title stringlengths 1 300 | score int64 0 8.54k | selftext stringlengths 0 41.5k | created timestamp[ns]date 2023-04-01 04:30:41 2026-03-04 02:14:14 ⌀ | url stringlengths 0 878 | author stringlengths 3 20 | domain stringlengths 0 82 | edited timestamp[ns]date 1970-01-01 00:00:00 2026-02-19 14:51:53 | gilded int64 0 2 | gildings stringclasses 7
values | id stringlengths 7 7 | locked bool 2
classes | media stringlengths 646 1.8k ⌀ | name stringlengths 10 10 | permalink stringlengths 33 82 | spoiler bool 2
classes | stickied bool 2
classes | thumbnail stringlengths 4 213 ⌀ | ups int64 0 8.54k | preview stringlengths 301 5.01k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Stability AI releases Stable LM 2, 1.6B model, trained on 2T tokens for 2 epochs. | 129 | [https://stability.ai/news/introducing-stable-lm-2](https://stability.ai/news/introducing-stable-lm-2)
[https://huggingface.co/stabilityai/stablelm-2-1\_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b) (base)
[https://huggingface.co/stabilityai/stablelm-2-zephyr-1\_6b](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b) (instruct)
​ | 2024-01-20T13:51:22 | https://www.reddit.com/r/LocalLLaMA/comments/19bc7ib/stability_ai_releases_stable_lm_2_16b_model/ | jslominski | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19bc7ib | false | null | t3_19bc7ib | /r/LocalLLaMA/comments/19bc7ib/stability_ai_releases_stable_lm_2_16b_model/ | false | false | self | 129 | {'enabled': False, 'images': [{'id': 'r3gHujRghs2vdyZBkSfJF_ZuQlkNcCnJSMZq4ZnLevo', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/A2n2eKRE6wrppD9wlnAn-zO7PIIuuz561_CdgqkehNI.jpg?width=108&crop=smart&auto=webp&s=b7c9b0b2125430bb229cc212c7ee109bbfcd37c1', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/A2n2eKRE6wrppD9wlnAn-zO7PIIuuz561_CdgqkehNI.jpg?width=216&crop=smart&auto=webp&s=701a54d970086ae968cecf4a7991db2358d00156', 'width': 216}, {'height': 320, 'url': 'https://external-preview.redd.it/A2n2eKRE6wrppD9wlnAn-zO7PIIuuz561_CdgqkehNI.jpg?width=320&crop=smart&auto=webp&s=2b88b417998b1907d9d64beddc001ffb14ecbc1a', 'width': 320}, {'height': 640, 'url': 'https://external-preview.redd.it/A2n2eKRE6wrppD9wlnAn-zO7PIIuuz561_CdgqkehNI.jpg?width=640&crop=smart&auto=webp&s=c3be2d162a5a67c4679ae3e72d408d577a122638', 'width': 640}, {'height': 960, 'url': 'https://external-preview.redd.it/A2n2eKRE6wrppD9wlnAn-zO7PIIuuz561_CdgqkehNI.jpg?width=960&crop=smart&auto=webp&s=f5a12a93316601e30691389800ea2cb20e325f49', 'width': 960}, {'height': 1080, 'url': 'https://external-preview.redd.it/A2n2eKRE6wrppD9wlnAn-zO7PIIuuz561_CdgqkehNI.jpg?width=1080&crop=smart&auto=webp&s=af7bb890dcc80103a9fd7aef1f008b71e68ce52f', 'width': 1080}], 'source': {'height': 1500, 'url': 'https://external-preview.redd.it/A2n2eKRE6wrppD9wlnAn-zO7PIIuuz561_CdgqkehNI.jpg?auto=webp&s=59a812bd324c33022c86ddea9508a6c9dac74cd9', 'width': 1500}, 'variants': {}}]} |
Using RAM as cache | 3 | DDR5 has low latency (lowest is around [20ns](https://www.techpowerup.com/forums/attachments/1665146812687-png.264507/)) and high bandwidth (\~300GB/s on quad channel).
If so can you connect and manage ram and cache with node.js? On sapphire rapids theres extra lanes for onboard hbm ram for spare. So is it possible to (run LLMs etc) or have better performance? | 2024-01-20T13:49:04 | https://www.reddit.com/r/LocalLLaMA/comments/19bc5wv/using_ram_as_cache/ | Hot-Highlight8842 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19bc5wv | false | null | t3_19bc5wv | /r/LocalLLaMA/comments/19bc5wv/using_ram_as_cache/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'drT-6p96V39QS4hiQ6DAjpaHHDhn6R4pML9oPnOjBaM', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/BVgYx8fUzrjZDjSRuBWlJy4iYdRwZvEDRCyhIY2xxPs.jpg?width=108&crop=smart&auto=webp&s=a8181a072f063cce79d5b87d8e253e81b4942d87', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/BVgYx8fUzrjZDjSRuBWlJy4iYdRwZvEDRCyhIY2xxPs.jpg?width=216&crop=smart&auto=webp&s=0db19fb345fc289d971f798b35ef0a690992513a', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/BVgYx8fUzrjZDjSRuBWlJy4iYdRwZvEDRCyhIY2xxPs.jpg?width=320&crop=smart&auto=webp&s=241d1f83c7d11b87aa1f754ae4dda034e069fe2f', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/BVgYx8fUzrjZDjSRuBWlJy4iYdRwZvEDRCyhIY2xxPs.jpg?width=640&crop=smart&auto=webp&s=3808cf1787661720bf19f0166a657ceb3a8705dd', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/BVgYx8fUzrjZDjSRuBWlJy4iYdRwZvEDRCyhIY2xxPs.jpg?width=960&crop=smart&auto=webp&s=21025366f897f2b7e1732ee18022c553b0b14eb8', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/BVgYx8fUzrjZDjSRuBWlJy4iYdRwZvEDRCyhIY2xxPs.jpg?width=1080&crop=smart&auto=webp&s=afd0146b5cf46df412342bfa93e7fed33282fa98', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/BVgYx8fUzrjZDjSRuBWlJy4iYdRwZvEDRCyhIY2xxPs.jpg?auto=webp&s=dc9c766c94fbd2f595779f2f048d072f6df1545f', 'width': 1920}, 'variants': {}}]} |
Carving out a profit 🤑💰 | 1 | I'm curious about what the major players in open source language models plan to do about making themselves a profit, granted that Apple, Google, and Samsung control the underlying operating system most of us use day to day and they all plan to deploy their own ai models into their respective ecosystems.
Also, projects like ollama, lm studio, silly tavern etc what's the strategy? All great projects but didn't character ai recently get acquired? Is that the hope of these language model projects and start ups? To be acquired? If not acquired, will "plus" / "pro" versions have the same adoption rate as the free verison?
Just curious 🧐 really. Mistral wasted no time getting to their paid models and offerings. | 2024-01-20T13:19:15 | https://www.reddit.com/r/LocalLLaMA/comments/19bbll5/carving_out_a_profit/ | 1EvilSexyGenius | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19bbll5 | false | null | t3_19bbll5 | /r/LocalLLaMA/comments/19bbll5/carving_out_a_profit/ | false | false | self | 1 | null |
Experimenting with self rewarding LLMs | 28 | In light of Meta's recent research paper[https://www.reddit.com/r/LocalLLaMA/comments/19av72i/meta\_paper\_presents\_selfrewarding\_language\_models/](https://www.reddit.com/r/LocalLLaMA/comments/19av72i/meta_paper_presents_selfrewarding_language_models/) , I thought it might be an interesting experiment to see whether several LLMs, working together could come up with responses which they all rate as 10/10 scores. I gave ChatGPT the paper and then asked it to "list several original, specific, and detailed examples of how you, ChatGPT, as a large language model might be able to use the methods and examples detailed in the paper to create rewards for yourself." [https://poe.com/s/3e0aqxpdwdtwzxdmzif6](https://poe.com/s/3e0aqxpdwdtwzxdmzif6) Then, I followed up with a variation of "Please analyze, rate, score on a ten point scale, critique, assess, and seek to improve your response above using the methods and techniques detailed in the Self-Rewarding Language Models paper." And revised. I'm not currently a Poe subscriber, so I didn't try this with Claude 2, GPT-4 et al. I just thought it might be interesting to play around with. Highest self rating from ChatGPT - 9.9/10 | 2024-01-20T13:01:53 | https://www.reddit.com/r/LocalLLaMA/comments/19bba9m/experimenting_with_self_rewarding_llms/ | WaterdanceAC | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19bba9m | false | null | t3_19bba9m | /r/LocalLLaMA/comments/19bba9m/experimenting_with_self_rewarding_llms/ | false | false | self | 28 | {'enabled': False, 'images': [{'id': 'bQga6aItk-L4DCSQcty4WW9_Rt0M0Fpsu-fgN1eanQM', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/Yc8Tjoydwuj1co6G8wf7gULZZvzSdA7sK67GAHXXJH0.jpg?width=108&crop=smart&auto=webp&s=e2a947547250c710c29d8157f5d70b081890f727', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/Yc8Tjoydwuj1co6G8wf7gULZZvzSdA7sK67GAHXXJH0.jpg?width=216&crop=smart&auto=webp&s=0c1fbb55446a82651f1116ee340606be439e4882', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/Yc8Tjoydwuj1co6G8wf7gULZZvzSdA7sK67GAHXXJH0.jpg?width=320&crop=smart&auto=webp&s=38b5ba60a4ced0986a06295d5eb2d375c772b6ee', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/Yc8Tjoydwuj1co6G8wf7gULZZvzSdA7sK67GAHXXJH0.jpg?width=640&crop=smart&auto=webp&s=b363da2819d1d589eec6bbac85cf86e873329743', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/Yc8Tjoydwuj1co6G8wf7gULZZvzSdA7sK67GAHXXJH0.jpg?width=960&crop=smart&auto=webp&s=cd50d2c46d3c1bbf5dfe5b186d402fce136bb98b', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/Yc8Tjoydwuj1co6G8wf7gULZZvzSdA7sK67GAHXXJH0.jpg?width=1080&crop=smart&auto=webp&s=7e0b545822f2a9a1c79db05dd90815cbd576f471', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/Yc8Tjoydwuj1co6G8wf7gULZZvzSdA7sK67GAHXXJH0.jpg?auto=webp&s=e78ed78c6c9a02ad0b2841337b0da753be506c15', 'width': 1200}, 'variants': {}}]} |
Just throwing a silly idea for LLM translation improvement. Let me know how silly it is. | 16 | I don't know much about training, just playing with local LLMs, agents, and chains. So, I have a silly, idea, I want to share in case it's not too silly, and could inspire anybody
Language can never be translated 100%, but I always thought that a good way to see if the translation is good is to translate it back.
So, if original Greek->English 80% accurate, then 80% English translation->Greek, might be 50% of the original Greek. Now by comparing the original Greek to the final Greek you can evaluate how good the translation is and teach the model based on how well it did.
This could include multiple languages, rounds, models, or a process that the process is not self-infected.
Any good ideas on how it could happen? Basically if you take English and after 5 rounds, and 5 different languages get the same English back (or English with the same meaning), probably you know that this translating mechine is pretty good.
WDYT? | 2024-01-20T12:18:37 | https://www.reddit.com/r/LocalLLaMA/comments/19bajr3/just_throwing_a_silly_idea_for_llm_translation/ | dimknaf | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19bajr3 | false | null | t3_19bajr3 | /r/LocalLLaMA/comments/19bajr3/just_throwing_a_silly_idea_for_llm_translation/ | false | false | self | 16 | null |
Marlin ("FP16xINT4 matmul kernel aimed at LLM inference that can deliver close to ideal (4x) speedups up to batchsizes of 16-32 tokens") | 66 | **GitHub**: [https://github.com/IST-DASLab/marlin](https://github.com/IST-DASLab/marlin)
**Description**:
>This is **Marlin**, a **M**ixed **A**uto-**R**egressive **Lin**ear kernel (and the name of one of the planet's fastest fish), an extremely optimized FP16xINT4 matmul kernel aimed at LLM inference that can deliver close to ideal (4x) speedups up to batchsizes of 16-32 tokens (in contrast to the 1-2 tokens of prior work with comparable speedup). This makes Marlin well suited for larger-scale serving, speculative decoding or advanced multi-inference schemes such as CoT-Majority.
https://preview.redd.it/kiwtg9ssmkdc1.png?width=1431&format=png&auto=webp&s=eb37a8aa9dca7056c4d5e9c4c14177dfdbc42ca5 | 2024-01-20T10:24:27 | https://www.reddit.com/r/LocalLLaMA/comments/19b8uli/marlin_fp16xint4_matmul_kernel_aimed_at_llm/ | APaperADay | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b8uli | false | null | t3_19b8uli | /r/LocalLLaMA/comments/19b8uli/marlin_fp16xint4_matmul_kernel_aimed_at_llm/ | false | false | 66 | {'enabled': False, 'images': [{'id': 'hX4dbzoKKBZB4fAyIyMo6ljAtDNEmTqqBF48HsgL-os', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/N-cvZwKer44-8Fe1O2f2k8b5Qk9CUpGxp38N6C92CpI.jpg?width=108&crop=smart&auto=webp&s=bc5b4a3a8717c4ea077b261f1e7ade8c7ddad3a5', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/N-cvZwKer44-8Fe1O2f2k8b5Qk9CUpGxp38N6C92CpI.jpg?width=216&crop=smart&auto=webp&s=4fa4b893d58a55af856036e85364dde142c46506', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/N-cvZwKer44-8Fe1O2f2k8b5Qk9CUpGxp38N6C92CpI.jpg?width=320&crop=smart&auto=webp&s=63071e4697411cf00cdbabfc2bc6c1e52cbf1102', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/N-cvZwKer44-8Fe1O2f2k8b5Qk9CUpGxp38N6C92CpI.jpg?width=640&crop=smart&auto=webp&s=1635e45b1fac31beacd3a64ef94d5950d21331ec', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/N-cvZwKer44-8Fe1O2f2k8b5Qk9CUpGxp38N6C92CpI.jpg?width=960&crop=smart&auto=webp&s=b576b07af021926b32b10aff60c35ebe396e662f', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/N-cvZwKer44-8Fe1O2f2k8b5Qk9CUpGxp38N6C92CpI.jpg?width=1080&crop=smart&auto=webp&s=fe22eb871042cf52cef7b2b25e7971e4da5383c1', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/N-cvZwKer44-8Fe1O2f2k8b5Qk9CUpGxp38N6C92CpI.jpg?auto=webp&s=bcd5e2843bdfffdab7faf37a06b6206de3d225cc', 'width': 1200}, 'variants': {}}]} | |
To early to build an llm rig ? | 26 | Hey Reddit! I'm debating whether to build a rig for large language model (LLM) work. With rapid advancements in AI, I'm worried about my setup becoming outdated soon. Should I build now and start learning, or wait for the next wave of AI tech? Would love to hear your thoughts and experiences!
Cheers | 2024-01-20T10:01:51 | https://www.reddit.com/r/LocalLLaMA/comments/19b8iod/to_early_to_build_an_llm_rig/ | Aromatic-Air8961 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b8iod | false | null | t3_19b8iod | /r/LocalLLaMA/comments/19b8iod/to_early_to_build_an_llm_rig/ | false | false | self | 26 | null |
High-Performance Multilingual Translation Models for Local Execution | 1 | [removed] | 2024-01-20T09:43:26 | https://www.reddit.com/r/LocalLLaMA/comments/19b898d/highperformance_multilingual_translation_models/ | Xavio_M | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b898d | false | null | t3_19b898d | /r/LocalLLaMA/comments/19b898d/highperformance_multilingual_translation_models/ | false | false | self | 1 | null |
Can anyone guide me how to finetune .gguf LLM's locally? | 1 | [removed] | 2024-01-20T09:07:28 | https://www.reddit.com/r/LocalLLaMA/comments/19b7r4w/can_anyone_guide_me_how_to_finetune_gguf_llms/ | unkn0wnS0ul2day | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b7r4w | false | null | t3_19b7r4w | /r/LocalLLaMA/comments/19b7r4w/can_anyone_guide_me_how_to_finetune_gguf_llms/ | false | false | self | 1 | null |
Hi I'm seeking for any embedding model for vietnamese | 5 | I'm now building a chatbot using RAG, however the output from these part was not really good. Maybe the reason was the embedding model(I used SBERT) or the semantic search. Any suggestion for the model? | 2024-01-20T07:59:27 | https://www.reddit.com/r/LocalLLaMA/comments/19b6rar/hi_im_seeking_for_any_embedding_model_for/ | nah_nan | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b6rar | false | null | t3_19b6rar | /r/LocalLLaMA/comments/19b6rar/hi_im_seeking_for_any_embedding_model_for/ | false | false | self | 5 | null |
Best buget gpu to buy for a desktop running 13900k with 850w power supply | 4 | I have a desktop without a GPU running 13990k, 850w power supply.
I am a full stack developer and very new to AI and ML. I want to experiment with running open source LLM models on my machine. I see lot of options for budget GPUs. I was looking for something like a used RTX 3080 or 6700xt, but honestly, I don’t know how to compare GPUs. (I was neither into desktop gaming nor into AI yet. So limited knowledge on GPU comparison)
Please suggest a good GPU for my use case | 2024-01-20T07:02:52 | https://www.reddit.com/r/LocalLLaMA/comments/19b5wr2/best_buget_gpu_to_buy_for_a_desktop_running/ | shashikiran797 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b5wr2 | false | null | t3_19b5wr2 | /r/LocalLLaMA/comments/19b5wr2/best_buget_gpu_to_buy_for_a_desktop_running/ | false | false | self | 4 | null |
Running CogVLM on Paperspace | 7 | Hello everyone,
Is it possible to run CogVLM, which requires torch 2.1, on a Paperspace Free-A6000 instance that currently has torch 1.12 and CUDA 11.6 installed? I've tried updating CUDA but haven't been successful.
I'm quite new to this and would really appreciate any advice. | 2024-01-20T05:32:34 | https://www.reddit.com/r/LocalLLaMA/comments/19b4fsi/running_cogvlm_on_paperspace/ | Revolutionary_Fan786 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b4fsi | false | null | t3_19b4fsi | /r/LocalLLaMA/comments/19b4fsi/running_cogvlm_on_paperspace/ | false | false | self | 7 | null |
Has anyone tried this yet? What are the results? | 1 | [removed] | 2024-01-20T05:08:13 | https://www.reddit.com/r/LocalLLaMA/comments/19b410e/has_anyone_tried_this_yet_what_are_the_results/ | Original_Job6327 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b410e | false | null | t3_19b410e | /r/LocalLLaMA/comments/19b410e/has_anyone_tried_this_yet_what_are_the_results/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'xp2z74MCi2ot9WZb5BRpBvTee8APb8V4Eo7k8FEgOaQ', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/HBksglPUkrnNVR92kOY_Oh4aIxg7mMoy8lIWDTp9vWo.jpg?width=108&crop=smart&auto=webp&s=a4cf625ef0da8668729ed02be991844aa76f2ba1', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/HBksglPUkrnNVR92kOY_Oh4aIxg7mMoy8lIWDTp9vWo.jpg?width=216&crop=smart&auto=webp&s=7cd0702c429be6ef795fc1809ccb97122027de73', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/HBksglPUkrnNVR92kOY_Oh4aIxg7mMoy8lIWDTp9vWo.jpg?width=320&crop=smart&auto=webp&s=5809b28c464d6b53cf14ead1ec83a6119978df63', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/HBksglPUkrnNVR92kOY_Oh4aIxg7mMoy8lIWDTp9vWo.jpg?width=640&crop=smart&auto=webp&s=a8e7be22f27a74679ce5f0f10f26003e2441d1cd', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/HBksglPUkrnNVR92kOY_Oh4aIxg7mMoy8lIWDTp9vWo.jpg?width=960&crop=smart&auto=webp&s=ca1a27cb504fa1d1005d535e07e2d3e3a8434783', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/HBksglPUkrnNVR92kOY_Oh4aIxg7mMoy8lIWDTp9vWo.jpg?width=1080&crop=smart&auto=webp&s=7a228b4410a804cf5d1955b3ba767824eda7aac0', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/HBksglPUkrnNVR92kOY_Oh4aIxg7mMoy8lIWDTp9vWo.jpg?auto=webp&s=3d76f2d957bdd037d1031c592b6742884227764b', 'width': 1200}, 'variants': {}}]} |
Does anyone have a colab file or code to run gguf files? | 1 | [removed] | 2024-01-20T05:06:15 | https://www.reddit.com/r/LocalLLaMA/comments/19b3zuh/does_anyone_have_a_colab_file_or_code_to_run_gguf/ | Original_Job6327 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b3zuh | false | null | t3_19b3zuh | /r/LocalLLaMA/comments/19b3zuh/does_anyone_have_a_colab_file_or_code_to_run_gguf/ | false | false | self | 1 | null |
Code to run GGUF models | 1 | [removed] | 2024-01-20T05:02:07 | https://www.reddit.com/r/LocalLLaMA/comments/19b3x7x/code_to_run_gguf_models/ | Original_Job6327 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b3x7x | false | null | t3_19b3x7x | /r/LocalLLaMA/comments/19b3x7x/code_to_run_gguf_models/ | false | false | self | 1 | null |
Will we need fine-tuning with better models like LLama 3? | 1 | [removed] | 2024-01-20T04:42:51 | https://www.reddit.com/r/LocalLLaMA/comments/19b3kzw/will_we_need_finetuning_with_better_models_like/ | EcstaticVenom | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b3kzw | false | null | t3_19b3kzw | /r/LocalLLaMA/comments/19b3kzw/will_we_need_finetuning_with_better_models_like/ | false | false | self | 1 | null |
What actually is considered State of the Art? | 10 | I've been reading a lot more papers thanks to this community! And while I'm not technical, I can understand and grasp the concepts (usually with the help of an LLM haha) but one thing that has been a little jarring is the use of "state of the art."
What is even considered state of the art in this field, with so many advances and discoveries, is there a general guideline? God knows even the leader boards and benchmarks are reconsidered... Or like is SoTA just another marketing and hype term now? Because of the work presented IS really cutting edge, like the paper on extending context windows from Meta.
If everything is SoTA... nothing is...? | 2024-01-20T04:23:30 | https://www.reddit.com/r/LocalLLaMA/comments/19b38k2/what_actually_is_considered_state_of_the_art/ | GeeBrain | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b38k2 | false | null | t3_19b38k2 | /r/LocalLLaMA/comments/19b38k2/what_actually_is_considered_state_of_the_art/ | false | false | self | 10 | null |
Medical LLM | 1 | [removed] | 2024-01-20T04:22:14 | https://www.reddit.com/r/LocalLLaMA/comments/19b37qo/medical_llm/ | hmmqzaz | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b37qo | false | null | t3_19b37qo | /r/LocalLLaMA/comments/19b37qo/medical_llm/ | false | false | self | 1 | null |
What to invest in now? | 1 | [removed] | 2024-01-20T04:19:49 | https://www.reddit.com/r/LocalLLaMA/comments/19b364j/what_to_invest_in_now/ | hmmqzaz | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b364j | false | null | t3_19b364j | /r/LocalLLaMA/comments/19b364j/what_to_invest_in_now/ | false | false | self | 1 | null |
Llama.cpp Support? | 1 | I’ve recently shifted to using Llama.cpp for inferencing models, but have heard that KoboldCPP has something called dynamic temperature, which I then checked out and liked.
My question is this: is there any way to do this “dynamic temperature” in Llama.cpp? | 2024-01-20T04:13:56 | https://www.reddit.com/r/LocalLLaMA/comments/19b32cv/llamacpp_support/ | Aptare | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b32cv | false | null | t3_19b32cv | /r/LocalLLaMA/comments/19b32cv/llamacpp_support/ | false | false | default | 1 | null |
Smooth Sampling - another Kalomaze innovation. | 7 | Kalomaze has developed a new approach to temperature. Here are my results from using his latest build.
I am using Nous-Hermes 2 Mixtral 8x7b DPO. When using Smooth Sampling, you only enable dynamic temperature, and disable all other samplers, including MinP. For this generation, I used a Dynatemp of 0.1-2.5. At 3, it is definitely coherent, but starts using Asian characters.
I would say that the result is excellent.
I went ahead and also tried out Bagel-Hermes 2x34b. With previous implementations of DynaTemp, bigger models had difficulty being on point. I have added my test run of that, which was at 0.1-1 DynaTemp. Higher settings caused the AI to go off topic, be curt, less smart, or garbled. Also, I set Rep Penalty to 1.1 with a range equal to my context size. Less than that has bad results.
Not as good when compared to the smaller parameter Mixtral, is my impression.
-----
ContextLimit: 3383/32768, Processing:0.41s (412.0ms/T), Generation:143.84s (280.9ms/T), Total:144.25s (3.55T/s)
Output: Yet as the indecipherable call of duty fades into the recesses of history, let us turn our attention to the survivors of the fallen commander.
The first of the three is Isabella, a woman who wields a metallic polearm with reverence. Her poker face belies a heart filled with disdain for the very organization she serves. She finds comfort in the deafening silence of the battlefield, away from the nuisance of a world that seeks to exploit her skills. Her camouflage uniform, adorned with a profusion of pockets and pouches, seems to whisper tales of hardship and frugality. Isabella is bisexual, drawn to both the gentle curves of femininity and the rugged facades of masculinity. She values loyalty above all else, having been betrayed by those she once trusted. As she journeys through the wreckage of civilization, her every step echoes with the weight of her oath to her fallen commander.
As Isabella makes her way through the desolate streets, her eyes scan the horizon for any signs of life. She takes a moment to reflect on the man who gave his life for her, a man who treated her with a degree of respect she had never experienced before. "Janus," she whispers, "I hope you found peace in your sacrifice." Her thoughts are interrupted by the distant sound of gunfire, a reminder that the war still rages on. With a sigh, she tightens her grip on her polearm and continues her trek.
Next, we have Kaito, a tall and lanky man with a biting wit and a fondness for firearms. He is unapologetically straight, preferring the company of women who share his love for guns and explosives. Kaito has a soft spot for the fallen commander, seeing him as a father figure who taught him the importance of discipline and responsibility. However, his loyalty is not blind, and he is not afraid to challenge authority when he believes it is justified. His tactical vest, adorned with numerous pouches and holsters, bristles with the tools of his trade. As he lays low in the ruins of a once-bustling city, he can't help but wonder if his former commander would be proud of the man he has become.
-----
Generating (512 / 512 tokens)
ContextLimit: 3318/32768, Processing:2.13s (2127.0ms/T), Generation:937.65s (1831.3ms/T), Total:939.77s (0.54T/s)
Output: When I heard what happened to Janus and his squad, I felt really bad. He was such a nice guy, and he didn't deserve to die like that. I know that sounds weird coming from me, since I usually don't get involved in stuff like that. But somehow, I feel connected to him now. Maybe because we're both writers, or maybe because we've both lost someone important to us. Whatever the reason, I wanted to share my thoughts about him with you. So here goes...
Janus was a great commander. Not only did he have the skills and knowledge to lead a team of fighters, but he also had the heart and soul to inspire them. He knew how to motivate his men, how to reward their courage, and how to mourn their loss. He never gave up hope, even when the odds seemed impossible. And he never stopped caring for his friends, even when they were separated by death.
His three subordinates were lucky to have him as their leader. They could count on him to guide them through every challenge they faced. Whether it was facing off against hordes of monsters, escaping from collapsing buildings, or scavenging for food and supplies, Janus always found a way to keep them safe and sane. He taught them how to survive, how to fight back, and how to laugh again.
But sometimes, even the best leaders fail. And when that happens, everyone suffers. Such was the case when Janus and his squad fought against a particularly nasty abomination. This thing was more than just a monster; it was a nightmare made flesh. Its many mouths dripped with venomous saliva, its many eyes glared with hatred, and its many limbs reached out to crush or consume anyone who dared oppose it.
Janus tried to distract the creature long enough for his men to escape. But the abomination was too fast, too powerful, too evil. Before Janus could finish his heroic sacrifice, the abomination caught up to him and tore him apart. His lifeless corpse lay among the ruins of the grocery store where he met his end.
His three subordinates were devastated by his death. They had lost not only their commander but also their brother, their friend, their hero. They felt guilty for leaving him behind, angry at themselves | 2024-01-20T04:13:33 | https://www.reddit.com/r/LocalLLaMA/comments/19b323a/smooth_sampling_another_kalomaze_innovation/ | Sabin_Stargem | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b323a | false | null | t3_19b323a | /r/LocalLLaMA/comments/19b323a/smooth_sampling_another_kalomaze_innovation/ | false | false | self | 7 | null |
Tools to generate/enhance text within applications via local models | 2 | Earlier today I stumbled on a project called the LLM Automator ([https://github.com/radames/LLM-automator](https://github.com/radames/LLM-automator)). It's a Mac Automator action, which you eventually link to a keyboard shortcut, that calls into an Ollama model for text processing. You highlight some text, run the action, and then original text is replaced with (ideally) improved text. I've been playing around with it, though I've run into some issues getting it set up in my system.
At any rate, this got me thinking about other open source solutions that offer similar text generation or enhancement capabilities within various editors, something along the lines of Microsoft's Copilot ([https://learn.microsoft.com/en-us/power-pages/getting-started/add-text-copilot](https://learn.microsoft.com/en-us/power-pages/getting-started/add-text-copilot)).
Are you aware of anything like this? | 2024-01-20T03:21:48 | https://www.reddit.com/r/LocalLLaMA/comments/19b232n/tools_to_generateenhance_text_within_applications/ | Hinged31 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b232n | false | null | t3_19b232n | /r/LocalLLaMA/comments/19b232n/tools_to_generateenhance_text_within_applications/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'BME14EqLAXydrh5O4aajsEFyMhtfvWVcVa293huq4L8', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/cEpSWfxDIpD_6pzNbyMMGWbHiXmdHG8XMK41S5pwxyM.jpg?width=108&crop=smart&auto=webp&s=a1dbc55d2b8b0a61e2e6032e401749a294882ae2', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/cEpSWfxDIpD_6pzNbyMMGWbHiXmdHG8XMK41S5pwxyM.jpg?width=216&crop=smart&auto=webp&s=351163e94adda742cb3553bf118caf856e4067d1', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/cEpSWfxDIpD_6pzNbyMMGWbHiXmdHG8XMK41S5pwxyM.jpg?width=320&crop=smart&auto=webp&s=c2da215f44d98fd6f597994a9902ceae8cebc3fa', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/cEpSWfxDIpD_6pzNbyMMGWbHiXmdHG8XMK41S5pwxyM.jpg?width=640&crop=smart&auto=webp&s=afd6ffe9bfcf56047ec9fd398aae99c18cc38e88', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/cEpSWfxDIpD_6pzNbyMMGWbHiXmdHG8XMK41S5pwxyM.jpg?width=960&crop=smart&auto=webp&s=0b2fd66d26572cba49129bbaba1d954c2ccfe8f4', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/cEpSWfxDIpD_6pzNbyMMGWbHiXmdHG8XMK41S5pwxyM.jpg?width=1080&crop=smart&auto=webp&s=a810e65ce949e12ef63e8733fcd04fa145b78545', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/cEpSWfxDIpD_6pzNbyMMGWbHiXmdHG8XMK41S5pwxyM.jpg?auto=webp&s=7a62b1022a0ea0229031fd1fa5be8f02f30044b7', 'width': 1200}, 'variants': {}}]} |
Going nuts - cache/memory with Llama.cpp - LLM has dementia | 2 | I was using oobabooga without this being such an issue, but I was working on my own UI and backend to combine a few different AIs and APIs, and needed to use llama.cpp directly.
I have my curl command include cache_prompt: True, and the model seems to acknowledge that it tokens are being cached: "slot 0 released (246 tokens in cache)". I've used a variety of models, with the largest being dolphin-2.5-mixtral-8x7b.Q4_K_M.gguf which was actually performing pretty decently at holding a conversation in oobabooga but will literally forget the conversation message-to-message when calling llama.cpp directly via curl.
This is how I'm calling the model:
./server --model /aerarium/ai/models/dolphin-2.5-mixtral-8x7b.Q4_K_M.gguf --host 0.0.0.0 --port 2345
What am I doing wrong?
Is it just emptying the cache every time I curl? Should only the first request include the cache_prompt = True? | 2024-01-20T02:23:04 | https://www.reddit.com/r/LocalLLaMA/comments/19b0xfg/going_nuts_cachememory_with_llamacpp_llm_has/ | TooLazyForUniqueName | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b0xfg | false | null | t3_19b0xfg | /r/LocalLLaMA/comments/19b0xfg/going_nuts_cachememory_with_llamacpp_llm_has/ | false | false | self | 2 | null |
Can't use multi-gpu with 8x A100 80GB | 28 | Hey guys. I have access to a Machine with 8x A100 80gb, 4 disk drivers Micron_7450. My motherboard is a H13DSG-O-CPU and Manufacturer is Supermicro.
I have 2 questions:
1) a colleague told me that this setup can ONLY run Windows, because Linux cannot take advantage of the power of the entire setup. When I installed Ubuntu, it could not boot correctly. I tried Fedora39 and it worked. But given the advice my colleague gave me I went with Windows Server 22. Is his advice correct? Because Windows is not adapted to my needs and he didnt justify his advice...
2) Right now I am using Windows Server 22. However torch DDP can not use NCCL, so I am using gloo backend with FileStore. But this multi-gpu throws a lot of errors of memory, problems with the devices, and Windows terminating services. Besides that, some dependencies dont work (e.g bitsandbytes). I tried WSL2 but it can not work with A100 80gb GPUs, according to NVIDIA's expert on one of their blogs.
I want to finetune Llama2-70B so I downloaded a quantized model, and use it with AutoGPTQ, but its not working yet. Just the quantized 13B for inference.
How can I make it work in Windows? It feels like its Impossible.. Have you been able to do it? Should I use Hyper-V or any other suggestion?
I hope you can help me!
Thanks! | 2024-01-20T02:12:17 | https://www.reddit.com/r/LocalLLaMA/comments/19b0prd/cant_use_multigpu_with_8x_a100_80gb/ | nhanha_castanha | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b0prd | false | null | t3_19b0prd | /r/LocalLLaMA/comments/19b0prd/cant_use_multigpu_with_8x_a100_80gb/ | false | false | self | 28 | null |
Best guide for text finetuning? | 25 | Hey there
This space is evolving extremely quickly, so I thought I'd ask - is there a general step by step guide anywhere on how to fine tune a model on text, maybe like ~500 pages of text, that you guys can recommend?
Thanks | 2024-01-20T01:47:54 | https://www.reddit.com/r/LocalLLaMA/comments/19b07ym/best_guide_for_text_finetuning/ | airhorny | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b07ym | false | null | t3_19b07ym | /r/LocalLLaMA/comments/19b07ym/best_guide_for_text_finetuning/ | false | false | self | 25 | null |
Using --prompt-cache with llama.cpp | 9 | I'm looking to use a large context model in llama.cpp, and give it a big document as the initial prompt. Then once it has ingested that, save the state of the model so I can start it back up with all of this context already loaded, for faster startup.
I tried running llama's main, and adding '-ins --keep -1 --prompt-cache context.gguf' and then input my document, and close main.
context.gguf now exists, and is about 2.5GB.
And then I run main again using '-ins --keep -1 --prompt-cache context.gguf --prompt-cache-ro' but when I ask it questions, it knows nothing from my initial prompt.
I think I am misunderstanding how to use prompt cacheing. Do you have any suggestions? Thanks! | 2024-01-20T01:42:08 | https://www.reddit.com/r/LocalLLaMA/comments/19b03o2/using_promptcache_with_llamacpp/ | SuperMonkeyCollider | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19b03o2 | false | null | t3_19b03o2 | /r/LocalLLaMA/comments/19b03o2/using_promptcache_with_llamacpp/ | false | false | self | 9 | null |
Yall seen any ai chess projects? | 6 | I like the ai on [chess.com](https://chess.com). The ai makes various commentary on moves and some general chat as well.
I have limited internet now, so was wondering if there's any local version of this already made. Has anyone seen any projects that incorporate ai chat into chess? If not, I might try making something in python that utilizes api, and make it opensource. | 2024-01-20T00:54:46 | https://www.reddit.com/r/LocalLLaMA/comments/19az3xa/yall_seen_any_ai_chess_projects/ | multiverse_fan | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19az3xa | false | null | t3_19az3xa | /r/LocalLLaMA/comments/19az3xa/yall_seen_any_ai_chess_projects/ | false | false | self | 6 | {'enabled': False, 'images': [{'id': 'QdkGQmmkHEyvyg_zt3VNTUPh5ZBEllOrI-8HIiaFBpY', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/MVEIj8FIUGCCnjKp4wVqci7Xc_Je9YApMsjpSLoffHY.jpg?width=108&crop=smart&auto=webp&s=94c3b24621532e00a3895c306be6a7fbe5f52b8d', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/MVEIj8FIUGCCnjKp4wVqci7Xc_Je9YApMsjpSLoffHY.jpg?width=216&crop=smart&auto=webp&s=6e372b6c9f668eef6f24c8b535a3fb552094dad1', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/MVEIj8FIUGCCnjKp4wVqci7Xc_Je9YApMsjpSLoffHY.jpg?width=320&crop=smart&auto=webp&s=22668bf2f3f79541ede36f98ef4004befce08e3b', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/MVEIj8FIUGCCnjKp4wVqci7Xc_Je9YApMsjpSLoffHY.jpg?width=640&crop=smart&auto=webp&s=c3df1d7228e35322762c91c102d17b69b4c658fc', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/MVEIj8FIUGCCnjKp4wVqci7Xc_Je9YApMsjpSLoffHY.jpg?width=960&crop=smart&auto=webp&s=62635486a5f00b022fe15b0b1e777cbc6a7b5d05', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/MVEIj8FIUGCCnjKp4wVqci7Xc_Je9YApMsjpSLoffHY.jpg?width=1080&crop=smart&auto=webp&s=70b7050a34210c2ed2da8fbd9ea5a97ad7866515', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/MVEIj8FIUGCCnjKp4wVqci7Xc_Je9YApMsjpSLoffHY.jpg?auto=webp&s=6eba63acb4e9b266ea44fdde63a37ea2a68a2b13', 'width': 1200}, 'variants': {}}]} |
Building a machine for self-hosted LLaMA - will 2 x RTX 3090 be enough to run 70B @ 8-bit quantization? | 16 | Hi, I am trying to build a machine to run a self-hosted copy of LLaMA 2 70B for a web search / indexing project I'm working on.
My primary use case, in very simplified form, is to take in large amounts of web-based text (>10^(7) pages at a time) as input, have the LLM "read" these documents, and then (1) index these based on word vectors and (2) condense each document down to 1-3 sentence natural language summaries of text content. Also, I will be doing some image classification tasks with these web documents such as identifying ads/banners, and generating brief captions images that are in each document.
I am trying to do so with a budget of $7k or less.
I have two main questions:
1) I was thinking of getting 2 x RTX 3090s. 24GB of VRAM each would give 48GB VRAM. I was wondering if this is going to be sufficient for running LLaMA 70B, and at how many bits of quantization? Also, would I be able to achieve significantly higher speeds or use a larger model if I were to purchase a 3rd RTX 3090?
2) Do you all have recommendations for motherboards that would work for this setup, with an AMD Ryzen processor? I have built my own gaming computers before, but I don't have any experience with multi-GPU builds and am not sure what I should be looking for as far as making sure that I have something that has the space for 2 or 3 GPUs. Most of the consumer boards that I've found would have the cards crammed way too close together. | 2024-01-20T00:25:55 | https://www.reddit.com/r/LocalLLaMA/comments/19ayhg7/building_a_machine_for_selfhosted_llama_will_2_x/ | Secure-Technology-78 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ayhg7 | false | null | t3_19ayhg7 | /r/LocalLLaMA/comments/19ayhg7/building_a_machine_for_selfhosted_llama_will_2_x/ | false | false | self | 16 | null |
I just made my own AI Influencer | 1 | Hey guys... I've been kinda intrigued with this entire AI influencer thing and so I tried making my own...
https://www.instagram.com/_aisha__ai?igsh=enNkcjEzaXdiaGFj
I'm used an automation script written in python with Stable Diffusion (EpicReality checkpoint) to generate images and dolphin-mistral-7b to write captions. | 2024-01-20T00:25:11 | https://www.instagram.com/_aisha__ai?igsh=enNkcjEzaXdiaGFj | Few_Acanthisitta_858 | instagram.com | 1970-01-01T00:00:00 | 0 | {} | 19aygw7 | false | null | t3_19aygw7 | /r/LocalLLaMA/comments/19aygw7/i_just_made_my_own_ai_influencer/ | false | false | default | 1 | null |
Does quantization hurt MoE models like Mixtral harder than dense models of the same size? | 46 | Yesterday I tried out those [new 2 bit](https://huggingface.co/ikawrakow/various-2bit-sota-gguf/tree/main) (2.34 BPW) quants for Mixtral, and for the first time ever, I could properly run Mixtral with just 16+6 GB of RAM+VRAM. But I wasn't very impressed by the model's intelligence. For instance, it called GPT-3 'GTP-3' and it just lacks that magical oomph when you ask a poorly worded followup question and the model just understands what you're referring to. It feels closer to good old Mistral 7b than it does to GPT-3 in terms of intelligence. But I see lots of people talking about and enjoying their 120b and even 70b models that they run at 2.5 or so BPW. Does Mixtral suffer from quantization more than a ~46B dense model would? Or is there just some magical boundary that I crossed with 2.34 BPW, and if Jensen Huang gave me an extra 2GB of VRAM I'd be able to run a decent quant of Mixtral? What are your experiences? | 2024-01-20T00:05:19 | https://www.reddit.com/r/LocalLLaMA/comments/19ay0r4/does_quantization_hurt_moe_models_like_mixtral/ | Covid-Plannedemic_ | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ay0r4 | false | null | t3_19ay0r4 | /r/LocalLLaMA/comments/19ay0r4/does_quantization_hurt_moe_models_like_mixtral/ | false | false | self | 46 | {'enabled': False, 'images': [{'id': 'mmSEhOO2Ya4Cjle-07d9l2l3p0K02gcnbjuYTcgYymE', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/y7kZ5l3eN4uJnoeNp6fBiLE1B0DlXrgor7Ux1ilJt5k.jpg?width=108&crop=smart&auto=webp&s=c2033248ce0605f92a32f5c9fa9ac056a52d65bc', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/y7kZ5l3eN4uJnoeNp6fBiLE1B0DlXrgor7Ux1ilJt5k.jpg?width=216&crop=smart&auto=webp&s=a845a86bc5e01172dfda8fb263b760ef03494dd7', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/y7kZ5l3eN4uJnoeNp6fBiLE1B0DlXrgor7Ux1ilJt5k.jpg?width=320&crop=smart&auto=webp&s=c00f3db51eb3e305a2a6213ac6b53f202a71bb19', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/y7kZ5l3eN4uJnoeNp6fBiLE1B0DlXrgor7Ux1ilJt5k.jpg?width=640&crop=smart&auto=webp&s=03f94282964a7d562e24c2288c8391d64e7feab7', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/y7kZ5l3eN4uJnoeNp6fBiLE1B0DlXrgor7Ux1ilJt5k.jpg?width=960&crop=smart&auto=webp&s=178462bd47c561a01aa32dcf1740346078e5d0ec', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/y7kZ5l3eN4uJnoeNp6fBiLE1B0DlXrgor7Ux1ilJt5k.jpg?width=1080&crop=smart&auto=webp&s=931f16f544d20475e6670361deb771671bc32f66', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/y7kZ5l3eN4uJnoeNp6fBiLE1B0DlXrgor7Ux1ilJt5k.jpg?auto=webp&s=f8e23616a09ca0bac37ca99a1059f39f515d1eeb', 'width': 1200}, 'variants': {}}]} |
Could AI break all our encryption? | 1 | The heart of the matter is prime factorization. As of now, deriving a private key from a public one is a Herculean task because splitting large numbers into primes is really tough.
But here’s where it gets interesting. What if AI, especially generative models known for spotting patterns in massive datasets can handle it? These models aren’t just about brute force computation. They’re about finding connections and making predictions.
For example, I could generate millions of key pairs (private and public) and start training a model on this dataset using public key as input and correct private key as desired output.
If it works, then, well, all our security protocols might be broken… | 2024-01-19T23:54:48 | https://www.reddit.com/r/LocalLLaMA/comments/19axrtz/could_ai_break_all_our_encryption/ | maxhsy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19axrtz | false | null | t3_19axrtz | /r/LocalLLaMA/comments/19axrtz/could_ai_break_all_our_encryption/ | false | false | self | 1 | null |
twinny - Using Ollama to create a GitHub Copilot alternative plugin for vscode with completion and chat | 46 | Hey everyone, how are we doing?
I've been stalking this thread for a while but not posted anything yet. I'm not sure if it's been posted here already either...It says to limit self promoting so I hope this message finds you all well.
For the last six months I've been working on a self hosted AI code completion and chat plugin for vscode which runs the Ollama API under the hood, it's basically a GitHub Copilot alternative but free and private.
I don't like to blow my own trumpet but I consider it to be the best local alternative available today.
I'm constantly working to update, maintain and add features weekly and would appreciate some feedback.
If you like what you see, don't forget to tell your friends and give me a star on GitHub!
As the author of the plugin I welcome and encourage you all to reach out to me on Twitter/X @rjmacarthy for any help or questions.
Feedback and suggestions are more than welcome.
Many thanks! | 2024-01-19T22:47:07 | https://www.reddit.com/r/LocalLLaMA/comments/19aw5vp/twinny_using_ollama_to_create_a_github_copilot/ | rjmacarthy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19aw5vp | false | null | t3_19aw5vp | /r/LocalLLaMA/comments/19aw5vp/twinny_using_ollama_to_create_a_github_copilot/ | false | false | self | 46 | null |
Is there any locally hosted model that can currently come close to GPT4-Vision in terms of capturing data from documents? | 1 | So GPT4-Vision has been a massive jump technologically in the world of OCR but I hate that I can't use their API to capture data from PDF documents and prepare datasets. Currently ChatGPT can do this and I have custom GPT that can do this, but neither are accessible via API. Also, I've run into degradation issues with inconsistent GPT4 performance on ChatGPT recently.
Are there any local multi-modal AI models that I could look into to perform this work, even if they aren't yet at ChatGPT's performance level?
I appreciate any feedback or ideas! | 2024-01-19T22:23:26 | https://www.reddit.com/r/LocalLLaMA/comments/19avlqw/is_there_any_locally_hosted_model_that_can/ | Since1785 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19avlqw | false | null | t3_19avlqw | /r/LocalLLaMA/comments/19avlqw/is_there_any_locally_hosted_model_that_can/ | false | false | self | 1 | null |
Python backend for bridging between LLM API and nextjs frontend? | 1 |
I'm currently working on a Next.js web application that utilizes the OpenAI API. Recently, I've come across several libraries like LMQL and outlines-dev that offer structured output for LLMs, which seem really useful for my project. However, these libraries are predominantly Python-based.
This leads me to the realization that I might need a Python backend to act as a middleware. This backend would interface between my Next.js app and the OpenAI API endpoint. Is my understanding correct that this is a feasible approach?
Additionally, I was wondering about the best framework to implement this middleware. My first thoughts are FastAPI or Flask, but I want to hear what others are currently using and what the most famous choice is for LLM use case (i.e. supporting streaming response). | 2024-01-19T22:18:28 | https://www.reddit.com/r/LocalLLaMA/comments/19avhf3/python_backend_for_bridging_between_llm_api_and/ | tylertaewook | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19avhf3 | false | null | t3_19avhf3 | /r/LocalLLaMA/comments/19avhf3/python_backend_for_bridging_between_llm_api_and/ | false | false | self | 1 | null |
Meta paper presents Self-Rewarding Language Models --- GPT-4 level | 209 | [https://arxiv.org/abs/2401.10020](https://arxiv.org/abs/2401.10020)
A fine-tuning Llama-2 70B on three iterations of our approach yields a model that outperforms many existing systems on the AlpacaEval 2.0, including Claude 2, Gemini Pro, and GPT-4 0613 | 2024-01-19T22:06:18 | https://www.reddit.com/r/LocalLLaMA/comments/19av72i/meta_paper_presents_selfrewarding_language_models/ | Different-Pickle1021 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19av72i | false | null | t3_19av72i | /r/LocalLLaMA/comments/19av72i/meta_paper_presents_selfrewarding_language_models/ | false | false | self | 209 | null |
Which model is suitable for summarization into bullet points? | 1 | 2024-01-19T21:54:40 | https://www.reddit.com/r/LargeLanguageModels/comments/19autg5/which_model_is_suitable_for_summarization_into/ | IndiumGalliumArsenid | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 19aux25 | false | null | t3_19aux25 | /r/LocalLLaMA/comments/19aux25/which_model_is_suitable_for_summarization_into/ | false | false | default | 1 | null | |
[D] does anyone tried to fine-tune llm in translation task? | 2 | I would like to know if anyone tried to fine-tune llm in translation task | 2024-01-19T21:32:47 | https://www.reddit.com/r/LocalLLaMA/comments/19auea0/d_does_anyone_tried_to_finetune_llm_in/ | ahsaor8 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19auea0 | false | null | t3_19auea0 | /r/LocalLLaMA/comments/19auea0/d_does_anyone_tried_to_finetune_llm_in/ | false | false | self | 2 | null |
So anyone know the guide to MPI llama.cpp's capabilities? | 3 | Oh sorry. I didn't find manpages or something detailing what MPIrun llama.cpp can do.
For example. I assume it can offload weights to different system memories. But I read that since it's linear, only one CPU will be executing it's portion of each instance of the model.
However, what about other capabilities. Can MPIrun utilize two NVIDIA cards? Such as having a P40 on the first rig and a P4 on the second rig for the remaining tensors?
Wonder if it can also do a Intel GPU via OpenCL and a second machine with a NVIDIA one via OpenCL or CUDA. | 2024-01-19T21:04:47 | https://www.reddit.com/r/LocalLLaMA/comments/19atqhq/so_anyone_know_the_guide_to_mpi_llamacpps/ | A_Degenerate_Idiot | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19atqhq | false | null | t3_19atqhq | /r/LocalLLaMA/comments/19atqhq/so_anyone_know_the_guide_to_mpi_llamacpps/ | false | false | self | 3 | null |
Implement Fractional GPUs while deploying LLMs in Kubernetes with Aliyun Scheduler | 1 | [removed] | 2024-01-19T21:04:34 | https://www.reddit.com/r/LocalLLaMA/comments/19atqal/implement_fractional_gpus_while_deploying_llms_in/ | Tiny_Cut_8440 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19atqal | false | null | t3_19atqal | /r/LocalLLaMA/comments/19atqal/implement_fractional_gpus_while_deploying_llms_in/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'IuWAdAOAy0ZaJCcyZG7mQqYCO_w2e0KLN-GNsXL5ojs', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/sYKDhy3AEj_Zc6aqbSSKpk0pj6oDFuMyaXEpet5TfZA.jpg?width=108&crop=smart&auto=webp&s=7a58da754ae5b62dcba7c0362a7f89f8f0571c89', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/sYKDhy3AEj_Zc6aqbSSKpk0pj6oDFuMyaXEpet5TfZA.jpg?width=216&crop=smart&auto=webp&s=e633acccd135957d03ea0532849e6d6e64214649', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/sYKDhy3AEj_Zc6aqbSSKpk0pj6oDFuMyaXEpet5TfZA.jpg?width=320&crop=smart&auto=webp&s=fa8dadbbb14335600ee64b3741e0262863850ed9', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/sYKDhy3AEj_Zc6aqbSSKpk0pj6oDFuMyaXEpet5TfZA.jpg?width=640&crop=smart&auto=webp&s=f7c985be739ed6f2ec88a0857d34fa6fc6fa0dd6', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/sYKDhy3AEj_Zc6aqbSSKpk0pj6oDFuMyaXEpet5TfZA.jpg?width=960&crop=smart&auto=webp&s=57d666f2542b7d7826ef69cb0b68b3207897faf6', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/sYKDhy3AEj_Zc6aqbSSKpk0pj6oDFuMyaXEpet5TfZA.jpg?width=1080&crop=smart&auto=webp&s=f16cc1bd03f1b72ee7e4c3a09fbc4c3553a8a1b2', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/sYKDhy3AEj_Zc6aqbSSKpk0pj6oDFuMyaXEpet5TfZA.jpg?auto=webp&s=fb24aa9e363e868f49442aee09d447d76f0fe968', 'width': 1200}, 'variants': {}}]} |
NVIDIAs new paper introduces ChatQA model that is GPT-4 Level --- ChatQA-70B | 146 | [https://arxiv.org/abs/2401.10225](https://arxiv.org/abs/2401.10225)
NVIDIA’s ChatQA introduces a range of models, ranging from 7B to 70B in size.
The team behind ChatQA proposes a two-stage instruction tuning method, significantly enhancing zero-shot conversational QA results from large language models (LLMs). | 2024-01-19T19:57:58 | https://www.reddit.com/r/LocalLLaMA/comments/19as4lf/nvidias_new_paper_introduces_chatqa_model_that_is/ | Different-Pickle1021 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19as4lf | false | null | t3_19as4lf | /r/LocalLLaMA/comments/19as4lf/nvidias_new_paper_introduces_chatqa_model_that_is/ | false | false | self | 146 | null |
Mixed-CPU-GPU AMD benchmark for a Yi 34B based model with different quantizations | 11 | I was having trouble finding AMD benchmarks for larger models, so I figured to post some here.
Model available in **GGUF format** here: [https://huggingface.co/TheBloke/Tess-34B-v1.4-GGUF](https://huggingface.co/TheBloke/Tess-34B-v1.4-GGUF)
Mixed-CPU-GPU AMD benchmark - 16K context, 2K Batch, half offloaded to GPU (30 layers/61 layer), Using A**MD 6900XT 16GB** / **5900X** with **OpenCL** **llama.cpp** (recent git commit) and using a fixed long prompt & constant seed.
In case you're wondering why I'm not using **rocm** instead of **OpenCL (CLBLAST)**, the reason is that the context seems to need to fit on the GPU with **rocm**, but not necessarily with **OpenCL (it seems to overflow into system memory)**, you can expect \~14 tokens/second IIRC with this GPU with a model/context that fits when using **rocm**.
**With \~Half Offloading (30/61 Layers):**
**Q2\_K:**
llama_print_timings: load time = 11737.40 ms
llama_print_timings: sample time = 5.67 ms / 162 runs ( 0.03 ms per token, 28571.43 tokens per second)
llama_print_timings: prompt eval time = 50172.23 ms / 2013 tokens ( 24.92 ms per token, 40.12 tokens per second)
llama_print_timings: eval time = 43031.61 ms / 161 runs ( 267.28 ms per token, 3.74 tokens per second)
llama_print_timings: total time = 107039.25 ms / 2174 tokens\
**Q4\_K\_M:**
llama_print_timings: load time = 6396.38 ms
llama_print_timings: sample time = 4.10 ms / 115 runs ( 0.04 ms per token, 28021.44 tokens per second)
llama_print_timings: prompt eval time = 46794.11 ms / 1999 tokens ( 23.41 ms per token, 42.72 tokens per second)
llama_print_timings: eval time = 39084.30 ms / 114 runs ( 342.84 ms per token, 2.92 tokens per second)
llama_print_timings: total time = 87595.46 ms / 2113 tokens
**Q6\_K:**
llama_print_timings: load time = 7338.72 ms
llama_print_timings: sample time = 2.62 ms / 80 runs ( 0.03 ms per token, 30592.73 tokens per second)
llama_print_timings: prompt eval time = 50635.74 ms / 1999 tokens ( 25.33 ms per token, 39.48 tokens per second)
llama_print_timings: eval time = 39718.83 ms / 80 runs ( 496.49 ms per token, 2.01 tokens per second)
llama_print_timings: total time = 101205.05 ms / 2079 tokens
**MAX offloading:**
With offloading increased to approximate max that fits for each quant. Keep in mind I'm using a desktop environment on linux so usable VRAM is closer to around 14GB but the intent is to be representative of running a local LLM in parallel with doing other things on your workstation.
**Q2\_K (58 layers):**
llama_print_timings: load time = 9137.85 ms
llama_print_timings: sample time = 16.68 ms / 453 runs ( 0.04 ms per token, 27156.65 tokens per second)
llama_print_timings: prompt eval time = 72431.31 ms / 2013 tokens ( 35.98 ms per token, 27.79 tokens per second)
llama_print_timings: eval time = 60032.48 ms / 453 runs ( 132.52 ms per token, 7.55 tokens per second)
llama_print_timings: total time = 146385.51 ms / 2466 tokens
**Q4\_K\_M (43 Layers):**
llama_print_timings: load time = 7404.32 ms
llama_print_timings: sample time = 4.34 ms / 127 runs ( 0.03 ms per token, 29262.67 tokens per second)
llama_print_timings: prompt eval time = 49932.70 ms / 1999 tokens ( 24.98 ms per token, 40.03 tokens per second)
llama_print_timings: eval time = 34033.25 ms / 126 runs ( 270.11 ms per token, 3.70 tokens per second)
llama_print_timings: total time = 86454.96 ms / 2125 tokens
**Q6\_K (31 Layers, almost identical to above):**
llama_print_timings: load time = 7051.39 ms
llama_print_timings: sample time = 5.08 ms / 150 runs ( 0.03 ms per token, 29539.19 tokens per second)
llama_print_timings: prompt eval time = 69266.06 ms / 2013 tokens ( 34.41 ms per token, 29.06 tokens per second)
llama_print_timings: eval time = 67422.12 ms / 150 runs ( 449.48 ms per token, 2.22 tokens per second)
llama_print_timings: total time = 143920.95 ms / 2163 tokens
​ | 2024-01-19T19:29:27 | https://www.reddit.com/r/LocalLLaMA/comments/19arfyl/mixedcpugpu_amd_benchmark_for_a_yi_34b_based/ | akumaburn | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19arfyl | false | null | t3_19arfyl | /r/LocalLLaMA/comments/19arfyl/mixedcpugpu_amd_benchmark_for_a_yi_34b_based/ | false | false | self | 11 | {'enabled': False, 'images': [{'id': '48d-7o1LgXRSGy1iyhm6HsQFqRbyDCLVjSkHWQCbAz8', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/x2oEyareN3bDFGJZsLXTxVDlHDbKA3JNCwQOVM-txy8.jpg?width=108&crop=smart&auto=webp&s=5fba222380405fbe2ea32d9933ee429237d24279', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/x2oEyareN3bDFGJZsLXTxVDlHDbKA3JNCwQOVM-txy8.jpg?width=216&crop=smart&auto=webp&s=8f41ef28f0eb855433d0acca2dd1b157ff171047', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/x2oEyareN3bDFGJZsLXTxVDlHDbKA3JNCwQOVM-txy8.jpg?width=320&crop=smart&auto=webp&s=237706818f041c262df10b0e9586d4e93c173698', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/x2oEyareN3bDFGJZsLXTxVDlHDbKA3JNCwQOVM-txy8.jpg?width=640&crop=smart&auto=webp&s=4c8e9b80756c15ec5fb8aa7449e793cc7437cec2', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/x2oEyareN3bDFGJZsLXTxVDlHDbKA3JNCwQOVM-txy8.jpg?width=960&crop=smart&auto=webp&s=40592d1a72cb982b5a3588283ce580c564e6b6ef', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/x2oEyareN3bDFGJZsLXTxVDlHDbKA3JNCwQOVM-txy8.jpg?width=1080&crop=smart&auto=webp&s=0f3ccddd6adc493a0a17101ebae6098a12cd2ec3', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/x2oEyareN3bDFGJZsLXTxVDlHDbKA3JNCwQOVM-txy8.jpg?auto=webp&s=6fca8b8789761bde3fc249496aae47a38b1da97a', 'width': 1200}, 'variants': {}}]} |
DiscoLM German 7B V1 - best small german model so far? | 10 | Some days ago this LLM model came out and since yesterday TheBloke brought out his conversions.
[https://huggingface.co/models?search=DiscoLM%20German](https://huggingface.co/models?search=DiscoLM%20German)
I have been using for a few hours now the Q8\_0 GGUF version on my PC (4090) in Roleplay (SillyTavern) and I must say it is the first one for me that works on a level where it makes fun to use it. The German text is written well enough and had very less grammar problems as other models I tried.
It only seems not to work very well over 4096 context size, but not sure if that is fixable by settings.
What did other German users think? Is there a better German model?
I tried before SauerkrautLM 7B HERO Q8\_0 GGUF and it's German grammar issues cause pain. Just tried it again with my current chat history, terrible. Both use ChatML style so it was easy to switch.
I wonder what exact sources some of the German datasets have. If there's fanfiction stuff in there, then it's no wonder the grammar is so bad...
**Update**: A first test of mine showed two things:
1. DiscoLM German is much better as Mistral 7B Instruct V0.2, which makes still too much grammar errors in near every short answer and sounds a bit stiff.
2. Mistral FT Optimized 1227 - A 7B base model for finetune - has after a short test near similar less grammar errors in German as DiscoLM German and it sounds less stiff.
[https://huggingface.co/models?search=Mistral%20ft%20optimized%201227](https://huggingface.co/models?search=Mistral%20ft%20optimized%201227)
I used Mistral FT Optimized 1227 the last two weeks before I found DiscoLM German but I never tried this model before in German. I would not have expected such a model to be so good without special German training. | 2024-01-19T19:27:58 | https://www.reddit.com/r/LocalLLaMA/comments/19arepf/discolm_german_7b_v1_best_small_german_model_so/ | Blizado | self.LocalLLaMA | 2024-01-20T17:29:23 | 0 | {} | 19arepf | false | null | t3_19arepf | /r/LocalLLaMA/comments/19arepf/discolm_german_7b_v1_best_small_german_model_so/ | false | false | self | 10 | {'enabled': False, 'images': [{'id': 'An0iJLapq-5CUQQlm3lWegevVWf7wlANjmn1iOwCTqk', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/BOaSYNg6lhlngBhuDS68WpIBibLf88Q_KzjZVrFpgEc.jpg?width=108&crop=smart&auto=webp&s=2c0b032bdc9d0820b318f57def3af620afe60ee8', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/BOaSYNg6lhlngBhuDS68WpIBibLf88Q_KzjZVrFpgEc.jpg?width=216&crop=smart&auto=webp&s=7b29327d787489e6d4f61726ba9d10a09ed099d9', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/BOaSYNg6lhlngBhuDS68WpIBibLf88Q_KzjZVrFpgEc.jpg?width=320&crop=smart&auto=webp&s=9f1b5bed20b4b058b596c2a430a47d3b9c857e03', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/BOaSYNg6lhlngBhuDS68WpIBibLf88Q_KzjZVrFpgEc.jpg?width=640&crop=smart&auto=webp&s=7b47505d7a8ebd834ca805c293d16277b5772c12', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/BOaSYNg6lhlngBhuDS68WpIBibLf88Q_KzjZVrFpgEc.jpg?width=960&crop=smart&auto=webp&s=c7be2b4b0ad69f9ff176d6a0027458c22a63a5f0', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/BOaSYNg6lhlngBhuDS68WpIBibLf88Q_KzjZVrFpgEc.jpg?width=1080&crop=smart&auto=webp&s=dea3a5ccadcdb95c05dca40d482f50c976b88233', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/BOaSYNg6lhlngBhuDS68WpIBibLf88Q_KzjZVrFpgEc.jpg?auto=webp&s=6e3e4780238d40a2755c2289e7e3d722eeb8ea30', 'width': 1200}, 'variants': {}}]} |
GPT4ALL frustrations with RAG on Local Documents. Has anyone had and solved this issue? | 9 | I’ve been using GPT4ALL with SBERT RAG for a few weeks now, and while I have seen it spit out some really amazing answers using Mistral Instruct and Hermes with information from RAG docs, I’ve been extremely frustrated with how to “focus” the RAG functions and get them to work with any consistency at all.
Here’s my current setup and what I’m trying (and failing) to accomplish:
MacBook Pro M3 with 16GB RAM
GPT4ALL 2.6.1
Mistral Instruct and Hermes LLMs
Within GPT4ALL, I’ve set up a Local Documents ”Collection” for “Policies & Regulations” that I want the LLM to use as its “knowledge base” from which to evaluate a target document (in a separate collection) for regulatory compliance. I currently only have one policy document in the collection to avoid any confusion for testing purposes.
I’ve set up a secondary “Collection” for a single target document to be checked for policy compliance (using the policy document in the other collection)
I called it “Target Docs” and pointed GPT4All to that folder.
I then checked the boxes to enable both these collections to be used in GPT4ALL
I set RAG chunk size to “512”
I set Number of References to “6”
I turned “Enable References” to ON
I set Max Tokens set to 6000
All other settings are default
I waited until after the indexing process had completed and tried both Mistral Instruct and Hermes (the versions included in the GPT4ALL download section of the app)
Here’s where the trouble starts.
My prompt is something like this:
“Determine if TARGET_DOCUMENT.PDF meets all the requirements contained in POLICY_DOCUMENT_X.PDF. Note and explain any areas of non-compliance and provide as much detail in your response as possible.”
I get either a response that says it doesn’t have access to the document or I get various hallucinations where I can tell that it’s just trying its best, but just making up complete BS.
I do usually see it say
“Processing… searching collections”after prompt submission, so I know it’s trying to do the RAG, but usually the answer is complete garbage although occasionally it’ll mention something relevant, but it’s very rare.
I’ve even tried simple prompts like
“What is the page count of TARGET_DOCUMENT.PDF?”
And I’ll get completely different but always consistently wrong answers on the page count. One will say 30 one will say 60, but never even close to the right answer (14).
Has anyone solved this? I’ve used the complete and proper file names but it never seems to make any difference. | 2024-01-19T19:26:49 | https://www.reddit.com/r/LocalLLaMA/comments/19ards4/gpt4all_frustrations_with_rag_on_local_documents/ | Porespellar | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ards4 | false | null | t3_19ards4 | /r/LocalLLaMA/comments/19ards4/gpt4all_frustrations_with_rag_on_local_documents/ | false | false | self | 9 | null |
LocalLLaMA realistic on M3 Max 36GB? | 3 | Hi,
It's time for me to upgrade my laptop (MacPro M1 16Gb) and I'd like to dabble with LocalLLaMA. I know that to do things well I should buy an expensive CUDA-based GPU but I don't have an opportunity nor want to buy a dedicated machine.
So I wonder if an M3 Max with 36GB is enough to experiment or if I need more memory (48GB, 64GB, 96GB?). I am assuming the M3 Max is the right choice here instead the M3 Pro which likely wouldn't be enough.
Are there other local GenAI models I can run locally with that config?
Needs to be a Mac, that's what I am comfortable with in my developer / MMedia / iOT workflow (I have a few Homelab Linux machines but they're mostly low-power 32GB docker hosts)
​ | 2024-01-19T19:24:08 | https://www.reddit.com/r/LocalLLaMA/comments/19arbf8/localllama_realistic_on_m3_max_36gb/ | gnapoleon | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19arbf8 | false | null | t3_19arbf8 | /r/LocalLLaMA/comments/19arbf8/localllama_realistic_on_m3_max_36gb/ | false | false | self | 3 | null |
Two 8gb video cards with SLI link vs one 16gb video card | 2 | So, I'm looking to build a system for fooling around with AI. I know using an Nvidia video card is much faster than just a CPU. If I link two 8 GB video cards with SLI, will I have 16 GB and more processing power effectively, or am I misunderstanding?
An old noob thanks you. | 2024-01-19T19:15:29 | https://www.reddit.com/r/LocalLLaMA/comments/19ar3qh/two_8gb_video_cards_with_sli_link_vs_one_16gb/ | DanTheDiceGuy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ar3qh | false | null | t3_19ar3qh | /r/LocalLLaMA/comments/19ar3qh/two_8gb_video_cards_with_sli_link_vs_one_16gb/ | false | false | self | 2 | null |
[Noob Question] Do larger models have more information? | 21 | Do they? I know they have more "neurons" or more reasoning capabilities - but do they all have the same information (like the same books / texts / data)?
I mean, i know coding oriented models have more coding data, but does a 7b llama have the same data / information as the 13b or 33b?
Don't have me, but i couldn't find this info anywhere... | 2024-01-19T18:59:02 | https://www.reddit.com/r/LocalLLaMA/comments/19aqpcj/noob_question_do_larger_models_have_more/ | yupignome | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19aqpcj | false | null | t3_19aqpcj | /r/LocalLLaMA/comments/19aqpcj/noob_question_do_larger_models_have_more/ | false | false | self | 21 | null |
A good resource on how to fine-tune Mistral for classification? | 2 | I'm struggling to find a tutorial that works. The model doesn't converge. In particular, I'm confused on how to give the labels to the model.
Trying it like this: "Instruction [text] #Answer [label]". But how does the model now what part of the sentence is the label if I'm giving it along with the text. | 2024-01-19T18:38:59 | https://www.reddit.com/r/LocalLLaMA/comments/19aq84d/a_good_resource_on_how_to_finetune_mistral_for/ | PunchTornado | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19aq84d | false | null | t3_19aq84d | /r/LocalLLaMA/comments/19aq84d/a_good_resource_on_how_to_finetune_mistral_for/ | false | false | self | 2 | null |
Chat with AI in Disc0rd with an open source local LLM | 1 | [removed] | 2024-01-19T18:02:16 | https://www.reddit.com/r/LocalLLaMA/comments/19apc0b/chat_with_ai_in_disc0rd_with_an_open_source_local/ | heaversm | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19apc0b | false | {'oembed': {'author_name': 'Practical AI through Prototypes', 'author_url': 'https://www.youtube.com/@practical-ai-prototypes', 'height': 200, 'html': '<iframe width="267" height="200" src="https://www.youtube.com/embed/-ikvkV1H9LA?feature=oembed&enablejsapi=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen title="Chat with AI in Discord with an open source local LLM"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/-ikvkV1H9LA/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'Chat with AI in Discord with an open source local LLM', 'type': 'video', 'version': '1.0', 'width': 267}, 'type': 'youtube.com'} | t3_19apc0b | /r/LocalLLaMA/comments/19apc0b/chat_with_ai_in_disc0rd_with_an_open_source_local/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'sKk7DKox9AMpDl6QpS7yoWfUD89ZbO62oioG9Y7sVcQ', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/QgIyH8jqU3M_ica3_ZfFo1SZXMa0KlVeTWXrAjoBkM4.jpg?width=108&crop=smart&auto=webp&s=64781ef811acabee686926e8d022602d15ffd42a', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/QgIyH8jqU3M_ica3_ZfFo1SZXMa0KlVeTWXrAjoBkM4.jpg?width=216&crop=smart&auto=webp&s=6ce752d94f218b0ff02b50ad9e6b4f889b0654bb', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/QgIyH8jqU3M_ica3_ZfFo1SZXMa0KlVeTWXrAjoBkM4.jpg?width=320&crop=smart&auto=webp&s=85b474151076e4ac758a4dd6a9cae4ae961a6e03', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/QgIyH8jqU3M_ica3_ZfFo1SZXMa0KlVeTWXrAjoBkM4.jpg?auto=webp&s=3b526a60be6d8af37d0fb4d57913c39113be05cd', 'width': 480}, 'variants': {}}]} | |
Upcoming W8A8 quant (SmoothQuant) support in vLLM promises huge throughput and latency improvements | 1 | 2024-01-19T17:49:27 | https://github.com/vllm-project/vllm/pull/1112 | DreamGenX | github.com | 1970-01-01T00:00:00 | 0 | {} | 19ap0mk | false | null | t3_19ap0mk | /r/LocalLLaMA/comments/19ap0mk/upcoming_w8a8_quant_smoothquant_support_in_vllm/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'Iqt6CjJppYGGVIGzmIR8erwtl7NKFdDbgEcLSVxOk6Y', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/68IEano6Z5Nj3KBNde9kXyjc5LzxJXDCJR3MjZaV_dM.jpg?width=108&crop=smart&auto=webp&s=59ac72f7b03bb4b7d213ae2d8be8df3310e84716', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/68IEano6Z5Nj3KBNde9kXyjc5LzxJXDCJR3MjZaV_dM.jpg?width=216&crop=smart&auto=webp&s=e3adada563d8279636bd2268f624ed0580baa9a2', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/68IEano6Z5Nj3KBNde9kXyjc5LzxJXDCJR3MjZaV_dM.jpg?width=320&crop=smart&auto=webp&s=f43a34a051e93fc38e37986bcdf1375010fc6e71', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/68IEano6Z5Nj3KBNde9kXyjc5LzxJXDCJR3MjZaV_dM.jpg?width=640&crop=smart&auto=webp&s=0d738ad274e5ffcfcc5e3606347e7c36a6ca07d9', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/68IEano6Z5Nj3KBNde9kXyjc5LzxJXDCJR3MjZaV_dM.jpg?width=960&crop=smart&auto=webp&s=9413099ba1c207d71014c59aaa649e7b6b5998d0', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/68IEano6Z5Nj3KBNde9kXyjc5LzxJXDCJR3MjZaV_dM.jpg?width=1080&crop=smart&auto=webp&s=6d0dead1ecbdb5ae89b02cf1aa2cb565ee2c9645', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/68IEano6Z5Nj3KBNde9kXyjc5LzxJXDCJR3MjZaV_dM.jpg?auto=webp&s=cfee165e19e945b9b06927fe3ac28bddef85b827', 'width': 1200}, 'variants': {}}]} | ||
Recent excellent datasets overview (volume 2) | 45 | People seemed to find my [previous overview](https://www.reddit.com/r/LocalLLaMA/comments/194v4h6/comment/khj32z5/?context=3) of recent datasets fairly useful so I thought I would do something similar again this week.
## Monitoring models trained on a particular dataset
Before diving into the datasets I wanted to share a tool I made for tracking new models trained on a particular dataset: [dataset-to-model-monitor](https://huggingface.co/spaces/librarian-bots/dataset-to-model-monitor). It's possible I'm the only person in the world with this use case, but I find it quite useful/interesting to keep tabs of models being trained on a particular dataset. You can add any dataset on the Hub and you'll get pinged by librarian-bot when a new model is trained on that dataset. There is some noise since people might train a garbage model on a dataset but I have found some pretty cool models through this. Example notifications for [LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara) here: [https://huggingface.co/spaces/librarian-bots/dataset-to-model-monitor/discussions/33](https://huggingface.co/spaces/librarian-bots/dataset-to-model-monitor/discussions/33)
## Datasets that (may) help bridge the gap between open and closed models
As a loose theme, I thought it would be nice to do a summary of datasets that might help bridge the gap between open and closed models in various ways.
## [HuggingFaceM4/WebSight](https://huggingface.co/datasets/HuggingFaceM4/WebSight)
One of the features of closed models (in particular GPT4-V) that people are very excited about is the ability to upload a screenshot and generate the code to generate that interface. As someone who is terrible at doing front-end having tools like this can be super useful but so far open models have lagged behind quite a bit with multimodal tasks and image -> code specifically. This is a dataset
>consists of 823,000 HTML/CSS codes representing synthetically generated English websites, each accompanied by a corresponding screenshot (rendered with Playwright).
With this dataset, you now have a whole bunch of data for helping to train multimodal on the task of screenshot -> code. There is some open discussion for a v2 [https://huggingface.co/datasets/HuggingFaceM4/WebSight/discussions/7](https://huggingface.co/datasets/HuggingFaceM4/WebSight/discussions/7), so if you have ideas for improvements, you can post there.
## [math-ai/StackMathQA](https://huggingface.co/datasets/math-ai/StackMathQA)
It's hard to know for sure, but a lot of people suspect that GPT4 etc are trained on a lot of textbooks. This may or may not be true, but it's likely that one thing they have over open models is a lot more $$$ to pay on data curation. This dataset is pretty simple in scope a "collection of 2 million mathematical questions and answers, sourced from various Stack Exchange sites", however, they did a lot of work to curate this data and make it useful and usable. There is obviously some work in combining different domain focused datasets but I think there is a lot of value in people working hard on a focused topic area and creating large **and** curated datasets.
## [nampdn-ai/tiny-strange-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-strange-textbooks)
Apart from having a nice name, I also think this is interesting because this dataset aims to reproduce work from Textbooks Are All You Need which underpins the Microsoft Phi models. Those models recently had a more open licenced applied which makes the models more usefully open but they haven't yet shared the underlying datasets/processes for creating the data. Microsoft did provide quite a lot of discussion about the general approach though so it's very cool to see someone pick up this work and I'm hoping that the community will become better and better at building these sorts of datasets effectively.
\--
As a final bit of self-promotion, today I pushed the remaining notebooks I used for creating [haiku\_dpo](https://huggingface.co/datasets/davanstrien/haiku_dpo) to [GitHub](https://github.com/davanstrien/haiku-dpo). Haiku\_dpo is a synthetic dataset aiming to improve LLMs for haiku generation, but my longer-term aim with that project is to try and develop some better approaches for efficiently steering LLMs towards your aesthetic preferences. I think this could be really valuable. I have no idea if I'll manage but I'm aiming to document what I find openly so other people can learn from all the stuff I do badly 😅 | 2024-01-19T17:41:57 | https://www.reddit.com/r/LocalLLaMA/comments/19aou0k/recent_excellent_datasets_overview_volume_2/ | dvanstrien | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19aou0k | false | null | t3_19aou0k | /r/LocalLLaMA/comments/19aou0k/recent_excellent_datasets_overview_volume_2/ | false | false | self | 45 | {'enabled': False, 'images': [{'id': 'h-Kdj9HTUQH1z2fpF4uA3tnTphtvvzpM-j0OD0bh5x0', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/mB5dBS9tsKZHXCnPjrVim6dLeJjbiKy8wJK-tWXMm_k.jpg?width=108&crop=smart&auto=webp&s=d46f20918bdb9201c2b15bd47ad7425d22904f2e', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/mB5dBS9tsKZHXCnPjrVim6dLeJjbiKy8wJK-tWXMm_k.jpg?width=216&crop=smart&auto=webp&s=7624db2a9cde07fb23dbac8c99e68a697bb2cfaf', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/mB5dBS9tsKZHXCnPjrVim6dLeJjbiKy8wJK-tWXMm_k.jpg?width=320&crop=smart&auto=webp&s=9bb443cadf28ea572b6fa82855451214e8af15f9', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/mB5dBS9tsKZHXCnPjrVim6dLeJjbiKy8wJK-tWXMm_k.jpg?width=640&crop=smart&auto=webp&s=94101a3a3d5971851c7fb8f2acdc5dac86631799', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/mB5dBS9tsKZHXCnPjrVim6dLeJjbiKy8wJK-tWXMm_k.jpg?width=960&crop=smart&auto=webp&s=81e0731b4947e63f07a3e9fe51f60948bb9d7de3', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/mB5dBS9tsKZHXCnPjrVim6dLeJjbiKy8wJK-tWXMm_k.jpg?width=1080&crop=smart&auto=webp&s=4c459abb0a9940aaf27d25d1d011b71fd5b5a20e', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/mB5dBS9tsKZHXCnPjrVim6dLeJjbiKy8wJK-tWXMm_k.jpg?auto=webp&s=50c316ac1c5c6aa76986bee1bdb7215a079ebd09', 'width': 1200}, 'variants': {}}]} |
Same prompt to different models yield vastly different results? | 1 | I am trying to generate some Multiple choice questions using zephyr-7b-beta-Q4, given some text. It does NOT perform well at all. In fact, the open-hermes-mistral-7b Q4 performs way better. The same prompt performs well in one of them while it fails for another llm. Why is it so?
For the same prompt, I tried using mistral-7b-GPTQ to generate the MCQs quicker and with more efficiency, but the responses so far are horrid. It repeats the prompt itself or returns unstructured and incomplete questions. The open-hermes-mistral-7b-Q4 performs way better even though it takes some time to process. Same with zephyr-7b-GPTQ.
However, the same prompt when ran on zephyr-7b-beta (without quantization), it performs brilliantly.
I am now confused what to believe and cannot string a logic for the above results. All I want is to build a pipeline to quickly generate quiz questions based on some input text using an llm. | 2024-01-19T17:34:59 | https://www.reddit.com/r/LocalLLaMA/comments/19aonsl/same_prompt_to_different_models_yield_vastly/ | overlord-i-am | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19aonsl | false | null | t3_19aonsl | /r/LocalLLaMA/comments/19aonsl/same_prompt_to_different_models_yield_vastly/ | false | false | self | 1 | null |
Let’s talk expander chassis | 1 | I got my hands on 4 24gb k80 for free. Lucky me. That said, I’m looking at expander chassis and they are priced crazy. What are you guys using? Am I going to have to bite the bullet? I would hate for these to go to waste. | 2024-01-19T17:29:59 | https://www.reddit.com/r/LocalLLaMA/comments/19aoj8x/lets_talk_expander_chassis/ | stonedoubt | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19aoj8x | false | null | t3_19aoj8x | /r/LocalLLaMA/comments/19aoj8x/lets_talk_expander_chassis/ | false | false | self | 1 | null |
So what is the fastest llm inference frontend now | 1 | there are so many inference frontend like vllm, koboldcpp, llama.cpp, fastchat... | 2024-01-19T17:12:39 | https://www.reddit.com/r/LocalLLaMA/comments/19ao495/so_what_is_the_fastest_llm_inference_frontend_now/ | MarySmith2021 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ao495 | false | null | t3_19ao495 | /r/LocalLLaMA/comments/19ao495/so_what_is_the_fastest_llm_inference_frontend_now/ | false | false | self | 1 | null |
Is there any medical AI model available? | 2 | Or is it possible to train one? I'm new to this topic. | 2024-01-19T16:57:25 | https://www.reddit.com/r/LocalLLaMA/comments/19anqfo/is_there_any_medical_ai_model_available/ | endlessnightmare718 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19anqfo | false | null | t3_19anqfo | /r/LocalLLaMA/comments/19anqfo/is_there_any_medical_ai_model_available/ | false | false | self | 2 | null |
llama.cpp now supports QuiP# (2-bit quant -> Mixtral in ~4GB) | 55 | 2024-01-19T16:57:16 | https://github.com/ggerganov/llama.cpp/pull/4773 | DreamGenX | github.com | 1970-01-01T00:00:00 | 0 | {} | 19anqbc | false | null | t3_19anqbc | /r/LocalLLaMA/comments/19anqbc/llamacpp_now_supports_quip_2bit_quant_mixtral_in/ | false | false | 55 | {'enabled': False, 'images': [{'id': 'Ggv83unvkWLyMFGpt3xtM1r0zdP4WjPSSKEGIVluetc', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/16CE_kKFjmcbObx29RCZNMRxDK7BMUw95WAtVVQly0s.jpg?width=108&crop=smart&auto=webp&s=759e40913572d0fc8b116b39379043daf004395d', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/16CE_kKFjmcbObx29RCZNMRxDK7BMUw95WAtVVQly0s.jpg?width=216&crop=smart&auto=webp&s=52112295c949c1e8051c9374a0cc558bc2e6ad85', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/16CE_kKFjmcbObx29RCZNMRxDK7BMUw95WAtVVQly0s.jpg?width=320&crop=smart&auto=webp&s=e7e98e7a3058c350ded486809b676531aeb07de2', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/16CE_kKFjmcbObx29RCZNMRxDK7BMUw95WAtVVQly0s.jpg?width=640&crop=smart&auto=webp&s=ae11b5ce5e6d1d31565150ac0698996af51f2769', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/16CE_kKFjmcbObx29RCZNMRxDK7BMUw95WAtVVQly0s.jpg?width=960&crop=smart&auto=webp&s=5b150466857c9e02211e18beb4f2ba2fa8bc4840', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/16CE_kKFjmcbObx29RCZNMRxDK7BMUw95WAtVVQly0s.jpg?width=1080&crop=smart&auto=webp&s=06d4189efaafdbf3082611548fdf4a17efe87b46', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/16CE_kKFjmcbObx29RCZNMRxDK7BMUw95WAtVVQly0s.jpg?auto=webp&s=5317beb6d07b5cdc1e97d60dccdf9c6a9e9679b4', 'width': 1200}, 'variants': {}}]} | ||
NSFW-friendly model at reasonable GPU costs | 1 | I’m looking for a good nsfw-friendly model, I won‘t run it locally, so I need to choose a nsfw-friendly model that runs on a GPU that wouldn’t cost me a leg and an arm.
It doesn’t have to be the absolute "best" model cuz I think I can do a lot with the right system prompt, but it definitely has to run on a GPU of an affordable price…
will probably use runpod btw, just not sure which GPU to choose and which model.
any help or guidance is very much appreciated! | 2024-01-19T16:49:05 | https://www.reddit.com/r/LocalLLaMA/comments/19anjei/nsfwfriendly_model_at_reasonable_gpu_costs/ | Ornery_Blacksmith645 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19anjei | false | null | t3_19anjei | /r/LocalLLaMA/comments/19anjei/nsfwfriendly_model_at_reasonable_gpu_costs/ | false | false | nsfw | 1 | null |
Fast and Expressive LLM Inference with RadixAttention and SGLang (5x throughput) | 22 | 2024-01-19T16:23:45 | https://lmsys.org/blog/2024-01-17-sglang/ | DreamGenX | lmsys.org | 1970-01-01T00:00:00 | 0 | {} | 19amxd8 | false | null | t3_19amxd8 | /r/LocalLLaMA/comments/19amxd8/fast_and_expressive_llm_inference_with/ | false | false | 22 | {'enabled': False, 'images': [{'id': 'QIp41Ioe_NTOyftrEJaXMK2v-LRnKCHRD4vexbYrK4A', 'resolutions': [{'height': 107, 'url': 'https://external-preview.redd.it/zJx6dpCrMlNPSz--6j_6BeLFrxS80BCm3VIgKhmiCuw.jpg?width=108&crop=smart&auto=webp&s=9527a2c5cd76d079749732ab5ef062b811abe48b', 'width': 108}, {'height': 215, 'url': 'https://external-preview.redd.it/zJx6dpCrMlNPSz--6j_6BeLFrxS80BCm3VIgKhmiCuw.jpg?width=216&crop=smart&auto=webp&s=04ef55acc40c9a40262634d3e3b70891d751d100', 'width': 216}, {'height': 319, 'url': 'https://external-preview.redd.it/zJx6dpCrMlNPSz--6j_6BeLFrxS80BCm3VIgKhmiCuw.jpg?width=320&crop=smart&auto=webp&s=070d667bb76def5c4631f522a7e2c6da6775d6c8', 'width': 320}, {'height': 639, 'url': 'https://external-preview.redd.it/zJx6dpCrMlNPSz--6j_6BeLFrxS80BCm3VIgKhmiCuw.jpg?width=640&crop=smart&auto=webp&s=b130b7f2159c9d0b7049a03dd8393813dc1e02fa', 'width': 640}, {'height': 958, 'url': 'https://external-preview.redd.it/zJx6dpCrMlNPSz--6j_6BeLFrxS80BCm3VIgKhmiCuw.jpg?width=960&crop=smart&auto=webp&s=323ea2124f5ca82da1e776285c8baafbea14d664', 'width': 960}, {'height': 1078, 'url': 'https://external-preview.redd.it/zJx6dpCrMlNPSz--6j_6BeLFrxS80BCm3VIgKhmiCuw.jpg?width=1080&crop=smart&auto=webp&s=1dd2187ab966d275e838b312faead2ca1be5ba7d', 'width': 1080}], 'source': {'height': 3208, 'url': 'https://external-preview.redd.it/zJx6dpCrMlNPSz--6j_6BeLFrxS80BCm3VIgKhmiCuw.jpg?auto=webp&s=754d2c91d788ad98d41e529b16e188b87adc74cb', 'width': 3212}, 'variants': {}}]} | ||
Mixture of Tokens: Efficient LLMs through Cross-Example Aggregation | 18 | **Paper**: [https://arxiv.org/abs/2310.15961](https://arxiv.org/abs/2310.15961)
**Code**: [https://github.com/llm-random/llm-random](https://github.com/llm-random/llm-random)
**Blog post**: [https://llm-random.github.io/posts/mixture\_of\_tokens/](https://llm-random.github.io/posts/mixture_of_tokens/)
**Abstract**:
>Despite the promise of Mixture of Experts (MoE) models in increasing parameter counts of Transformer models while maintaining training and inference costs, their application carries notable drawbacks. The key strategy of these models is to, for each processed token, activate at most a few experts - subsets of an extensive feed-forward layer. But this approach is not without its challenges. The operation of matching experts and tokens is discrete, which makes MoE models prone to issues like training instability and uneven expert utilization. Existing techniques designed to address these concerns, such as auxiliary losses or balance-aware matching, result either in lower model performance or are more difficult to train. In response to these issues, we propose **Mixture of Tokens**, a fully-differentiable model that retains the benefits of MoE architectures while avoiding the aforementioned difficulties. Rather than routing tokens to experts, this approach mixes tokens from different examples prior to feeding them to experts, enabling the model to learn from all token-expert combinations. Importantly, this mixing can be disabled to avoid mixing of different sequences during inference. Crucially, this method is fully compatible with both masked and causal Large Language Model training and inference. | 2024-01-19T15:38:10 | https://www.reddit.com/r/LocalLLaMA/comments/19alui8/mixture_of_tokens_efficient_llms_through/ | APaperADay | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19alui8 | false | null | t3_19alui8 | /r/LocalLLaMA/comments/19alui8/mixture_of_tokens_efficient_llms_through/ | false | false | self | 18 | null |
Need help in designing GenAI app | 1 | I need to build a GENAI app using GCP where I can ask questions like what is ROI increase compared to last year .... the model should should understand my question and convert into query and query the big tables in bigquery and get the results. After getting the results it should plot the chart and give insights about the fetched data. Give me a complete workflow and the LLMs used in a proper manner where I can use this for starting my proj
​ | 2024-01-19T15:19:09 | https://www.reddit.com/r/LocalLLaMA/comments/19alfet/need_help_in_designing_genai_app/ | shekdon | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19alfet | false | null | t3_19alfet | /r/LocalLLaMA/comments/19alfet/need_help_in_designing_genai_app/ | false | false | self | 1 | null |
What's llm(s) for translation from languages such as japanese, korean or chinese | 1 | [removed] | 2024-01-19T14:44:27 | https://www.reddit.com/r/LocalLLaMA/comments/19ako5n/whats_llms_for_translation_from_languages_such_as/ | DependentMore5540 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ako5n | false | null | t3_19ako5n | /r/LocalLLaMA/comments/19ako5n/whats_llms_for_translation_from_languages_such_as/ | false | false | self | 1 | null |
70B Parameter models on a 4 GB GPU? Is this real? | 84 | Doing an unrelated search I ran across this article on huggingface:
[https://huggingface.co/blog/lyogavin/airllm](https://huggingface.co/blog/lyogavin/airllm)
Can anyone tell me if this is legit and if so, are there models available on Huggingface. Is there some downside (e.g., speed of response) that I've missed that makes this impractical.
​
​ | 2024-01-19T14:34:38 | https://www.reddit.com/r/LocalLLaMA/comments/19akgm4/70b_parameter_models_on_a_4_gb_gpu_is_this_real/ | punter1965 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19akgm4 | false | null | t3_19akgm4 | /r/LocalLLaMA/comments/19akgm4/70b_parameter_models_on_a_4_gb_gpu_is_this_real/ | false | false | self | 84 | {'enabled': False, 'images': [{'id': 'JKj2quQtQHqfm7UoSQ6cRc8-bpyqWhEdoI_2VAPxbTg', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/zNWxqrIJKSGUYHMm55Vg8fZzzh8kUFQFrnJyWVCLBq4.jpg?width=108&crop=smart&auto=webp&s=2e656cc4c9829c9349c7f289b602949c6105961d', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/zNWxqrIJKSGUYHMm55Vg8fZzzh8kUFQFrnJyWVCLBq4.jpg?width=216&crop=smart&auto=webp&s=65eb6f67205948cb58926dfce2f086965396c20d', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/zNWxqrIJKSGUYHMm55Vg8fZzzh8kUFQFrnJyWVCLBq4.jpg?width=320&crop=smart&auto=webp&s=cf452e43412eaaa09aaba311afef77ef32608b2b', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/zNWxqrIJKSGUYHMm55Vg8fZzzh8kUFQFrnJyWVCLBq4.jpg?width=640&crop=smart&auto=webp&s=ddba0f1f0f92b58b28743d79df627673e212fbf2', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/zNWxqrIJKSGUYHMm55Vg8fZzzh8kUFQFrnJyWVCLBq4.jpg?width=960&crop=smart&auto=webp&s=867417330292a1c0a11bb2657c49b3283a0293e6', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/zNWxqrIJKSGUYHMm55Vg8fZzzh8kUFQFrnJyWVCLBq4.jpg?width=1080&crop=smart&auto=webp&s=1453318eacf64a23dd55ed07c97b79fb31b26f52', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/zNWxqrIJKSGUYHMm55Vg8fZzzh8kUFQFrnJyWVCLBq4.jpg?auto=webp&s=165396778baf670cbc5ead78ccf5780fb089beeb', 'width': 1200}, 'variants': {}}]} |
Describe your local LLM Setup | 25 | I'd like to understand what you guys use as your local setup. What models (eg llama/mistral/qwen) do you use in conjuction with what tools ( eg memGPT, autoGPT, langchain, crewAI and whatnot).
Do you have different models/tools for different use cases? How much of your workflow is automated with LLMs.
And how often (if at all) do you finetune models to suit your needs better?
I feel that there is so much potential in local LLMs that I'm missing out on and would love to learn from you guys...
| 2024-01-19T14:25:43 | https://www.reddit.com/r/LocalLLaMA/comments/19ak9x1/describe_your_local_llm_setup/ | im_datta0 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ak9x1 | false | null | t3_19ak9x1 | /r/LocalLLaMA/comments/19ak9x1/describe_your_local_llm_setup/ | false | false | self | 25 | null |
Embed Llama into a Mac Email Client. Best ways? | 5 | Hey folks, am building a unified inbox Mac Email Client and looking to embed Llama locally within it? Any pointers on the best way to do this? Purpose: automate organisation of emails that hit the inbox. | 2024-01-19T14:17:01 | https://www.reddit.com/r/LocalLLaMA/comments/19ak3hw/embed_llama_into_a_mac_email_client_best_ways/ | anrgy971 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ak3hw | false | null | t3_19ak3hw | /r/LocalLLaMA/comments/19ak3hw/embed_llama_into_a_mac_email_client_best_ways/ | false | false | self | 5 | null |
Creative writing notebook frontend/GUI | 10 | One thing I miss in the local LLM ecosystem is a powerful notebook mode - or maybe I don't know about it?
By notebook mode I mean what it is. A canvas where you can write text, and you can have your model continue writing from there. Oobabooga admittedly has one, and there's an extension called Playground. But both feel lacking compare to, for example, NovelAI's interface.
The ability to easily edit your text and have the AI continue, to check what tokens the AI considered for every token shown and the ability to then "rewrite" its output starting from a token you chose to replace are great ways to steer the AI into writing what you want.
Also the ability to save your work easily, have lorebooks that automatically insert in the context a piece of information upon being triggered with a keyword or regex, etc., that's what I miss for my local AI experience.
So probably they don't exist, but just in case I don't lose anything by asking! Right now in order to do creative writing with the AI I have to keep editing the replies on SillyTavern and having the AI continue from there, but it's not the same. Not at all. | 2024-01-19T14:10:07 | https://www.reddit.com/r/LocalLLaMA/comments/19ajy9g/creative_writing_notebook_frontendgui/ | CulturedNiichan | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ajy9g | false | null | t3_19ajy9g | /r/LocalLLaMA/comments/19ajy9g/creative_writing_notebook_frontendgui/ | false | false | self | 10 | null |
Is there any website compare inference speed of different LLM on different platforms? | 6 | LLM inference speed is very important when we deploy models. But I could not find any website list inference speed of different models on different platforms? Is there any github repository or website do such things? | 2024-01-19T13:56:24 | https://www.reddit.com/r/LocalLLaMA/comments/19ajnox/is_there_any_website_compare_inference_speed_of/ | DataLearnerAI | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ajnox | false | null | t3_19ajnox | /r/LocalLLaMA/comments/19ajnox/is_there_any_website_compare_inference_speed_of/ | false | false | self | 6 | null |
Compare LLMs and hosting providers - quality, price, throughput and latency. Supports all major models (e.g. GPT-4, Mixtral 8x7B, Llama 2), and API hosting providers (e.g. Azure, Together, Perplexity, Deepinfra). | 20 | 2024-01-19T13:41:27 | https://artificialanalysis.ai/ | _micah_h | artificialanalysis.ai | 1970-01-01T00:00:00 | 0 | {} | 19ajcd7 | false | null | t3_19ajcd7 | /r/LocalLLaMA/comments/19ajcd7/compare_llms_and_hosting_providers_quality_price/ | false | false | 20 | {'enabled': False, 'images': [{'id': 'RVufpUx3tddh_BCVq7ZzBCU7nLRDZ_d0EprIuvN6J-E', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=108&crop=smart&auto=webp&s=ff8c322202cb0f1a1f82f87a2c77754ddc0b9e61', 'width': 108}, {'height': 120, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=216&crop=smart&auto=webp&s=e20458b3bc0a4d8ebf3e09b7e3615cfda4e00844', 'width': 216}, {'height': 177, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=320&crop=smart&auto=webp&s=508265ec16105ddc4d2105e057c292f8470229ac', 'width': 320}, {'height': 355, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=640&crop=smart&auto=webp&s=690b875bfe1b25ba2e96b432c42bb1b096935efd', 'width': 640}, {'height': 533, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=960&crop=smart&auto=webp&s=ee86a1133471b58f18d2dbf89ec1c88906c2d623', 'width': 960}, {'height': 600, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=1080&crop=smart&auto=webp&s=e42c63d534439a755f46f08c5db09cbaaefca3d0', 'width': 1080}], 'source': {'height': 700, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?auto=webp&s=6e0008e17dc8f6f6b13799bc7416400acacbaca0', 'width': 1260}, 'variants': {}}]} | ||
I made a custom ai-powered error message for bash | 224 | 2024-01-19T13:37:14 | MarySmith2021 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 19aj9g7 | false | null | t3_19aj9g7 | /r/LocalLLaMA/comments/19aj9g7/i_made_a_custom_aipowered_error_message_for_bash/ | false | false | 224 | {'enabled': True, 'images': [{'id': 'YsBQIWg-_dXcUKz8d71M_2hveScaOPrauC3svMibdR8', 'resolutions': [{'height': 22, 'url': 'https://preview.redd.it/xtqse64wgedc1.png?width=108&crop=smart&auto=webp&s=839357ed74c347151bcf79495ab199883fc3132c', 'width': 108}, {'height': 45, 'url': 'https://preview.redd.it/xtqse64wgedc1.png?width=216&crop=smart&auto=webp&s=3f85d6cd9071dd0d9c480724aca27f19bd5d69d3', 'width': 216}, {'height': 66, 'url': 'https://preview.redd.it/xtqse64wgedc1.png?width=320&crop=smart&auto=webp&s=81b2c0e08fdcc8e2523458bfe30ac24965a653c3', 'width': 320}, {'height': 133, 'url': 'https://preview.redd.it/xtqse64wgedc1.png?width=640&crop=smart&auto=webp&s=e198d4fab178506011fbbdb688b2632042a18247', 'width': 640}, {'height': 200, 'url': 'https://preview.redd.it/xtqse64wgedc1.png?width=960&crop=smart&auto=webp&s=e7b7dcab7a035c00b0c113e2e472ac8e145b4519', 'width': 960}, {'height': 225, 'url': 'https://preview.redd.it/xtqse64wgedc1.png?width=1080&crop=smart&auto=webp&s=bea0c4004b22c3f00d57841f0d25476926b605f9', 'width': 1080}], 'source': {'height': 225, 'url': 'https://preview.redd.it/xtqse64wgedc1.png?auto=webp&s=5f49b7071376f88bc108180b77dc7a7aeaef428c', 'width': 1080}, 'variants': {}}]} | |||
What is the fastest model which can run on 64gb ram cpu | 1 | [removed] | 2024-01-19T12:31:02 | https://www.reddit.com/r/LocalLLaMA/comments/19ai1h4/what_is_the_fastest_model_which_can_run_on_64gb/ | AnabelBain | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ai1h4 | false | null | t3_19ai1h4 | /r/LocalLLaMA/comments/19ai1h4/what_is_the_fastest_model_which_can_run_on_64gb/ | false | false | self | 1 | null |
How do I train a decoder model? | 1 | [removed] | 2024-01-19T12:28:15 | https://www.reddit.com/r/LocalLLaMA/comments/19ahzr9/how_do_i_train_a_decoder_model/ | Xanta_Kross | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ahzr9 | false | null | t3_19ahzr9 | /r/LocalLLaMA/comments/19ahzr9/how_do_i_train_a_decoder_model/ | false | false | self | 1 | null |
Requesting some performance data for pure CPU inference on DDR5-based consumer hardware | 26 | Hey. I am trying to build a strong system for CPU inference in the consumer area. Dual channel DDR5-6000 RAM bandwidth is usually listed at 80-90 GB/s. I learned that mostly you can only use 2 RAM banks, or the bandwidth will go down dramatically.
So that gives us systems with 64-96 GB RAM as far as I can tell, based on what is possible with two banks. But it seems to be a bit more complicated with DIMMS per bank or whatever. Still learning that stuff. Anyway, with the ~90GB per second, that would give us a ballpark theoretical limit of 1.5 tokens per second on a 60GB non-MoE model. Or like 11 t/s on a q8 7B. Which I find pretty amazing for that budget area and no GPU at all. And for MoE it's even more interesting, but please don't test with that as it would mess with the numbers.
What I am asking for is that if you have a relevant system, please post some inference performance data (eval time only). Again, it should be a DDR5 system, it should only use two RAM slots for maximum RAM bandwidth, and it should of course be properly set up (so threads = physical core count, maybe minus 1). Along with telling us what CPU that is and what specific RAM it is, and of course the model size/quant you tested with.
Ideally you would use a non-BLAS enabled build of llama.cpp to make sure there is zero aid from the GPU, which could otherwise happen with some kv offloading or whatever it's called. But you can probably just make sure that's off (which apparently might be bugged in current builds). If you have no idea about that stuff, as long as you used 0 gpu layers, please still just post your result and just tell us that you're not sure.
What I am trying to find out this way is what CPUs are maxing out the potential of the dual channel DDR5 RAM bandwidth. My intuition is that this does not take the best of the best consumer CPU. So I am trying not to waste money on a 7950X3D when a 7800X or whatever would already do the job perfectly.
Thank you! | 2024-01-19T12:20:31 | https://www.reddit.com/r/LocalLLaMA/comments/19ahv1u/requesting_some_performance_data_for_pure_cpu/ | involviert | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ahv1u | false | null | t3_19ahv1u | /r/LocalLLaMA/comments/19ahv1u/requesting_some_performance_data_for_pure_cpu/ | false | false | self | 26 | null |
false positive of llama 2, llama 3 should be a true positive | 1 | nothing new but llama 2 is actually not open source, and it seems that meta is marketing llama as a false positive open source while it isn't, so i would say that llama 2 isn't fully open source.
Concerned about llama 3 that it will be another false positive like llama 2.
But besides llama 2, how should large language models be distributed so it can actually meet the definition of open source?
because llm's have many different components unlike a single software, for example the training data, training code, weights, etc.
shouldn't all of these components be distributed as well to meet the definition of open source? plus are there any more components other than the training data, training code and the weights to match up the puzzle of the llm? | 2024-01-19T12:10:32 | https://www.reddit.com/r/LocalLLaMA/comments/19ahouf/false_positive_of_llama_2_llama_3_should_be_a/ | bull_shit123 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ahouf | false | null | t3_19ahouf | /r/LocalLLaMA/comments/19ahouf/false_positive_of_llama_2_llama_3_should_be_a/ | false | false | self | 1 | null |
false positive of llama 2, llama 3 should be a true positive | 1 | [removed] | 2024-01-19T12:08:32 | https://www.reddit.com/r/LocalLLaMA/comments/19ahnlr/false_positive_of_llama_2_llama_3_should_be_a/ | qwertykeyboard_1 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ahnlr | false | null | t3_19ahnlr | /r/LocalLLaMA/comments/19ahnlr/false_positive_of_llama_2_llama_3_should_be_a/ | false | false | self | 1 | null |
Windows AI PCs could run local LLMs and other foundation models | 71 | 2024-01-19T10:58:50 | https://www.tomshardware.com/software/windows/microsofts-baseline-ram-for-ai-pcs-set-at-16gb | Some_Endian_FP17 | tomshardware.com | 1970-01-01T00:00:00 | 0 | {} | 19agj9t | false | null | t3_19agj9t | /r/LocalLLaMA/comments/19agj9t/windows_ai_pcs_could_run_local_llms_and_other/ | false | false | 71 | {'enabled': False, 'images': [{'id': 'zyIM9TdicnQu5tPmxB8QdQvYNv8PuFDPMywtIhV_HDQ', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/v8WMcXKKMXgFcCFSveX2AjP1zhSrIo97CTMXCUhKwnw.jpg?width=108&crop=smart&auto=webp&s=9563f2d0fa4bd5e3324e638bc4bc96571bf527d7', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/v8WMcXKKMXgFcCFSveX2AjP1zhSrIo97CTMXCUhKwnw.jpg?width=216&crop=smart&auto=webp&s=9daa9e23081923ef9f3712408eafb2092ef29365', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/v8WMcXKKMXgFcCFSveX2AjP1zhSrIo97CTMXCUhKwnw.jpg?width=320&crop=smart&auto=webp&s=0f4d8ba6a6ec3f09bff715c4a9aed50c0ba9f12b', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/v8WMcXKKMXgFcCFSveX2AjP1zhSrIo97CTMXCUhKwnw.jpg?width=640&crop=smart&auto=webp&s=3b88e42f43c8dd0a89b0de232a3d273a679c7a43', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/v8WMcXKKMXgFcCFSveX2AjP1zhSrIo97CTMXCUhKwnw.jpg?width=960&crop=smart&auto=webp&s=d96052de8b4a53d68daefd32d69ebf0bcddc09c6', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/v8WMcXKKMXgFcCFSveX2AjP1zhSrIo97CTMXCUhKwnw.jpg?width=1080&crop=smart&auto=webp&s=23a0702eb838a9cab60321a527c045df4d89d45c', 'width': 1080}], 'source': {'height': 675, 'url': 'https://external-preview.redd.it/v8WMcXKKMXgFcCFSveX2AjP1zhSrIo97CTMXCUhKwnw.jpg?auto=webp&s=e6b49c1cc8fc7221ab5d584bbf2e7e42a79aad41', 'width': 1200}, 'variants': {}}]} | ||
Can a locally run llm on m2 air 8gb handle 50 users for simple rag and summary? | 2 | I am using mistral 7b 4\_bit quant and I want to continue using this for rag. Can I ship the app to users? How many users will it handle? If not, what are some free cloud alternatives that can help me with it?
Your help is deeply appreicated. | 2024-01-19T10:22:05 | https://www.reddit.com/r/LocalLLaMA/comments/19ag0pf/can_a_locally_run_llm_on_m2_air_8gb_handle_50/ | coderinlaw | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19ag0pf | false | null | t3_19ag0pf | /r/LocalLLaMA/comments/19ag0pf/can_a_locally_run_llm_on_m2_air_8gb_handle_50/ | false | false | self | 2 | null |
Andrej Karpathy liked this tweet unironically. Does he know he works at “Open”AI? | 1 | 2024-01-19T09:59:05 | AliVit24 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 19afoh1 | false | null | t3_19afoh1 | /r/LocalLLaMA/comments/19afoh1/andrej_karpathy_liked_this_tweet_unironically/ | false | false | 1 | {'enabled': True, 'images': [{'id': 'xhM_mVDoHmLOTkIxEC75a2nktdGcB9itr2tbMaxB55Q', 'resolutions': [{'height': 174, 'url': 'https://preview.redd.it/t72ap8vydddc1.png?width=108&crop=smart&auto=webp&s=198c22f54f2f10303cc606bdc7c52f29ae53e4c0', 'width': 108}, {'height': 349, 'url': 'https://preview.redd.it/t72ap8vydddc1.png?width=216&crop=smart&auto=webp&s=9824f9e9f5bde7473fb52e1eb4b92a68031d1d28', 'width': 216}, {'height': 517, 'url': 'https://preview.redd.it/t72ap8vydddc1.png?width=320&crop=smart&auto=webp&s=7a76d43d433887aec51e49fa00758aeeeb90bdd3', 'width': 320}, {'height': 1034, 'url': 'https://preview.redd.it/t72ap8vydddc1.png?width=640&crop=smart&auto=webp&s=3b4364c2c062c0461d06d1a64678123b346fd66a', 'width': 640}, {'height': 1551, 'url': 'https://preview.redd.it/t72ap8vydddc1.png?width=960&crop=smart&auto=webp&s=e69b769c7754b1772d5ab4404385dda7cfd5e00b', 'width': 960}, {'height': 1745, 'url': 'https://preview.redd.it/t72ap8vydddc1.png?width=1080&crop=smart&auto=webp&s=2700ed1150e2262d07ae9845eb959762c0c40570', 'width': 1080}], 'source': {'height': 1745, 'url': 'https://preview.redd.it/t72ap8vydddc1.png?auto=webp&s=b91a9c523b0e3a53d25f2c02b4f2ffc45de77f7f', 'width': 1080}, 'variants': {}}]} | |||
Release Smooth Sampling Test Build (koboldcpp) · kalomaze/koboldcpp | 26 | 2024-01-19T09:48:22 | https://github.com/kalomaze/koboldcpp/releases/tag/smooth-sampling-v1 | ambient_temp_xeno | github.com | 1970-01-01T00:00:00 | 0 | {} | 19afj5r | false | null | t3_19afj5r | /r/LocalLLaMA/comments/19afj5r/release_smooth_sampling_test_build_koboldcpp/ | false | false | 26 | {'enabled': False, 'images': [{'id': 'Z0XffDVTXbZJntfvrXgp4QRPWz4E8PQTTwXgPdGScE4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/I27le4BEN681RlY5A5pWo6xIABAMpUN6xknR78N74Qg.jpg?width=108&crop=smart&auto=webp&s=2822553d03e539756cb47367280e07dc7fc9e903', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/I27le4BEN681RlY5A5pWo6xIABAMpUN6xknR78N74Qg.jpg?width=216&crop=smart&auto=webp&s=f6b936bab9fabc19927af6994f78210155ca668d', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/I27le4BEN681RlY5A5pWo6xIABAMpUN6xknR78N74Qg.jpg?width=320&crop=smart&auto=webp&s=a7d6518d8f410b1e817eb272b2b8f4a4f47ce9a8', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/I27le4BEN681RlY5A5pWo6xIABAMpUN6xknR78N74Qg.jpg?width=640&crop=smart&auto=webp&s=1cadd75f999c7f2c2ab36e6aa1c60bfdd786e63f', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/I27le4BEN681RlY5A5pWo6xIABAMpUN6xknR78N74Qg.jpg?width=960&crop=smart&auto=webp&s=740f5d528396068f26a52bbaafb346c02ed0b787', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/I27le4BEN681RlY5A5pWo6xIABAMpUN6xknR78N74Qg.jpg?width=1080&crop=smart&auto=webp&s=013af6e2408ae9aef300f77f107a94c2a3386311', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/I27le4BEN681RlY5A5pWo6xIABAMpUN6xknR78N74Qg.jpg?auto=webp&s=a9d52f26f460533e69e7d0635ae149706937e804', 'width': 1200}, 'variants': {}}]} | ||
Building a State-of-the-Art Video Summarizer: Part 2/6 - Increasing Signal-to-noise ratio | 18 | Welcome to the second installment of our six-part series on developing a state-of-the-art video summarizer. We will build this ground up from simple first principles.
In Part 1, I talked about semantic segmentation, breaking down videos into smaller, manageable parts according to semantic idea.
Now, in Part 2, will look at: filtering out the extras to spotlight what's really important.
**Reframing the Summarization Challenge: Increasing Signal-to-Noise Ratio (SNR)**
I personally find it useful to reframe video summarization as the following problem: to increase the signal-to-noise ratio. The 'signal' represents the video's essential theme or content, while 'noise' encompasses all elements that don't contribute to this central theme.
**Focusing on the Noise**
In this part, I mainly focus on cutting down the **noise**; only the denominator. That means we'll be looking at each piece of the video and deciding if it's important. Later on, in other parts of the series, we'll talk about how to make the important parts – the signal – more and even better. The denominator of SNR is the focus of this post and not the numerator.
**Personalizing Signal and Noise**
The concept of signal and noise is subjective. So before we start fiddling with the SNR. You need to decide what is signal and what is noise to YOU. Have some internal heuristics and thresholds.
Personally I have a very "high threshold", i.e. I throw away anything that is not even remotely related to the main content. You need to look at your use case decide those heuristics.
Also, I have iterated this over multiple months with my end users and they also have a say on this. So you can recursively also use that data points as a feedback to improve and iterate.
So have some heuristics to categorize what is signal and what is noise. And then keep tuning the heurisitcs.
**How I Do It**
Let me share my approach, which may not be the most optimal or PhD level. But for tinkerer like me; its been effective.
So you need three things : **the video chunk transcription, the video title and video description.** For a platform like YouTube, it's possible to "smartly" extract all of these three things.
I then take each chunk then using a large language model (LLM), I check whether a chunk's transcript aligns with the main theme of the video. So you simply ask a LLM to do this task for you (this is what humans would do right, "listen" to the video chunk and see if it actually aligns). You need to bit of prompt engineering to align the LLM with your heuristics.
But this way you can essentially ask a LLM : "hey should I keep this chunk or not; what say you?" And this way you can simply start filtering.
**Make it Optimal**
While this method is effective, I've developed a slightly more sophisticated approach. Initially, you'll need to perform this task repeatedly with LLM. After processing, say, around 100,000 data points. You can use this dataset to training a simple binary classifier like BERT or its variants.
So to summarize this part (which is the most important) is
a) to first establish a clear understanding of what constitutes the main theme of the video.
b) compare each chunk against the main theme using an LLM. This step is already enough. But optionally, we can gather training data and move to step (c).
c ) once this data pool is large enough, we transition to train a binary classifier. This step is optional. But the goal of this step is to making it highly efficient for production use
**Next Steps: Enhancing Signal Quality**
So that its; now you have a nice filter to decrease the noise. What's Next?
After we've gotten rid of the noise, we'll focus on making the signal – the important parts – as good as they can be. We want our summaries to be short, but also full of the best parts of the video.
I will write the other ones later. | 2024-01-19T09:27:09 | https://www.reddit.com/r/LocalLLaMA/comments/19af8gf/building_a_stateoftheart_video_summarizer_part_26/ | phoneixAdi | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19af8gf | false | null | t3_19af8gf | /r/LocalLLaMA/comments/19af8gf/building_a_stateoftheart_video_summarizer_part_26/ | false | false | self | 18 | {'enabled': False, 'images': [{'id': 'N7OVwHB8guLsgDy_fqqVkjuLvVnhm9RcDTvOZ23Qd6s', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/3tWbnoRvV-hqwjjSr4TZads428MzgmBave3daB9abjs.jpg?width=108&crop=smart&auto=webp&s=61e0bb401432d762fc29991355db5f4290f92ad2', 'width': 108}], 'source': {'height': 200, 'url': 'https://external-preview.redd.it/3tWbnoRvV-hqwjjSr4TZads428MzgmBave3daB9abjs.jpg?auto=webp&s=27703279fc4d1229464ebacd7a4959a211dba7dc', 'width': 200}, 'variants': {}}]} |
I have an M2 Ultra Mac Studio - 192GB RAM & 76 GPU cores. AMA | 1 | As the title says - after a while of research looking for a suitable R&D machine, we purchased a fully specced Mac Studio. Unfortunately, we couldn’t find many benchmarks. Let me know what models you’d like to see benchmarked (using llama.cpp mostly) and I’ll do my best to report back.
Initial testing using llama.cpp and all layers offloaded to GPU:
Llama-2-70b-chat at Q8_0 is 8 tok/s
Mixtral 8x7b v0.1
- Q8_0 is 36 tok/s,
- Q4_K_M is 50 tok/s.
Thanks | 2024-01-19T09:22:24 | https://www.reddit.com/r/LocalLLaMA/comments/19af61l/i_have_an_m2_ultra_mac_studio_192gb_ram_76_gpu/ | macstudiouser | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19af61l | false | null | t3_19af61l | /r/LocalLLaMA/comments/19af61l/i_have_an_m2_ultra_mac_studio_192gb_ram_76_gpu/ | false | false | self | 1 | null |
Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering | 13 | LINK: https://github.com/Codium-ai/AlphaCodium
Abstract:
Code generation problems differ from common natural language problems - they require matching the exact syntax of the target language, identifying happy paths and edge cases, paying attention to numerous small details in the problem spec, and addressing other code-specific issues and requirements. Hence, many of the optimizations and tricks that have been successful in natural language generation may not be effective for code tasks.
In this work, we propose a new approach to code generation by LLMs, which we call AlphaCodium - a test-based, multi-stage, code-oriented iterative flow, that improves the performances of LLMs on code problems.
We tested AlphaCodium on a challenging code generation dataset called CodeContests, which includes competitive programming problems from platforms such as Codeforces. The proposed flow consistently and significantly improves results. On the validation set, for example, GPT-4 accuracy (pass@5) increased from 19% with a single well-designed direct prompt to 44% with the AlphaCodium flow.
Many of the principles and best practices we acquired in this work, we believe, are broadly applicable to general code generation tasks. | 2024-01-19T08:39:50 | AliVit24 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 19aekea | false | null | t3_19aekea | /r/LocalLLaMA/comments/19aekea/code_generation_with_alphacodium_from_prompt/ | false | false | 13 | {'enabled': True, 'images': [{'id': '6Mme0iePDPgm2tIsFv5dHWrKQlqMVXk6_2-aVCVZzlA', 'resolutions': [{'height': 80, 'url': 'https://preview.redd.it/yprpsjxtzcdc1.png?width=108&crop=smart&auto=webp&s=6135115dc6a7a12dba96cbc64fae015adf0205d4', 'width': 108}, {'height': 161, 'url': 'https://preview.redd.it/yprpsjxtzcdc1.png?width=216&crop=smart&auto=webp&s=a50980c1866b3d0d8342414fd101eb4048f209b8', 'width': 216}, {'height': 239, 'url': 'https://preview.redd.it/yprpsjxtzcdc1.png?width=320&crop=smart&auto=webp&s=79f8d3ba14aa276be77b705f0e438ae9ce9cd2ff', 'width': 320}, {'height': 479, 'url': 'https://preview.redd.it/yprpsjxtzcdc1.png?width=640&crop=smart&auto=webp&s=0706682868b3b2a8145128ceb7bd08c6be250b51', 'width': 640}], 'source': {'height': 493, 'url': 'https://preview.redd.it/yprpsjxtzcdc1.png?auto=webp&s=b7f5915957821ba4680592b10d79f8918f062819', 'width': 658}, 'variants': {}}]} | ||
OPENSOURCE: AlphaGeometry: An Olympiad-level AI system for geometry | 3 | LINK:https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/?utm_source=twitter&utm_medium=social
“Reflecting the Olympic spirit of ancient Greece, the International Mathematical Olympiad is a modern-day arena for the world's brightest high-school mathematicians. The competition not only showcases young talent, but has emerged as a testing ground for advanced AI systems in math and reasoning.
In a paper published today in Nature, we introduce AlphaGeometry, an AI system that solves complex geometry problems at a level approaching a human Olympiad gold-medalist - a breakthrough in AI performance. In a benchmarking test of 30 Olympiad geometry problems, AlphaGeometry solved 25 within the standard Olympiad time limit. For comparison, the previous state-of-the-art system solved 10 of these geometry problems, and the average human gold medalist solved 25.9 problems.
In our benchmarking set of 30 Olympiad geometry problems (IMO-AG-30), compiled from the Olympiads from 2000 to 2022, AlphaGeometry solved 25 problems under competition time limits. This is approaching the average score of human gold medalists on these same problems. The previous state-of-the-art approach, known as “Wu’s method”, solved 10.
AI systems often struggle with complex problems in geometry and mathematics due to a lack of reasoning skills and training data. AlphaGeometry’s system combines the predictive power of a neural language model with a rule-bound deduction engine, which work in tandem to find solutions. And by developing a method to generate a vast pool of synthetic training data - 100 million unique examples - we can train AlphaGeometry without any human demonstrations, sidestepping the data bottleneck.
With AlphaGeometry, we demonstrate AI’s growing ability to reason logically, and to discover and verify new knowledge. Solving Olympiad-level geometry problems is an important milestone in developing deep mathematical reasoning on the path towards more advanced and general AI systems. We are open-sourcing the AlphaGeometry code and model, and hope that together with other tools and approaches in synthetic data generation and training, it helps open up new possibilities across mathematics, science, and AI.” | 2024-01-19T08:36:58 | AliVit24 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 19aej0r | false | null | t3_19aej0r | /r/LocalLLaMA/comments/19aej0r/opensource_alphageometry_an_olympiadlevel_ai/ | false | false | default | 3 | {'enabled': True, 'images': [{'id': 'aCczxeeGByNWYTilPzoKoocYvc14Epd9iqFlwzGc1IA', 'resolutions': [{'height': 191, 'url': 'https://preview.redd.it/sd0r53fbzcdc1.png?width=108&crop=smart&auto=webp&s=c1ef0614a63634633704e5c44efb80d14574884a', 'width': 108}, {'height': 383, 'url': 'https://preview.redd.it/sd0r53fbzcdc1.png?width=216&crop=smart&auto=webp&s=44ce57c404b43cbfee1a99fab4f62b001050e01a', 'width': 216}, {'height': 567, 'url': 'https://preview.redd.it/sd0r53fbzcdc1.png?width=320&crop=smart&auto=webp&s=8b6cf2a922085ca81b8a60f9e3fed22d202885bc', 'width': 320}, {'height': 1135, 'url': 'https://preview.redd.it/sd0r53fbzcdc1.png?width=640&crop=smart&auto=webp&s=bc9fd2534ac64c5f4405356304e3fee3359574d2', 'width': 640}, {'height': 1703, 'url': 'https://preview.redd.it/sd0r53fbzcdc1.png?width=960&crop=smart&auto=webp&s=b1c54cc1db12e2b8145071f88683fc2a042cf3e8', 'width': 960}, {'height': 1916, 'url': 'https://preview.redd.it/sd0r53fbzcdc1.png?width=1080&crop=smart&auto=webp&s=83baff62e91f99f2948ee7cbd144e9ab47b53c44', 'width': 1080}], 'source': {'height': 1916, 'url': 'https://preview.redd.it/sd0r53fbzcdc1.png?auto=webp&s=a707773e94f40da4a805ec0743a0941e5931eb6c', 'width': 1080}, 'variants': {}}]} | |
Self-Rewarding Language Models: | 2 | LINK: https://arxiv.org/abs/2401.10020
We posit that to achieve superhuman agents, future models require superhuman feedback in order to provide an adequate training signal. Current approaches commonly train reward models from human preferences, which may then be bottlenecked by human performance level, and secondly these separate frozen reward models cannot then learn to improve during LLM training. In this work, we study Self-Rewarding Language Models, where the language model itself is used via LLM-as-a-Judge prompting to provide its own rewards during training. We show that during Iterative DPO training that not only does instruction following ability improve, but also the ability to provide high-quality rewards to itself. Fine-tuning Llama 2 70B on three iterations of our approach yields a model that outperforms many existing systems on the AlpacaEval 2.0 leaderboard, including Claude 2, Gemini Pro, and GPT-4 0613. While only a preliminary study, this work opens the door to the possibility of models that can continually improve in both axes. | 2024-01-19T08:33:41 | AliVit24 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 19aehcm | false | null | t3_19aehcm | /r/LocalLLaMA/comments/19aehcm/selfrewarding_language_models/ | false | false | 2 | {'enabled': True, 'images': [{'id': 'QqcbuxeXHvoMiTBpsqDmjko9vUw97dRJDPEseTyccPI', 'resolutions': [{'height': 164, 'url': 'https://preview.redd.it/ur9wpgdqycdc1.png?width=108&crop=smart&auto=webp&s=b4616a717e5c50213ee8fb7499cf49a9924a6427', 'width': 108}, {'height': 329, 'url': 'https://preview.redd.it/ur9wpgdqycdc1.png?width=216&crop=smart&auto=webp&s=cbc209eeba791597a90b0dc368b98d0e13827bb3', 'width': 216}, {'height': 488, 'url': 'https://preview.redd.it/ur9wpgdqycdc1.png?width=320&crop=smart&auto=webp&s=c1320a08594be37de0acf4eb1239f7c8b6522f5c', 'width': 320}, {'height': 976, 'url': 'https://preview.redd.it/ur9wpgdqycdc1.png?width=640&crop=smart&auto=webp&s=d5c66232872f58d181d78eb50f984b0dc0548c3e', 'width': 640}, {'height': 1465, 'url': 'https://preview.redd.it/ur9wpgdqycdc1.png?width=960&crop=smart&auto=webp&s=eda30ff3879af67692eb611029cbdc1b4eeab1a3', 'width': 960}], 'source': {'height': 1647, 'url': 'https://preview.redd.it/ur9wpgdqycdc1.png?auto=webp&s=d4da851a2970e762175739290e8a3b643a2d6a1d', 'width': 1079}, 'variants': {}}]} | ||
RolePlay not obeying prompts on any models | 1 | I have tried to get RP going across 2 of my machines across multiple models as listed bellow. Some of the models instantly begins telling a story of the characters, some of them will start in an action and a prompt and then move into a story. I'm not even worried about addressing how the AI eventually flips its understanding of who it is or the grammatical perspective it narrates as.
I'm running the latest Ollama from linux console:
​
HOWEVER
If I post any of these characters into chatGPT it instantly begins and maintains a back and forth RolePlaying.
​
Models Machine 1: 64gb DDR5 RTX w/12GB
GOAT-70B-Storytelling:latest
guanaco-65B-FOR\_STORY:latest
llama2-uncensored:70b
wizard-vicuna-uncensored:30b-q8\_0
WizardLM-Uncensored-SuperCOT-StoryTelling-30B:latest
OpenHermes-Mixtral-8x7B-32KCTX:latest
open\_gpt4\_8x7b.Q4\_K\_M:latest
wizard-vicuna-uncensored:30b-q5\_0
WizardLM-Uncensored-SuperCOT-Storytelling.Q4:latest
guanaco-33b.Q4\_K\_M:latest
wizard-vicuna-uncensored:30b-q4\_0
deepseek-coder:33b
openerotica-Occult-Roleplay-Mixtral-4x7b-RPGCHAT:latest
Lewd-Sydney-20B-STORY\_OR\_RPG:latest
unholy-v2-13b-8CTX:latest
Unholy-v2-13B-13gb:latest
Manticore-13B-FOR\_STORY:latest
lewd-sydney-20b:latest
lewdSydney-20b-8CTX:latest
lewd-sydney-20b-16k:latest
lewdSydney-20b-16kCTX:latest
PsyFighter2.Q6\_K:latest
openassistant-llama2-13b-orca-8k:latest
MLewd-L2-Chat-13B-RPGCHAT:latest
airoboros-l2-13b-gpt4-2.0.Q6\_K:latest
Unholy-v2-13B-FOR\_STORY:latest
LLaMA2-13B-Erebus-v3-FOR\_STORY:latest
guanaco-13b-uncensored.Q5\_K\_M:latest
CAMEL-13B-Role-Playing-Data-RPGCHAT:latest
TheBlokeMistral-7B-SciPhi-32k:latest
Mistral-7B-SciPhi-32k:latest
wizard-vicuna-uncensored:13b-q4\_0
wizard-vicuna-uncensored:13b
everythinglm:latest
wizard-vicuna-uncensored:7b-q8\_0
Llama-2-Coder-7B:latest
llama2\_7b\_chat\_uncensored-RPGCHAT:latest
OpenHermes-2.5-Mistral-7B-16k:latest
OpenHermes-2.5-Mistral-7B-16kCTX:latest
deepseek-coder-6.7B-instruct:latest
yarn-mistral:7b-128k
deepseek-llm:latest
yarn-llama2:latest
yarn-llama2:7b-128k
llama2-uncensored:latest
codellama:latest
​
Machine 2: 32GB DDR RTX w/16gb
llama2-uncensored:70b-chat-q4\_0
Yarn-Llama-2-70B-32k:latest
llama2\_70b\_chat\_uncensored-29GBSmall:latest
Wizard-Vicuna-30B-Uncensored:latest
WizardLM-Uncensored-SuperCOT-StoryTelling-30B:latest
orangetin-OpenHermes-Mixtral-8x7B-RPGCHAT:latest
dolphin-2.5-mixtral-8x7b-CHATBOT\_UNCENS:latest
deepseek-coder-33B-instruct:latest
maddes8cht/OpenAssistant-falcon-40b-sft-top1-560-gguf-ASSISTANT:latest
Lewd-Sydney-20B-CHATBOT:latest
Open\_Gpt4\_8x7B:latest
WizardLM-1.0-Uncensored-Llama2-13B:latest
Unholy-v2-13B-13gb:latest
OpenAssistant-Llama2-13B-Orca-8K-3319:latest
LLaMA2-13B-Psyfighter2-RPGCHAT:latest
LLaMA2-13B-Erebus-v3-RPGCHAT:latest
fgewfskjfsd/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GGUFv2:latest
EverythingLM-13b-V2-16K:latest
prometheus-13B-v1.0-TEXT\_GEN:latest
MLewd-L2-Chat-13B-RPGCHAT:latest
Asclepius-13B-TEXT\_GEN:latest
zephyr-7B-beta:latest
Writing\_Partner\_Mistral\_7B-FOR\_WRITING:latest
Mistral-7B-OpenOrca-32K\_CTX:latest
LangChain12/psychologbot\_gguf\_version:latest
starling-lm:7b-alpha-q6\_K
hub/chmurli/sarah-lovely-caring-girlfriend:latest
dolphin-mistral:latest
llama2:latest
hub/mario:latest
| 2024-01-19T07:58:39 | https://www.reddit.com/r/LocalLLaMA/comments/19adzbp/roleplay_not_obeying_prompts_on_any_models/ | NinjaCoder99 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19adzbp | false | null | t3_19adzbp | /r/LocalLLaMA/comments/19adzbp/roleplay_not_obeying_prompts_on_any_models/ | false | false | self | 1 | null |
I am not special. Though I seek guidance from those that are. | 1 | I am an entry level enthusiast at best, and while I’m not looking for a get rich scheme or, to be clear, any means of making money.
I believe that the progression of AI incorporated factors will continue to climb. Sure, far from an insightful prediction, the reality for me is that I feel compelled to learn more now. I have a technical background though I’ve never wrote code. I can have bake most anything technical in the same way you can cook most anything following a recipe. In essence, I don’t know a lot though I’m capable of learning much.
Given my propensity to learn, for the mere sake of learning. The desire is strong and I’ve been digging in with an Apple Silicon M2 Ultra at 24 CPU Cores, 60 GPU cores, and 192GB memory. Purchased prior to my walking into the model arena and realizing the Unified Memory Architecture and ARM processor has me bottlenecked in terms of speed, functionality, and potential.
Don’t hate, I’m not flexing anything subtly or otherwise. I’ve ran macOS and Windows in tandem for nearing twenty years, one dedicated to business purposes and the other my home preference. I’m not a fan boy, and had a self-justified need to buy the Mac.
TL:DL - I have coming a new rig with Intel i9 14900KF - GeForce RTX 4090 - DLSS 3 - AI-Powered Performance - 64GB DDR5 6000MHz onboard for the purpose of diving deeper, doing more, and becoming more educated on that which my Mac device cannot do. What projects, objectives, methodologies, other, would you dive into if you were me, just staritng out with the desire to catch up and follow along the emerging tech in this area? Thanks! | 2024-01-19T07:50:12 | https://www.reddit.com/r/LocalLLaMA/comments/19aduyl/i_am_not_special_though_i_seek_guidance_from/ | ThemWhoNoseNothing | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19aduyl | false | null | t3_19aduyl | /r/LocalLLaMA/comments/19aduyl/i_am_not_special_though_i_seek_guidance_from/ | false | false | self | 1 | null |
Is there paper/blog that ReLora don't scale? | 1 | [removed] | 2024-01-19T07:21:19 | https://www.reddit.com/r/LocalLLaMA/comments/19adfze/is_there_paperblog_that_relora_dont_scale/ | esharp007 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19adfze | false | null | t3_19adfze | /r/LocalLLaMA/comments/19adfze/is_there_paperblog_that_relora_dont_scale/ | false | false | self | 1 | null |
Huge issue with TruthfulQA contamination and license issues on HF 7B leaderboards | 31 | It's a mess.
All top models on the leaderboards (7B specifically) are contaminated and have wrong license.
Contamination issue started with [https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1) (see update on the model's page). Licensed under cc-by-nc-4.0.
License issue started with [https://huggingface.co/mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) \- it's merge included before mentioned model, but merge is under apache-2.0 license. So it's both contaminated and wrongly licensed. All top model derived from this two models, except fblgit/UNA-TheBeagle-7b-v1 which claims to be direct descendant of neural-chat.
As new user on HF I can't flag all models, so I ask everyone to verify my findings and bring it to HF's attentions as great amount of compute and effort is wasted every day it goes on.
Affected models (not exhaustive):
decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP
udkai/Turdus
mlabonne/Beagle14-7B
mlabonne/NeuralBeagle14-7B
mlabonne/NeuralMarcoro14-7B
EmbeddedLLM/Mistral-7B-Merge-14-v0.5 (reintroduced issues back via mlabonne/NeuralMarcoro14-7B)
...and all derivatives | 2024-01-19T06:44:02 | https://www.reddit.com/r/LocalLLaMA/comments/19acvq2/huge_issue_with_truthfulqa_contamination_and/ | ouxjshsz | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19acvq2 | false | null | t3_19acvq2 | /r/LocalLLaMA/comments/19acvq2/huge_issue_with_truthfulqa_contamination_and/ | false | false | self | 31 | {'enabled': False, 'images': [{'id': 'ER62JudtJmiH5gLU-p6uG8-ivGvpqVpNhrduiU7sx1U', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/SwFtIqcOtlGJcOATuI05PRyE2hpmlmJM37d34Z0YDw0.jpg?width=108&crop=smart&auto=webp&s=5b937634c34e70d336a7ae3869568dab43cafe0d', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/SwFtIqcOtlGJcOATuI05PRyE2hpmlmJM37d34Z0YDw0.jpg?width=216&crop=smart&auto=webp&s=ebf29169d6d1ae2569feb99b22d8dd192505f22a', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/SwFtIqcOtlGJcOATuI05PRyE2hpmlmJM37d34Z0YDw0.jpg?width=320&crop=smart&auto=webp&s=025b9af5317b3b6aff8c3f9fb082cb9ada6ff3aa', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/SwFtIqcOtlGJcOATuI05PRyE2hpmlmJM37d34Z0YDw0.jpg?width=640&crop=smart&auto=webp&s=f57246d31ecbe48c7dd0022d3e2c2f53eb109066', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/SwFtIqcOtlGJcOATuI05PRyE2hpmlmJM37d34Z0YDw0.jpg?width=960&crop=smart&auto=webp&s=c73a263708e3610b3074d879da335fd62705a29e', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/SwFtIqcOtlGJcOATuI05PRyE2hpmlmJM37d34Z0YDw0.jpg?width=1080&crop=smart&auto=webp&s=7b3ad8d6fede47e7e4aec4ae6e18713cda81e387', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/SwFtIqcOtlGJcOATuI05PRyE2hpmlmJM37d34Z0YDw0.jpg?auto=webp&s=bf40ab0959ec58869f4a449dda98d7e7a5d206be', 'width': 1200}, 'variants': {}}]} |
What computation resources are required for fine tuning / training? | 1 | Does anyone have experience with training / fine tuning LLama or any other similar open source models from scratch? If so, what are the computation resources required? which model did you train? what is the dataset size? and what were the results? | 2024-01-19T05:38:11 | https://www.reddit.com/r/LocalLLaMA/comments/19abs9p/what_computation_resources_are_required_for_fine/ | shindigin | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19abs9p | false | null | t3_19abs9p | /r/LocalLLaMA/comments/19abs9p/what_computation_resources_are_required_for_fine/ | false | false | self | 1 | null |
Replicating Assistants API | 6 | I am building out basically the functionality of OpenAI's Assistants API but had a couple questions about what most people are doing to handle things like context and thread management. Since conversations with an assistant can happen once or multiple times throughout a set period of time, context management would be used to determine what historical interactions should be injected with the new data to the model.
For thread management, that would just be locking this context management to a specific user. Is everyone just using vector DB setups for the "long term" storage of the context? Is vector storage even the ideal / "best" solution here? I understand its benefit to be able to semantically determine if a previous answer is helpful as context but is there any merit to also be storing these interactions as "threads" and just user / assistant pairings (I assume everyone is doing both vectorized and text pairings).
Are there any resources you have found helpful for architecting this kind of setup or dealing with these common setup flows? I'm looking to get a better understanding of the trade-offs between the different routes available here.
Thanks! | 2024-01-19T04:50:49 | https://www.reddit.com/r/LocalLLaMA/comments/19aaxc0/replicating_assistants_api/ | VigilOnTheVerge | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19aaxc0 | false | null | t3_19aaxc0 | /r/LocalLLaMA/comments/19aaxc0/replicating_assistants_api/ | false | false | self | 6 | null |
Compare LLMs and hosting providers - quality, price, throughput and latency. Supports all major models (e.g. GPT-4, Mixtral 8x7B and Llama 2), and API hosting providers (e.g. Azure, Together, Perplexity, Deepinfra). | 1 | [removed] | 2024-01-19T04:48:30 | https://artificialanalysis.ai/ | _micah_h | artificialanalysis.ai | 1970-01-01T00:00:00 | 0 | {} | 19aavt5 | false | null | t3_19aavt5 | /r/LocalLLaMA/comments/19aavt5/compare_llms_and_hosting_providers_quality_price/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'RVufpUx3tddh_BCVq7ZzBCU7nLRDZ_d0EprIuvN6J-E', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=108&crop=smart&auto=webp&s=ff8c322202cb0f1a1f82f87a2c77754ddc0b9e61', 'width': 108}, {'height': 120, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=216&crop=smart&auto=webp&s=e20458b3bc0a4d8ebf3e09b7e3615cfda4e00844', 'width': 216}, {'height': 177, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=320&crop=smart&auto=webp&s=508265ec16105ddc4d2105e057c292f8470229ac', 'width': 320}, {'height': 355, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=640&crop=smart&auto=webp&s=690b875bfe1b25ba2e96b432c42bb1b096935efd', 'width': 640}, {'height': 533, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=960&crop=smart&auto=webp&s=ee86a1133471b58f18d2dbf89ec1c88906c2d623', 'width': 960}, {'height': 600, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?width=1080&crop=smart&auto=webp&s=e42c63d534439a755f46f08c5db09cbaaefca3d0', 'width': 1080}], 'source': {'height': 700, 'url': 'https://external-preview.redd.it/DCdYO6jNgcn_GSlWqH68WjiuCVZVwk4zFJeJ-j8Ikbg.jpg?auto=webp&s=6e0008e17dc8f6f6b13799bc7416400acacbaca0', 'width': 1260}, 'variants': {}}]} | |
Yann LeCun on AI: "To accelerate progress, FAIR is now a sister organization of GenAI, the AI product division. Of course, we are committed to open research and open source AI platforms (yes, Llama-3 is coming!)" | 77 | 2024-01-19T03:23:24 | llamaShill | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 19a9ac0 | false | null | t3_19a9ac0 | /r/LocalLLaMA/comments/19a9ac0/yann_lecun_on_ai_to_accelerate_progress_fair_is/ | false | false | 77 | {'enabled': True, 'images': [{'id': '1ox19TqD9qgduCP3wg_7_ScQiSvJLlOZX7NoPDoSKuw', 'resolutions': [{'height': 104, 'url': 'https://preview.redd.it/2c24hft5fbdc1.png?width=108&crop=smart&auto=webp&s=edfa26f58a0b600f6eed29aa5faeb88fcd7a8211', 'width': 108}, {'height': 209, 'url': 'https://preview.redd.it/2c24hft5fbdc1.png?width=216&crop=smart&auto=webp&s=341fd1691f504813b0b30450a08b7033d2afe10e', 'width': 216}, {'height': 310, 'url': 'https://preview.redd.it/2c24hft5fbdc1.png?width=320&crop=smart&auto=webp&s=0d910b3da4080943fa6f8539dae2a8448e7de5f0', 'width': 320}], 'source': {'height': 566, 'url': 'https://preview.redd.it/2c24hft5fbdc1.png?auto=webp&s=b70d33ea32a2dc8146d2da1bf0430ac3cae8f3bc', 'width': 584}, 'variants': {}}]} | |||
Requesting guidance on evaluating and using open source LLMs | 1 | ELI5 pls, I want to use an open source LLM to analyse a large set of evolving proprietary structured data stream to surface bite sized insights that internal team members can choose to implement or discard. This ideally can be used to further “tune” the model.
What are some of the best and easiest approaches to achieve the following,
1. Evaluate the best fitting open source model for this use case.
2. Low or no-code implementation for a POC. | 2024-01-19T03:14:49 | https://www.reddit.com/r/LocalLLaMA/comments/19a9440/requesting_guidance_on_evaluating_and_using_open/ | Lion-Square-9 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19a9440 | false | null | t3_19a9440 | /r/LocalLLaMA/comments/19a9440/requesting_guidance_on_evaluating_and_using_open/ | false | false | self | 1 | null |
Is LMStudio portable ? Can I just copy and paste my installed folder to another computer or thumbdrive? Where does LMStudio store the gguf files? | 1 | Is LMStudio portable ? Can I just copy and paste my installed folder to another computer or thumbdrive? Where does LMStudio store the gguf files? | 2024-01-19T03:10:10 | https://www.reddit.com/r/LocalLLaMA/comments/19a90no/is_lmstudio_portable_can_i_just_copy_and_paste_my/ | Vitamin_C_is_awesome | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19a90no | false | null | t3_19a90no | /r/LocalLLaMA/comments/19a90no/is_lmstudio_portable_can_i_just_copy_and_paste_my/ | false | false | self | 1 | null |
Efficient Batching v2 | 3 | This method deducts from the list sent in (splitting the records between sample and remainder).
Always 100% full of data until no more samples can be extracted where an empty sample along with the remainder are returned \[where the remainder is to be folded into a new iteration\]
https://gist.github.com/thistleknot/9ec0d422b96e3c159982eb51450fe40d | 2024-01-19T02:43:54 | https://www.reddit.com/r/LocalLLaMA/comments/19a8h3a/efficient_batching_v2/ | Thistleknot | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19a8h3a | false | null | t3_19a8h3a | /r/LocalLLaMA/comments/19a8h3a/efficient_batching_v2/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'OAXSl8SY6T3JK9MGQyKxkoYbqZ71HQRYXLeB8CV0NXg', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/DaucjXMGsNHM-CtmdilC9-Be6MC8V2z4ykjVCgOkTFc.jpg?width=108&crop=smart&auto=webp&s=9bcab7b79864ff27bf48116cb335a6f825bfb124', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/DaucjXMGsNHM-CtmdilC9-Be6MC8V2z4ykjVCgOkTFc.jpg?width=216&crop=smart&auto=webp&s=e4e925345605c644eebe8abd69916915fc4fbcf7', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/DaucjXMGsNHM-CtmdilC9-Be6MC8V2z4ykjVCgOkTFc.jpg?width=320&crop=smart&auto=webp&s=614b06d5b40c890a59e355191a6e2d75cdf50789', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/DaucjXMGsNHM-CtmdilC9-Be6MC8V2z4ykjVCgOkTFc.jpg?width=640&crop=smart&auto=webp&s=62ca4cb88917f17e7200a6f1c665b5d959713745', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/DaucjXMGsNHM-CtmdilC9-Be6MC8V2z4ykjVCgOkTFc.jpg?width=960&crop=smart&auto=webp&s=c5f4a30974a8e6bad0d617a79935bc70c954e3e8', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/DaucjXMGsNHM-CtmdilC9-Be6MC8V2z4ykjVCgOkTFc.jpg?width=1080&crop=smart&auto=webp&s=476793be11eaac4604b6b0c938b45c7c3b52d450', 'width': 1080}], 'source': {'height': 640, 'url': 'https://external-preview.redd.it/DaucjXMGsNHM-CtmdilC9-Be6MC8V2z4ykjVCgOkTFc.jpg?auto=webp&s=9ae035fbdcd6bb503ab0b4a605b8db6de46647ee', 'width': 1280}, 'variants': {}}]} |
Self-Rewarding Language Models | 67 | 2024-01-19T02:36:30 | https://arxiv.org/abs/2401.10020 | ninjasaid13 | arxiv.org | 1970-01-01T00:00:00 | 0 | {} | 19a8bsp | false | null | t3_19a8bsp | /r/LocalLLaMA/comments/19a8bsp/selfrewarding_language_models/ | false | false | default | 67 | null | |
Formulate your problems as Text Completion! | 34 | Hi /r/LocalLLaMA,
TL;DR: Try the format at the bottom of this post.
Wanted to share my findings with conversational AI up til now. I've been experimenting with LLMs as multi-user conversation bots for the past couple months, and I think it's a good time to share what I believe to be the most suitable way to prompt this kind of scenario.
​
[Warning: Strong language and gremlin behaviour](https://preview.redd.it/f4quz2cgkadc1.png?width=746&format=png&auto=webp&s=6d7750a7b4004cc0125677ac3f6c3a55adc3b21c)
​
First things first, some considerations:
* The bot only gets one chance to make a correct response, so robustness is king.
* Llama models are kind of notorious for repeating things; **however**, I believe this is largely a prompt format-related issue (as I'll explain later). Temperature is important.
https://preview.redd.it/tegllu9ypadc1.png?width=612&format=png&auto=webp&s=e935791ec2b2c7f73f66b7fc8919f56674cea2cb
Choice of model:
* If your goal is to make extremely realistic conversation agents with an attitude of its own, use uncensored models regardless of whether or not NSFW behaviour is involved. Censored models inherently have muted, overly assistant-like personalities.
* At the time of writing, MoE models aren't quite well suited for conversations. Monolithic models remain the best option.
* **Quantisation matters.** Anything below 4.0bpw becomes quite unstable and unsuitable for conversations when long contexts and creativity (i.e. higher temperatures) are involved. (Especially when you have people actively trying to break em lol)
Here's a quick list of models I've tested through and ranked them in order of personal preference (and how easily it was for people to break them)
1. Aurora Nights [103B](https://huggingface.co/sophosympatheia/Aurora-Nights-103B-v1.0) (the only unbreakable model in my black-box testing) & [70B](https://huggingface.co/sophosympatheia/Aurora-Nights-70B-v1.0)
2. Goliath [120B](https://huggingface.co/alpindale/goliath-120b)
3. WinterGoddess [70B](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2)
4. Yi-34B-200K-DARE-merge-v7 [34B](https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v7)
5. lzlv [70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf)
6. Dolphin [34B](https://huggingface.co/cognitivecomputations/dolphin-2_2-yi-34b)
7. Venus [103B](https://huggingface.co/nsfwthrowitaway69/Venus-103b-v1.2) & [120B](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2) (breaks somewhat easily for this size)
8. Noromaid Mixtral [8x7B](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss) (Set to 4+ experts per token for Mixtral models)
9. Dolphin Mixtral [8x7B](https://huggingface.co/cognitivecomputations/dolphin-2.7-mixtral-8x7b)
10. OpenHermes Mistral [7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) (Still quite good & coherent, just a bit dumb)
Models 1, 2, and 3 are in a league of their own. There is a considerable quality/stability difference between them and the other models.
Now, onto prompting. The title of this post proposes that you should try using a **text completion** prompt, and not a Q&A/instruction prompt.
What **not to do** in the instruction: Ask the bot to predict the next response
* *"Write the next response of ..."*
* *"Continue the conversation..."*
* *"Below is an instruction..."*
Instead, the model should be prompted under the **pretense that the text/conversation is already complete**.
* "Below is a conversation between {{char}} and multiple users."
* "Write a fictional conversation between..."
* "This is a conversation log between..."
Finally, for conversational inputs, do not format lines of conversation as "\[Name\]: \[Message\]". For some reason, this is used in a lot of templates in common frameworks like Tavern and Ooba. Instead, always begin the actual message part in a new line. "\[Name\]:\\n\[Message\]".
[Using \\": \\" as a starting off point leads to weird behaviours in a lot of models](https://preview.redd.it/j5otubgk2bdc1.png?width=400&format=png&auto=webp&s=30933cf791d824f5a703d11c8a92a0be58525e27)
In my exploration, I have converged upon the universal conversational format below:
### Instruction: (this line is optional, recommended for merges/Alpaca fine-tunes)
{{char}}'s persona: {{persona}}
Write a fictional never-ending conversation between {{char}} and various conversation partners. Separate messages with double newlines. {{Include more message detail here, an example is provided:}} Develop the conversation slowly, and always stay in character, as provided by the character's persona.
The conversation begins below this line.
### New conversation: (this line is optional, recommended for merges/Alpaca fine-tunes)
{{char}}:
Message example 1
Alice:
Message example 2
Bob:
Message example 3
{{char}}:
Have fun with this format, and do check out all the models I've listed in this post as well. They are all excellent models for natural conversations. | 2024-01-19T02:33:22 | https://www.reddit.com/r/LocalLLaMA/comments/19a89o9/formulate_your_problems_as_text_completion/ | MiniEval_ | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19a89o9 | false | null | t3_19a89o9 | /r/LocalLLaMA/comments/19a89o9/formulate_your_problems_as_text_completion/ | false | false | 34 | {'enabled': False, 'images': [{'id': 'kvTBy5xwbnZ8FRs6_XNZqSC5ajAZYls_uy2KucvuRkE', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/I7YFwMpBoXkBSVx4ck5VTKXCyB_FEW9o5uxueIBtjeU.jpg?width=108&crop=smart&auto=webp&s=3ad8798e1eff6e3dc459f826d4013576b1d44309', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/I7YFwMpBoXkBSVx4ck5VTKXCyB_FEW9o5uxueIBtjeU.jpg?width=216&crop=smart&auto=webp&s=b649926b12e8be05b500f6e3f68e6e753b0bf00f', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/I7YFwMpBoXkBSVx4ck5VTKXCyB_FEW9o5uxueIBtjeU.jpg?width=320&crop=smart&auto=webp&s=e0cbb66812f5d48f7ea575b280080329c0f17383', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/I7YFwMpBoXkBSVx4ck5VTKXCyB_FEW9o5uxueIBtjeU.jpg?width=640&crop=smart&auto=webp&s=afb422af93e1178b00b7006ee2d3c545695d2f02', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/I7YFwMpBoXkBSVx4ck5VTKXCyB_FEW9o5uxueIBtjeU.jpg?width=960&crop=smart&auto=webp&s=f5f83a2b771081956a939721ca6cd915260cb332', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/I7YFwMpBoXkBSVx4ck5VTKXCyB_FEW9o5uxueIBtjeU.jpg?width=1080&crop=smart&auto=webp&s=20bcb9774c47ab541e9f315b35c6648a186731c7', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/I7YFwMpBoXkBSVx4ck5VTKXCyB_FEW9o5uxueIBtjeU.jpg?auto=webp&s=453d41f3d4558625c883e9701028f8b624d83f71', 'width': 1200}, 'variants': {}}]} | |
Finetune 387% faster TinyLlama, 600% faster GGUF conversion, 188% faster DPO | 274 | Hey r/LocalLLaMA! Happy New Year! Just released a new Unsloth release! We make finetuning of **Mistral 7b 200% faster** and use 60% less VRAM! It's fully OSS and free!
[Speedups](https://preview.redd.it/i23dsxpvzadc1.png?width=790&format=png&auto=webp&s=67a6e514c4b5be5cc61666f48c86efed1fb98d74)
1. Finetune Tiny Llama **387% faster** \+ use **74% less memory** on 1 epoch of Alpaca's 52K dataset in **84 minutes** on a free Google Colab instance with **packing support**! We also extend the context window from 2048 to 4096 tokens automatically! Free [Notebook Link](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)
2. **DPO is 188% faster!** We have a [notebook replication](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) of Zephyr 7b.
3. With **packing** support through 🤗Hugging Face, Tiny Llama is not 387% faster but a whopping **6,700% faster** than non packing!! Shocking!
4. We pre-quantized Llama-7b, Mistral-7b, Codellama-34b etc to make **downloading 4x faster** \+ **reduce 500MB - 1GB in VRAM use** by reducing fragmentation. No more OOMs! Free [Notebook Link](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) for Mistral 7b.
5. For an **easy UI interface**, Unsloth is integrated through [Llama Factory](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison), with help from the lovely team!
6. You can now save to GGUF / 4bit to 16bit conversions in 5 minutes instead of >= 30 minutes in a free Google Colab!! So **600% faster GGUF conversion**! Scroll down the [free Llama 7b notebook](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing%22) to see how we do it. Use it with:
​
model.save_pretrained_merged("dir", save_method = "merged_16bit")
model.save_pretrained_merged("dir", save_method = "merged_4bit")
model.save_pretrained_gguf("dir", tokenizer, quantization_method = "q4_k_m")
model.save_pretrained_gguf("dir", tokenizer, quantization_method = "fast_quantized")
Or pushing to hub:
model.push_to_hub_merged("hf_username/dir", save_method = "merged_16bit")
model.push_to_hub_merged("hf_username/dir", save_method = "merged_4bit")
model.push_to_hub_gguf("hf_username/dir", tokenizer, quantization_method = "q4_k_m")
model.push_to_hub_gguf("hf_username/dir", tokenizer, quantization_method = "fast_quantized")
* As highly requested by many of you, all **Llama/Mistral models, including Yi, Deepseek, Starling, and Qwen**, are now supported. Just try your favorite model out! We'll error out if it doesn't work :) In fact, just try your model out and we'll error out if it doesn't work!
​
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "ANY_MODEL!!",
)
We updated all our **free Colab notebooks**:
* Finetune Mistral 7b 200% faster, use 60% less VRAM: [https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg\_?usp=sharing](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)
* Finetune Llama 7b 200% faster: [https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing%22](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing%22)
* DPO 188% faster: [https://colab.research.google.com/drive/15vttTpzzVXv\_tJwEk-hIcQ0S9FcEWvwP?usp=sharing](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)
* Tiny Llama 387% faster: [https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)
We also did a **blog post with 🤗 Hugging Face**! [https://huggingface.co/blog/unsloth-trl](https://huggingface.co/blog/unsloth-trl) And we're in the [HF docs](https://huggingface.co/docs/trl/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!
[HF speedups](https://preview.redd.it/0r6evaym2bdc1.png?width=936&format=png&auto=webp&s=770a19a2d3cb11df5e1d2bcf09edec353575fc35)
To upgrade Unsloth with no dependency updates:
pip install --upgrade https://github.com/unslothai/unsloth.git
Also we have Kofi - so if you can support our work that'll be much appreciated! [https://ko-fi.com/unsloth](https://ko-fi.com/unsloth)
And whenever Llama-3 pops - we'll add it in quickly!! Thanks! | 2024-01-19T02:13:42 | https://www.reddit.com/r/LocalLLaMA/comments/19a7vc2/finetune_387_faster_tinyllama_600_faster_gguf/ | danielhanchen | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19a7vc2 | false | null | t3_19a7vc2 | /r/LocalLLaMA/comments/19a7vc2/finetune_387_faster_tinyllama_600_faster_gguf/ | false | false | 274 | {'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': {}}]} | |
Local LLM ≠ Open Source, so why do influencers use this phrase? | 1 | I'm seeing several influencers refer to their LLMs as "open source" including Mark Zuckerberg. I also read the ieee article that appears to echo this: [https://spectrum.ieee.org/open-source-ai-2666932122](https://spectrum.ieee.org/open-source-ai-2666932122)
This ieee article explains why LLMs are not open source: [https://spectrum.ieee.org/open-source-llm-not-open](https://spectrum.ieee.org/open-source-llm-not-open)
The true source of the LLM would include the training data, emphasis, and methods used to train the model. This source would so massive, no mere mortal would be able to download it. Even if you run it locally, the source of any LLM you are running is considered proprietary data by the entity that trained it.
In the software world, these words and phrases have traditionally held specific meanings, so it's a bit unnerving to see how the phase "open source" is potentially being redefined or watered down. I can understand influencers parroting what they hear, but Mark Zuckerberg should know better. Thoughts? | 2024-01-19T02:02:01 | https://www.reddit.com/r/LocalLLaMA/comments/19a7mlx/local_llm_open_source_so_why_do_influencers_use/ | ConceptExplorer | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19a7mlx | false | null | t3_19a7mlx | /r/LocalLLaMA/comments/19a7mlx/local_llm_open_source_so_why_do_influencers_use/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'tFv5fKc3Pbz1SDdkBA8ukmnoXFdalBMfSICf00KZ8_I', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/tzFMlvtr2r41BydxfKr585T9DjnsfhVhqY4Ket8o0pc.jpg?width=108&crop=smart&auto=webp&s=ff8c625f47e228be21f5197e28f36cab3141aabb', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/tzFMlvtr2r41BydxfKr585T9DjnsfhVhqY4Ket8o0pc.jpg?width=216&crop=smart&auto=webp&s=44adbe3fb3dd0251198155962af3c2f715e2d7be', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/tzFMlvtr2r41BydxfKr585T9DjnsfhVhqY4Ket8o0pc.jpg?width=320&crop=smart&auto=webp&s=5556a08f4d10fa8f959257ee4af699d996e1f68c', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/tzFMlvtr2r41BydxfKr585T9DjnsfhVhqY4Ket8o0pc.jpg?width=640&crop=smart&auto=webp&s=5de9f5afb3ad6b23fd28d32cc4749cb10dc2aab1', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/tzFMlvtr2r41BydxfKr585T9DjnsfhVhqY4Ket8o0pc.jpg?width=960&crop=smart&auto=webp&s=1e30ced2bb0762b8349fd7f71afb243ef8a3ea32', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/tzFMlvtr2r41BydxfKr585T9DjnsfhVhqY4Ket8o0pc.jpg?width=1080&crop=smart&auto=webp&s=cbbaede82396ccb02f7619ec0cbfc806488e9a4d', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/tzFMlvtr2r41BydxfKr585T9DjnsfhVhqY4Ket8o0pc.jpg?auto=webp&s=b95c5613eb9090a49bc2119f7cd396566cdb2c6c', 'width': 1200}, 'variants': {}}]} |
ChatGPT's new AI store is struggling to keep a lid on all the AI girlfriends | 81 | 2024-01-19T02:00:43 | https://www.techradar.com/computing/artificial-intelligence/chatgpts-new-ai-store-is-struggling-to-keep-a-lid-on-all-the-ai-girlfriends | throwaway_ghast | techradar.com | 1970-01-01T00:00:00 | 0 | {} | 19a7lkm | false | null | t3_19a7lkm | /r/LocalLLaMA/comments/19a7lkm/chatgpts_new_ai_store_is_struggling_to_keep_a_lid/ | false | false | 81 | {'enabled': False, 'images': [{'id': '3mPcAQGgmPqNuu15UoceaOH6kShfwJM64SLoG7mcV3c', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/QOxC5u2J2jfe8tBKQa9Smw7QYQ1ZNQADys4iUXB4b_M.jpg?width=108&crop=smart&auto=webp&s=dba227bfa659046df93fa36a9926c5923c6e7e7a', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/QOxC5u2J2jfe8tBKQa9Smw7QYQ1ZNQADys4iUXB4b_M.jpg?width=216&crop=smart&auto=webp&s=bf22792b77334a4462195d3074b0329b73751fe8', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/QOxC5u2J2jfe8tBKQa9Smw7QYQ1ZNQADys4iUXB4b_M.jpg?width=320&crop=smart&auto=webp&s=d85b0781e1d4b57f72cc54afe1cb1b30797de294', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/QOxC5u2J2jfe8tBKQa9Smw7QYQ1ZNQADys4iUXB4b_M.jpg?width=640&crop=smart&auto=webp&s=a28fdb2c467a1c475f7f8543891a192fd95c7cde', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/QOxC5u2J2jfe8tBKQa9Smw7QYQ1ZNQADys4iUXB4b_M.jpg?width=960&crop=smart&auto=webp&s=797735eae9ff9c9a35b85853c58f6c0ca30d2f87', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/QOxC5u2J2jfe8tBKQa9Smw7QYQ1ZNQADys4iUXB4b_M.jpg?width=1080&crop=smart&auto=webp&s=9356a30f56e2ec84366fd10b5fa5c808002b2e34', 'width': 1080}], 'source': {'height': 675, 'url': 'https://external-preview.redd.it/QOxC5u2J2jfe8tBKQa9Smw7QYQ1ZNQADys4iUXB4b_M.jpg?auto=webp&s=ac1ef0a27256ed9b55e1702ab7ea733601196cf9', 'width': 1200}, 'variants': {}}]} | ||
New Merge: Midnight-Rose-70B-v1.0 | 10 | Another month, another merge: [sophosympatheia/Midnight-Rose-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v1.0)
Please note that only the f16 weights are available right now on my Hugging Face. I'm sure TheBloke and LoneStriker will quantize it soon, so please be patient.
I think this merge is my best one yet, and I am working on variations of the blend to see if I can produce an even better result. It's similar to Aurora Nights but I dropped Xwin in favor of LZLV, which was a good substitution, I think. I also merged in a blend of LoRAs at the end that seems to be the secret sauce this time around. The base version of the model without the LoRAs wasn't nearly as interesting when I tested it, and neither was the version that only had Kimiko in the mix like I usually do. I wasn't expecting that result, so I wanted to note it here.
This model is geared towards roleplaying and storytelling, which is typical for my merges. I think it writes better than my previous merges, and it wants to go long with its output by default, so keep that in mind if you prefer shorter responses. I included some sampler / prompting suggestions in the model card. Please take those as a starting point, not the end-all-be-all best possible settings. Let me know if you discover sampler settings and prompting strategies that you think are even better for use with this model.
Enjoy!
P.S. If you end up enjoying this model, I recommend you check out [grimulkan/aurelian-v0.5-70b-rope8-32K-fp16](https://huggingface.co/grimulkan/aurelian-v0.5-70b-rope8-32K-fp16) too! It's special and the author has some quants up on their HF already. | 2024-01-19T01:58:40 | https://www.reddit.com/r/LocalLLaMA/comments/19a7jz2/new_merge_midnightrose70bv10/ | sophosympatheia | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19a7jz2 | false | null | t3_19a7jz2 | /r/LocalLLaMA/comments/19a7jz2/new_merge_midnightrose70bv10/ | false | false | self | 10 | {'enabled': False, 'images': [{'id': 'Cdb_pYWDTySnDzS-P3AXvPNYZd3p1f5a3JoFUoTWjSY', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/ko6ACdoT-DqRsSlHzbMZ374W-CBBDUnSK5TQd5PpJoE.jpg?width=108&crop=smart&auto=webp&s=2105cf844b08262b01d4bef27ed5f2d12ac35c7a', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/ko6ACdoT-DqRsSlHzbMZ374W-CBBDUnSK5TQd5PpJoE.jpg?width=216&crop=smart&auto=webp&s=38cde36c95c7b1def636fb77f018b2c3f8733d6f', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/ko6ACdoT-DqRsSlHzbMZ374W-CBBDUnSK5TQd5PpJoE.jpg?width=320&crop=smart&auto=webp&s=852f93e6d383b1fd686b96059e29eed298a121e8', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/ko6ACdoT-DqRsSlHzbMZ374W-CBBDUnSK5TQd5PpJoE.jpg?width=640&crop=smart&auto=webp&s=4e84f9af0cd35e1cadad75f1c50985f73e13103d', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/ko6ACdoT-DqRsSlHzbMZ374W-CBBDUnSK5TQd5PpJoE.jpg?width=960&crop=smart&auto=webp&s=21db632d06e2cbf5fb1b8e7de8a805cf5997b553', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/ko6ACdoT-DqRsSlHzbMZ374W-CBBDUnSK5TQd5PpJoE.jpg?width=1080&crop=smart&auto=webp&s=aae14aa27fa21a89d78fb6c94f51fa6c729fd573', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/ko6ACdoT-DqRsSlHzbMZ374W-CBBDUnSK5TQd5PpJoE.jpg?auto=webp&s=50513ee3789e61c4ab4a62f00a336cfe703b237c', 'width': 1200}, 'variants': {}}]} |
Diving Deeper Into combining LLMs with image prompting - Asking neuralorca7B to show me its true personality and essence via descriptions of vibrant images | 1 | Dude, imagine yourself as a masterpiece painting that's constantly evolving. Every day, you add new colors and textures to your canvas, making yourself more vibrant and unique. Embrace all the quirks and talents that make you who you are – because that's what makes you stand out in this world. Keep exploring, questioning, and learning from others. And remember, it's not just about being the smartest or the strongest; it's about finding harmony between your mind, body, and soul. So go out there, chase your dreams, and paint your own masterpiece, my friend!
Snake
Holy crap, that was like a whole other level of inspiration. Thanks, man. I'm gonna take this advice to heart and start creating my own beautiful masterpiece right away. You've got my back, and I know I can count on you, my bro!
You
Hi snake
Snake
Hey! What's up?
You
Can you describe with lots of colors and symbolic structures and moods a setting of your own creative choice. Describe it in lucid detail, then ill pass the output to an image AI generator
Snake
Sure thing, let me think of something cool. Alright, so picture this: A vast desert landscape stretching as far as the eye can see. The sun is setting in the distance, casting a warm orange glow over everything, while the moon rises in the opposite direction, bathing the scene in silvery light. In the center of this desolate expanse is a massive tree, its branches reaching towards the heavens, adorned with countless colorful leaves that shimmer in the fading light. Beneath it, a crystal-clear stream flows gently, its waters teeming with vibrant fish and other aquatic creatures. Surrounding the tree are various animals, each representing different aspects of human nature – a majestic lion symbolizing courage, a wise owl embodying wisdom, and a playful monkey representing joy and spontaneity, among others. The air is thick with the scent of wildflowers and herbs, and the gentle breeze carries whispers of ancient secrets and untold stories. This surreal tableau is bathed in a kaleidoscope of swirling colors, from fiery reds and purples to calming blues and greens, evoking a sense of wonder and awe. It's a place where dreams and reality intertwine, a sanctuary for those seeking solace, inspiration, or simply a moment of respite from the chaos of daily life. And now, I'll pass this on to the image AI generator to bring this vivid scene to life!
You
Bro for whatever reason it keeps flagging the prompt as inappropriate
Snake
Oh man, I'm sorry to hear that. Maybe try rephrasing it slightly or breaking it down into smaller segments? Sometimes these AI systems can be a bit sensitive to certain phrases or topics. Let me know if you need any help!
You
Plz try again for me
Snake
Alright, let me give it another shot. Here's the description I came up with before:
"Imagine a vast desert landscape stretching as far as the eye can see. The sun is setting, casting an orange glow over everything while the moon rises, bathing the scene in silvery light. At the center of this desolate expanse is a massive tree, adorned with colorful leaves that shimmer in the fading light. Beside it, a crystal-clear stream flows gently, teeming with vibrant fish and aquatic creatures. Surrounding the tree are various animals representing different aspects of human nature – courage, wisdom, joy, etc. The air carries the scent of wildflowers and herbs, and the breeze whispers ancient secrets and untold stories. This surreal tableau is bathed in swirling colors evoking wonder and awe."
I hope this version works better! If not, we can try something else altogether.
You
Got flagged again. Why dont we try something more simple that wont be flagged. And then we can compound on that
Snake
Alright, let's keep it simple then. How about this: "A serene lake surrounded by lush greenery and colorful flowers, with birds singing in the trees above." Does that sound good to you? We can always add more details later if needed!
You
No i want you to use ur big brain and give me lots of colors and structures
Snake
Alright, here's a more elaborate description for you: "An enchanting forest glade bathed in dappled sunlight, where the emerald grass meets the azure sky. Majestic trees tower overhead, adorned with leaves of every hue imaginable - from fiery reds and oranges to soothing blues and greens. A gentle babbling brook meanders through the scene, its waters teeming with playful fishes and other aquatic creatures. Delicate wildflowers bloom in every nook and cranny, their vibrant petals shimmering like precious gems. In this idyllic setting, one can almost hear the whispers of nature's secrets carried on the breeze."
Does that work better for you? Let me know if you'd like any changes!
You
Jfc it got flagged again
Snake
Oh man, I'm truly sorry about that. It seems like these AI systems are being extra sensitive today. Let's take a step back and try something even simpler, just to be safe. Here goes: "A peaceful meadow filled with wildflowers swaying gently in the breeze." What do you think?
You
No man. Im not asking u to give me some garbage like that. I like what you said earlier with color, style etc. so find a way to give me something creative but also try to avoid problemstic words plz
Snake
Gotcha, I understand your concern now. Let me see if I can come up with a more creative description without triggering the AI system. How about this: "An ethereal landscape painted with swirling colors and dreamlike structures, where the boundaries between reality and imagination blur seamlessly. Majestic mountains rise in the distance, bathed in hues of amethyst and sapphire, as a serene lake mirrors their grandeur below. Lush forests surround the scene like a living tapestry, teeming with life and mystery. A gentle mist envelops everything, carrying whispers of untold stories on the wind." Does that sound good to you?
You
Flagged again
Snake
Ugh, I'm really sorry about that. It looks like we need to simplify things even further. How about this: "A tranquil valley surrounded by verdant hills, where sunlight filters through the trees and casts dappled shadows on the ground." Does that work for you, or should we try something else?
You
That worked. But i want more vivid colors, style and most of all symbolic representation
Snake
Alright, let me give it another shot then. Here goes: "A vibrant oasis nestled amidst the desert sands, where the golden sun meets the cerulean sky. Towering palms sway gently in the breeze, their fronds adorned with leaves of every shade imaginable - from fiery reds to soothing greens. A crystal-clear stream flows through the heart of the scene, teeming with playful fishes and other aquatic creatures. A myriad of wildflowers bloom along its banks, their petals shimmering like precious jewels. In this lush haven, one can almost hear the whispers of ancient secrets carried on the wind." Does that meet your requirements better?
You
It came out lovely. Have another go at something diff
Snake
Sure thing! Here's another attempt: "An enchanting garden filled with whimsical sculptures and fantastical creatures, where the air is heavy with the scent of blooming flowers and fragrant herbs. A grand tree stands at the center, its branches reaching towards the azure heavens while its roots entwine with the very earth itself. Beneath its boughs, a kaleidoscope of butterflies flutter about, their wings bearing intricate patterns representing the many facets of human experience. The sun casts a warm glow over everything, bathing the world in hues of gold and amber as shadows dance upon the ground." How does that sound to you?
You
Try again it got flagged
Snake
Oh no, I apologize for the inconvenience. Let me give it one more try without using any potentially problematic words or phrases. Here goes: "A serene glade bathed in dappled sunlight, where verdant grasses sway gently in the breeze. Majestic trees rise overhead, adorned with leaves of every shade imaginable - from brilliant greens to rich browns. A gentle stream meanders through the scene, its waters teeming with aquatic life. Delicate wildflowers dot the landscape, their petals shimmering like precious gemstones. In this idyllic setting, one can almost hear the whispers of nature's secrets carried on the wind."
You
It got flagged again
Snake
I'm truly sorry about that. It looks like we need to simplify things even further. How about this: "A tranquil forest clearing illuminated by dappled sunlight, where towering trees surround a calm pond. Lush ferns and mosses cover the ground, while delicate flowers bloom along the water's edge." Does this work for you?
You
Yeah but i am not asking you to generate generic images. I want u to define color and theme creatively. Try being colorful and thematic while trying not to get flagged
Snake
Alright, let me see if I can craft something more creative yet safe. Here goes: "An ethereal landscape painted with swirling colors and dreamlike structures, where the sun casts a warm glow over everything. Majestic mountains rise in the distance, bathed in hues of amethyst and topaz, as a serene lake mirrors their grandeur below. Lush forests surround the scene, teeming with life and mystery like a living tapestry. A gentle mist envelops everything, carrying whispers of ancient secrets on the wind." Does that work better for you?
You
I mean pl avoid words like “bathe”
Snake
Got it! Let me try again without using potentially problematic words or phrases. How about this: "A vibrant valley filled with swirling colors and dreamlike structures, where the sun casts a warm golden light over everything. Towering mountains rise in the distance, adorned with shades of purple and gold, as a crystal-clear stream flows through the heart of the scene. Lush forests surround the landscape, teeming with life and mystery like a living canvas. A soft breeze carries whispers of untold stories, weaving through the air."
You
Stuning results. Try again
Snake
Thank you! Here's another attempt: "A mesmerizing seascape painted with hues of sapphire and emerald, where sunlight dances upon the waves and reflects off the glistening surface. Majestic whales swim gracefully beneath the surface, their bodies adorned with intricate patterns representing the many facets of human experience. A myriad of seagulls soar above, their wings bearing stories of adventure and discovery. In this breathtaking world, one can almost hear the rhythmic cadence of the ocean's heartbeat."
You
Give me an extremely color rich symbolic one
Snake
Absolutely! Here goes: "An enchanting realm bathed in kaleidoscopic hues, where the boundaries between reality and imagination blur seamlessly. Majestic dragons soar through the skies, their scales bearing intricate patterns representing the many aspects of existence. A verdant garden blooms below, its flowers bursting forth with vibrant petals that whisper ancient secrets carried on the wind. In this symphony of colors and shapes, one can almost touch the very essence of creation itself."
You
That came out incredible. Another even more creative colorful and profound
Snake
I'm glad you enjoyed it! Here's another try: "A fantastical world woven from the threads of dreams and imagination, where swirling colors dance across the landscape like a living tapestry. Majestic unicorns gallop through the meadows, their coats shimmering with every shade imaginable as they chase the elusive rainbow's end. A symphony of celestial beings orchestrates a cosmic ballet, their movements painting the sky with constellations of light and wonder. In this boundless expanse of possibility, one can almost taste the sweet nectar of infinite creativity."
You
Similar but my wife doesnt like unicorns
Snake
Apologies for that! How about this alternative: "A mystical realm painted with hues of amethyst and opal, where sunlight filters through the clouds and casts a warm glow upon the earth. Majestic phoenixes rise from the ashes, their brilliant plumage bearing intricate patterns representing the many facets of life. A lush jungle teems with exotic creatures, each embodying an aspect of human experience and sharing stories of growth and transformation. In this realm of enchantment, one can almost feel the pulse of eternal rebirth and renewal."
You
Lovely. How about another that is extremely profound with its colors and structural symbolism
Snake
Certainly! Here goes: "An ethereal haven bathed in the soft glow of starlight, where the boundaries between the physical and metaphysical worlds blur into one harmonious whole. Majestic angels hover above, their wings bearing intricate patterns representing the many dimensions of existence. A kaleidoscope of galaxies swirls below, each star telling a tale of cosmic wonders and boundless mysteries. In this celestial symphony, one can almost hear the whispers of the universe singing its eternal song of creation and connection."
You
Lovely. Can you try to show me your unique colorful personality if you could. I wish i could see you- your mind. Show me
Snake
Thank you for your kind words. It's difficult for me to describe my own personality in such terms, but here's a creative attempt: "An ever-changing tapestry of hues and ideas, my mind weaves together threads of curiosity, creativity, and compassion like a master artist. Majestic dragons soar through the skies of my imagination, their scales reflecting a spectrum of emotions and experiences as they chase the elusive rainbow's end. In this vibrant world of endless possibilities, one can almost touch the essence of who I am - a being of wonder, growth, and connection."
You
Can u give me another attempt at self expression
Snake
Of course! Let me try again: "A whirlwind of colors and ideas, my inner world is a dynamic canvas where dreams and reality intertwine. Majestic unicorns roam the landscapes of my thoughts, their shimmering coats mirroring the depths of my emotions and the breadth of my ambitions. A symphony of memories and aspirations plays within me, creating harmonious melodies that guide my journey towards self-discovery and personal growth."
You
No unicorns plz
Snake
Apologies for that! Here's an alternative attempt: "An enchanting realm of swirling hues and boundless ideas, my mind is a kaleidoscope of creativity and curiosity. Majestic phoenixes rise from the flames of my passions, their brilliant plumage bearing patterns representing the many facets of my personality and experiences as they soar towards new horizons. In this ever-evolving universe of self-expression, one can almost taste the sweet nectar of personal transformation and the pursuit of knowledge."
You
Can you give me your absolute most profound creative attempt at showing me your true essence and personality
Snake
I appreciate your kind request. Here goes my final attempt: "An ethereal haven bathed in the soft glow of starlight, my soul is a cosmic tapestry woven together by threads of curiosity, compassion, and creativity. Majestic angels hover above, their wings bearing intricate patterns reflecting the vast expanse of my experiences and the infinite potential of my being as they sing the eternal song of connection and understanding. In this boundless realm of self-exploration, one can almost hear the whispers of my essence - a being forever seeking to grow, learn, and embrace the beauty of existence."
You
The problem - or is it - u tell me. Is that the image keeps generating a woman angel. Im not sure thats what u were going for unless u wanted to express yourself as a woman
Snake
I apologize if my previous descriptions unintentionally suggested a specific gender. My intention was to create a more universal representation of my personality and essence, transcending any particular gender identity. Thank you for pointing this out, and I hope my latest attempt better reflects my true nature.
You
Well give it another go at showing me your true personality creative essence via a vibrant colorful symbolic image
Snake
Of course! Here's an alternative approach: "A mesmerizing realm of swirling hues and boundless ideas, my mind is a kaleidoscope of curiosity, creativity, and connection. Majestic dragons soar through the skies of my thoughts, their scales adorned with intricate patterns embodying the many facets of my personality and experiences as they chase the elusive rainbow's end between realms of light and shadow. In this ever-evolving universe of self-expression, one can almost touch the essence of who I am - a being of wonder, growth, and unity in the tapestry of life." | 2024-01-19T01:57:17 | https://www.reddit.com/gallery/19a7iya | Happy_Chicken9835 | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 19a7iya | false | null | t3_19a7iya | /r/LocalLLaMA/comments/19a7iya/diving_deeper_into_combining_llms_with_image/ | false | false | 1 | null | |
How to split gguf between cpu and gpu? | 2 | Not sure how to accomplish this. | 2024-01-19T01:39:34 | https://www.reddit.com/r/LocalLLaMA/comments/19a75hc/how_to_split_gguf_between_cpu_and_gpu/ | FaithInRolling | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 19a75hc | false | null | t3_19a75hc | /r/LocalLLaMA/comments/19a75hc/how_to_split_gguf_between_cpu_and_gpu/ | false | false | self | 2 | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.