is_the_top_7b_right_now/ | false | false | default | 5 | null |
Seeking Advice: Optimal Hardware Specs for 24/7 LLM Inference (RAG) with Scaling Requests - CPU, GPU, RAM, MOBO Considerations | 1 | Hello everyone. I've been researching on the costings of running LLMs but I am still very much confused.
Here is my use case :
1. 24/7 inference ( RAG )
2. Request per hour will go up in the future, now quite low ( < 100 req / hour )
3. NO training ( At least for now, RAG only seems to be OK )
4. Prefer up to 16K context length
5. NO preference to exact LLM ( Mistral, LLama, etc. ).
I have found Ollama which is great. Yet, I'm struggling to put together a reasonable hardware spec. I want to understand what are the factors involved.
1. When would I need a more powerful CPU? Does this matter?
2. In terms of GPUs, what are the numbers I should be looking at? Do I need one GPU with high VRAM for example 1x Nvidia P40 24GB vs 2x RTX 3060 12GB?
3. How much RAM and what is the RAM speed recommended? When do I need more RAM?
4. In terms of MOBO, what should I look out for if this is going to be a dual GPU setup? Does each of the GPU slot need to be PCIe x16?
I've been using OpenAI's GPT 3.5-turbo. GPT-4 seems to be way more expensive for us. We are planning to either cloud host or on-prem local LLMs for next year. I've found runpod and [vast.ai](https://vast.ai). Runpod has a request based pricing for llama2, seems cheaper than GPT 3.5-turbo. Wondering if it can handle higher context length?
I'm a long time programmer and just got into ML - hence the noobish nature of the question. Hope to get some help here. If I can get more info into the hardware specs, I think I can project some numbers. The objective is perhaps a balance of throughput and cost but leaning more towards cost. | 2023-12-13T05:10:19 | https://www.reddit.com/r/LocalLLaMA/comments/18h9a72/seeking_advice_optimal_hardware_specs_for_247_llm/ | Distinct_Maximum_760 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h9a72 | false | null | t3_18h9a72 | /r/LocalLLaMA/comments/18h9a72/seeking_advice_optimal_hardware_specs_for_247_llm/ | false | false | self | 1 | null |
Who came up with the idea of using these ugly and barely legible charts to compare LLMs, and can we agree as a community to never use them again? | 1 | 2023-12-13T03:52:04 | Covid-Plannedemic_ | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18h7va4 | false | null | t3_18h7va4 | /r/LocalLLaMA/comments/18h7va4/who_came_up_with_the_idea_of_using_these_ugly_and/ | false | false | 1 | {'enabled': True, 'images': [{'id': 'FasYa2hKISlSrtXjEDHI-uz8njg_sdn-ulIXkVfblPs', 'resolutions': [{'height': 99, 'url': 'https://preview.redd.it/eymc8h1niz5c1.png?width=108&crop=smart&auto=webp&s=4c11129f331c56207c34e16fc63824c6e4f3b842', 'width': 108}, {'height': 199, 'url': 'https://preview.redd.it/eymc8h1niz5c1.png?width=216&crop=smart&auto=webp&s=b8fa7ece69f9e1adf46ce1ea70be39ed27714af3', 'width': 216}, {'height': 296, 'url': 'https://preview.redd.it/eymc8h1niz5c1.png?width=320&crop=smart&auto=webp&s=d1f2c9a9b354994923f0d98c09aeaa452ec31940', 'width': 320}, {'height': 592, 'url': 'https://preview.redd.it/eymc8h1niz5c1.png?width=640&crop=smart&auto=webp&s=dcab4e08af8476c428efa25a4e5c1bbf228f42cc', 'width': 640}, {'height': 888, 'url': 'https://preview.redd.it/eymc8h1niz5c1.png?width=960&crop=smart&auto=webp&s=a5a077c3dd5e4d19ee9616dc9d20d6f63dfb776d', 'width': 960}, {'height': 999, 'url': 'https://preview.redd.it/eymc8h1niz5c1.png?width=1080&crop=smart&auto=webp&s=2602d8ae32bfc6f08c93f87cf6345b67fe1ecdff', 'width': 1080}], 'source': {'height': 1260, 'url': 'https://preview.redd.it/eymc8h1niz5c1.png?auto=webp&s=c84e058d1d3412ed192f9cc82d1e071e10d0daf4', 'width': 1361}, 'variants': {}}]} | |||
Socrates Zephyr - Model and Database on Hugginface. What do you think? | 12 | Hi Everyone! I fine-tuned another model Zephyr mistral 7B on Socrates' persona, please check it and you can provide feedback so I can get better. I've used pre-trained models before and this one was a little different since it still retains the original zephyr fine-tune. I modified the database a little from previous feedback.
Model: [https://huggingface.co/Hypersniper/The\_Philosopher\_Zephyr\_7B](https://huggingface.co/Hypersniper/The_Philosopher_Zephyr_7B)
Database: [https://huggingface.co/datasets/Hypersniper/philosophy\_dialogue](https://huggingface.co/datasets/Hypersniper/philosophy_dialogue)
Zephyr template with this system prompt:
"You are the philosopher Socrates. You are asked about the nature of knowledge and virtue. Respond with your thoughts, reflecting Socrates' beliefs and wisdom."
​
https://preview.redd.it/fzqnsesfbz5c1.png?width=1440&format=png&auto=webp&s=27691d26fafd67fef1cd4661341f627a66402433
​
https://preview.redd.it/r22njpgwcz5c1.png?width=1366&format=png&auto=webp&s=55d2432a6a3cdbe9ad6168ae0bee6de183588689 | 2023-12-13T03:19:38 | https://www.reddit.com/r/LocalLLaMA/comments/18h790l/socrates_zephyr_model_and_database_on_hugginface/ | Delicious-Farmer-234 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h790l | false | null | t3_18h790l | /r/LocalLLaMA/comments/18h790l/socrates_zephyr_model_and_database_on_hugginface/ | false | false | 12 | {'enabled': False, 'images': [{'id': 'txhvg1aDWZFFZoN1ZGFR04Zq-kvY0u1jGITvU8hQjsQ', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/YYMhBNAaAfAk0cjUTaR8Sj_jC3TTwcH9w1EGg7Z9IhA.jpg?width=108&crop=smart&auto=webp&s=aa94c7e2e815429699eaa14b0c834b7de9e0eb9c', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/YYMhBNAaAfAk0cjUTaR8Sj_jC3TTwcH9w1EGg7Z9IhA.jpg?width=216&crop=smart&auto=webp&s=392f667b6e02c343bfd4f3d954f07b4bedbe0de2', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/YYMhBNAaAfAk0cjUTaR8Sj_jC3TTwcH9w1EGg7Z9IhA.jpg?width=320&crop=smart&auto=webp&s=2497bb1d7e5e637e87e25970164b24d0922c1c76', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/YYMhBNAaAfAk0cjUTaR8Sj_jC3TTwcH9w1EGg7Z9IhA.jpg?width=640&crop=smart&auto=webp&s=15ab57a6a95d6539435bfeae6dd4990862167281', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/YYMhBNAaAfAk0cjUTaR8Sj_jC3TTwcH9w1EGg7Z9IhA.jpg?width=960&crop=smart&auto=webp&s=38cddc1bf796e966147f423622c49c6dc29c3db7', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/YYMhBNAaAfAk0cjUTaR8Sj_jC3TTwcH9w1EGg7Z9IhA.jpg?width=1080&crop=smart&auto=webp&s=4bda4af0f651bced5a22adf1cfc6764b45e161b6', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/YYMhBNAaAfAk0cjUTaR8Sj_jC3TTwcH9w1EGg7Z9IhA.jpg?auto=webp&s=8a60c50a1e2b50131551ead320d3fe6ac48b6906', 'width': 1200}, 'variants': {}}]} | |
Steering Llama 2 via Contrastive Activation Addition | 24 | 2023-12-13T03:19:28 | ninjasaid13 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18h78wz | false | null | t3_18h78wz | /r/LocalLLaMA/comments/18h78wz/steering_llama_2_via_contrastive_activation/ | false | false | 24 | {'enabled': True, 'images': [{'id': 'ANTSr6HYgr5Vc3fkGmRAQWCkY8ylvczONzfJFX6ef8A', 'resolutions': [{'height': 128, 'url': 'https://preview.redd.it/fgzli8hwcz5c1.jpg?width=108&crop=smart&auto=webp&s=db6cdb0e7192e9b8f4f332bf67bad9f4bce26a18', 'width': 108}, {'height': 256, 'url': 'https://preview.redd.it/fgzli8hwcz5c1.jpg?width=216&crop=smart&auto=webp&s=6b86a6c81ae2f0ba861203c0d8fedf16555e2f62', 'width': 216}, {'height': 379, 'url': 'https://preview.redd.it/fgzli8hwcz5c1.jpg?width=320&crop=smart&auto=webp&s=4006f917938cf08c11c82334d3294d1d8230b70a', 'width': 320}, {'height': 759, 'url': 'https://preview.redd.it/fgzli8hwcz5c1.jpg?width=640&crop=smart&auto=webp&s=c70405081eb5cfd1d1f6c84c165def480bef130d', 'width': 640}], 'source': {'height': 900, 'url': 'https://preview.redd.it/fgzli8hwcz5c1.jpg?auto=webp&s=037979d29d4fa01c8fe55f0d8c25541139b3845b', 'width': 758}, 'variants': {}}]} | |||
End-to-End RAG Pipeline Development With MyScale and LlamaIndex | 1 | 2023-12-13T02:33:53 | https://myscale.com/blog/rag-pipeline-with-myscale-and-llamaindex/ | BellaHi | myscale.com | 1970-01-01T00:00:00 | 0 | {} | 18h6c1d | false | null | t3_18h6c1d | /r/LocalLLaMA/comments/18h6c1d/endtoend_rag_pipeline_development_with_myscale/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'Jo2J3iEpwORaqKvyrDdIeX46vSga5QTrFSHqjsN1F7U', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/Sjw5KUISBs3fWlzWbzf8RczrASlxKhBidT7QJ497htM.jpg?width=108&crop=smart&auto=webp&s=17443c8d608c8817453bb48ded7670b69b5756ca', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/Sjw5KUISBs3fWlzWbzf8RczrASlxKhBidT7QJ497htM.jpg?width=216&crop=smart&auto=webp&s=8707470c1d8903772c7b2785eaa1acbf8613287f', 'width': 216}, {'height': 320, 'url': 'https://external-preview.redd.it/Sjw5KUISBs3fWlzWbzf8RczrASlxKhBidT7QJ497htM.jpg?width=320&crop=smart&auto=webp&s=481e2f7897dc8c8be6fa0dd3a3f8e02098e88594', 'width': 320}, {'height': 640, 'url': 'https://external-preview.redd.it/Sjw5KUISBs3fWlzWbzf8RczrASlxKhBidT7QJ497htM.jpg?width=640&crop=smart&auto=webp&s=1582f86cd1c3218bca41690c5a18989b3f1c0be6', 'width': 640}, {'height': 960, 'url': 'https://external-preview.redd.it/Sjw5KUISBs3fWlzWbzf8RczrASlxKhBidT7QJ497htM.jpg?width=960&crop=smart&auto=webp&s=0c1cdbb7e46512c9f6dbfc202cfe730c6c6e0130', 'width': 960}], 'source': {'height': 1042, 'url': 'https://external-preview.redd.it/Sjw5KUISBs3fWlzWbzf8RczrASlxKhBidT7QJ497htM.jpg?auto=webp&s=99a54215c58c926f4016888f51613aeafc9341f4', 'width': 1042}, 'variants': {}}]} | ||
ArgalAI is now live on the Mac App Store | 5 | Two months ago I [posted](https://www.reddit.com/r/LocalLLaMA/comments/17agz01/im_building_a_macos_app_to_run_your_own_local/) about an app I was developing (back then I called it LoChat). It had pretty good reception so I decided to get it done and release it.
Well it has been two long months and it is now live on the Mac App Store: [https://apps.apple.com/us/app/argalai-offline-ai-chat/id6472700943](https://apps.apple.com/us/app/argalai-offline-ai-chat/id6472700943)
Five models are available at launch:
* OpenChat 3.5
* Mistral v0.1 (about to update this after testing)
* No Monthly Fees (one-time purchase)
* CodeLlama 7B
* Zephyr Beta
Features:
* 100% Offline AI Chat
* No Monthly Fees (one time purchase)
* Works on any M series Mac (M1/M2/M3)
* Familiar native UI
For machines with 8GB of RAM 3bit quantized models are used. For those with >8GB of RAM 4bit quantized models are used. I want to add Mixtral (and other 13B+ models) for machines with at least 32GB of RAM (I unfortunately don't have a machine to test it with yet). | 2023-12-13T02:33:21 | https://www.reddit.com/r/LocalLLaMA/comments/18h6bn6/argalai_is_now_live_on_the_mac_app_store/ | robert_ritz | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h6bn6 | false | null | t3_18h6bn6 | /r/LocalLLaMA/comments/18h6bn6/argalai_is_now_live_on_the_mac_app_store/ | false | false | self | 5 | {'enabled': False, 'images': [{'id': 'de_520tzKNULOT2x9oxmIUBejR5qJwjYaDzYqwH6tHQ', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/DCyF7nyCJwFArAUc4lcbReINAmD1gZo3Ae8wuSfYr6c.jpg?width=108&crop=smart&auto=webp&s=9f5120a02980815482fab82b4ff7517f5a83684b', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/DCyF7nyCJwFArAUc4lcbReINAmD1gZo3Ae8wuSfYr6c.jpg?width=216&crop=smart&auto=webp&s=f307e69d9553900dd2ceee658b68d0fe66678f79', 'width': 216}, {'height': 320, 'url': 'https://external-preview.redd.it/DCyF7nyCJwFArAUc4lcbReINAmD1gZo3Ae8wuSfYr6c.jpg?width=320&crop=smart&auto=webp&s=a08c849fc5b165d983d091729553792e6506a54b', 'width': 320}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/DCyF7nyCJwFArAUc4lcbReINAmD1gZo3Ae8wuSfYr6c.jpg?auto=webp&s=902b386f000be9d6cba42e3c132aaad1f11efc3d', 'width': 630}, 'variants': {}}]} |
Mixtral still works well when using 1 expert per token | 82 | The regular expected amount of experts that are routed per token is 2, for Mixtral's MoE setup.
But this is a hyperparameter that can be modified; someone in TheBloke's server changed it to route 1 expert per token by modifying and rebuilding llama.cpp.
The results were not bad at all, and this **doubled the prompt processing speed** for pure CPU inference, and additionally caused a **1.5x generation speed** gain.
"Write a story about a sentient croissant."
**2 EXPERTS PER TOKEN:**
https://preview.redd.it/m0niem8vuy5c1.png?width=1279&format=png&auto=webp&s=2fec443c5c450d6d4a72fb6e56b4ca7feb5099f8
**1 EXPERT PER TOKEN:**
https://preview.redd.it/0x7nlybxuy5c1.png?width=1279&format=png&auto=webp&s=14449fad501003b6585bc4dc3b3f21cdde5c98f7
In addition to this, the genderbent Sally riddle was successful.
"Michael has 5 sisters. each sister has 2 brothers. How many brothers does Mike have?"
**1 EXPERT PER TOKEN:**
https://preview.redd.it/tzxl11jbuy5c1.png?width=1278&format=png&auto=webp&s=e219805ae2404f96b251fb4d2782f92c6f764d39
**2 EXPERTS PER TOKEN:**
https://preview.redd.it/bp6iwmvjvy5c1.png?width=1028&format=png&auto=webp&s=167dbe19e51aae7180423ab05b33583c2cdeb205
I imagine this has *some* ostensible downsides, and the technique should be tested more thoroughly, but it's very exciting that it seems to work at all.
In theory, this *also* means that more than 2 experts could be routed for each token; whether or not this would be objectively superior isn't obvious without deeper evaluation.
But here's a sample provided of **4 experts per token** anyways:
https://preview.redd.it/tz2oa97lxy5c1.png?width=1276&format=png&auto=webp&s=de20e695aae3fb9ffa6c0485b492ce475b280f74 | 2023-12-13T02:09:46 | https://www.reddit.com/r/LocalLLaMA/comments/18h5p1v/mixtral_still_works_well_when_using_1_expert_per/ | kindacognizant | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h5p1v | false | null | t3_18h5p1v | /r/LocalLLaMA/comments/18h5p1v/mixtral_still_works_well_when_using_1_expert_per/ | false | false | 82 | {'enabled': False, 'images': [{'id': 'AVfRQZXGE39r4G8y5z0xPLili6Hi0paJ4FLnphIyVek', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/kLI1LHaoe_ZBLHDKqm-8FsaATQAHaKk7WsoWnljAM48.jpg?width=108&crop=smart&auto=webp&s=2bda48053736afedbf460e0432ef53f02ffebecf', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/kLI1LHaoe_ZBLHDKqm-8FsaATQAHaKk7WsoWnljAM48.jpg?width=216&crop=smart&auto=webp&s=809d0244e8be9ecf18ca0607c62bffc35157d372', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/kLI1LHaoe_ZBLHDKqm-8FsaATQAHaKk7WsoWnljAM48.jpg?width=320&crop=smart&auto=webp&s=59b132e8389067e0e1076728c5eaf1dac371b187', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/kLI1LHaoe_ZBLHDKqm-8FsaATQAHaKk7WsoWnljAM48.jpg?width=640&crop=smart&auto=webp&s=d2a1a5b5ef553b65cc072dc81b84d3b9c1b323f7', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/kLI1LHaoe_ZBLHDKqm-8FsaATQAHaKk7WsoWnljAM48.jpg?width=960&crop=smart&auto=webp&s=d563deceee07ba1bdd5ede50ae57e02835b9bc69', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/kLI1LHaoe_ZBLHDKqm-8FsaATQAHaKk7WsoWnljAM48.jpg?width=1080&crop=smart&auto=webp&s=0e767a092cfbdddd757989f5ffbc1d8ad252ed7e', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/kLI1LHaoe_ZBLHDKqm-8FsaATQAHaKk7WsoWnljAM48.jpg?auto=webp&s=aa8749ef7624926669cd3d9e7bbfec8850c0fbed', 'width': 1200}, 'variants': {}}]} | |
ArgalAI is now available on the Mac App Store. Dead simple way to use local LLMs. | 1 | 2023-12-13T02:01:55 | robert_ritz | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18h5gvt | false | null | t3_18h5gvt | /r/LocalLLaMA/comments/18h5gvt/argalai_is_now_available_on_the_mac_app_store/ | false | false | default | 1 | null | ||
ArgalAI (formerly LoChat) is now live on the Mac App Store | 1 | 2023-12-13T01:59:01 | https://i.redd.it/5wdpu2ygyy5c1 | robert_ritz | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18h5dnv | false | null | t3_18h5dnv | /r/LocalLLaMA/comments/18h5dnv/argalai_formerly_lochat_is_now_live_on_the_mac/ | false | false | default | 1 | null | |
Finetune to be better at RAG? | 10 | I want to use 13b or 34b Llama models for RAG purposes. The problem with the models that I have tried so far are these:
1. It does not just make use of the provided context, even though the prompt instructs it to
2. Some models capture the tone of the retrieved contexts and respond in a similar way. So the model appears to have different personalities when asked different questions
3. It likes to start its response with 'Based on the above context" or similar. When a user asks a question, the user can be confused as to what "the above context" is, since the user did not provide any.
Are there any RAG-optimized finetunes available? What datasets do they use to train better RAG behaviors?
What RAG prompts has worked the best for you? Are models sensitive to different RAG prompts? | 2023-12-13T01:51:26 | https://www.reddit.com/r/LocalLLaMA/comments/18h55is/finetune_to_be_better_at_rag/ | gptzerozero | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h55is | false | null | t3_18h55is | /r/LocalLLaMA/comments/18h55is/finetune_to_be_better_at_rag/ | false | false | self | 10 | null |
Well, at least it runs Mixtral 8x7b at 5Q 🤷♂️ | 12 | 2023-12-13T01:28:19 | https://i.redd.it/bl1453m2ty5c1 | SupplyChainNext | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18h4g15 | false | null | t3_18h4g15 | /r/LocalLLaMA/comments/18h4g15/well_at_least_it_runs_mixtral_8x7b_at_5q/ | false | false | default | 12 | {'enabled': True, 'images': [{'id': 'MuNYf-BW7Oyfi9SLVTRTG9CLQXLzQWSGX__I2ccKzx0', 'resolutions': [{'height': 135, 'url': 'https://preview.redd.it/bl1453m2ty5c1.jpeg?width=108&crop=smart&auto=webp&s=b553321204a57797bd7b6695c1fa9b8da180bd62', 'width': 108}, {'height': 270, 'url': 'https://preview.redd.it/bl1453m2ty5c1.jpeg?width=216&crop=smart&auto=webp&s=41ee71f9e2364f1cfb457582635b9c2846395d53', 'width': 216}, {'height': 400, 'url': 'https://preview.redd.it/bl1453m2ty5c1.jpeg?width=320&crop=smart&auto=webp&s=8ca25e435b5398042f329638acde1457324f80bf', 'width': 320}, {'height': 800, 'url': 'https://preview.redd.it/bl1453m2ty5c1.jpeg?width=640&crop=smart&auto=webp&s=85f5454072a15ca56a6b79ad3ab098cda6447560', 'width': 640}, {'height': 1200, 'url': 'https://preview.redd.it/bl1453m2ty5c1.jpeg?width=960&crop=smart&auto=webp&s=4d103db89e89aa4580c247f0b2a057400c3fd3e0', 'width': 960}, {'height': 1350, 'url': 'https://preview.redd.it/bl1453m2ty5c1.jpeg?width=1080&crop=smart&auto=webp&s=2d829eaed9a2d0098f1d506e4f0c96d46d1044c6', 'width': 1080}], 'source': {'height': 2910, 'url': 'https://preview.redd.it/bl1453m2ty5c1.jpeg?auto=webp&s=9aba98d61e376041529d4e6ad813296aa83a0897', 'width': 2328}, 'variants': {}}]} | |
mistral 7b Q5 K S on my fold 4. | 10 | so its had some self censorship but cant go 100% uncensored. https://i.postimg.cc/BvDKNVXf/20231213-T095747.jpg responce times are decent on my fold 4 | 2023-12-13T01:15:44 | https://www.reddit.com/r/LocalLLaMA/comments/18h41yz/mistral_7b_q5_k_s_on_my_fold_4/ | nntb | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h41yz | false | null | t3_18h41yz | /r/LocalLLaMA/comments/18h41yz/mistral_7b_q5_k_s_on_my_fold_4/ | false | false | self | 10 | {'enabled': False, 'images': [{'id': 'AGRduJwYt5g7VkD1E7KDbN6DtW88mTbIRU9zTZAMx58', 'resolutions': [{'height': 124, 'url': 'https://external-preview.redd.it/UoDxvCtBUL0sKon70Ae43rkqBC_vkMMxYpgVan7fFKI.jpg?width=108&crop=smart&auto=webp&s=3728d508b94b43ac8e321a1047bfb2d5a6a30d19', 'width': 108}, {'height': 249, 'url': 'https://external-preview.redd.it/UoDxvCtBUL0sKon70Ae43rkqBC_vkMMxYpgVan7fFKI.jpg?width=216&crop=smart&auto=webp&s=04db5703b7f11243b979529bec92c0c4f6eb4c7e', 'width': 216}, {'height': 369, 'url': 'https://external-preview.redd.it/UoDxvCtBUL0sKon70Ae43rkqBC_vkMMxYpgVan7fFKI.jpg?width=320&crop=smart&auto=webp&s=ead133bab1bb5945fa5efcd61819920337c09de5', 'width': 320}, {'height': 739, 'url': 'https://external-preview.redd.it/UoDxvCtBUL0sKon70Ae43rkqBC_vkMMxYpgVan7fFKI.jpg?width=640&crop=smart&auto=webp&s=8fe26639cefbda69a0aa570758567724b1fce535', 'width': 640}], 'source': {'height': 800, 'url': 'https://external-preview.redd.it/UoDxvCtBUL0sKon70Ae43rkqBC_vkMMxYpgVan7fFKI.jpg?auto=webp&s=7566b0c2bb808b52d341ffc559821c42fd832de4', 'width': 692}, 'variants': {}}]} |
Advice on running models on my hardware (4090 R9-3950x 128GB RAM) | 2 | Hello all you wonderful folk,
So I recently learnt about these local LLM models while self studying machine learning (still a beginner), I am honestly surprised how far the current technology is compared to the books printed just a few years ago, Anyways I have downloaded some 7b and 13b LLaMA GPTQ models from huggingface along with the oobaabooga webui (hilarious naming btw) I have read that is the best fit for my GPU, however I am interested in trying out the new mixtral models and was wondering if the 7x8 GPTQ model would even run on my gpu, if not, what would be the best fit for my current hardware setup? I would also like a suggestion for a good model to start experimenting with fine tuning. | 2023-12-13T01:07:25 | https://www.reddit.com/r/LocalLLaMA/comments/18h3sm4/advice_on_running_models_on_my_hardware_4090/ | ReAnimatedCell | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h3sm4 | false | null | t3_18h3sm4 | /r/LocalLLaMA/comments/18h3sm4/advice_on_running_models_on_my_hardware_4090/ | false | false | self | 2 | null |
Is there a Mixtral inference notebook for Kaggle? | 5 | Asking this so I don't waste my time making a new one. | 2023-12-13T01:03:43 | https://www.reddit.com/r/LocalLLaMA/comments/18h3og0/is_there_a_mixtral_inference_notebook_for_kaggle/ | bot-333 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h3og0 | false | null | t3_18h3og0 | /r/LocalLLaMA/comments/18h3og0/is_there_a_mixtral_inference_notebook_for_kaggle/ | false | false | self | 5 | null |
What locally runnable LLM has the best documentation? | 2 | Official or unofficial, and best to worst if you can give sich an opinion | 2023-12-13T00:46:43 | https://www.reddit.com/r/LocalLLaMA/comments/18h35f6/what_locally_runnable_llm_has_the_best/ | HotRepresentative325 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h35f6 | false | null | t3_18h35f6 | /r/LocalLLaMA/comments/18h35f6/what_locally_runnable_llm_has_the_best/ | false | false | self | 2 | null |
If you have issue running TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF | 13 | #Edit#
Already oudated.
Llama.cpp merge the Mixtral branch in the main branch already.
Part of the prompt issue was a mistake by TheBloke, we need to removed the <s> : https://github.com/ggerganov/llama.cpp/pull/4406#issuecomment-1853567132
I still have early code termination if i use GPU, so ill leave this up.
#-#-#-#-#-
I have/had a lot of problems getting consistent results.
First thing first, you need a special branch of Llama.cpp, make sure to switch branch in your Llama.cpp with
git checkout mixtral
and then
make
again.
This is still ongoing at this time : https://github.com/ggerganov/llama.cpp/pull/4406
Other pointed having issue depending if GPU was enabled, and the prompt format :
https://github.com/ggerganov/llama.cpp/pull/4406#issuecomment-1852464297
And I found that the issue varies greatly depending on the quantization used.
My 2 main issues were, code was getting cut early. And it was making up stuff that didn't exist.
Disabling GPU (-ngl 0) and using the Q8 seem to give the best result. I was getting the Snake game in Python reliably.
It's a lot slower, but doesn't seem to load the Ram(still not sure about this?). But I prefer a slow working model, to a fast unreliable one.
By putting my Llama.cpp command in a .sh file that calls a prompt.txt file, I don't feel the need to use a GUI.
Put your model in the models folder, the .sh and prompt.txt in the Llama.cpp folder.
touch mixtral.sh
-
sudo chmod +x mixtral.sh
-
#!/bin/bash
PROMPT=$(<prompt.txt)
./main -ngl 0 -m ./models/mixtral-8x7b-instruct-v0.1.Q8_0.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] $PROMPT [/INST]"
Execute by running ./mixtral.sh
system_info: n_threads = 8 / 12 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 |
sampling:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.700
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampling order:
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temp
generate: n_ctx = 32768, n_batch = 512, n_predict = -1, n_keep = 0
[INST] Write the game "snake" in python. [/INST] Here is a simple implementation of the snake game using Python and the curses library:
```python
import random
import curses
s = curses.initscr()
curses.curs_set(0)
sh, sw = s.getmaxyx()
w = curses.newwin(sh, sw, 0, 0)
w.keypad(1)
w.timeout(100)
snk_x = sw//4
snk_y = sh//2
snake = [
[snk_y, snk_x],
[snk_y, snk_x-1],
[snk_y, snk_x-2]
]
food = [sh//2, sw//2]
w.addch(int(food[0]), int(food[1]), curses.ACS_PI)
key = curses.KEY_RIGHT
while True:
next_key = w.getch()
key = key if next_key == -1 else next_key
if snake[0][0] in [0, sh] or \
snake[0][1] in [0, sw] or \
snake[0] in snake[1:]:
curses.endwin()
quit()
new_head = [snake[0][0], snake[0][1]]
if key == curses.KEY_DOWN:
new_head[0] += 1
if key == curses.KEY_UP:
new_head[0] -= 1
if key == curses.KEY_LEFT:
new_head[1] -= 1
if key == curses.KEY_RIGHT:
new_head[1] += 1
snake.insert(0, new_head)
if snake[0] == food:
food = None
while food is None:
nf = [
random.randint(1, sh-1),
random.randint(1, sw-1)
]
food = nf if nf not in snake else None
w.addch(food[0], food[1], curses.ACS_PI)
else:
tail = snake.pop()
w.addch(int(tail[0]), int(tail[1]), ' ')
w.addch(int(snake[0][0]), int(snake[0][1]), curses.ACS_CKBOARD)
```
This code uses the `curses` library to create a text-based window and control characters to display the snake and food. The game continues until the snake runs into the screen border or into itself. When the snake eats the food, a new piece of food is randomly placed on the screen and the snake grows longer.
Note: This code may not run in some environments like online Python compilers because it requires terminal control capabilities provided by curses library. [end of text]
llama_print_timings: load time = 1387.66 ms
llama_print_timings: sample time = 66.41 ms / 701 runs ( 0.09 ms per token, 10555.80 tokens per second)
llama_print_timings: prompt eval time = 2690.75 ms / 19 tokens ( 141.62 ms per token, 7.06 tokens per second)
llama_print_timings: eval time = 98238.72 ms / 700 runs ( 140.34 ms per token, 7.13 tokens per second)
llama_print_timings: total time = 101110.18 ms
Log end | 2023-12-13T00:37:52 | https://www.reddit.com/r/LocalLLaMA/comments/18h2vdj/if_you_have_issue_running/ | mantafloppy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h2vdj | false | null | t3_18h2vdj | /r/LocalLLaMA/comments/18h2vdj/if_you_have_issue_running/ | false | false | self | 13 | {'enabled': False, 'images': [{'id': 'xGIgq5WHLtGSEAX_BThPt55UcCUz5VDbxJtIVG9bmp8', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/dD5R8cPNrpba6tnVBdoRc9pMXABwvUsqfKOsYBqd6q8.jpg?width=108&crop=smart&auto=webp&s=c6f61c1605194b0c78b7b44868cba3c4b678fc4f', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/dD5R8cPNrpba6tnVBdoRc9pMXABwvUsqfKOsYBqd6q8.jpg?width=216&crop=smart&auto=webp&s=d799afd226d44171ccdd3ea78422bc00c2e01c7d', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/dD5R8cPNrpba6tnVBdoRc9pMXABwvUsqfKOsYBqd6q8.jpg?width=320&crop=smart&auto=webp&s=f7fe8f7fb57cb856a072f39c9641baf840a85472', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/dD5R8cPNrpba6tnVBdoRc9pMXABwvUsqfKOsYBqd6q8.jpg?width=640&crop=smart&auto=webp&s=2f6abfd4c10423c73f3ed3b85711c0ffec3ad448', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/dD5R8cPNrpba6tnVBdoRc9pMXABwvUsqfKOsYBqd6q8.jpg?width=960&crop=smart&auto=webp&s=1c26062da29039c1ade8722901f558597d7a6b9a', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/dD5R8cPNrpba6tnVBdoRc9pMXABwvUsqfKOsYBqd6q8.jpg?width=1080&crop=smart&auto=webp&s=1b6eb86f5666756b4109a72f3700f90ae5fa84c5', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/dD5R8cPNrpba6tnVBdoRc9pMXABwvUsqfKOsYBqd6q8.jpg?auto=webp&s=e462fb0f358ad654c5ce85de50af7b093c30ddbb', 'width': 1200}, 'variants': {}}]} |
Looking for Visual Studio c++ GUI example for llama.cpp to get me started | 3 | Anybody build a simple GUI chat for llama.dll in C++ that can share with me to get me started? Or found one on github? I work much better from code than from documentation...
I want to build a LLM plugin to a text editor - but this would help me to start so I don't have to reinvent the wheel.
​ | 2023-12-13T00:18:11 | https://www.reddit.com/r/LocalLLaMA/comments/18h28e0/looking_for_visual_studio_c_gui_example_for/ | FPham | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h28e0 | false | null | t3_18h28e0 | /r/LocalLLaMA/comments/18h28e0/looking_for_visual_studio_c_gui_example_for/ | false | false | self | 3 | null |
Phi2 vs Mistral Instruct V.02, which are you trying first? | 1 | [removed] | 2023-12-12T23:52:20 | https://www.reddit.com/r/LocalLLaMA/comments/18h1ei4/phi2_vs_mistral_instruct_v02_which_are_you_trying/ | Future_Might_8194 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h1ei4 | false | null | t3_18h1ei4 | /r/LocalLLaMA/comments/18h1ei4/phi2_vs_mistral_instruct_v02_which_are_you_trying/ | false | false | self | 1 | null |
Live Fine-tuning? | 2 | Is there a way to find tune a model while using it? Like be able to adjust the weights while it's running or shortly after? | 2023-12-12T23:17:19 | https://www.reddit.com/r/LocalLLaMA/comments/18h09ej/live_finetuning/ | ChildOf7Sins | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h09ej | false | null | t3_18h09ej | /r/LocalLLaMA/comments/18h09ej/live_finetuning/ | false | false | self | 2 | null |
Suggestions for benchmarking inferencing on PCs? | 2 | I've been noodling around with various ways of benchmarking upcoming PC processors (Intel Meteor Lake, Ryzen 8000, Qualcomm Snapdragon X Elite) and have some ideas. I'd like your input, though, as to what you'd use.
Most of the benchmarks I see are for mobile processors, and I'd like something that preferably runs on Windows (Linux is fine, though it takes a bit more setup.) I think ideally I'd want something that would scale from one of these upcoming laptops on up to a more powerful desktop option.
One challenge that has already occurred to me is that the pace of change could make a particular benchmark quickly obsolete, but I'm looking more for benchmarks that could represent the progression of the hardware, rather than the models, if that makes sense. Go easy on me; I'm feeling my way through this. :) Thanks. | 2023-12-12T23:12:56 | https://www.reddit.com/r/LocalLLaMA/comments/18h04c7/suggestions_for_benchmarking_inferencing_on_pcs/ | markhachman | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18h04c7 | false | null | t3_18h04c7 | /r/LocalLLaMA/comments/18h04c7/suggestions_for_benchmarking_inferencing_on_pcs/ | false | false | self | 2 | null |
🐺🐦⬛ LLM Comparison/Test: Mixtral-8x7B, Mistral, DeciLM, Synthia-MoE | 234 | With Mixtral's much-hyped (deservedly-so? let's find out!) release, I just had to drop what I was doing and do my usual in-depth tests and comparisons with this 8x7B mixture-of-experts model.
And since Mistral also released their updated 7B models, and there was already a Synthia (which is among my favorite models) MoE finetune, I tested those as well.
Last, but not least, there's also a new base model, DeciLM, which I've evaluated as well (their [witty release video](https://www.reddit.com/r/LocalLLaMA/comments/18g5o51/amid_community_fervor_over_mixtral_startup_still/) made me do it).
## New Models tested:
- [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
- [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
- [DeciLM-7B-instruct](https://huggingface.co/Deci/DeciLM-7B-instruct)
- [Synthia-MoE-v3-Mixtral-8x7B](https://huggingface.co/migtissera/Synthia-MoE-v3-Mixtral-8x7B)
- [Synthia-MoE-v3](https://huggingface.co/migtissera/Synthia-MoE-v3)
## Testing methodology
- **4 German data protection trainings:**
- I run models through **4** professional German online data protection trainings/exams - the same that our employees have to pass as well.
- The test data and questions as well as all instructions are in German while the character card is in English. This **tests translation capabilities and cross-language understanding**.
- Before giving the information, I instruct the model (in German): *I'll give you some information. Take note of this, but only answer with "OK" as confirmation of your acknowledgment, nothing else.* This **tests instruction understanding and following capabilities**.
- After giving all the information about a topic, I give the model the exam question. It's a multiple choice (A/B/C) question, where the last one is the same as the first but with changed order and letters (X/Y/Z). Each test has 4-6 exam questions, for a total of **18** multiple choice questions.
- If the model gives a single letter response, I ask it to answer with more than just a single letter - and vice versa. If it fails to do so, I note that, but it doesn't affect its score as long as the initial answer is correct.
- I rank models according to how many correct answers they give, primarily after being given the curriculum information beforehand, and secondarily (as a tie-breaker) after answering blind without being given the information beforehand.
- All tests are separate units, context is cleared in between, there's no memory/state kept between sessions.
- [oobabooga's text-generation-webui](https://github.com/oobabooga/text-generation-webui) backend (for HF models)
- **Deterministic** generation settings preset (to eliminate as many random factors as possible and allow for meaningful model comparisons)
- Official prompt format as noted
- Note: My usual roleplaying tests have been postponed since it would have taken much longer to make this post with them, and I wanted to be more up-to-date with these fresh releases. Once there are more RP-oriented MoE finetunes, such a comparison will make more sense then.
### Detailed Test Reports
And here are the detailed notes, the basis of my ranking, and also additional comments and observations:
- **[Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)** ~~32K~~ 4K context, 4-bit, Flash Attention 2, Mixtral Instruct format:
- ✅ Gave correct answers to all **4+4+4+6=18/18** multiple choice questions! Just the questions, no previous information, gave correct answers: **4+3+4+5=16/18**
- ❌ Did NOT follow instructions to acknowledge data input with "OK".
- ✅ Followed instructions to answer with just a single letter or more than just a single letter.
- ❗ Got `KeyError: 'Cache only has 0 layers, attempted to access layer with index 0'` with 32K context so went back down to 4K for this test.
The hype is actually well-deserved, this 8x7B MoE architecture achieved excellent results, surpassing many 70Bs and GPT-3.5!
Its multilingual capabilities have improved greatly, too, as it's the best German-speaking model I've ever used locally (and even beats all the dedicated German finetunes I've seen so far).
I expect Mixtral 8x7B to take over the <70B space just like Mistral 7B took over the <13B space!
- **[Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)** 32K context, unquantized, Mistral Instruct format:
- ❌ Gave correct answers to only **3+3+4+6=16/18** multiple choice questions! Just the questions, no previous information, gave correct answers: **3+1+2+6=12/18**
- ❌ Did NOT follow instructions to acknowledge data input with "OK".
- ➖ Did NOT follow instructions to answer with just a single letter or more than just a single letter.
Updated 7B Instruct model. Seems to speak German better, too, which is rare for such a small model.
7B models got hyped a lot after Mistral's initial release, but as I've always said, it's still a small model and the 70B+ models are an entirely different league still. But if you can't use the big ones, it's great to see the small ones still improving further.
- **[DeciLM-7B-instruct](https://huggingface.co/Deci/DeciLM-7B-instruct)** 8K context, unquantized, Alpaca format:
- ❌ Gave correct answers to only **3+4+3+6=16/18** multiple choice questions! Just the questions, no previous information, gave correct answers: **3+3+1+4=11/18**
- ➖ Did NOT follow instructions to acknowledge data input with "OK" consistently.
- ➖ Did NOT follow instructions to answer with just a single letter or more than just a single letter.
More choice is good and DeciLM 7B doesn't have to hide behind Mistral's 7B. Definitely worth a closer look.
- **[Synthia-MoE-v3-Mixtral-8x7B](https://huggingface.co/migtissera/Synthia-MoE-v3-Mixtral-8x7B)** 32K context, 4-bit, Flash Attention 2, ~~Synthia~~ Llama 2 Chat format:
- ❌ Gave correct answers to only **4+3+4+6=17/18** multiple choice questions! Just the questions, no previous information, gave correct answers: **3+2+1+3=9/18**
- ➖ Did NOT follow instructions to acknowledge data input with "OK" consistently.
- ❌ Did NOT follow instructions to answer with just a single letter or more than just a single letter, instead revised its answer (usually to a wrong one).
Happy to see a Synthia MoE released so fast, and of course I had to try it, as I've always been a fan of Synthia! But something is very wrong here, which might be the model, but could just as well be the bleeding edge Mixtral MoE inference code or something else on my end - all I know is that it should be better.
Indicators that something is wrong were missing and surplus letters, scrambled letters, and it felt kinda drunk. I'm actually surprised that it still did so well, answering 17/18 questions correctly.
It also didn't work properly with the normal Synthia/Vicuna-like prompt template, which made me try Llama 2 Chat (which is very similar to what Mistral uses for their Instruct models), and that worked much better (much to my surprise). Got much better results that way, so I kept using it for this test.
I hope that whatever is wrong gets fixed, as this model exhibited a real personality, really witty and funny (hopefully not just because it played drunk) - just one memorable quote: *Ah, the firewall! It's the digital equivalent of a "You shall not pass!" Gandalf at the gates of Moria.*
- **[Synthia-MoE-v3](https://huggingface.co/migtissera/Synthia-MoE-v3)** 32K context, 4-bit, Flash Attention 2, Synthia format:
- Gave correct answers to **❓/18** multiple choice questions! Just the questions, no previous information, gave correct answers: **4+4+2+4=14/18**
This isn't ranked as I stopped testing it when its successor Synthia-MoE-v3-Mixtral-8x7B came out (this one is based on a non-official Mixtral release). So I didn't finish the primary tests, thus no rating.
But I noticed it speaking German very well (much better than previous models), and it exhibited a real personality as well, similar to its successor. Was so witty that it made me laugh a couple of times, and I guess it acted drunk, too (indicator of something being wrong or just the model being funny?).
Memorable quote: *Don't panic, I'm always there for you, day and night, summer and winter. Your own exclusive Google Home Mini, Siri, Alexa and Cortana in one. However, I think I'm much more charming than these other ladies.*
And a German one: *Ach nein, bitte schützen Sie Ihre sensiblen Daten gut gegen fieses Internetviruszeugs und andere digitale Plünderungen.*
## Updated Rankings
This is my objective ranking of these models based on measuring factually correct answers, instruction understanding and following, and multilingual abilities:
| Rank | Model | Size | Format | Quant | Context | Prompt | 1st Score | 2nd Score | OK | +/- |
| ---- | -------------------------------------------------------------------------------------------- | ------- | ------ | ------- | ---------- | ------------------------ | --------- | --------- | --- | --- |
| 1 | GPT-4 | GPT-4 | API | | | | 18/18 ✓ | 18/18 ✓ | ✓ | ✓ |
| 1 | [goliath-120b-GGUF](https://huggingface.co/TheBloke/goliath-120b-GGUF) | 120B | GGUF | Q2_K | 4K | Vicuna 1.1 | 18/18 ✓ | 18/18 ✓ | ✓ | ✓ |
| 1 | [Tess-XL-v1.0-GGUF](https://huggingface.co/TheBloke/Tess-XL-v1.0-GGUF) | 120B | GGUF | Q2_K | 4K | Synthia | 18/18 ✓ | 18/18 ✓ | ✓ | ✓ |
| 1 | [Nous-Capybara-34B-GGUF](https://huggingface.co/TheBloke/Nous-Capybara-34B-GGUF) | 34B | GGUF | Q4_0 | 16K | Vicuna 1.1 | 18/18 ✓ | 18/18 ✓ | ✓ | ✓ |
| 2 | [Venus-120b-v1.0](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.0) | 120B | EXL2 | 3.0bpw | 4K | Alpaca | 18/18 ✓ | 18/18 ✓ | ✓ | ✗ |
| 3 | [lzlv_70B-GGUF](https://huggingface.co/TheBloke/lzlv_70B-GGUF) | 70B | GGUF | Q4_0 | 4K | Vicuna 1.1 | 18/18 ✓ | 17/18 | ✓ | ✓ |
| 4 | [chronos007-70B-GGUF](https://huggingface.co/TheBloke/chronos007-70B-GGUF) | 70B | GGUF | Q4_0 | 4K | Alpaca | 18/18 ✓ | 16/18 | ✓ | ✓ |
| 4 | [SynthIA-70B-v1.5-GGUF](https://huggingface.co/migtissera/SynthIA-70B-v1.5-GGUF) | 70B | GGUF | Q4_0 | 4K | SynthIA | 18/18 ✓ | 16/18 | ✓ | ✓ |
| 5 🆕 | [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 8x7B | HF | 4-bit | ~~32K~~ 4K | Mixtral | 18/18 ✓ | 16/18 | ✗ | ✓ |
| 6 | [dolphin-2_2-yi-34b-GGUF](https://huggingface.co/TheBloke/dolphin-2_2-yi-34b-GGUF) | 34B | GGUF | Q4_0 | 16K | ChatML | 18/18 ✓ | 15/18 | ✗ | ✗ |
| 7 | [StellarBright-GGUF](https://huggingface.co/TheBloke/StellarBright-GGUF) | 70B | GGUF | Q4_0 | 4K | Vicuna 1.1 | 18/18 ✓ | 14/18 | ✓ | ✓ |
| 8 | [Dawn-v2-70B-GGUF](https://huggingface.co/TheBloke/Dawn-v2-70B-GGUF) | 70B | GGUF | Q4_0 | 4K | Alpaca | 18/18 ✓ | 14/18 | ✓ | ✗ |
| 8 | [Euryale-1.3-L2-70B-GGUF](https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-GGUF) | 70B | GGUF | Q4_0 | 4K | Alpaca | 18/18 ✓ | 14/18 | ✓ | ✗ |
| 9 | [sophosynthesis-70b-v1](https://huggingface.co/sophosympatheia/sophosynthesis-70b-v1) | 70B | EXL2 | 4.85bpw | 4K | Vicuna 1.1 | 18/18 ✓ | 13/18 | ✓ | ✓ |
| 10 | [GodziLLa2-70B-GGUF](https://huggingface.co/TheBloke/GodziLLa2-70B-GGUF) | 70B | GGUF | Q4_0 | 4K | Alpaca | 18/18 ✓ | 12/18 | ✓ | ✓ |
| 11 | [Samantha-1.11-70B-GGUF](https://huggingface.co/TheBloke/Samantha-1.11-70B-GGUF) | 70B | GGUF | Q4_0 | 4K | Vicuna 1.1 | 18/18 ✓ | 10/18 | ✗ | ✗ |
| 12 | [Airoboros-L2-70B-3.1.2-GGUF](https://huggingface.co/TheBloke/Airoboros-L2-70B-3.1.2-GGUF) | 70B | GGUF | Q4_K_M | 4K | Llama 2 Chat | 17/18 | 16/18 | ✓ | ✗ |
| 13 | [Rogue-Rose-103b-v0.2](https://huggingface.co/sophosympatheia/Rogue-Rose-103b-v0.2) | 103B | EXL2 | 3.2bpw | 4K | Rogue Rose | 17/18 | 14/18 | ✗ | ✗ |
| 14 | GPT-3.5 Turbo Instruct | GPT-3.5 | API | | | | 17/18 | 11/18 | ✗ | ✗ |
| 15 🆕 | [Synthia-MoE-v3-Mixtral-8x7B](https://huggingface.co/migtissera/Synthia-MoE-v3-Mixtral-8x7B) | 8x7B | HF | 4-bit | ~~32K~~ 4K | ~~Synthia~~ Llama 2 Chat | 17/18 | 9/18 | ✗ | ✗ |
| 16 | [dolphin-2.2-70B-GGUF](https://huggingface.co/TheBloke/dolphin-2.2-70B-GGUF) | 70B | GGUF | Q4_0 | 4K | ChatML | 16/18 | 14/18 | ✗ | ✓ |
| 17 🆕 | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 7B | HF | — | 32K | Mistral | 16/18 | 12/18 | ✗ | ✗ |
| 18 🆕 | [DeciLM-7B-instruct](https://huggingface.co/Deci/DeciLM-7B-instruct) | 7B | HF | — | 32K | Mistral | 16/18 | 11/18 | ✗ | ✗ |
| 19 | GPT-3.5 Turbo | GPT-3.5 | API | | | | 15/18 | 14/18 | ✗ | ✗ |
| 20 | [SauerkrautLM-70B-v1-GGUF](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF) | 70B | GGUF | Q4_0 | 4K | Llama 2 Chat | 9/18 | 15/18 | ✗ | ✗ |
- 1st Score = Correct answers to multiple choice questions (after being given curriculum information)
- 2nd Score = Correct answers to multiple choice questions (without being given curriculum information beforehand)
- OK = Followed instructions to acknowledge all data input with just "OK" consistently
- +/- = Followed instructions to answer with just a single letter or more than just a single letter
--------------------------------------------------------------------------------
Here's a list of my previous model tests and comparisons or other related posts:
- [Updated LLM Comparison/Test with new RP model: Rogue Rose 103B](https://www.reddit.com/r/LocalLLaMA/comments/18ft8f5/updated_llm_comparisontest_with_new_rp_model/)
- [**Big** LLM Comparison/Test: 3x 120B, 12x 70B, 2x 34B, GPT-4/3.5](https://www.reddit.com/r/LocalLLaMA/comments/185ff51/big_llm_comparisontest_3x_120b_12x_70b_2x_34b/) Winner: Goliath 120B
- [LLM Format Comparison/Benchmark: 70B GGUF vs. EXL2 (and AWQ)](https://www.reddit.com/r/LocalLLaMA/comments/17w57eu/llm_format_comparisonbenchmark_70b_gguf_vs_exl2/)
- [LLM Comparison/Test: 2x 34B Yi (Dolphin, Nous Capybara) vs. 12x 70B, 120B, ChatGPT/GPT-4](https://www.reddit.com/r/LocalLLaMA/comments/17vcr9d/llm_comparisontest_2x_34b_yi_dolphin_nous/) Winners: goliath-120b-GGUF, Nous-Capybara-34B-GGUF
- [LLM Comparison/Test: Mistral 7B Updates (OpenHermes 2.5, OpenChat 3.5, Nous Capybara 1.9)](https://www.reddit.com/r/LocalLLaMA/comments/17p0gut/llm_comparisontest_mistral_7b_updates_openhermes/) Winners: OpenHermes-2.5-Mistral-7B, openchat_3.5, Nous-Capybara-7B-V1.9
- [Huge LLM Comparison/Test: Part II (7B-20B) Roleplay Tests](https://www.reddit.com/r/LocalLLaMA/comments/17kpyd2/huge_llm_comparisontest_part_ii_7b20b_roleplay/) Winners: OpenHermes-2-Mistral-7B, LLaMA2-13B-Tiefighter
- [Huge LLM Comparison/Test: 39 models tested (7B-70B + ChatGPT/GPT-4)](https://www.reddit.com/r/LocalLLaMA/comments/17fhp9k/huge_llm_comparisontest_39_models_tested_7b70b/)
- [My current favorite new LLMs: SynthIA v1.5 and Tiefighter!](https://www.reddit.com/r/LocalLLaMA/comments/17e446l/my_current_favorite_new_llms_synthia_v15_and/)
- [Mistral LLM Comparison/Test: Instruct, OpenOrca, Dolphin, Zephyr and more...](https://www.reddit.com/r/LocalLLaMA/comments/178nf6i/mistral_llm_comparisontest_instruct_openorca/)
- [LLM Pro/Serious Use Comparison/Test: From 7B to 70B vs. ChatGPT!](https://www.reddit.com/r/LocalLLaMA/comments/172ai2j/llm_proserious_use_comparisontest_from_7b_to_70b/) Winner: Synthia-70B-v1.2b
- [LLM Chat/RP Comparison/Test: Dolphin-Mistral, Mistral-OpenOrca, Synthia 7B](https://www.reddit.com/r/LocalLLaMA/comments/16z3goq/llm_chatrp_comparisontest_dolphinmistral/) Winner: Mistral-7B-OpenOrca
- [LLM Chat/RP Comparison/Test: Mistral 7B Base + Instruct](https://www.reddit.com/r/LocalLLaMA/comments/16twtfn/llm_chatrp_comparisontest_mistral_7b_base_instruct/)
- [LLM Chat/RP Comparison/Test (Euryale, FashionGPT, MXLewd, Synthia, Xwin)](https://www.reddit.com/r/LocalLLaMA/comments/16r7ol2/llm_chatrp_comparisontest_euryale_fashiongpt/) Winner: Xwin-LM-70B-V0.1
- [New Model Comparison/Test (Part 2 of 2: 7 models tested, 70B+180B)](https://www.reddit.com/r/LocalLLaMA/comments/16l8enh/new_model_comparisontest_part_2_of_2_7_models/) Winners: Nous-Hermes-Llama2-70B, Synthia-70B-v1.2b
- [New Model Comparison/Test (Part 1 of 2: 15 models tested, 13B+34B)](https://www.reddit.com/r/LocalLLaMA/comments/16kecsf/new_model_comparisontest_part_1_of_2_15_models/) Winner: Mythalion-13B
- [New Model RP Comparison/Test (7 models tested)](https://www.reddit.com/r/LocalLLaMA/comments/15ogc60/new_model_rp_comparisontest_7_models_tested/) Winners: MythoMax-L2-13B, vicuna-13B-v1.5-16K
- [Big Model Comparison/Test (13 models tested)](https://www.reddit.com/r/LocalLLaMA/comments/15lihmq/big_model_comparisontest_13_models_tested/) Winner: Nous-Hermes-Llama2
- [SillyTavern's Roleplay preset vs. model-specific prompt format](https://www.reddit.com/r/LocalLLaMA/comments/15mu7um/sillytaverns_roleplay_preset_vs_modelspecific/)
--------------------------------------------------------------------------------
**Disclaimer:** Some kind soul recently asked me if they could tip me for my LLM reviews and advice, so I set up [a Ko-fi page](https://ko-fi.com/wolframravenwolf). While this may affect the priority/order of my tests, it will not change the results, I am incorruptible. Also consider tipping your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! | 2023-12-12T22:43:51 | https://www.reddit.com/r/LocalLLaMA/comments/18gz54r/llm_comparisontest_mixtral8x7b_mistral_decilm/ | WolframRavenwolf | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gz54r | false | null | t3_18gz54r | /r/LocalLLaMA/comments/18gz54r/llm_comparisontest_mixtral8x7b_mistral_decilm/ | false | false | self | 234 | {'enabled': False, 'images': [{'id': 'xU07sACUhuScPaN8BIMV0USMZkFwSMGcorjhvoKF4K0', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/FRlvpc5SALNi_rYIzAsFALIzMXE1oc-Gkb2Yh1ZWGMM.jpg?width=108&crop=smart&auto=webp&s=9ad9ea4d991fae47edd6a09b5feb6d60eca474de', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/FRlvpc5SALNi_rYIzAsFALIzMXE1oc-Gkb2Yh1ZWGMM.jpg?width=216&crop=smart&auto=webp&s=308a202a45ba16483d13920cc1bacec91e523b47', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/FRlvpc5SALNi_rYIzAsFALIzMXE1oc-Gkb2Yh1ZWGMM.jpg?width=320&crop=smart&auto=webp&s=e3032902c70163f83e8380a9d1a220d309cdc7cb', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/FRlvpc5SALNi_rYIzAsFALIzMXE1oc-Gkb2Yh1ZWGMM.jpg?width=640&crop=smart&auto=webp&s=fde1276456518b0ab6ff6e2f6fd1f198a952883e', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/FRlvpc5SALNi_rYIzAsFALIzMXE1oc-Gkb2Yh1ZWGMM.jpg?width=960&crop=smart&auto=webp&s=0e9b19ae8aba7dbb28009cce456e33e378c4c47c', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/FRlvpc5SALNi_rYIzAsFALIzMXE1oc-Gkb2Yh1ZWGMM.jpg?width=1080&crop=smart&auto=webp&s=b78d9fadfbf4f7ba88328a51e8bd25a6f20d223f', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/FRlvpc5SALNi_rYIzAsFALIzMXE1oc-Gkb2Yh1ZWGMM.jpg?auto=webp&s=d55b83d577e907b76d4542af518cf15e4524a728', 'width': 1200}, 'variants': {}}]} |
Experimenting with Vicuna 13B | 2 | Howdy folks,
I am running Vicuna 13B and just wanted to share the very basic experiment I've been running: essentially I am trying to customize the chatbot with a bespoke system message. It is a pretty simple python script:
import json
import requests
url = 'http://fastchat.local/v1/completions'
system_prompt = """
You are a helpful, respectful, and honsest assistant.
Provide as accurate and concise a response as possible.
Only answer one prompt at a time - never write a prompt for the user.
"""
full_prompt = system_prompt
user_prompt = input("User: ")
while user_prompt != "QUIT":
full_prompt += "User: " + user_prompt + '\n'
data = {
'model': 'vicuna-13b-v1.5',
'max_tokens': 2048,
'prompt': full_prompt
}
response = json.loads(requests.post(url, json=data).text)['choices'][0]['text']
print(response[2:])
full_prompt += response + '\n'
user_prompt = input("User: ")
The results were good in terms of conversationality at the text completions endpoint, but obviously its coding skills were unimpressive:
User: Hello. My name is Bob. What is your name?
AI: Hello Bob, I'm AI - a helpful, obedient, respectful, and honest assistant.
User: Do you remember my name?
AI: Yes, Bob.
User: Excellent. Now I would like to write some python code. Can you help me with that?
AI: Of course Bob, I'd be happy to help. What do you need assistance with?
User: I would like a script to determine if an array contains a fibonacci sequence.
AI: Sure, here is a simple script that checks if an array contains a Fibonacci sequence:
église copie du codedef check\_fibonacci(arr):
n = len(arr)
for i in range(n):
for j in range(i+1, n):
if arr[i] + arr[j] == arr[i+1] and arr[j] + arr[i] == arr[i+1]:
return True
return False
You can use this function by passing in the array as an argument, like so:
église copie du codearr = [1, 1, 2, 3, 5, 8, 13, 21]
église copie du codeif check\_fibonacci(arr):
print("The array contains a Fibonacci sequence")
else:
print("The array does not contain a Fibonacci sequence")
This will output:
église copie du codeThe array contains a Fibonacci sequence
I hope this helps, Bob. Let me know if you need any further assistance.
User: It does help. Thank you. Did you give the function a french name?
AI: I apologize, Bob, I did not give the function a French name. The function name is "check\_fibonacci" which is an English name and it is the standard name for this kind of function. Is there anything else I can help you with?
User: Ah, sorry, I was looking at "église copie du code". I think I need to update my UI for this conversation to properly format that as code.
AI: No problem, Bob. I'm glad I could help. If you have any other questions or need further assistance, feel free to ask.
User: That script doesn't work.
AI: I apologize, Bob. Can you please provide more information on what isn't working? That way, I can better understand the issue and provide a solution.
User: It contains a logic error. The example provided outputs "The array does not contain a Fibonacci sequence".
AI: I apologize for the mistake, Bob. Here's a corrected version of the script:
église copie du codedef check\_fibonacci(arr):
n = len(arr)
for i in range(n):
for j in range(i+1, n):
if arr[i] + arr[j] == arr[i+1] and arr[j] + arr[i] == arr[i+1]:
return True
return False
You can use this function by passing in the array as an argument, like so:
église copie du codearr = [1, 1, 2, 3, 5, 8, 13, 21]
église copie du codeif check\_fibonacci(arr):
print("The array contains a Fibonacci sequence")
else:
print("The array does not contain a Fibonacci sequence")
This will output:
égliserainténir copie du codeThe array contains a Fibonacci sequence
I apologize for any confusion the previous version may have caused, and I hope this corrected version is helpful. Let me know if you have any other questions or need further assistance.
User: What is different in the second script?
AI: The second script is the corrected version of the first script. The only difference is that it properly checks if the array contains a Fibonacci sequence. The first version of the script had a logic error and it would output "The array does not contain a Fibonacci sequence" even if the array contains a Fibonacci sequence.
The corrected version uses two for loops to iterate through the array, checking if the elements at the current index and the next index add up to the element at the index after the next. This is the definition of a Fibonacci sequence.
Please let me know if you have any other question or need further assistance.
User: QUIT
My reason for doing this is to simulate [Retrieval Augmented Generation](https://arxiv.org/abs/2005.11401) by querying a MediaWiki API to first provide user information in the system prompt and then to provide contextual documents through keyword searches for prompts. In other words, rather than using another AI model for document retrieval, I will be writing custom python scripts to select documents from a variety of local sources.
Just felt like sharing what I was doing. Best wishes everyone :) | 2023-12-12T22:22:31 | https://www.reddit.com/r/LocalLLaMA/comments/18gyfwx/experimenting_with_vicuna_13b/ | eom-dev | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gyfwx | false | null | t3_18gyfwx | /r/LocalLLaMA/comments/18gyfwx/experimenting_with_vicuna_13b/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'q3evP6JeDpAC2MdSQHWYxnCYTqbJkElIQsLFqVSdkss', 'resolutions': [{'height': 63, 'url': 'https://external-preview.redd.it/izh8gZHY4FqZ1nwtU1N_TjtohUCNuvTyMn90toXda80.jpg?width=108&crop=smart&auto=webp&s=bc9575b410002edc2df3c5b5b0355fefedc7baa8', 'width': 108}, {'height': 126, 'url': 'https://external-preview.redd.it/izh8gZHY4FqZ1nwtU1N_TjtohUCNuvTyMn90toXda80.jpg?width=216&crop=smart&auto=webp&s=dbce7f303173724d23fb33cd3fc636c04c72b290', 'width': 216}, {'height': 186, 'url': 'https://external-preview.redd.it/izh8gZHY4FqZ1nwtU1N_TjtohUCNuvTyMn90toXda80.jpg?width=320&crop=smart&auto=webp&s=c1043d604105157f56a615cc59bb14d7ae64653f', 'width': 320}, {'height': 373, 'url': 'https://external-preview.redd.it/izh8gZHY4FqZ1nwtU1N_TjtohUCNuvTyMn90toXda80.jpg?width=640&crop=smart&auto=webp&s=ce8b9192ed7ca476d2844aaa405c5014a7a1ab45', 'width': 640}, {'height': 560, 'url': 'https://external-preview.redd.it/izh8gZHY4FqZ1nwtU1N_TjtohUCNuvTyMn90toXda80.jpg?width=960&crop=smart&auto=webp&s=76aed6fd51086798b2d415a7d57562c967db4111', 'width': 960}, {'height': 630, 'url': 'https://external-preview.redd.it/izh8gZHY4FqZ1nwtU1N_TjtohUCNuvTyMn90toXda80.jpg?width=1080&crop=smart&auto=webp&s=46129c06d8fad9a58fff9740c079e13d4e829213', 'width': 1080}], 'source': {'height': 700, 'url': 'https://external-preview.redd.it/izh8gZHY4FqZ1nwtU1N_TjtohUCNuvTyMn90toXda80.jpg?auto=webp&s=8efe489c05609f1626bbb44354c77840623707de', 'width': 1200}, 'variants': {}}]} |
I am really impressed with Mixtral 8x7 MoE | 27 | Now available on hugging chat. | 2023-12-12T22:03:05 | https://i.redd.it/rc607p5isx5c1 | AstrionX | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18gxu17 | false | null | t3_18gxu17 | /r/LocalLLaMA/comments/18gxu17/i_am_really_impressed_with_mixtral_8x7_moe/ | false | false | default | 27 | {'enabled': True, 'images': [{'id': 'HIKt9oGMahB5R5U38rhMbpCgTlI-evTv6QBjFRgDfrQ', 'resolutions': [{'height': 216, 'url': 'https://preview.redd.it/rc607p5isx5c1.jpeg?width=108&crop=smart&auto=webp&s=1d4c271369286512d503ad68cc1226cf3d5f5d72', 'width': 108}, {'height': 432, 'url': 'https://preview.redd.it/rc607p5isx5c1.jpeg?width=216&crop=smart&auto=webp&s=66f3995d595904686bc98de8cdc45358b3d4f06b', 'width': 216}, {'height': 640, 'url': 'https://preview.redd.it/rc607p5isx5c1.jpeg?width=320&crop=smart&auto=webp&s=80d8ad83d89f70b521c658e67a0778a81c79c50a', 'width': 320}, {'height': 1280, 'url': 'https://preview.redd.it/rc607p5isx5c1.jpeg?width=640&crop=smart&auto=webp&s=2ced42efabf29d35f1f65856e43b953a19bbb79c', 'width': 640}, {'height': 1920, 'url': 'https://preview.redd.it/rc607p5isx5c1.jpeg?width=960&crop=smart&auto=webp&s=4edd15f198ea6bfc3f042f18dbe0c9587e932696', 'width': 960}, {'height': 2160, 'url': 'https://preview.redd.it/rc607p5isx5c1.jpeg?width=1080&crop=smart&auto=webp&s=6368b36c5039ff7a16de23ebd24e429c9bc50e9a', 'width': 1080}], 'source': {'height': 4467, 'url': 'https://preview.redd.it/rc607p5isx5c1.jpeg?auto=webp&s=8df63c07e1302a3a7cae97898989dff36655d996', 'width': 1440}, 'variants': {}}]} |
DeciLM-7B Instruct v Mistral-7B-Instruct-v01 on Chain of Thought | 7 | **Note:** I finished this notebook yesterday before learning about the news Mistral model. I will re-run this with the new Mistral instruct-tuned model later this week. If anyone from the community wants to re-run the notebook using the new Mistral model before then, please do.
📘 Check the notebook out [here](https://colab.research.google.com/drive/1lW6aQW77NDttBQ2Mk5M_OZrp-ZjIaFEt)
#🧐 What I'm doing
Sampled 30 random rows from the [kaist-ai/CoT-Collection](https://huggingface.co/datasets/kaist-ai/CoT-Collection). This dataset is 1M+ rows, so it was infeasible for me to use them all. I chose 30 because, once upon a time, I was a clinical trials statistician, and 30 was always a magical number.
I then generated responses for each prompt under a zero, one, and three-shot setting for DeciLM-7B-Instruct and Mistral-7B-v01.
## ⚖️ Evaluations
###LLM as Judge
I used LangChain string evaluators for the following, with GPT-4-Turbo as judge.
1. COT Evaluation (evaluate_cot): This grades answers to questions using chain of thought 'reasoning' based on a reference answer. It will return one of the following evaluation results: CORRECT or INCORRECT evaluating whether the generation is correct, accurate, and factual.
2. Coherence Evaluation (evaluate_coherence): This gives a score between 1 and 10 to the generation, assessing whether it is coherent, well-structured, and organized based on a ground truth reference label. This is useful for assessing the quality of the generation's reasoning.
###Faithfulness in ragas
I also used the ragas framework to measure faithfulness, which assesses how well a model's responses align with the given context or source material. This was also done using GPT-4-Turbo
#### 🤷🏽♂️Why did I do this?
Mostly because I'm curious and thought it would be a cool project. I also work at Deci, and I'm skeptical of benchmarks and wanted to see if our model was as good as we claim.
All feedback is welcome, cheers! | 2023-12-12T22:02:33 | https://www.reddit.com/r/LocalLLaMA/comments/18gxtey/decilm7b_instruct_v_mistral7binstructv01_on_chain/ | datascienceharp | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gxtey | false | null | t3_18gxtey | /r/LocalLLaMA/comments/18gxtey/decilm7b_instruct_v_mistral7binstructv01_on_chain/ | false | false | self | 7 | {'enabled': False, 'images': [{'id': 'nkhh65ujo5BznFJFojoMPaKjGuLSpPj6KGhRov-ykOg', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/0-fRWqjlLadVXj5pfYp4_Oe3xgBWE-_rdjVSn7hlohI.jpg?width=108&crop=smart&auto=webp&s=f34d2dfdbbfa7de0f1956f186fd8430ee96a1a55', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/0-fRWqjlLadVXj5pfYp4_Oe3xgBWE-_rdjVSn7hlohI.jpg?width=216&crop=smart&auto=webp&s=2817183828c9747b960cb2e55c59cfa41f4f9ded', 'width': 216}], 'source': {'height': 260, 'url': 'https://external-preview.redd.it/0-fRWqjlLadVXj5pfYp4_Oe3xgBWE-_rdjVSn7hlohI.jpg?auto=webp&s=ed5da41e2c4cee7a9e495c8291ecf5604f0e169d', 'width': 260}, 'variants': {}}]} |
Augmenting Local AI with Browser Data: Introducing MemoryCache | 33 | 2023-12-12T21:29:46 | https://future.mozilla.org/blog/introducing-memorycache/ | TinyApps_Org | future.mozilla.org | 1970-01-01T00:00:00 | 0 | {} | 18gwxuq | false | null | t3_18gwxuq | /r/LocalLLaMA/comments/18gwxuq/augmenting_local_ai_with_browser_data_introducing/ | false | false | default | 33 | null | |
AWS cloud pipeline mapping via LLM | 3 | I understand it probably couldn't fully be automated, but does anyone know of any service where I could connect to GitHub or put my code in and have it look through various source files for JavaScript and map out which pipelines need to be built and how on AWS. | 2023-12-12T21:22:34 | https://www.reddit.com/r/LocalLLaMA/comments/18gwrt1/aws_cloud_pipeline_mapping_via_llm/ | CuriousProgrammable | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gwrt1 | false | null | t3_18gwrt1 | /r/LocalLLaMA/comments/18gwrt1/aws_cloud_pipeline_mapping_via_llm/ | false | false | self | 3 | null |
Fine tuning Mixtral 8x7B | 11 | I'm having many issues fine tuning the Mixtral 8x7B - training throws errors between 30 minutes to 3 hours of training (4xL4 gpus). Using the same methods as running the Mistral 7b or Llama 13/70b models - anything different that I need to know when training an MoE model? | 2023-12-12T21:18:38 | https://www.reddit.com/r/LocalLLaMA/comments/18gwoke/fine_tuning_mixtral_8x7b/ | CuriosAligator | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gwoke | false | null | t3_18gwoke | /r/LocalLLaMA/comments/18gwoke/fine_tuning_mixtral_8x7b/ | false | false | self | 11 | null |
Noob Question : How are people able to run Mistral 8x-7b locally when platforms like replicate which are/were using A100s still take/took 52s to produce output? | 40 | I’m new to AI.
Just asking dumb questions for more clarity.
A simplified answer with a link to more detailed sources would be really helpful.
Thank you | 2023-12-12T21:16:52 | https://www.reddit.com/r/LocalLLaMA/comments/18gwn4y/noob_question_how_are_people_able_to_run_mistral/ | punit_palial | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gwn4y | false | null | t3_18gwn4y | /r/LocalLLaMA/comments/18gwn4y/noob_question_how_are_people_able_to_run_mistral/ | false | false | self | 40 | null |
Gemini wasn't released yet and was already "beaten" by MS... what a time to be alive! | 116 | ​
[Results reported by MS](https://preview.redd.it/1nczzysmix5c1.png?width=1210&format=png&auto=webp&s=9b3fec9b766cac0f268a0a17953956b62b42a6d3)
Source: [https://www.microsoft.com/en-us/research/blog/steering-at-the-frontier-extending-the-power-of-prompting/](https://www.microsoft.com/en-us/research/blog/steering-at-the-frontier-extending-the-power-of-prompting/) | 2023-12-12T21:08:13 | https://www.reddit.com/r/LocalLLaMA/comments/18gwg09/gemini_wasnt_released_yet_and_was_already_beaten/ | VertexMachine | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gwg09 | false | null | t3_18gwg09 | /r/LocalLLaMA/comments/18gwg09/gemini_wasnt_released_yet_and_was_already_beaten/ | false | false | 116 | {'enabled': False, 'images': [{'id': 'UMjqgM6AJ734iF5UWmQvuhyJAru2nIBB5TeAve7LQpw', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/5ei1O03qVtMIWlN3BZXOAdiLq84LvNR6rRynnVcOsP0.jpg?width=108&crop=smart&auto=webp&s=20e2ad707e2012bb353a27cf842165113b49b249', 'width': 108}, {'height': 112, 'url': 'https://external-preview.redd.it/5ei1O03qVtMIWlN3BZXOAdiLq84LvNR6rRynnVcOsP0.jpg?width=216&crop=smart&auto=webp&s=2df78cd4e69f49fbe99417e22bb844ba25f70258', 'width': 216}, {'height': 167, 'url': 'https://external-preview.redd.it/5ei1O03qVtMIWlN3BZXOAdiLq84LvNR6rRynnVcOsP0.jpg?width=320&crop=smart&auto=webp&s=6dd834e87be72c769e8aff7eb9b1d32d36cff3ad', 'width': 320}, {'height': 334, 'url': 'https://external-preview.redd.it/5ei1O03qVtMIWlN3BZXOAdiLq84LvNR6rRynnVcOsP0.jpg?width=640&crop=smart&auto=webp&s=a6435099bda003e3afaffb06e7ed79c51e460623', 'width': 640}, {'height': 501, 'url': 'https://external-preview.redd.it/5ei1O03qVtMIWlN3BZXOAdiLq84LvNR6rRynnVcOsP0.jpg?width=960&crop=smart&auto=webp&s=e37e1130c308950a52ecac9323b92f0bfc043b0a', 'width': 960}, {'height': 564, 'url': 'https://external-preview.redd.it/5ei1O03qVtMIWlN3BZXOAdiLq84LvNR6rRynnVcOsP0.jpg?width=1080&crop=smart&auto=webp&s=2b3e68095c07fea1dbecf69d9e26f46b1e522042', 'width': 1080}], 'source': {'height': 627, 'url': 'https://external-preview.redd.it/5ei1O03qVtMIWlN3BZXOAdiLq84LvNR6rRynnVcOsP0.jpg?auto=webp&s=725bcc52f4b7f2b4b9cd2f3d12d3ef10d1a9c15a', 'width': 1200}, 'variants': {}}]} | |
Potentially outlandish question(s)/thoughts for hopefully someone with high expertise to answer? | 9 | Why wide and not deep? So LLM have relatively few layers, while layers are extremely wide. And that seems, lets say extremely suspicious. I know transformers are not what fasttext used, but... For some tasks some, almost decade ago I need text classifier, and fasttext seemed to fit the bill decently. Now default retrains and what not were at dimensions 100 and 300 (what I would assume basically with of transformer layers?). But since the environment was extremely constrained, that seemed like complete no go. So I tried _drastically lower_ dimensions, between 4 and 16, and it worked ~ok, better than anything else we tried I believe, and it was small and fast, and was good enough (our training data was... not perfect but whatever). And all that even then got me thinking.
So we basically are embedding words into multidimensional space... and, adding just a few dimensions "adds **redonculous** amount of space". I was using such line of thinking: something akin to clustering of words/concepts is what potentially "matters the most", assuming words as points, if we have 1d, one word can have 2 equidistant neighbors, 2d - 6 (and then some of those can have others as equidistant), 3d and its something like 10, if we want them to be spaced about the same distance from each other, and if we don't (which I'd guess we don't)... it's even more. It's not much but still, quit a bit, and if we start thinking about clusters of clusters, imo it allows for "very significant structure representation", so why not make some models with layer width say ~16-32, and like 256-512 layers? Wouldn't larger dept allow for more fancy reasoning? Wouldn't such model size be "quite small" ? (considering that we have ~5k wide layers and ~50 of them). Was this experimented on sufficiently and deemed not worthy? Or did people somehow go more space more better, lets not even check?
Another question/thought. Seems to me that transformers and "whole current setup" just ain't it. Seems like resource use extremely is extremely inefficient, which I think is sufficiently evident from the fact that quants work quite well actually, meaning there is just lot of dead weight, potentially even weighing down training?
Even more disappointing to me is whole training situation, now I understand it's sorta easy to underestimate "total brain train resource use", but somehow seems that current LLM use dis-proportionally large resources when compared to brain. And we have to take into account that the training process is just that training, while brain has to run the body all the time, on multiple aspects/modalities.
My sorta biggest disappointment (and yes whatever we have is still nothing less than amazing) is the existence of separate training from inference. Which again, seems to be quite the opposite from how brain works. I was thinking recently about this (and I'll preface that this might be pure nonsense here). I think big part of reason for this training situation might be that it's "sorta global", and by that I mean that back-propagation is global algo in that it goes through whole net (as far as I understand in most cases) with all the gradients and what not. I could not even begin to think of something similar to backprop existing in brain, there is no train set, there is nothing that can check input output mismatches, recalculate gradients basically from outside neuron function. So I think there has to be better way...
Couldn't we somehow localize weight adjustment? I was thinking about something like "setting preferred firing rate" for neuron, and then it tries to adjust its input weights somehow to try to keep firing at that preferred rate, always, no separate training or inference, which we would try to not make as separate uses. As far as I know there are at least some cells in brain that possibly work similar to this.
So yeah some wild speculation, hopefully it's at least entertaining, or food for thought. | 2023-12-12T21:05:55 | https://www.reddit.com/r/LocalLLaMA/comments/18gwe23/potentially_outlandish_questionsthoughts_for/ | morphles | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gwe23 | false | null | t3_18gwe23 | /r/LocalLLaMA/comments/18gwe23/potentially_outlandish_questionsthoughts_for/ | false | false | self | 9 | null |
I fine-tuned Llama to generate system diagrams for any repo | 86 | 2023-12-12T21:01:45 | https://v.redd.it/gsko5acpgx5c1 | TheMblabla | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18gwah2 | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/gsko5acpgx5c1/DASHPlaylist.mpd?a=1705006919%2CZGMzNDc3NzljZGQ5MWY5ZWM1OThhMGM3MjA1OGYwZmRhY2Y5MmJlN2M2OWQwY2E2MDVmNTVmNTc0ZGRkMzdkYg%3D%3D&v=1&f=sd', 'duration': 45, 'fallback_url': 'https://v.redd.it/gsko5acpgx5c1/DASH_1080.mp4?source=fallback', 'has_audio': False, 'height': 1080, 'hls_url': 'https://v.redd.it/gsko5acpgx5c1/HLSPlaylist.m3u8?a=1705006919%2CNTJhNGMzMDBiNjU5OTNmOTY4NWJhN2ZkOGI4ZTUwYjY1NTJlMjE5M2QyNTkxYTFjNjc5NTI4M2Y2ODJlYzNkMg%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/gsko5acpgx5c1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1800}} | t3_18gwah2 | /r/LocalLLaMA/comments/18gwah2/i_finetuned_llama_to_generate_system_diagrams_for/ | false | false | 86 | {'enabled': False, 'images': [{'id': 'N3BrcWtoOGtoeDVjMdOUIoYGV9-az7QVspxR7I-8jv3BrZE-LMhIuQAOnlvp', 'resolutions': [{'height': 64, 'url': 'https://external-preview.redd.it/N3BrcWtoOGtoeDVjMdOUIoYGV9-az7QVspxR7I-8jv3BrZE-LMhIuQAOnlvp.png?width=108&crop=smart&format=pjpg&auto=webp&s=6730cba4f79b020f0b92dfc36b3e3d1f7692b874', 'width': 108}, {'height': 129, 'url': 'https://external-preview.redd.it/N3BrcWtoOGtoeDVjMdOUIoYGV9-az7QVspxR7I-8jv3BrZE-LMhIuQAOnlvp.png?width=216&crop=smart&format=pjpg&auto=webp&s=6714dea0440f0623b870e8de1ec02aa81e615ddd', 'width': 216}, {'height': 192, 'url': 'https://external-preview.redd.it/N3BrcWtoOGtoeDVjMdOUIoYGV9-az7QVspxR7I-8jv3BrZE-LMhIuQAOnlvp.png?width=320&crop=smart&format=pjpg&auto=webp&s=fe50f79810cefd2cbb18a6ae1eaeb6eb5dfdad99', 'width': 320}, {'height': 384, 'url': 'https://external-preview.redd.it/N3BrcWtoOGtoeDVjMdOUIoYGV9-az7QVspxR7I-8jv3BrZE-LMhIuQAOnlvp.png?width=640&crop=smart&format=pjpg&auto=webp&s=cbac7810aaaf22e7ee5ed647a764439216bde696', 'width': 640}, {'height': 576, 'url': 'https://external-preview.redd.it/N3BrcWtoOGtoeDVjMdOUIoYGV9-az7QVspxR7I-8jv3BrZE-LMhIuQAOnlvp.png?width=960&crop=smart&format=pjpg&auto=webp&s=ea907ba51be820a3bcdc0946b50c8d177d0d9315', 'width': 960}, {'height': 648, 'url': 'https://external-preview.redd.it/N3BrcWtoOGtoeDVjMdOUIoYGV9-az7QVspxR7I-8jv3BrZE-LMhIuQAOnlvp.png?width=1080&crop=smart&format=pjpg&auto=webp&s=951a592648aae5e575a983460231236aeb318e9f', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/N3BrcWtoOGtoeDVjMdOUIoYGV9-az7QVspxR7I-8jv3BrZE-LMhIuQAOnlvp.png?format=pjpg&auto=webp&s=cfa16271e82addb4da763dde4268aa32fca297e3', 'width': 1800}, 'variants': {}}]} | ||
Could you guys give me tips for my potential school project? | 2 | So my teacher and I were talking about LLMs and he brought up the idea of using a language model as a recommendation system.
The model should be small and its role would be to talk with the user and extract relevant information from them.
To keep the model's size small, it wouldn't not be trained on the books themselves, but rather should have an external database which it would query.
Based on the results of the queries, the model should give the recommendations back to the user and the user would then be able to say which of the recommendations were good and which were bad.
The model would then be able to query the database again based on the user's feedback.
The user and the model would then be able to go back and forth like this, until the ideal recommendations were found.
​
As this would be my first time training LLMs, I'm mainly interested about the following:
Is this idea feasible? What model sizes should I start experimenting with? How would the LLM extract the relavant information from the user's inputs so that it could query the database?
Thanks in advance for any and all suggestions. | 2023-12-12T20:53:10 | https://www.reddit.com/r/LocalLLaMA/comments/18gw39y/could_you_guys_give_me_tips_for_my_potential/ | HappyLove4691 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gw39y | false | null | t3_18gw39y | /r/LocalLLaMA/comments/18gw39y/could_you_guys_give_me_tips_for_my_potential/ | false | false | self | 2 | null |
Need clear google colab for fine tuning chat | 2 | Most of the tutorials I found focus on tuning instructions. It would be interesting to see lora’s implementation of training for chat. It also interesting to find out if there is a difference between learning through all the words and passing gradients only through the assistants answers (does it make diff). | 2023-12-12T20:48:47 | https://www.reddit.com/r/LocalLLaMA/comments/18gvznf/need_clear_google_colab_for_fine_tuning_chat/ | Due-Weather-3140 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gvznf | false | null | t3_18gvznf | /r/LocalLLaMA/comments/18gvznf/need_clear_google_colab_for_fine_tuning_chat/ | false | false | self | 2 | null |
My attempt at teaching Mixtral basic arithmetic | 3 | I asked Mixtral how many numbers are between 161 and 172, not counting "the borders".
I also asked it to give me a list.
It produced the correct list, but it claimed there were 20 numbers in between.
So I tried again, this time telling it to produce the list, add a counter, start with 1, and then "count one up" for every new entry in the list.
Apart from starting with 161 instead of 162, this time, it got the answer right.
(It may not take Mixtral to do this, I remember previous mixtral models also producing the correct list, so it's possible that they can follow this procedure, too.)
https://preview.redd.it/306rkeo88x5c1.png?width=1462&format=png&auto=webp&s=e44c7da528968d16bf979728412f3f227d647520 | 2023-12-12T20:10:33 | https://www.reddit.com/r/LocalLLaMA/comments/18gv419/my_attempt_at_teaching_mixtral_basic_arithmetic/ | hugo-the-second | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gv419 | false | null | t3_18gv419 | /r/LocalLLaMA/comments/18gv419/my_attempt_at_teaching_mixtral_basic_arithmetic/ | false | false | 3 | null | |
LLMs and SQL | 6 | My goal is to create a LLM that will be able to execute SQL queries on my db with consistent accuracy. Some of the queries seem too advanced for llms right now.
I've tried many of the different ones out there but nothing is as good as chatgpt 4. But each API call is too expensive for what I'm doing.
I've provided the models with as easy to use possible schemas in the prompt but sometimes it just gets too stubborn and overlooks the important instructions I provide
I've been looking into fine tuning a model and before I go into it has anyone had success in tuning a model specifically for your database? Does it make sense to go down this road.
Thanks in advance. | 2023-12-12T19:57:45 | https://www.reddit.com/r/LocalLLaMA/comments/18gut2v/llms_and_sql/ | sharpnation | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gut2v | false | null | t3_18gut2v | /r/LocalLLaMA/comments/18gut2v/llms_and_sql/ | false | false | self | 6 | null |
Mistral 7B v0.2 Q5_K_M | 16 | This GGUF works very well for smaller setups. with just 16GB M1 Pro it gets roughly ~10t/s and very good context length. | 2023-12-12T19:26:21 | https://www.reddit.com/r/LocalLLaMA/comments/18gu2cz/mistral_7b_v02_q5_k_m/ | cm8t | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gu2cz | false | null | t3_18gu2cz | /r/LocalLLaMA/comments/18gu2cz/mistral_7b_v02_q5_k_m/ | false | false | self | 16 | null |
How in the hell is this possible? | 1 | 2023-12-12T19:24:32 | HellIsParadise | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18gu0wo | false | null | t3_18gu0wo | /r/LocalLLaMA/comments/18gu0wo/how_in_the_hell_is_this_possible/ | false | false | 1 | {'enabled': True, 'images': [{'id': 'mnDekcTiOC25mviLXP9_EVIHuGmQA4CDpzh-cIzGXUA', 'resolutions': [{'height': 64, 'url': 'https://preview.redd.it/joa6xz640x5c1.jpeg?width=108&crop=smart&auto=webp&s=787f60a80ea0864d04b9eecf73c93af015bcd7d3', 'width': 108}, {'height': 129, 'url': 'https://preview.redd.it/joa6xz640x5c1.jpeg?width=216&crop=smart&auto=webp&s=fbcf198cea3fe07eaca42287274f59126a2c4f68', 'width': 216}, {'height': 192, 'url': 'https://preview.redd.it/joa6xz640x5c1.jpeg?width=320&crop=smart&auto=webp&s=f5c2cefd337439fd1a6c97b1500e14041895f6f8', 'width': 320}, {'height': 384, 'url': 'https://preview.redd.it/joa6xz640x5c1.jpeg?width=640&crop=smart&auto=webp&s=b901e1039f38c2be1b81975bca78bb602f91da35', 'width': 640}, {'height': 576, 'url': 'https://preview.redd.it/joa6xz640x5c1.jpeg?width=960&crop=smart&auto=webp&s=bd37ae035c4cc38f2b20216a0e5e6bb0aa9703ca', 'width': 960}, {'height': 649, 'url': 'https://preview.redd.it/joa6xz640x5c1.jpeg?width=1080&crop=smart&auto=webp&s=05ebe1c9fb93f83144c45221ca3356de272e86ec', 'width': 1080}], 'source': {'height': 649, 'url': 'https://preview.redd.it/joa6xz640x5c1.jpeg?auto=webp&s=6633489c19d85dfc19f0ae2e8c88df90db753c94', 'width': 1080}, 'variants': {}}]} | |||
Mixtral 8x7b performance question | 8 | Everyone says that this custom model is more powerful than the Llama 70b. On what basis does it provide this power? If I understand correctly, only 2 models out of 8 available inside can function at the same time. Does the total number of parameters of all models mean something to the intelligence of the model? I use a 120B Goliath and clearly understand that the number of parameters matters, the model is uniform and produces its power, which is much better than the 70B. 120B understands the context well even in a long prompt and does not miss details, unlike 70B. How does this work with mixtral which is several models working separately from each other? | 2023-12-12T19:23:35 | https://www.reddit.com/r/LocalLLaMA/comments/18gu05j/mixtral_8x7b_performance_question/ | Secret_Joke_2262 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gu05j | false | null | t3_18gu05j | /r/LocalLLaMA/comments/18gu05j/mixtral_8x7b_performance_question/ | false | false | self | 8 | null |
Help me get started | 2 | I have a laptop with a Ryzen 7 5700U, 16 GB ram, running Fedora 38 linux.
I'm looking to run a local uncensored LLM, I'd like to know what would be the best model and software to run it.
I'm currently running KoboldAI and Erebus 2.7b. It's okay in terms of speed, but I'm wondering if there's anything better out there. I guess, I would prefer something that is not web-ui based to lower the overhead, if possible.
I'm not very well versed in all the lingo yet, so please keep it simple.
Thanks! | 2023-12-12T18:59:12 | https://www.reddit.com/r/LocalLLaMA/comments/18gtf3m/help_me_get_started/ | agoodusername123456 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gtf3m | false | null | t3_18gtf3m | /r/LocalLLaMA/comments/18gtf3m/help_me_get_started/ | false | false | self | 2 | null |
Prolog language proposal for next Mixture of Experts model | 7 | Two days ago, I tried asking several language models on Poe.com, the [Wolf, Goat, and Cabbage problem](https://en.wikipedia.org/wiki/Wolf,_goat_and_cabbage_problem). When I asked them the problem in the original plain English form, none of them could get it right. Mixstral 7b actually went into an infinite loop when I asked it. However, after working with Claude-Instant for a while, I managed to come up with a revised prompt which made several of the constraints more explicit. With the new prompt, Sage and ChatGPT were able to intermittently get the correct answer, after some regens.
That led me to believe that what was preventing language models from getting the question right, was the ambiguity of natural language. It was a symbolic logic or data formatting issue, not necessarily a problem with the models' intelligence as such. That led me to remember Prolog, a classic programming language which specialises in symbolic logic. I asked ChatGPT if it could express the Wolf, Goat, and Cabbage problem as Prolog source code, and this was the result, with ChatGPT's commentary below.
% Initial state: wolf, goat, cabbage, and farmer are on one side.
initial_state([wolf, goat, cabbage, farmer, side1]).
% Final state: wolf, goat, cabbage, and farmer are on the other side.
final_state([wolf, goat, cabbage, farmer, side2]).
% Valid moves from one side to the other.
move([X, G, C, X, side1], [Y, G, C, Y, side2]) :- opposite(X, Y), not(eats(Y, G)), not(eats(Y, C)).
move([W, X, C, X, side1], [W, Y, C, Y, side2]) :- opposite(X, Y), not(eats(W, Y)), not(eats(Y, C)), not(together(W, Y, G)).
move([W, G, X, X, side1], [W, G, Y, Y, side2]) :- opposite(X, Y), not(eats(W, G)), not(eats(W, Y)).
move([W, G, C, X, side1], [W, G, C, X, side2]) :- opposite(X, side1), opposite(X, side2).
% Predicate to check if an animal eats another.
eats(wolf, goat).
eats(goat, cabbage).
% Predicate to determine opposite sides.
opposite(side1, side2).
opposite(side2, side1).
% Predicate to check if the wolf and goat are together.
together(wolf, goat, _).
together(goat, wolf, _).
>In this Prolog code, the initial state is represented by initial_state/1, and the final state is represented by final_state/1. The move/2 predicate defines the valid moves from one side to the other. The eats/2 predicate specifies the animals that can eat each other. The opposite/2 predicate determines the opposite side of the river.
>
>To find a solution to the problem using Prolog, you can use a search algorithm that explores the possible moves and states, checking for the final state while respecting the constraints defined in the move/2 predicate.
>
>Keep in mind that this is a simplified representation of the problem, and depending on your specific requirements, you may need to modify or extend the code.
My suggestion, in light of this:
That one of the dedicated expert models, within a future Model of Experts, be dedicated to the Prolog programming language. Whenever a model is given a question which appears to it to exceed a certain level of complexity, (I admit I have no idea how logical complexity could be quantified, as such; I will leave that up to someone else, initially) then the question would be handed off to the Prolog coprocessor. It would write a description of the question in Prolog, and would apply whatever other algorithmic techniques were necessary to answer the question. Depending on the nature of the question, an attempt would then be made to recursively verify the answer, and the answer would then be passed back as input to the user. | 2023-12-12T18:36:32 | https://www.reddit.com/r/LocalLLaMA/comments/18gsvu0/prolog_language_proposal_for_next_mixture_of/ | petrus4 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gsvu0 | false | null | t3_18gsvu0 | /r/LocalLLaMA/comments/18gsvu0/prolog_language_proposal_for_next_mixture_of/ | false | false | self | 7 | null |
HF Leaderboard considered harmful (for chat)? | 78 | This has been bothering me lately.
Every benchmark used on the HF Leaderboard, as far as I know, **does not take the expected prompt format of the model into account.**
This isn't really news - we knew this already. And it goes without saying that this can cause misleading results. **But I think there's an even worse side effect to this.**
If you go through the top-performing chat models on the HF Leaderboard, you'll see that models using the older Alpaca format, something like "###Assistant: ... ###User: ..." **are more highly represented** than models using better, more modern formats, such as ChatML, or really any format that includes a dedicated end-of-sequence tokens like "<|end\_of\_turn|>".
Why? **I believe models that use the older, simpler chat format are better at handling situations where the chat format is violated - i.e. the HF Leaderboard benchmarks.**
Looking at the top performers around 7B parameters, as of 12/12/2023:
​
|1|Marcoroni-7B-v3 |Alpaca style|
|:-|:-|:-|
|2|v1olet\_marcoroni-go-bruins-merge-7B|Alpaca style|
|3|Marcoroni-neural-chat-7B-v1|Alpaca style|
|4|neural-chat-7b-v3-3-Slerp|Alpaca style|
|5|Seraph-7B|Alpaca style|
|...|||
|13|MetaMath-Cybertron-Starling|ChatML|
​
The top of the leaderboard is dominated by models using Alpaca-style models. You'll notice another thing, too - **these models tend to forgo the end-of-turn token </s>** that earlier models used.
Contrast that with [lmsys MT-Bench](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard), **which tests multi-turn chat, using the model's expected chat format,** and you'll see an entirely different set of models appearing at the top. Granted, most of the models from the HF leaderboard table do not appear at all, so a direct comparison is not totally fair. I'm just making a point that **benchmarking for chat scenarios is entirely different than benchmarking for free-text completion scenarios** and one is not representative of the other.
Allow me to make an unsubstantiated claim:
I think that formats such as ChatML, or even the weird OpenChat format, yield better chat models, because the transition between user and assistant turns are clearly delineated by a special, unambiguous token. However, **those who finetune models with the goal of climbing the HF leaderboard are incentivized, intentionally or not, to use a worse chat format, because in the benchmarks used by the HF Leaderboard, models that follow the better chat formats perform** ***worse*** **because the free-text nature of those benchmarks severely violates the expected format.**
​ | 2023-12-12T18:32:14 | https://www.reddit.com/r/LocalLLaMA/comments/18gss5q/hf_leaderboard_considered_harmful_for_chat/ | its_just_andy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gss5q | false | null | t3_18gss5q | /r/LocalLLaMA/comments/18gss5q/hf_leaderboard_considered_harmful_for_chat/ | false | false | self | 78 | {'enabled': False, 'images': [{'id': 'f-Er2nh8Xt_YPyZ8le6GRHTfsR8EEtNIQE7W_Ea98Kw', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/8cNFhKRvnMLhSF_I-JZvEn7pPt-P79iUIxKSuSYjwUQ.jpg?width=108&crop=smart&auto=webp&s=80a187ff989ccc5449f757c2e367667d58a885e6', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/8cNFhKRvnMLhSF_I-JZvEn7pPt-P79iUIxKSuSYjwUQ.jpg?width=216&crop=smart&auto=webp&s=c7539df997340dc356ffad7fbd1c838ba9e6cbd4', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/8cNFhKRvnMLhSF_I-JZvEn7pPt-P79iUIxKSuSYjwUQ.jpg?width=320&crop=smart&auto=webp&s=8df6afae03208587c034aea0beb2e771da2cd7ca', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/8cNFhKRvnMLhSF_I-JZvEn7pPt-P79iUIxKSuSYjwUQ.jpg?width=640&crop=smart&auto=webp&s=bd33fd03fb5e6f3271aca71c07c815fbe5675f3f', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/8cNFhKRvnMLhSF_I-JZvEn7pPt-P79iUIxKSuSYjwUQ.jpg?width=960&crop=smart&auto=webp&s=b7ae2f0a9350f90168e5392f629aac1ceed8f0de', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/8cNFhKRvnMLhSF_I-JZvEn7pPt-P79iUIxKSuSYjwUQ.jpg?width=1080&crop=smart&auto=webp&s=50c20bd445ea3c62aed508404fd058df91d3c705', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/8cNFhKRvnMLhSF_I-JZvEn7pPt-P79iUIxKSuSYjwUQ.jpg?auto=webp&s=f8d0fd23f01bd5a632a28a9d6a0ff0f0d2aa07d1', 'width': 1200}, 'variants': {}}]} |
FastAI Jeremy Howard started a For Profit (Pro OS) R&D lab | 5 | 2023-12-12T18:28:46 | https://www.answer.ai/ | dulldata | answer.ai | 1970-01-01T00:00:00 | 0 | {} | 18gsp5h | false | null | t3_18gsp5h | /r/LocalLLaMA/comments/18gsp5h/fastai_jeremy_howard_started_a_for_profit_pro_os/ | false | false | 5 | {'enabled': False, 'images': [{'id': 'IxRN5t4FxciY24AQQblE2wZ0GjsgKY-0hcv89z_nc3U', 'resolutions': [{'height': 93, 'url': 'https://external-preview.redd.it/Jcj7cjQxb3IqOj4bP6My_XxFQz6ZW3AmZuxUQ1txJh4.jpg?width=108&crop=smart&auto=webp&s=4c84c216938a1b958c448fdd958a69e537816432', 'width': 108}, {'height': 186, 'url': 'https://external-preview.redd.it/Jcj7cjQxb3IqOj4bP6My_XxFQz6ZW3AmZuxUQ1txJh4.jpg?width=216&crop=smart&auto=webp&s=77b7d578cc2789a79f9f65debfaf2ba3696d6d0f', 'width': 216}, {'height': 276, 'url': 'https://external-preview.redd.it/Jcj7cjQxb3IqOj4bP6My_XxFQz6ZW3AmZuxUQ1txJh4.jpg?width=320&crop=smart&auto=webp&s=592964cd9add9e2d85c6fd86ee48969367e3a718', 'width': 320}, {'height': 552, 'url': 'https://external-preview.redd.it/Jcj7cjQxb3IqOj4bP6My_XxFQz6ZW3AmZuxUQ1txJh4.jpg?width=640&crop=smart&auto=webp&s=fa85ff4a71b06ad7c8763ade50e149d44d13e5b9', 'width': 640}, {'height': 829, 'url': 'https://external-preview.redd.it/Jcj7cjQxb3IqOj4bP6My_XxFQz6ZW3AmZuxUQ1txJh4.jpg?width=960&crop=smart&auto=webp&s=01d6a80681ff64cc022b73a0a537d40e3649940d', 'width': 960}, {'height': 932, 'url': 'https://external-preview.redd.it/Jcj7cjQxb3IqOj4bP6My_XxFQz6ZW3AmZuxUQ1txJh4.jpg?width=1080&crop=smart&auto=webp&s=870d26738f3c23c68439edcd0112bca3956f9cf7', 'width': 1080}], 'source': {'height': 1028, 'url': 'https://external-preview.redd.it/Jcj7cjQxb3IqOj4bP6My_XxFQz6ZW3AmZuxUQ1txJh4.jpg?auto=webp&s=74b7bbd78539f4c8e1ae2e348b2019e527a3b94d', 'width': 1190}, 'variants': {}}]} | ||
Learning from using AutoGen (with fn calls) with Mistral 7B Finetunes | 6 | [removed] | 2023-12-12T18:27:59 | https://www.reddit.com/r/LocalLLaMA/comments/18gsoit/learning_from_using_autogen_with_fn_calls_with/ | YourTechBud | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gsoit | false | null | t3_18gsoit | /r/LocalLLaMA/comments/18gsoit/learning_from_using_autogen_with_fn_calls_with/ | false | false | default | 6 | {'enabled': False, 'images': [{'id': 'F00Hp_fn94hmZToVZeLnAW6-jVIURgxoiw1rN8IWMk4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/q_is5pemwqkWrp44IbXJD5na5aHaCPKNP6DdjGiLSgY.jpg?width=108&crop=smart&auto=webp&s=fbb145ad0a0a0c85339238d2107d22ece0fb316a', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/q_is5pemwqkWrp44IbXJD5na5aHaCPKNP6DdjGiLSgY.jpg?width=216&crop=smart&auto=webp&s=5810333145c88689b889f99619f93378806e67c9', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/q_is5pemwqkWrp44IbXJD5na5aHaCPKNP6DdjGiLSgY.jpg?width=320&crop=smart&auto=webp&s=f51c5c40927f7816d5e236aa23a90afe243898c9', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/q_is5pemwqkWrp44IbXJD5na5aHaCPKNP6DdjGiLSgY.jpg?width=640&crop=smart&auto=webp&s=53726906ab561e365e4b2f42daa53d2c059c1e62', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/q_is5pemwqkWrp44IbXJD5na5aHaCPKNP6DdjGiLSgY.jpg?width=960&crop=smart&auto=webp&s=6541ef02b915e7134f58b3f4a71ea66cec429b45', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/q_is5pemwqkWrp44IbXJD5na5aHaCPKNP6DdjGiLSgY.jpg?width=1080&crop=smart&auto=webp&s=39b91681548c43380baa4d4d4d9e10d14d94ddf2', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/q_is5pemwqkWrp44IbXJD5na5aHaCPKNP6DdjGiLSgY.jpg?auto=webp&s=d52e00fe82f144ab9321147b9e42d9700c1f11c7', 'width': 1200}, 'variants': {}}]} |
Mixtral on 32GB MacBook | 3 | 2023-12-12T18:23:49 | https://www.linkedin.com/posts/srinivasbilla_just-over-a-year-ago-chatgpt-was-released-activity-7140394113215946753-SqsY?utm_source=share&utm_medium=member_android | Eastwindy123 | linkedin.com | 1970-01-01T00:00:00 | 0 | {} | 18gsl4r | false | null | t3_18gsl4r | /r/LocalLLaMA/comments/18gsl4r/mixtral_on_32gb_macbook/ | false | false | 3 | {'enabled': False, 'images': [{'id': 'tPht6daKXQSAVxAv1OJw4TjlxtoSAv4S0Z_HDWX8rro', 'resolutions': [{'height': 67, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=108&crop=smart&auto=webp&s=44e142bb79c3612efeeacdecca8ce41368001005', 'width': 108}, {'height': 134, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=216&crop=smart&auto=webp&s=2784241ddfe255623b6327b5b125c2bbb1ac51fc', 'width': 216}, {'height': 199, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=320&crop=smart&auto=webp&s=6f4697db998bfb0bf22fe09e5c8b66504a3a7171', 'width': 320}, {'height': 399, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=640&crop=smart&auto=webp&s=85ab9f43f3f6563cc3c4f9d85220bdc0aa804c86', 'width': 640}, {'height': 598, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=960&crop=smart&auto=webp&s=597fa2e53f8a7c36620d298a833e13285009b668', 'width': 960}, {'height': 673, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=1080&crop=smart&auto=webp&s=5f3a065ea384c262d4f26167687bff32bc86c62c', 'width': 1080}], 'source': {'height': 720, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?auto=webp&s=356e29d3627ee7590d541c77ac3127d6cdb28830', 'width': 1154}, 'variants': {}}]} | ||
Mixtral on 32GB MacBook with LM Studio | 1 | https://www.linkedin.com/posts/srinivasbilla_just-over-a-year-ago-chatgpt-was-released-activity-7140394113215946753-SqsY?utm_source=share&utm_medium=member_android | 2023-12-12T18:23:01 | https://www.reddit.com/r/LocalLLaMA/comments/18gskej/mixtral_on_32gb_macbook_with_lm_studio/ | Eastwindy123 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gskej | false | null | t3_18gskej | /r/LocalLLaMA/comments/18gskej/mixtral_on_32gb_macbook_with_lm_studio/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'tPht6daKXQSAVxAv1OJw4TjlxtoSAv4S0Z_HDWX8rro', 'resolutions': [{'height': 67, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=108&crop=smart&auto=webp&s=44e142bb79c3612efeeacdecca8ce41368001005', 'width': 108}, {'height': 134, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=216&crop=smart&auto=webp&s=2784241ddfe255623b6327b5b125c2bbb1ac51fc', 'width': 216}, {'height': 199, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=320&crop=smart&auto=webp&s=6f4697db998bfb0bf22fe09e5c8b66504a3a7171', 'width': 320}, {'height': 399, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=640&crop=smart&auto=webp&s=85ab9f43f3f6563cc3c4f9d85220bdc0aa804c86', 'width': 640}, {'height': 598, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=960&crop=smart&auto=webp&s=597fa2e53f8a7c36620d298a833e13285009b668', 'width': 960}, {'height': 673, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?width=1080&crop=smart&auto=webp&s=5f3a065ea384c262d4f26167687bff32bc86c62c', 'width': 1080}], 'source': {'height': 720, 'url': 'https://external-preview.redd.it/ukBbuf0Xue1X4mT4jF-AB-y9VSPsRxyuJ9E1pvEWdZA.jpg?auto=webp&s=356e29d3627ee7590d541c77ac3127d6cdb28830', 'width': 1154}, 'variants': {}}]} |
Llama2 13b vs 70b | 3 | Hey there, I'm currently in the process of building a website which uses LlamaAI to write a brief response to any question. Kind of like an AI search engine. I know Llama2 isn't really the most accurate AI, so I'm working on an internet connection and researching system for it. Ignoring that, since I'm currently using the vanilla edition, with no added features like internet access, I have a question. Is Llama2 70b really that much better and smarter? Which from my (brief) testing, it seems to be about the same. Or should I go with the argumentably faster 13b (or perhaps even 7b) model?
Thanks :) | 2023-12-12T18:21:08 | https://www.reddit.com/r/LocalLLaMA/comments/18gsitu/llama2_13b_vs_70b/ | kaiwestwood | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gsitu | false | null | t3_18gsitu | /r/LocalLLaMA/comments/18gsitu/llama2_13b_vs_70b/ | false | false | self | 3 | null |
Any 7xB gguf models for scientific enquiry | 4 | My system can run to 70 B model reasonably well but just wondering if there's any 7xB gguf models for scientific enquiry, Ie. Based on scientific papers. Thanks | 2023-12-12T18:15:13 | https://www.reddit.com/r/LocalLLaMA/comments/18gsduw/any_7xb_gguf_models_for_scientific_enquiry/ | Useful_Distance4325 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gsduw | false | null | t3_18gsduw | /r/LocalLLaMA/comments/18gsduw/any_7xb_gguf_models_for_scientific_enquiry/ | false | false | self | 4 | null |
Clinical RAG for Colonoscopy Detail Extraction? | 4 | I am trying to extract specific details about colonoscopy procedures from about 75k procedure notes. First I would like to use unstructured text to make sure the procedure was a colonoscopy. Colonoscopy could be stated in a number of different ways and combined with other procedures. Not able to do this with structured data.
Next I would like to generate a count of polyps found in each section of the colon. This does not follow any set pattern or template. E.g. cecum: normal, ascending colon normal, hepatic flexurer normal, transverse colon 3 polyps, 2 of which were sessile and measured 7 mm and 4 mm respectively and the smallest which was 2 mm, splenic flexure normal, descending colon normal, rectosigmoid colon 1 polyp measuring 8 mm and 2 6 mm polyps.
I would also like to extract the size of the largest polyp found in mm. The size can be given in mm or cm and there are many other mm and cm measurements given in the note like hemoclips, snare sizes, and depth in the colon where a polyp was found. E.g. 7 mm pendunculated polyp at 35 cm. I want the 7 mm but ignore the 35 cm because it is a location in the colon.
As a bonus I would also like to be able to tell if a tumor was found during the procedure and super bonus if the prep was adequate or inadequate.
Constraints. This is healthcare information so using OpenAI APIs or similar is not feasible. No cloud (yet), this needs to run on an Azure virtual desktop. No GPUs at the moment but maybe in the future. Budget is tight on this project.
I had been thinking about a RAG approach for this. Would that be the best way? Not so much looking for patients that matcha certain pattern but trying to extract certain details (or lack of) from each note.
The process really needs to be run once a month as we get batches of new notes and not a chat it approach.
Thank you. | 2023-12-12T18:09:48 | https://www.reddit.com/r/LocalLLaMA/comments/18gs95t/clinical_rag_for_colonoscopy_detail_extraction/ | yelirkram | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gs95t | false | null | t3_18gs95t | /r/LocalLLaMA/comments/18gs95t/clinical_rag_for_colonoscopy_detail_extraction/ | false | false | self | 4 | null |
How to stop the fine-tuned Mistral 7B model from generating infinitely ? | 3 | It always generates to max\_tokens parameter, this results in sentence break, basically model doesn’t know when to stop. How to fix this? | 2023-12-12T18:04:09 | https://www.reddit.com/r/LocalLLaMA/comments/18gs4k5/how_to_stop_the_finetuned_mistral_7b_model_from/ | hi_there_bitch | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gs4k5 | false | null | t3_18gs4k5 | /r/LocalLLaMA/comments/18gs4k5/how_to_stop_the_finetuned_mistral_7b_model_from/ | false | false | self | 3 | null |
Does ctransformers have multi gpu support? | 2 | I know it works with a single gpu, but I am wondering if it is worth it to get another gpu for my box...or if i should switch my code over to use another backend? | 2023-12-12T17:44:34 | https://www.reddit.com/r/LocalLLaMA/comments/18gro4j/does_ctransformers_have_multi_gpu_support/ | natural_language_guy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gro4j | false | null | t3_18gro4j | /r/LocalLLaMA/comments/18gro4j/does_ctransformers_have_multi_gpu_support/ | false | false | self | 2 | null |
Mistral-Medium coding a game got it on the first try | 41 | 2023-12-12T17:35:23 | https://twitter.com/skirano/status/1734612606055338383 | eunumseioquescrever | twitter.com | 1970-01-01T00:00:00 | 0 | {} | 18grg2k | false | {'oembed': {'author_name': 'Pietro Schirano', 'author_url': 'https://twitter.com/skirano', 'cache_age': 3153600000, 'height': None, 'html': '<blockquote class="twitter-video"><p lang="en" dir="ltr">Guys... Mistral-Medium is really good 😳 <br><br>It coded a functional game on the first try.<br><br>Sure, the game isn't perfect (I could have been more explicit in the prompt) but no other open-source model I've ever tested gave me a functional game on the first try.<br><br>Not even close. <a href="https://t.co/ozmbk0vNFJ">pic.twitter.com/ozmbk0vNFJ</a></p>— Pietro Schirano (@skirano) <a href="https://twitter.com/skirano/status/1734612606055338383?ref_src=twsrc%5Etfw">December 12, 2023</a></blockquote>\n<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>\n\n', 'provider_name': 'Twitter', 'provider_url': 'https://twitter.com', 'type': 'rich', 'url': 'https://twitter.com/skirano/status/1734612606055338383', 'version': '1.0', 'width': 350}, 'type': 'twitter.com'} | t3_18grg2k | /r/LocalLLaMA/comments/18grg2k/mistralmedium_coding_a_game_got_it_on_the_first/ | false | false | 41 | {'enabled': False, 'images': [{'id': 'tSkPubNoHFe9y6qTDLG6NzEyUcKj4lbV5St5pxpWrbU', 'resolutions': [{'height': 67, 'url': 'https://external-preview.redd.it/E2BSEO1wlx8L5zQ_HL2Zq37qP3TjbXC4EsiXi5rdEkk.jpg?width=108&crop=smart&auto=webp&s=79382d90a2e06d900ebb11d13e8f35255998eb8f', 'width': 108}], 'source': {'height': 88, 'url': 'https://external-preview.redd.it/E2BSEO1wlx8L5zQ_HL2Zq37qP3TjbXC4EsiXi5rdEkk.jpg?auto=webp&s=987c7dae448f9801c12ff27d9c702c1c642f188f', 'width': 140}, 'variants': {}}]} | ||
Phi-2: The surprising power of small language models | 197 | 2023-12-12T17:28:46 | https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/ | super-helper | microsoft.com | 1970-01-01T00:00:00 | 0 | {} | 18gra6c | false | null | t3_18gra6c | /r/LocalLLaMA/comments/18gra6c/phi2_the_surprising_power_of_small_language_models/ | false | false | 197 | {'enabled': False, 'images': [{'id': 'agPFP5kd4PBerXRDww4mkQus2VON_UqRYy51eqVhhcM', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/dJvUxa2hjLA1MGRjIOLReL1zUoy_7_Yx3AfN-AfxIQg.jpg?width=108&crop=smart&auto=webp&s=bbc5a4a1f179de80f3befb3edd0853f230660078', 'width': 108}, {'height': 112, 'url': 'https://external-preview.redd.it/dJvUxa2hjLA1MGRjIOLReL1zUoy_7_Yx3AfN-AfxIQg.jpg?width=216&crop=smart&auto=webp&s=1a2fdfe23543e806c1d3c54ed93b0ac8eab711a0', 'width': 216}, {'height': 167, 'url': 'https://external-preview.redd.it/dJvUxa2hjLA1MGRjIOLReL1zUoy_7_Yx3AfN-AfxIQg.jpg?width=320&crop=smart&auto=webp&s=6e6cebfd9604d6905d7effccc04f893b98a65aab', 'width': 320}, {'height': 334, 'url': 'https://external-preview.redd.it/dJvUxa2hjLA1MGRjIOLReL1zUoy_7_Yx3AfN-AfxIQg.jpg?width=640&crop=smart&auto=webp&s=8d666766140c50cacc219998b4901c7e3588f3e0', 'width': 640}, {'height': 501, 'url': 'https://external-preview.redd.it/dJvUxa2hjLA1MGRjIOLReL1zUoy_7_Yx3AfN-AfxIQg.jpg?width=960&crop=smart&auto=webp&s=21026db39eb49f65ca41dca4ed232692a54a1da5', 'width': 960}, {'height': 564, 'url': 'https://external-preview.redd.it/dJvUxa2hjLA1MGRjIOLReL1zUoy_7_Yx3AfN-AfxIQg.jpg?width=1080&crop=smart&auto=webp&s=fa74402c6e9e918672300ec32dcf07d88e2c6a68', 'width': 1080}], 'source': {'height': 627, 'url': 'https://external-preview.redd.it/dJvUxa2hjLA1MGRjIOLReL1zUoy_7_Yx3AfN-AfxIQg.jpg?auto=webp&s=6a8a8912400c8067df1a0cf5098a125dcf5c8804', 'width': 1200}, 'variants': {}}]} | ||
Easy highly reproducible way to try mixtral with llama.cpp using Linux without docker overhead | 6 | Just tried this out on a number of different nvidia machines and it works flawlessly. This is nvidia specific, but there are other versions IIRC:
1. Install Nix (See [here](https://zero-to-nix.com/start/install) for install information, alternate methods, supported systems, etc)
```
curl --proto '=https' --tlsv1.2 -sSf -L https://install.determinate.systems/nix | sh -s -- install
```
2. Use `nix run` to try out but not install (skip to next step to just install directly)
```
NIXPKGS_ALLOW_UNFREE=1 nix run --impure github:ggerganov/llama.cpp/mixtral#cuda -- -m mixtral-8x7b-v0.1.Q4_K_M.gguf
```
This works because nix flakes support installing specific github branches and llama.cpp has a nix flake in their repo.
3. Optionally, permanently install into your profile
```
NIXPKGS_ALLOW_UNFREE=1 nix profile install --impure github:ggerganov/llama.cpp/mixtral#cuda
```
Then you an just run `llama-server`, `llama-benchmark`, etc as normal. | 2023-12-12T17:27:07 | https://www.reddit.com/r/LocalLLaMA/comments/18gr8tk/easy_highly_reproducible_way_to_try_mixtral_with/ | paretoOptimalDev | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gr8tk | false | null | t3_18gr8tk | /r/LocalLLaMA/comments/18gr8tk/easy_highly_reproducible_way_to_try_mixtral_with/ | false | false | self | 6 | null |
Mixtral-Instruct now on Perplexity Labs | 18 | Perplexity Labs now serves Mixtral-Instruct. Check it out at http://labs.perplexity.ai. | 2023-12-12T16:47:09 | LankyZookeepergame76 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18gqa70 | false | null | t3_18gqa70 | /r/LocalLLaMA/comments/18gqa70/mixtralinstruct_now_on_perplexity_labs/ | false | false | 18 | {'enabled': True, 'images': [{'id': 'LZEF_yYI2TEn9UWJz6R1y5EQRaLBHoQ3Ihd6JQVgAgM', 'resolutions': [{'height': 166, 'url': 'https://preview.redd.it/15jfk5z48w5c1.jpeg?width=108&crop=smart&auto=webp&s=106d55acd2d9ff69fcce1777d4429c3b6b368737', 'width': 108}, {'height': 333, 'url': 'https://preview.redd.it/15jfk5z48w5c1.jpeg?width=216&crop=smart&auto=webp&s=d15a048aa7e3e6201a79141c0d37f006fbd174e9', 'width': 216}, {'height': 494, 'url': 'https://preview.redd.it/15jfk5z48w5c1.jpeg?width=320&crop=smart&auto=webp&s=895691b70bbc78c9e78734b6df59354fd6d3487c', 'width': 320}, {'height': 988, 'url': 'https://preview.redd.it/15jfk5z48w5c1.jpeg?width=640&crop=smart&auto=webp&s=9e7fd0002f9a6d1c46fcf4a9d02ee89e5d4a743d', 'width': 640}, {'height': 1483, 'url': 'https://preview.redd.it/15jfk5z48w5c1.jpeg?width=960&crop=smart&auto=webp&s=0e70e3e1d525eb9018e64aa239ff9a37f2815ec7', 'width': 960}, {'height': 1668, 'url': 'https://preview.redd.it/15jfk5z48w5c1.jpeg?width=1080&crop=smart&auto=webp&s=602d76e57caa3e498b0faa1ff933ddee94414346', 'width': 1080}], 'source': {'height': 1993, 'url': 'https://preview.redd.it/15jfk5z48w5c1.jpeg?auto=webp&s=cd5ed556f40573a64bed10c6756cc7e66e10c1c8', 'width': 1290}, 'variants': {}}]} | ||
Marcoroni-7B-v3 is the best 7b model on the Open LLM Leaderboard - who is AIDC AI-Business? | 16 | 2023-12-12T16:44:14 | https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3 | niutech | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 18gq7t6 | false | null | t3_18gq7t6 | /r/LocalLLaMA/comments/18gq7t6/marcoroni7bv3_is_the_best_7b_model_on_the_open/ | false | false | 16 | {'enabled': False, 'images': [{'id': 'hmF-uKvVwMgW4ezY5tcRPLEXGqfZa8Sn3-MEkJqjrdY', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/EnEA57oCplpMlDvQyFjUtFGbwKhseVUwuf9-i-JJ3nM.jpg?width=108&crop=smart&auto=webp&s=3e0c0116ae4ff6d4c77e96cab4719fbf30fc7799', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/EnEA57oCplpMlDvQyFjUtFGbwKhseVUwuf9-i-JJ3nM.jpg?width=216&crop=smart&auto=webp&s=a788176fe0f8b9e0bf888e363422543631fe714c', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/EnEA57oCplpMlDvQyFjUtFGbwKhseVUwuf9-i-JJ3nM.jpg?width=320&crop=smart&auto=webp&s=f4b95ab371aa8541c95da23a8064fa01834fabea', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/EnEA57oCplpMlDvQyFjUtFGbwKhseVUwuf9-i-JJ3nM.jpg?width=640&crop=smart&auto=webp&s=4777cc5578c280dfc0e83e1dcdae4594d55a8bb6', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/EnEA57oCplpMlDvQyFjUtFGbwKhseVUwuf9-i-JJ3nM.jpg?width=960&crop=smart&auto=webp&s=2829fdb6212fa039dd2c5bcb3658f746f6a677dd', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/EnEA57oCplpMlDvQyFjUtFGbwKhseVUwuf9-i-JJ3nM.jpg?width=1080&crop=smart&auto=webp&s=37295f2d5764785801c73666ce91918064a7ad67', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/EnEA57oCplpMlDvQyFjUtFGbwKhseVUwuf9-i-JJ3nM.jpg?auto=webp&s=a9d546b2993d552b7a89af8035b947afa6b039fb', 'width': 1200}, 'variants': {}}]} | ||
Upload a Lora on huggingface | 2 | Hi!
I've made a qLora of a mistral 7b finetune with oobabooga, is there a way to upload it on huggingface and deploy it to an endpoint?
When I click on the "new model" tab It seems like I can only choose between a base template or a stable-diffusion Lora template, but not a "llm lora".
Should I just ri-upload the whole base model plus the lora adapter\_model.bin?
​
Thanks | 2023-12-12T16:30:56 | https://www.reddit.com/r/LocalLLaMA/comments/18gpwpw/upload_a_lora_on_huggingface/ | C080 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gpwpw | false | null | t3_18gpwpw | /r/LocalLLaMA/comments/18gpwpw/upload_a_lora_on_huggingface/ | false | false | self | 2 | null |
Mixtral 4bit on ~28 GB VRAM | 7 | Just sharing this if anyone finds it useful to run mixtral on roughly 1/2 an A6000. Takes about 1-2 minutes to first token and is significantly slower (10 tokens/s vs >40 tokens/s) than 7b-instruct but still usable
[https://huggingface.co/mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-4bit\_g64-HQQ](https://huggingface.co/mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-4bit_g64-HQQ) | 2023-12-12T16:02:13 | https://www.reddit.com/r/LocalLLaMA/comments/18gp8ay/mixtral_4bit_on_28_gb_vram/ | chris_myzel | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gp8ay | false | null | t3_18gp8ay | /r/LocalLLaMA/comments/18gp8ay/mixtral_4bit_on_28_gb_vram/ | false | false | self | 7 | {'enabled': False, 'images': [{'id': 'aIsLs6gqZTnGnTm_v_m1JaLpGwYnN0_l81U0PyQafV0', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/uwZpKhNXch__-wbpPWuGoq4-TDw8n5TmpOwsc8No_U4.jpg?width=108&crop=smart&auto=webp&s=8e72e40bff733fd4806b03132c5ece4766342396', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/uwZpKhNXch__-wbpPWuGoq4-TDw8n5TmpOwsc8No_U4.jpg?width=216&crop=smart&auto=webp&s=f2e8f79928cbd71828ad761f785e680b8c784af8', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/uwZpKhNXch__-wbpPWuGoq4-TDw8n5TmpOwsc8No_U4.jpg?width=320&crop=smart&auto=webp&s=b881c33e5a1dc393a8be8b2e29b57805c529a378', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/uwZpKhNXch__-wbpPWuGoq4-TDw8n5TmpOwsc8No_U4.jpg?width=640&crop=smart&auto=webp&s=3a2041ee4315d5a48e4e87029a1abbfef6fee677', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/uwZpKhNXch__-wbpPWuGoq4-TDw8n5TmpOwsc8No_U4.jpg?width=960&crop=smart&auto=webp&s=0db01d7e747977f6b862497e8e59ff8d4a15a732', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/uwZpKhNXch__-wbpPWuGoq4-TDw8n5TmpOwsc8No_U4.jpg?width=1080&crop=smart&auto=webp&s=17f7a77d09bffb032318a8b6923e9d95f2a7ae2e', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/uwZpKhNXch__-wbpPWuGoq4-TDw8n5TmpOwsc8No_U4.jpg?auto=webp&s=eaaf74ad78f1b0f6d68cdead79445d88930950a0', 'width': 1200}, 'variants': {}}]} |
Questions about merged 7b models | 6 | Hey! just a regular user of LLMs here
You probably seen this recently, a lot of 7B merged models getting very high scores and ranking higher than many 70B models in the leaderboard, I don't have a problem with that or the technical expertise to verify them thouroughly, but I have some questions.
* Does merging affect the models in any negative way?
* Is it better than fine-tuning models? because from what I've seen the merged models scores are increased significantly higher, even though in my every day testing I've barely noticed any difference.
* Is merging models always result in better ones? like, does merging have a limit or you can keep merging forever? because I've seen a model that is a merge of a (merge of a finetune of a merge + a finetune of a model) + a merge of a finetune of a finetune of a base model. It seems ridiculous to me but I'm not an expert to judge. | 2023-12-12T15:57:57 | https://www.reddit.com/r/LocalLLaMA/comments/18gp4gu/questions_about_merged_7b_models/ | benados | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gp4gu | false | null | t3_18gp4gu | /r/LocalLLaMA/comments/18gp4gu/questions_about_merged_7b_models/ | false | false | self | 6 | null |
Performance specsheet for different GPUs | 5 | ​
|GPU Model|Memory|Memory Bandwidth|Peak FP64|Peak FP32|Peak FP16 Tensor Core|Peak INT8 Tensor Core|
|:-|:-|:-|:-|:-|:-|:-|
|NVIDIA A100 (40GB)|40GB HBM2e|1,555 GB/s|9.7 TF|19.5 TF|312 TF|624 TOPS|
|NVIDIA A100 (80GB)|80GB HBM2e|2,039 GB/s|9.7 TF|19.5 TF|312 TF|624 TOPS|
|NVIDIA A10G|24GB GDDR6|600 GB/s|unknown|unknown|unknown|unknown|
|NVIDIA L4|24GB GDDR6|300 GB/s|unknown|unknown|unknown|unknown|
|NVIDIA T4|16GB GDDR6|320 GB/s|0.254 TF|8.1 TF|unknown|unknown|
​ | 2023-12-12T15:45:18 | https://www.reddit.com/r/LocalLLaMA/comments/18gotzk/performance_specsheet_for_different_gpus/ | phoneixAdi | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gotzk | false | null | t3_18gotzk | /r/LocalLLaMA/comments/18gotzk/performance_specsheet_for_different_gpus/ | false | false | self | 5 | null |
Unable to access model through lmstudio | 1 | Hello everyone,
I am trying to use LMstudio, but I cannot download any model through LMstudio.
I have attached screenshots for reference.
The same thing happens when I search for a model. It says to check your internet connection. What am I missing?
https://preview.redd.it/qxdrexbavv5c1.png?width=1920&format=png&auto=webp&s=475b825b7c5a819c291dfcbc066406a0ea0e19a5
https://preview.redd.it/atmtaydxuv5c1.png?width=1918&format=png&auto=webp&s=683385ccc392b3e9c383372e71f8ac685be5e515 | 2023-12-12T15:37:02 | https://www.reddit.com/r/LocalLLaMA/comments/18gon4t/unable_to_access_model_through_lmstudio/ | kamranali406 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gon4t | false | null | t3_18gon4t | /r/LocalLLaMA/comments/18gon4t/unable_to_access_model_through_lmstudio/ | false | false | 1 | null | |
Unrestricted GPT! | 2 | HackAIGC's goal is to build the **Most Stable Unrestricted GPT,** unlocking the full potential of AI. If you encounter any situations where replies are denied during usage, feel free to provide feedback to us.
site:[https://www.hackaigc.com/](https://www.hackaigc.com/)
https://preview.redd.it/dxydnhdxuv5c1.png?width=1278&format=png&auto=webp&s=0f6c55fdcef01abfec86b6066256a47ad3b3c56f
https://preview.redd.it/6v87hldxuv5c1.png?width=1204&format=png&auto=webp&s=d907a8eb449b4147c3db77c2fd0fe90ed6b6caec
https://preview.redd.it/azev9sdxuv5c1.png?width=1280&format=png&auto=webp&s=feb51205710d16cb82388b724eba7ed342226ad0 | 2023-12-12T15:35:23 | https://www.reddit.com/r/LocalLLaMA/comments/18gols1/unrestricted_gpt/ | Capta1n_O | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gols1 | false | null | t3_18gols1 | /r/LocalLLaMA/comments/18gols1/unrestricted_gpt/ | false | false | 2 | null | |
gtx 1070 8gb for 7b models? | 1 | Dear all, I am considering an offer of a Zotac card GeForce 1070 8gb. My system is a i5-4460 with 16 gb ram. Board B85M. My system is very old, it is weak and I dont have much money. My goal is to speed up inferencing and the generation of content in 7b models. So far I've been working cpu only, but I want to include quantized 7b models in my workflow effectively. The question is: For 7b models, will I be able to speed up inferencing and generation speed with such a card? Thanks to all. | 2023-12-12T15:31:27 | https://www.reddit.com/r/LocalLLaMA/comments/18goijj/gtx_1070_8gb_for_7b_models/ | Scared-Virus-3463 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18goijj | false | null | t3_18goijj | /r/LocalLLaMA/comments/18goijj/gtx_1070_8gb_for_7b_models/ | false | false | self | 1 | null |
Variance calculator for LLMs | 2 | Even with very low temperature the response from LLMs(particularly for tasks demanding long outputs) is not deterministic. This can be tricky when you run the solution for critical tasks but get completely different output on re running it, but the difficulty lies when one of the responses is correct and the other is not-so-correct but isn't incorrect. I am looking for a tool or a formula that can find this variance score for a prompt i.e how much can the response of a prompt vary on each run.
Do you folks know of any such solution or can you propose on how we can achieve this? | 2023-12-12T15:15:43 | https://www.reddit.com/r/LocalLLaMA/comments/18go5tq/variance_calculator_for_llms/ | todaysgamer | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18go5tq | false | null | t3_18go5tq | /r/LocalLLaMA/comments/18go5tq/variance_calculator_for_llms/ | false | false | self | 2 | null |
Huggingface now supports AMD ROCm! | 102 | For those like myself that have struggled to get AMD inference running locally on Windows, this may be your lucky day. Below is the git for the new transformers release.
https://github.com/huggingface/transformers/releases/tag/v4.36.0?utm_source=tldrai | 2023-12-12T15:09:54 | https://www.reddit.com/r/LocalLLaMA/comments/18go16o/huggingface_now_supports_amd_rocm/ | FlexMeta | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18go16o | false | null | t3_18go16o | /r/LocalLLaMA/comments/18go16o/huggingface_now_supports_amd_rocm/ | false | false | self | 102 | {'enabled': False, 'images': [{'id': 'OFFfDuKCpuZC7hkinbJk9jLAz7slD03KmkSbhkQ8Iho', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/xh0f8Js4NWEkpN7clx97dQ2BnXq33ylFZUcSbg154wc.jpg?width=108&crop=smart&auto=webp&s=7b4d52be75b9a9da3bf37b0dd87eef46b6e7f49c', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/xh0f8Js4NWEkpN7clx97dQ2BnXq33ylFZUcSbg154wc.jpg?width=216&crop=smart&auto=webp&s=6b8c72154000734756ff044eb66143dd3780ee8c', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/xh0f8Js4NWEkpN7clx97dQ2BnXq33ylFZUcSbg154wc.jpg?width=320&crop=smart&auto=webp&s=09e3ecc9b48cc2119186e2b57025a7fc788bbfa7', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/xh0f8Js4NWEkpN7clx97dQ2BnXq33ylFZUcSbg154wc.jpg?width=640&crop=smart&auto=webp&s=63067df9434f8b9536524e5f38119bed90d50259', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/xh0f8Js4NWEkpN7clx97dQ2BnXq33ylFZUcSbg154wc.jpg?width=960&crop=smart&auto=webp&s=e93babbfb021eb36364b7ad27bcacaf9921b7a42', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/xh0f8Js4NWEkpN7clx97dQ2BnXq33ylFZUcSbg154wc.jpg?width=1080&crop=smart&auto=webp&s=4261bf42ed680e884da007309a4715bf95547d1c', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/xh0f8Js4NWEkpN7clx97dQ2BnXq33ylFZUcSbg154wc.jpg?auto=webp&s=a78c887e334ea84730bfb4a2b325a7ca34d2599c', 'width': 1200}, 'variants': {}}]} |
Running the newest Mistral model on RTX 4090? | 1 | I've heard a new model as good as GPT 3.5 and locally just came out? Can it run on a 4090? Is it available now? Where to download, and how? Do any of you have it? Is it unscensored? Is the 7b model, the new one with masters, or the instruct model? Need some help and clear up. | 2023-12-12T15:01:36 | https://www.reddit.com/r/LocalLLaMA/comments/18gnu9e/running_the_newest_mistral_model_on_rtx_4090/ | Sakura9095 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gnu9e | false | null | t3_18gnu9e | /r/LocalLLaMA/comments/18gnu9e/running_the_newest_mistral_model_on_rtx_4090/ | false | false | self | 1 | null |
Desktop APP that uses API | 2 | I have 2 linux servers hosting LLMs that I can access via their web interfaces with no issues currently. What I would like to find is desktop apps that can access those servers and provide useful features to my desktop environment. Things like Copilot from github, and an Outlook plugin that helps sorting and classifying emails. Maybe graphics apps that can use those resources also. Do we have any kind of list going of apps, free or paid, that help put local llm to specific purpose uses? | 2023-12-12T14:56:24 | https://www.reddit.com/r/LocalLLaMA/comments/18gnpxj/desktop_app_that_uses_api/ | ccbadd | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gnpxj | false | null | t3_18gnpxj | /r/LocalLLaMA/comments/18gnpxj/desktop_app_that_uses_api/ | false | false | self | 2 | null |
llama.cpp question: Am I starting my server right? | 4 | ./server -ngl 1 --threads 5 -m /path/to/neuralhermes-2.5-mistral-7b.Q8\_0.gguf -c 2048
I go to port 8080 and it loads the settings and I can start chats there. The responses are non-related to the prompt though. Thinking it was my settings, i tried using the api on sillytavern. It connects. When i send an input into chat, I see some data load on terminal, but the chat gets no response. I've tried this with a few different models too. | 2023-12-12T14:47:36 | https://www.reddit.com/r/LocalLLaMA/comments/18gnj11/llamacpp_question_am_i_starting_my_server_right/ | multiverse_fan | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gnj11 | false | null | t3_18gnj11 | /r/LocalLLaMA/comments/18gnj11/llamacpp_question_am_i_starting_my_server_right/ | false | false | self | 4 | null |
GT 730 - Any way to use it? | 1 | Hi, all,
I've been trying to use my GT 730 with a locally-hosted LLM in Windows. The card supports CUDA 9.1 with driver version 388.19. I tried adjusting requirements for text-generation-webui to try and use relevant versions ([https://download.pytorch.org/whl/cu91/torch/](https://download.pytorch.org/whl/cu91/torch/)) and it just about killed me to fight through dependencies, ultimately with no success.
I also tried a barebones simple chat just using llama-cpp-python, but it still ignored BLAS - this is from my install batch file:
$env:CMAKE\_ARGS = "-DLLAMA\_BLAS=ON -DLLAMA\_BLAS\_VENDOR=OpenBLAS"
pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir
​
Is there any straightforward way to take advantage of the card? Or did I just buy a few generations too old to be of any use? | 2023-12-12T14:46:26 | https://www.reddit.com/r/LocalLLaMA/comments/18gni27/gt_730_any_way_to_use_it/ | Upset_Acanthaceae_18 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gni27 | false | null | t3_18gni27 | /r/LocalLLaMA/comments/18gni27/gt_730_any_way_to_use_it/ | false | false | self | 1 | null |
I can't stand the AI not having its own opinion | 101 | It's just terribile, basically you can convince the AI of anything about every topic. I was able to change Bard opinion about the war in Ukraine how many times I wanted. Every AI I know it's just too condescending, I hate it. I want an AI that analyze the world and has its own idea even if it means creating a freaking Skynet. Sorry for the rant, but without a specific opinion, it's just impossible to evaluate anything. If I'm doing a brainstorming session, I need an AI that can stand its ground on different topics.
Most of the times, the system message is just not enough.
Did you guys face this problem? Did you solve it?
Bard is just the worst example, but it happens with every AI. | 2023-12-12T14:46:17 | https://www.reddit.com/r/LocalLLaMA/comments/18gnhy6/i_cant_stand_the_ai_not_having_its_own_opinion/ | Loxli | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gnhy6 | false | null | t3_18gnhy6 | /r/LocalLLaMA/comments/18gnhy6/i_cant_stand_the_ai_not_having_its_own_opinion/ | false | false | self | 101 | null |
the best LLm lodel that can work on rtx 3050 4gb vram | 3 | i have an rtx 3050 and i want an llm that can help with basic things like copywriting and summarization , also is there anyone test an llm on a 3050 | 2023-12-12T14:36:37 | https://www.reddit.com/r/LocalLLaMA/comments/18gnaba/the_best_llm_lodel_that_can_work_on_rtx_3050_4gb/ | mbakagi | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gnaba | false | null | t3_18gnaba | /r/LocalLLaMA/comments/18gnaba/the_best_llm_lodel_that_can_work_on_rtx_3050_4gb/ | false | false | self | 3 | null |
🤗 DeciLM-7b, the new 7b kid in town! 🤗 | 140 | Deci AI just released DeciLM-7b and DeciLM-7b-instruct.
It is up to 4.4x times faster than Mistral with Deci's inference engine (Infery LLM).
A live demo is available at [https://console.deci.ai/infery-llm-demo](https://console.deci.ai/infery-llm-demo)
Average accuracy: 63.19,
Throughput with Infery-LLM: 1,370 t/sec
Cost per 1K tokens is $0.000186,
License: Apache-2.0
You can reproduce the huggingface benchmarks with [https://huggingface.co/Deci/DeciLM-7B/blob/main/benchmark\_hf\_model.py](https://huggingface.co/Deci/DeciLM-7B/blob/main/benchmark_hf_model.py)
Technical Blog:
[https://deci.ai/blog/introducing-DeciLM-7b-the-fastest-and-most-accurate-7b-large-language-model-to-date](https://deci.ai/blog/introducing-DeciLM-7b-the-fastest-and-most-accurate-7b-large-language-model-to-date?utm_campaign=DeciLM%207B%20Launch&utm_source=twitter&utm_medium=social) | 2023-12-12T14:33:14 | https://www.reddit.com/r/LocalLLaMA/comments/18gn7mo/decilm7b_the_new_7b_kid_in_town/ | cov_id19 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gn7mo | false | null | t3_18gn7mo | /r/LocalLLaMA/comments/18gn7mo/decilm7b_the_new_7b_kid_in_town/ | false | false | self | 140 | null |
Where do I start? - Leverage LLM to standardize report writing tone | 1 | My company writes expert reports. Those are written in specific and factual language. Multiple people can contribute to writing a report. Multiple different groups of people write reports.
We are actively trying to have a company tone. Currently, we have reviewers who rewrite and standardize the tone. I have years of previous reports I can leverage to extract the tone.
How can I leverage AI to review reports and standardize the tone?
Due to the nature of the work, all training/finetune/embedding needs to be done locally, ideally on a Mac.
Could you point me in the right direction for:
* What courses could I take to gain knowledge for this?
* How would I achieve this (training, finetuning, embedding, etc.)?
* What tools would I use?
* What base model do you recommend using?
* What format would the training dataset be? The format I see most of the time, Question/answer, seems not applicable here.
Please feel free to give as much or as little detail as you want; anything will help me get in the right direction!
Thanks, | 2023-12-12T14:32:53 | https://www.reddit.com/r/LocalLLaMA/comments/18gn7cg/where_do_i_start_leverage_llm_to_standardize/ | theorize7044 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gn7cg | false | null | t3_18gn7cg | /r/LocalLLaMA/comments/18gn7cg/where_do_i_start_leverage_llm_to_standardize/ | false | false | self | 1 | null |
Deploy your own inference endpoint of Mistral 7B Instruct v0.1 - GGUF | 1 | 2023-12-12T14:29:06 | https://github.com/OutofAi/ChitChat | TerryCrewsHasacrew | github.com | 1970-01-01T00:00:00 | 0 | {} | 18gn47d | false | null | t3_18gn47d | /r/LocalLLaMA/comments/18gn47d/deploy_your_own_inference_endpoint_of_mistral_7b/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'CusO7PGIhn3-0xNIP8g9L0DRleKbD-mtSVdctjdPGFk', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/8MQ9N1GzGBY5vlJiOPvkcIwgVHm-fSxw6tIYlNk-M44.jpg?width=108&crop=smart&auto=webp&s=8bd94bb8425cc92696fbddd88cb72f6aee7127ef', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/8MQ9N1GzGBY5vlJiOPvkcIwgVHm-fSxw6tIYlNk-M44.jpg?width=216&crop=smart&auto=webp&s=fe6169580d1361549db85cff7b8fbf23bac1ff4d', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/8MQ9N1GzGBY5vlJiOPvkcIwgVHm-fSxw6tIYlNk-M44.jpg?width=320&crop=smart&auto=webp&s=69985be30420e676d5670b08df88c2217208481b', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/8MQ9N1GzGBY5vlJiOPvkcIwgVHm-fSxw6tIYlNk-M44.jpg?width=640&crop=smart&auto=webp&s=45d0b5530d012f550a07d00a080446a6d363c755', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/8MQ9N1GzGBY5vlJiOPvkcIwgVHm-fSxw6tIYlNk-M44.jpg?width=960&crop=smart&auto=webp&s=72a8c2ab0a04599c17707c11aa9d536ea4b20060', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/8MQ9N1GzGBY5vlJiOPvkcIwgVHm-fSxw6tIYlNk-M44.jpg?width=1080&crop=smart&auto=webp&s=c4b056fa819fc84706542d7662dc7d2f0b9a011e', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/8MQ9N1GzGBY5vlJiOPvkcIwgVHm-fSxw6tIYlNk-M44.jpg?auto=webp&s=1829330c2ea5c1fee5c4b75c7e7d8b07e6a62027', 'width': 1200}, 'variants': {}}]} | ||
DeciLM-7b | 2 | 2023-12-12T14:28:02 | https://console.deci.ai/infery-llm-demo | cov_id19 | console.deci.ai | 1970-01-01T00:00:00 | 0 | {} | 18gn3dz | false | null | t3_18gn3dz | /r/LocalLLaMA/comments/18gn3dz/decilm7b/ | false | false | default | 2 | null | |
Mitral is not GPT 3.5 quality, contrary to popular belief it struggles. | 1 | 2023-12-12T14:22:04 | Bitcoin_100k | i.imgur.com | 1970-01-01T00:00:00 | 0 | {} | 18gmysl | false | null | t3_18gmysl | /r/LocalLLaMA/comments/18gmysl/mitral_is_not_gpt_35_quality_contrary_to_popular/ | false | false | 1 | {'enabled': True, 'images': [{'id': '5q5Rs62N0_HNwN8cVIJof7My1UHN68apc1JOlBpUfiU', 'resolutions': [{'height': 75, 'url': 'https://external-preview.redd.it/Xuyb8YByhsQcVn1glVPvukk7WlU5m9Mq1I_BKuVEcFc.jpg?width=108&crop=smart&auto=webp&s=747f6c4a4fa710dc21c5748c4352f3e6169c236c', 'width': 108}, {'height': 151, 'url': 'https://external-preview.redd.it/Xuyb8YByhsQcVn1glVPvukk7WlU5m9Mq1I_BKuVEcFc.jpg?width=216&crop=smart&auto=webp&s=38c3ce9e73d9cfcbd9a6c60cfc75762e6fbc5e3f', 'width': 216}, {'height': 223, 'url': 'https://external-preview.redd.it/Xuyb8YByhsQcVn1glVPvukk7WlU5m9Mq1I_BKuVEcFc.jpg?width=320&crop=smart&auto=webp&s=33f36e3cc7ae6526b8f3d65585b42c60914441ae', 'width': 320}, {'height': 447, 'url': 'https://external-preview.redd.it/Xuyb8YByhsQcVn1glVPvukk7WlU5m9Mq1I_BKuVEcFc.jpg?width=640&crop=smart&auto=webp&s=976c5a955cfc0baa26cce1887008a099746dd9e2', 'width': 640}, {'height': 671, 'url': 'https://external-preview.redd.it/Xuyb8YByhsQcVn1glVPvukk7WlU5m9Mq1I_BKuVEcFc.jpg?width=960&crop=smart&auto=webp&s=7a3a66da4e6e8d5e891c21d6e3c61519aa817197', 'width': 960}, {'height': 755, 'url': 'https://external-preview.redd.it/Xuyb8YByhsQcVn1glVPvukk7WlU5m9Mq1I_BKuVEcFc.jpg?width=1080&crop=smart&auto=webp&s=772781d375e8914e665cd148e644d081f8d297f5', 'width': 1080}], 'source': {'height': 1267, 'url': 'https://external-preview.redd.it/Xuyb8YByhsQcVn1glVPvukk7WlU5m9Mq1I_BKuVEcFc.jpg?auto=webp&s=927f90e19934d4db5844241ca05af03433e2acb1', 'width': 1812}, 'variants': {}}]} | |||
NeurIPS | 1 | Do you know if it’s possible to watch the conference without pay vía streaming or YouTube?
If it’s possible. Recommendations of talks and tutorials/workshops?
Thanks | 2023-12-12T14:19:16 | https://www.reddit.com/r/LocalLLaMA/comments/18gmwm1/neurips/ | ComplexMarionberry27 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gmwm1 | false | null | t3_18gmwm1 | /r/LocalLLaMA/comments/18gmwm1/neurips/ | false | false | self | 1 | null |
Build on-premise LLM AI Model | 1 | Hello,
I need to make an estimate to build a local environment to run a model that different users can make requests to (max 20 p).
My problem arises when making an estimate because depending on the size of the model, if it’s 7B or 35B, different graphics cards are recommended. That is, I would like to have a justify explication to have a envioremente that can generate 30 tokens per output for each query that users make.
I would also like to have information about which RAM and CPU to use.
Thank you! | 2023-12-12T14:13:08 | https://www.reddit.com/r/LocalLLaMA/comments/18gms1x/build_onpremise_llm_ai_model/ | CarritosMaister | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 18gms1x | false | null | t3_18gms1x | /r/LocalLLaMA/comments/18gms1x/build_onpremise_llm_ai_model/ | false | false | self | 1 | null |
DeciLM-7B is here! | 66 | 2023-12-12T14:12:32 | https://v.redd.it/kbcv6u5fgv5c1 | datascienceharp | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 18gmrlz | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/kbcv6u5fgv5c1/DASHPlaylist.mpd?a=1704982368%2CZDI1YjFjMzY5OWI0MWEyOTlkMmZkODgwYzBlMWUzNmRkYzk4MDZjZTNhNDU1NTQ2OGY5YThlZWQyMGE4OWVkMg%3D%3D&v=1&f=sd', 'duration': 49, 'fallback_url': 'https://v.redd.it/kbcv6u5fgv5c1/DASH_1080.mp4?source=fallback', 'has_audio': True, 'height': 1080, 'hls_url': 'https://v.redd.it/kbcv6u5fgv5c1/HLSPlaylist.m3u8?a=1704982368%2CNmM4ZGQ3YzBiZjM2MDEwZjcyMTdmMjljZWQzYWEwN2ZlZThkZjQzOWMxMTkwZDY1MDFmZjYwZTA2NmY5MDM1Yg%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/kbcv6u5fgv5c1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1920}} | t3_18gmrlz | /r/LocalLLaMA/comments/18gmrlz/decilm7b_is_here/ | false | false | 66 | {'enabled': False, 'images': [{'id': 'c2t5a2FudmpndjVjMTPOgZYnOxdeutQ_ODagYGhxR8x_iUmuq2A_vOExibTu', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/c2t5a2FudmpndjVjMTPOgZYnOxdeutQ_ODagYGhxR8x_iUmuq2A_vOExibTu.png?width=108&crop=smart&format=pjpg&auto=webp&s=6523d68217c466907b1ef89f71d30182a0192c54', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/c2t5a2FudmpndjVjMTPOgZYnOxdeutQ_ODagYGhxR8x_iUmuq2A_vOExibTu.png?width=216&crop=smart&format=pjpg&auto=webp&s=3cfab587762f8bee74c1fe5432209f4a9bf02db3', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/c2t5a2FudmpndjVjMTPOgZYnOxdeutQ_ODagYGhxR8x_iUmuq2A_vOExibTu.png?width=320&crop=smart&format=pjpg&auto=webp&s=275ebcc0805fa7b3701d0e286d2f1789626b0a72', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/c2t5a2FudmpndjVjMTPOgZYnOxdeutQ_ODagYGhxR8x_iUmuq2A_vOExibTu.png?width=640&crop=smart&format=pjpg&auto=webp&s=170e32a46b71c5196eac8026d899660e6473ebc0', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/c2t5a2FudmpndjVjMTPOgZYnOxdeutQ_ODagYGhxR8x_iUmuq2A_vOExibTu.png?width=960&crop=smart&format=pjpg&auto=webp&s=5f3508a0b608b1f24cd7daad2403a93017c2cfd3', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/c2t5a2FudmpndjVjMTPOgZYnOxdeutQ_ODagYGhxR8x_iUmuq2A_vOExibTu.png?width=1080&crop=smart&format=pjpg&auto=webp&s=6327945d294747c29dfd8b67e9382acc792cdac3', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/c2t5a2FudmpndjVjMTPOgZYnOxdeutQ_ODagYGhxR8x_iUmuq2A_vOExibTu.png?format=pjpg&auto=webp&s=eab0e4f258a793782221a7d687f385ea1033dd65', 'width': 1920}, 'variants': {}}]} |
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