Repetition penalty llama reddit. I prefer the Orca-Hashes prompt style over airoboros.
Repetition penalty llama reddit 2 and anything less than 2. 08 still keeps repetitiveness under control in most cases, while generating vastly longer outputs for many prompts. Response: I wrote a fantasy story about LOTR. It seems that you insist to kiss Elon's ass and tell everyone that his model is the best one. 25, and start with 1. 9s vs 39. 1B model only, settings: repetition_penalty=1. I could not reproduce this when using Llama 3 Instruct 8B loaded in BF16 (unquantized) and repeatedly regenerating a new message at least over 50 times giving the exact same result each time when using 0 temperature, 1 repetition penalty and rest is off/default (through SillyTavern). 4 + repetition penalty range 2048 solved that problem for me. 8 with 0. View community ranking In the Top 5% of largest communities on Reddit. 2. It writes well in general but it doesn't take long before it continually outputs repeated phrases ('strange, new world' has wound up at the end of nearly every post it makes, for example). 15" or "1. So I upped the repetition tokens from 256 to 512 and it fixed it for one message, then it just carried on repeating itself. <|eot_id|> is Llama 3's stop token Instruct or non Instruct? With the new Llama 3 models, Meta released both the base model and also the "Instruct" version as is usual. Takes about ~6-8GB RAM depending As for repetition on 70b: - REDUCE your repetition penalty. In my experience, repetition in the outputs are an everyday occurance with "greedy decoding" This sampling, used in speculative decoding, generates unusable output, 2-3x faster. The training has started on 2023-09-01. Since you're using completely different inference software, it's either a problem with the Llama 2 base or a fundamental If the repetition penalty is too high, most models get trapped and just send "safe" or broken responses. 1, and making the repetition penalty too high makes the answer nonsense. KoboldAI instead uses a Here are my two problems: The answer ends, and the rest of the tokens until it reaches max_new_tokens are all newlines. 172K subscribers in the LocalLLaMA community. Therefore, a repetition penalty would start punishing writing these tags correctly, thus destroying the conversation Repetition Penalty: Repetition penalty is a technique that penalizes or reduces the probability of generating tokens that have recently appeared in the generated text. This penalty works by down-weighting the probability of tokens that have previously appeared We would like to show you a description here but the site won’t allow us. . Or check it out in the app stores TOPICS. Conclusion: That's unusual. But this kind of repetition isn't of tokens per se, but of sentence structure, so can't be solved by repetition penalty and happens with other presets as well. For the context template and instruct, I'm using the llama3 specific ones. As far as I understand, it was trained on about twice the data that llama 2 was trained on. It will beat all llama-1 finetunes easily, except orca possibly. generate function. Can't get 34B to run locally so far, but am using an online version (https://labs. The best base models at each size right now are Llama 3 8b, Yi 1. 5 in most areas! However, that was back when llama-2 was fairly new. Like in your example applying viruses to everything. Prompt: instruction: Write a fantasy story about LOTR. The lower the value, the smaller the set A huge problem I still have no solution for with repeat penalties in general is that I can not blacklist a series of tokens used for conversation tags. Since then, I figured Repetition Penalty is kind of redundant and model breaking when it's >1,15. Repetition - How to reduce it? I've tried llama 2 13b, llama 1 13/33b models, loads of types. So, I've noticed that Llama repeats the same phrase multiple times despite the AI instruction saying to avoid repetition. Reddit iOS Reddit Android Reddit Premium About Reddit Advertise Blog Careers Press. gguf` on the second message. I've had it go into pretty much infinite loops in the second or third response already, which is way worse than any other model I've tried. 2ish. 7 slope which provides what our community agrees to be relatively decent results across most models. This size keeps a good variety of interactions in the context. cpp on Termux. 5 is high enough that you very well might see stuff like this happen. eos_token_id, This is based on the 1 trillion token Checkpoint of tiny llama, there is not released chat version for the 1. I haven't really gotten the AI Instructions to work very well, especially with Llama. Repetition penalty application in proportion to historical token frequency. If you are playing on 6B however it will break if you set repetition penalty over 1. cpp on npm: all 3 fail to build (this is what you get when things are this fresh/recent I guess), and they also don't have all the options the command line llama. Loader is Exllama v2 HF. cpp If the repetition penalty gets too high, the AI gets nonsensical. All llama 2 models with stochastic sampling have this same issue. Reddit . It is called "The Lord of the Rings: The Battle of the Five Armies. 5, eos_token_id=tokenizer. I generally agree, although what they recommend is what I've referred to as "LLaMA-Precise. 15 (probably would be better to change it to 0 tbh), rest is 0 0 0 1 1 0 0 0 as you go down in the UI. 25, especially trying out 1. It seems like this is much more prone to repetition than GPT-3 was. These are way better, and DRY prevents repetition way better without hurting the model. Frustrating to see such excellent writing ruined by the extreme repetition. Repetition penalty range also makes no difference. Apologies if this is well known on the sub. 05 (and repetition penalty range at 3x the token limit). 9 top_p, 0 top_k, 1 typical_p, 0. 1) and the repetition and sudden loss of articles/punctuation issues just vanish. Personally I run 0. bin -p "Act as a helpful Health IT consultant" -n -1. So I've been using llama-cpp-python's server: python3 -m llama_cpp. Yi runs HOT. Share Add a Comment. Keskar et al. cpp doesn't interpret a top_k of 0 as "unlimited", so I ended up setting it to 160 for creative mode (though any arbitrarily high value would've likely worked) and got good results. Once you get a huge context going, the initial prompt processing takes a LONG time, but after that prompts are cached and its fast. It's basically unusable from my testing. The model answers to the request just fine, but can't finish its response nevertheless. Then there are plethora of smaller models, with the honorary mention of Mistral 7B, performing absolutely amazing for its size. e. then I use the continue command to finish the response. The models that have LLaMa seems to take high temp well, but doesn't do well with repetition_penalty over 1. 5-mixtral-8x7b-GGUF Q4_K_M Repetition penalty makes no difference whatsoever. Presence penalty makes it choose less used tokens. Also increase the repeated token penalty. 2: 428: October 14, 2024 Llama-2 7B-hf repeats context of question directly Mancer seems to be using mythomax GPTQ models. 0 --tfs 0. So, here’s my question - has anyone else experienced similar issues? I need to run these tests on other models, will probably test Internlm2 today since on it these repetition issues Subreddit to discuss about Llama, the large language model created by Meta AI. I think it is caused by the "<|image|>" token whose id is 128256, and meta-llama/Llama-3. Get the Reddit app Scan this QR code to download the app now. I almost never use it now, instead set a Min_P of 0,2-0,32. Aside from that, do you know of a list of general DRY sequence breakers I can paste into Ooba that works for most model types like Mistral, Llama, Gemma, etc. See #385 re: CUDA 12 it seems to already work if you build from source? Reply reply We are currently private in protest of Reddit's poor management and decisions related to third party platforms and content management. Upped to Temperature 2. 10 to about 1. 7 --repeat_penalty 1. It uses RAG and local embeddings to provide better results and show sources. Im not super familiar with LMstudio but things such as temperature, repetition penalty, and correct system prompt and such can make a huge difference. I'd just start changing variables, using different models and presets. Use min-P (around 0. Amount generation: 128 Tokens Context Size: 1124 (If you have enough VRAM increase the value if not lower it!!. 1 Reply reply More replies Top 1% Rank by size What's worse, the only weapon against it (repetition penalty) distorts language structure, affecting the output quality. Internet Culture (Viral) Amazing Frequency penalty is like normal repetition penalty. 1 to 1. 21, 1. This penalty is more of a bandaid fix than a good solution to preventing repetition; However, Mistral 7b models especially struggle without it. " It is a sequel to the first movie, "The Lord of the Rings: The Fellowship of the Ring. /r/StableDiffusion is back open after the protest of Reddit killing open API Then we will have llama 2 70B and Grok is somewhere at this level. 29, 1. 10 repetition penalty over 1024 tokens. server Any way to fake repetition penalty? I've just registered with Moemate and have been having a decent time so far, but I've been having a number of frustrations with the Mixtral 8x7B model. ChatGPT: Sure, I'll try to explain these concepts in a simpler For any good model, repetition penalty (and even more frequence penalty) should degrade performance That because (at least in my viewfeel free to correct me) the concept - Repetition Penalty. That's why I basically don't use repeat penalty, and I think that somehow crept back in with mirostat, even at penalty 1. (2048 for original LLaMA, 4096 for Llama 2, or higher with extended context - but not hundreds of thousands of tokens). Using codellama-13b-oasst-sft-v10. The Silph Road is a grassroots network of trainers whose communities span the globe and hosts resources to help trainers learn about the game, Subreddit to discuss about Llama, the large language model created by Meta AI. q4_0. I’d highly recommend either jsonformer or prompt engineering with StarChat Beta, XGen 7b, and Raven v4 14b (World, the newest version isn’t as good at output parsing) for all of these I recommend no repetition penalty, multi shot, around 0. cpp when streaming, since you can start reading right away. 18, Rep. 0 now, it's producing more prometheus-aware stuff now, but funny enough (so far - not done yet) it's not giving much explainer: It's just normal content. Temperature : 1. uses ChatML format That looks like an emerging standard and I saw surprisingly good results with that in my latest model test/comparison. Internet Culture (Viral) Amazing; Animals & Pets Subreddit to discuss about Llama, the large language model created by Meta AI. 05 min_p, repetition penalty 1, frequency penalty 0, presence penalty 0) That's an interesting question! After conducting a thorough search, I found that there are a few words in the English language that rhyme with exactly 13 other words. I'm not sure if this setting is more important for low bpw models, or if 2x gain is considered consistent for 4. It complements the regular repetition penalty, which targets single token repetitions, by mitigating repetitions of token sequences and breaking loops. 3 and even tried mirostat mode 1,2 on the kobold. Or it just doesn’t generate any text and the entire response is newlines. The models are trained to understand x amount of context, and get confused on anything Get the Reddit app Scan this QR code to download the app now. The settings show when I have no model loaded. How should I change the repetition penalty if my character keeps giving similar responses? Do I lower it? Coins. And magically, the repetition was gone again. Llama 3 prefers lower temperature and repetition penalty. I prefer the Orca-Hashes prompt style over airoboros. Slope 0 pipeline, or model. 1 samplers. Much higher and the penalty stops it from being able to end sentences (because . 5 (exl2) or 1. Sports. 1. Pure, non-fine-tuned LLaMA-65B-4bit is able to come with very impressive and creative translations, given the right settings (relatively high temperature and repetition penalty) but fails to do so consistently and on the other hand, produces quite a lot of spelling and other mistakes, which take a lot of manual labour to iron out. I find it incredible that such a small open-source model outperforms gpt-3. generate doesn't seems to support generate text token by token, instead, they will give you all the output text at once when it's It is now about as fast as using llama. As far as llama-2 finetunes, very few exist so far, so it’s probably the best for everything, but that will change when more models release. Try at least 0. Adding a I switched up the repetition penalty from 1. 4-Mixtral-Instruct-8x7b-Zloss-GGUF (Q5_K_M) Another member of the community did a lot of testing and found a repetition penalty of 1/0. 466 votes, 198 comments. There is not a lot of difference from my experience. When setting repetition penalty from 1. With Mistral and Llama-3, I think we barely have any objective data about samplers. 'The TinyLlama project aims to pretrain a 1. 5, (the higher the temperature the more creative the model) depending on your tests, which works best. Q5_K_M. For instance, if we had the penalty scaled on a curve so that the first few times are weighted heavily, but then the subsequent repetition is weighed less severely. As the context limit is reached, older stuff gets discarded (and smart frontends manipulate the context to always This is the repetition penalty value applied as a sigmoid interpolation between the Repetition Penalty value (at the most recent token) and 1. Well, that was the goal of inverse DPO, I suppose. 2-1. 2023-08-19: After extensive testing, I've switched to Repetition Penalty 1. As far as I know, the EOS token doesn't get special treatment so it is affected by repetition penalty like any other token. cpp, special tokens like <s> and </s> are tokenized correctly. txt file and name it whatever you want and put it in the presets folder in the Oobabooga install directory. - Some models are less capable of answering specific questions, or talk on specific themes. Also excited for the updates, which Llama really needs. 10. mistral require a higher repetition penalty than vicuna, vicuna truncates messages if repetition is too high etc) Get the Reddit app Scan this QR code to download the app now. As soon as I load any . 5 trillion version yet! Definitely worthwhile checking the repo every now and then for updates :) Would recommend using the chat version for now, even if you intend to further fine-tune. I would be willing to improve the docs with a PR once I get this. With adjustments to temperature and repetition penalty, the speed becomes 1. The benefit of this over straight llama chat is that it is uncensored (it doesn’t refuse requests). 1B, almost on par with Llama 1 7B models. 1 rep pen, 1024 range and 0. 99 temperature, 1. 7, repetition_penalty 1. org) So I just recently set up Oobabooga's Text Generation Web UI (TGWUI) and was playing around with different models and character creations within the UI. As a model I use upstage 70b. At this point I usually have hundreds of generations from the model. Not claiming that it's perfect, but it works well for me. Yeah, the model is batshit crazy. 15 and 1. Instructions for deployment on your own system can be found here: LLaMA Int8 ChatBot Guide v2 (rentry. Pen. Gaming. I wouldn't expect llama 3 70b performance, but it absolutely obliterates the 8b model. , top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, do_sample=True, num_return_sequences=1, num_beams = num_beams, remove_invalid_values=True, ) output_text = self. If you are wondering what Amateur Radio is about, it's basically a two way radio service where licensed operators throughout the world experiment and communicate with each other on frequencies reserved for license holders. Just consider that, depending on repetition penalty settings, what's already part of the context will affect what tokens will be output. 4bpw might do better if you can fit it in 24GB. 10, Rep. 12, depending on whether the model repeats too much, then increase the penalty. What's more The typical solution to fix this is the Repetition Penalty, which adds a bias to the model to avoid repeating the same tokens, but this has issues with 'false positives'; imagine a language model frequency_penalty: Higher values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. 🤗Transformers. I am open to sampler suggestions here myself. I noticed some problems with repetition, no matter how much you crank up the penalty of the temperature, when you hit retry or continue, you'll probably see the same thing again. Most presets have repetition_penalty set to a value somewhere between 1. I have tried token forcing, beam search, repetition penalty - nothing solves the problem; I tried other prompt formats. gguf, Might have even better results by lowering 'repetition penalty' too. The following are all skipped: llama_sample_top_k llama_sample_tail_free llama_sample_typical llama_sample_top_p Similar logic is found in text-generation-webui's code where all samplers other than temperature is disabled when Mirostat is enabled. But with the default settings preset this and most other Posted by u/Enkay55 - 3 votes and 14 comments Min_p at 0. Someone on Reddit also said that Repetition penalty is also used, but I never tried messing with that in Mirostat. 01 temp, 0. 85), top_k 40, and top_p 0. 0. So for example, if you want to generate code, there is going to be a lot of repetition, if you want to generate markdown table, there is going to be even more repetition, similar for HTML, etc. Reply jackfood2004 Interesting question that pops here quite often, rarely at least with the most obvious answer: lift the repetition penalty (round 1. I had to set both fairly high to get the best results. cpp (locally typical sampling and mirostat) which I haven't tried yet. Transformers parameters like epsilon_cutoff, eta_cutoff, and encoder_repetition_penalty can be used. Valheim Genshin Impact Minecraft Pokimane Halo Infinite Call of Duty: Warzone Path of Exile Hollow Knight: Silksong Escape from Tarkov Watch Dogs: Legion. I found that playing around with temperature and repetition penalty didn't do anything to fix this, but switching my quick preset back to Default and then raising the temperature seems to have fixed the problem. Using LLaMA 13B 4bit running on an RTX 3080. Themed models like Adventure, Skein or one of the NSFW ones will generally be able to handle shorter introductions the best and give you the best experiences. The prompt format is also fairly critical as well, I am actually having good luck with "novel style" raw prompting. I’d say you should proofread a bunch of your model’s outputs and lower the rep penalty if you do. Repetition penalty 1. However, I haven’t come across a similar mathematical description for the repetition_penalty in LLaMA-2 (including its research paper). 2 and that fixed it for one message. 07 Llama 3 has 8K context size, even fine tuned models don't work that well above 8K. Important: Top P at 1. 05 MinP and all other samplers disabled, but Mirostat with low Tau also works. 6, Min-P at 0. 我重新微调了qwen-14b-chat, internlm-20b-chat，都是这个现象，原始模型（非Loram）没有这个问题. 05 Minp, low temperature, mirostat with a tau of 1. 2 across 15 different LLaMA (1) and Llama 2 models. But yes, it really depends on the model. However after a while now i am beginning to notice "AI styled writing" I tried pumping up the temperature to 1. My solution is to edit the response, removing all text from the point where it starts to repeat itself in its response and then add in a word of two, to create a partial sentence that pushes the response in a different direction from what it was repeating. 1). I Yep, that Llama 2 repetition issue is a terrible problem and makes these newer models useless for chat/RP. 37! are 1. reReddit: Top posts of February 12, 2023. Deterministic preset, so temperature and top_k don't apply - it always picks the most probable token. Make sure the repetition penalty range is set at 2048, this seems to remove repetition for me. 0 coins. 73 votes, 30 comments. 0 Just copy and paste that into a . perplexity. I have used GPT-3 as a base model. I've done a lot of testing with repetition penalty values 1. The key is to disable top-P, top-K and user very low repetition penalty (around 1. :) Parasitic really outdid himself with that one. 89 The first open weight model to match a GPT-4-0314 I've been trying all sorts of combinations for hours and my best result so far is this. Greedy sampling selects the token the model finds most probable, and anything else is an attempt to compensate for a particular model's particular shortcomings. Saved searches Use saved searches to filter your results more quickly Welcome to Reddit's own amateur (ham) radio club. Playing around with LZLV-70b 4QM, i am having a great time with the long form responses. This further confirms that existing llama models are severely under trained. After testing so many models, I think "general intelligence" is a - or maybe "the" - key to success: The smarter a model is, the less it seems to suffer from the repetition issue. cpp is not just 1 or 2 percent faster; it's a whopping 28% faster than llama-cpp-python: 30. There have been many reports of this Llama 2 repetition issue here and in other posts, and few if any other people use the deterministic settings as much as I do. 05 and no Repetition Penalty at all, and I did not have any weirdness at least through only 2~4K context. 36 repetition_penalty=1. cpp directly, but with the following benefits: More samplers. Premium Powerups Explore Gaming. 18" are the best, but in my experience it isn't. 27 votes, 18 comments. I've been looking into and talking about the Llama 2 repetition issues a lot, and TheBloke/Nous-Hermes-Llama2-GGML (q5_K_M) suffered the least from it. Along with a repetition penalty of about 1. cpp is equivalent to a presence penalty, adding an additional penalty based on frequency of tokens in the penalty window might be worth exploring too. Try KoboldCPP with the GGUF model and see if it persists. Slope 0. In my experience it's better than top-p for natural/creative output. I was looking through the sample settings for Llama. 25bpw is maybe too low for it to be usable 2. Have been running a Yi 200k based model for quite some time now, and in full context too (now 65k thanks to 4-bit cache), and it’s the best model I’ve ever used. Llama API llamafile LLM Predictor LM Studio LocalAI Maritalk MistralRS LLM MistralAI ModelScope LLMS Monster API <> LLamaIndex Reddit Remote Remote depth S3 Sec filings Semanticscholar Simple directory reader Singlestore Slack Smart pdf loader Snowflake Spotify repetition_penalty: float = Field (description = "Penalty for repeated words in generated text; 1 View community ranking In the Top 5% of largest communities on Reddit. do_sample=True top_p=1 top_k=12 temperature=0. Sure I could get a bit format The current implementation of rep pen in llama. 7B models are usually not as smart and good at reading „in between the lines” to my liking. For answers that do generate, they are copied word for word from the given context. 05 (for 1024 range) and then I only use Dynamic Temperature, and that’s it, no other Yea, what mcmoose said, use Dynamic Temperature from now on when at all possible. However, one point I'm concerned about is the EOS token <|im_end|> being part of the prompt template: . Also, mouse over the scary looking numbers in the settings, they are far from scary you cant break them they explain using tooltips very well. For my settings, I keep my Min P at 0. Valheim; Genshin Impact; Subreddit to discuss about Llama, the large language model created by Meta AI. 37, 1. For example, its **Part 0 - Why do we want repetition penalties?** For reasons of various hypotheses, **LLMs have a tendency to repeat themselves and get stuck in `repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1. sh and then do "docker compose up --build" to start it with new parameters. " Like technically any amount of whitespace between more tokens in JSON (in the JSON tokenizer sense, not the language model tokenizer) is valid JSON, but baking into the grammar a repetition penalty might be a better (longer term) solution, or even a linter/formatter that follows along with the grammar (weighting whitespace tokens higher in certain places). /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app It's worth mentioning, bigger context means higher RAM/VRAM requirement. 15, 1. 3 (llama. Any advice? comments sorted by Best Top New Controversial Q&A Add a Comment sh221B777 • Additional comment actions. Any penalty calculation must track wanted, formulaic repitition imho. 02). 7 were good for me. 5 or so, and really goes wonky over 2. 15 repetition_penalty, 75 top_k, 0. The 128k context version is very useful for having large pdfs in context, which it can handle surprisingly well. Works on my laptop with 8GB RAM. After that there is a repetition penalty parameter, which I set to 1. Roleplay instruct mode preset: Showed personality and wrote extremely well, much better than I'd expect from a 7B or even 13B. Top K at 0. This is Llama. I use Contrastive Search with a slightly increased repetition penalty. 7B is likely to loop in general. ? The default sequence breakers should do the trick already. Stop doing the same old mistake of cranking it way up every time you see some repetition. I came across this issue two days ago and spent half a day conducting thorough tests and creating a detailed bug report for llama-cpp-python. 我跑了1万数据条做测试，在多轮对话情况下，聊几轮到十多轮以后，输出的长度开始变短，到最后就只有十多个字，怎么问都说不详细。 We would like to show you a description here but the site won’t allow us. 131K subscribers in the LocalLLaMA community. It's set up to launch the 7b llama model, but you can edit launch parameters in run. Basically, context size is not in bytes, it's in "things" the model sees as a fundamental unit of text, and it not only needs that much memory to store it, but memory to process it too. 5, num_tokens_to_generate = 100. MM does this much less often. Then it did it again. Special tokens. It's just the same things over and over and over again. 50) Repetition Penalty : 1. Although, a little note here — I read on Reddit that any Nous-Capy models work best with recalling context to up to In Text completion presets, set the temperature between 1 and 2. Sort by: You can think of it as a top-p with built-in repetition penalty. They also added a couple other sampling methods to llama. 05 typical_p=1. 5s. It helps fight against llama2's tendency to repeat itself, and gives diverse responses with each regeneration. 149K subscribers in the LocalLLaMA community. It's like a traumatized person who can only think bad/nasty things. This is done by dividing the token if it is above zero, and multiplying it by the penalty if it is below zero. Repetition penalty between 1 and 1. Internet Culture (Viral) Amazing . Check your presets and sampler order, especially Temperature, Mirostat (if enabled), Repetition Penalty and the sampler values. Using it is very simple. repetition_penalty: 1 repetition_penalty_range: 0 encoder_repetition_penalty: 1 top_k: 0 min_length: 0 no_repeat_ngram_size: 0 num_beams: 1 penalty_alpha: 0 length_penalty: 1 I find 13B great, exceeding my expectations. Catbox Link. 33 and repetition penalty at 1. The defaults we use for this are 1. 03 or so. 37 (Also good results but !not as good as with 1. Add the bos token, skip special tokens and activate text streaming is checked, auto_max_new_tokens and ban the eos_token is Subreddit to discuss about Llama, the large language model created by Meta AI. 1B Llama model on 3 trillion tokens. Takes about ~4-5GB RAM depending on context length. Also as others have noted 2. 7B: Nous Hermes Mistral 7B DPO. Mixtral, MythoMax and TieFighter are good, but I really feel like this is a step up. I'm also getting constant repetition of very long sentences with dolphin-2. GGUF model, the setting `additive_repetition_penalty`, along with many other settings, all disappear. is penalized) and soon loses all sense entirely. The sweet spot for responses is around 200 tokens. on 13B mistral based model mirostat 2 with repetition penalty 1. ggmlv3. ai/). and with a temperature so close to 1, all it's really doing is repetition penalty and top_p. After ~30 messages, fell into a repetition loop. --top_k 0 --top_p 1. Yes Exllama is much faster but the speed is ok with llama. " Get the Reddit app Scan this QR code to download the app now. I hope Meta addresses this for llama 3. 1. true. I did search around reddit and Google for a while, and couldn't find any comprehensive explanation of the various samplers. Sometimes it is necessary though, like for Mistral 7b models. But there is hope! I have submitted a pull request to text-generation-webui that introduces a new type of repetition penalty that specifically targets looping, while leaving the basic structure of language unaffected. Goal: Observing changes in output helps me understand how each parameter influences the model’s responses. decode(output[0], skip_special_tokens=True) output_text = Because you have your temperatures too low brothers. 20, but I find that lowering this to around 1. 1 as recommended here) Reddit's #1 spot for Pokémon GO™ discoveries and research. 0 (at the end of the Repetition Penalty Range). I was using konichi-7b-v2-DPO which is considered a fairly uncensored model (no recommendations, just downloaded the other day, heard good Hey, thanks fot the prompt and samplers recommendation! I’ll give them a go! Really cool that you figured how to reel in Repetition without Repetition Penalty! Also, I’m very happy to read you’ve been enjoying the model. Min P to 0. Most commonly suggested repetition penalty 1 was not good in some cases (it was repeating even within same response) For a more precise chat, use temp 0. Maybe I should turn the repetition penalty up comments sorted by Best Top New Controversial Q&A Add a Comment demonfire737 Mod • Additional comment It seems when users set the repetition_penalty>1 in the generate() function will cause "index out of bound error". 7B: Nous Hermes 2 SOLAR 10. Repetition penalty is something greatly misunderstood. 5/hr on vast. Generation parameters preset: LLaMA-Precise (temp 0. ', do_sample=True, top_k=10, num_return_sequences=1, repetition_penalty=1. Just wondering if this is by design? interestingly, the repetition problem happened with `pygmalion-2-7b. /main -ins -t 6 -ngl 10 --color -c 2048 --temp 0. I tried using llama_HF with those quants to add the correct tokenizer back in, but I got a garbled mess as a result. Even with a high repetition penalty and temperature ND likes to repeat phrases, sometimes ones that were not essential to the story to the point of irrelevance. Then I set repetition penalty to 600 like in your screenshot and it didn't loop but the logic of the storywriting seemed flawed and all over the place, starting to repeat View community ranking In the Top 5% of largest communities on Reddit. 4 Likes. For 30b though, like WizardLM uncensored 30b, it's gotta be GPTQ and even then the speed isn't great (RTX 3090). It's not really necessarily documented in the commandline what this is doing, so one has to read the code to find this out. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. 18, range 0 (full context). And this was using mirostat and high repetition penalty. I'm thinking something like the function sqrt log(x) would help when generating long form outputs that have the potential for high repetition. If the repetition penalty is high, the model could end up writing something weird like “ the largest country in the America”. 05-1. 18 with Repetition Penalty Slope 0! What is repetition penalty slope and how do I set this parameter within llama. Testing was done with TheBloke's q3_k_s ggml Phrase Repetition Penalty (PRP) Originally intended to be called Magic Mode, PRP is a new and exclusive preset option. Subreddit to discuss about Llama, the large language model created by Meta AI. - Repetition Penalty should be used lightly, if at all, (1. For Quality: NeverSleep/Noromaid-v0. cpp "main" program does (like grammar and many others). 03. But suffered from severe repetition (even within the same message) after ~15 messages. Anyway, it seems to be a decently intelligent model based on the first part of that response, somewhat similar to Alpaca. 18, Range 2048, Slope 0 (same settings simple-proxy-for-tavern has been using for months) which has fixed or improved many issues I occasionally encountered (model talking as user from the start, high context models being too dumb, repetition/looping). I'm hoping we get a lot of alpaca finetunes soon though, since it always works the best, imo. 157K subscribers in the LocalLLaMA community. I just followed the basic example character profile that is provided to create a new character to chat with (not for providing knowledge like an assistent, but just for having fun with interesting personas). I see many people struggle to find a sweet spot for LLama 3. To avoid contamination, most of our human-written documents are taken. cpp should be a framework that offers the most possible options to "play" around with llms, which in my understanding implies that it adresses educational and advanced use-cases as well, I think we should let the possibility open to experiment with even higher repetition (repeat-penalty < 1). Also, set repetition penalty to 1. 7B. It's silly to base anti-repetition penalty on individual sub-word tokens rather than longer sequences, but that's the state of nonsense we are still dealing with in the open source world at least. Much less, and it keeps getting shorter; much more, and it tends to repeat itself like you see. This remains the same with repetition_penalty=1. 1, 1. 2 MAX) because it works as a multiplier based on how many times the token was seen in the previous context; it also runs before all other samplers. 12 top_p, typical_p 1, length penalty 1. shawwn/llama-dl: High-speed download of LLaMA, Facebook's 65B parameter GPT model (github. I did try setting repetition penalty from about 1. It's somewhat In my experience, repetition in the outputs are an everyday occurance with "greedy decoding" This sampling, used in speculative decoding, generates unusable output, 2-3x faster. 05) and DRY instead. min_p 0, top_k 20, repetition penalty 1. (2019)’s repetition penalty when avail-able. Thanks. Could anyone provide insights? 1 Like. Members Online Finetuned Miqu (Senku-70B) - EQ Bench 84. People sometimes say "1. more control for min-p top-k and repetition penalty are useful, especially if you can save a per-model default (i. Like a lot higher. These two are different beasts compared to poor Llama-2. I was unsure tbh, but since in my opinion llama. But repetition penalty is not a silver bullet, unfortunately, because as I said in the beginning, there is a lot of repetition in our ordinary lives. For immediate help and problem solving, please join us at There are 3 nodejs libraries for llama. 1, smoothing at 0. By using the transformers Llama tokenizer with llama. I think some early results are using bad repetition penalty and/or temperature settings. OTarumi July 1, 2023, 12:59am 3. Can't be that all combinations cause these issues for you with LLaMA (1) models. 05 to 1. With a lot of EOS tokens in the prompt, you make it less likely for the model to output it as repetition penalty will eventually suppress it, leading to rambling on and derailing I used no repetition penalty at all at first and it entered a loop immediately. 95 --temp 0. 0, Min-P at 0. If the rumors are true about a 120b model it could end up being scary good if they also drastically increase the training dataset. Adding a repetition_penalty of 1. 7 top p. I did a penalty range of about 1200-2000. 85 to produce the best results when combined with those other parameters. $1. cpp and I found a thread around the creation of the initial repetition samplers where someone comments that the Kobold repetition sampler has an option for a "slope" parameter. (0. cpp recently add tail-free sampling with the --tfs arg. If you’re in a situation to run a 13B GGML version yourself, use Mirostat sampling (2, 5, and 0. Additionally seems to help: - Make a very compact bot character description, using W++ We are Reddit's primary hub for all things modding, from troubleshooting for beginners to creation of mods by experts. For the hyperparameter repetition_penalty, while I comprehend that a higher repetition_penalty promotes the generation of more diverse tokens, I’m seeking a more quantitative explanation of its mechanism. Q4_K_S. For a more precise chat, use temp 0. 33 votes, 46 comments. always so damn satisfying to see, ha ha. main: build = 938 (c574bdd) main: seed = 42 Confused about Takes over ~2GB RAM and tested on my 3GB 32-bit phone via llama. 2-11B-Vision-Instruct · Issue about using "repetition_penalty" parameter in model. 18 since everyone says that's the magic number. Both models have the slop that all models do, but it seems somehow more endearing when it comes from MM. 6 temp and 0. 5 This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, which break third-party apps and moderation tools. 1 and no Repetition Penalty too and no problem, again, I could test only until 4K context. com) LLaMA has been leaked on 4chan, above is a link to the github repo. 5 34B, Cohere Command R 34B, Llama 3 70B, and Cohere Command R+ 103B Reply reply Great-Investigator30 I'm running LLaMA-65B on a single A100 80GB with 8bit quantization. In the llama_sample_repetition_penalty function, we expect to penalize a token based upon how many times it is used. We ask Much less repetitive. LLaMA +sampling +penalty Figure 1: Detectors for machine-generated text are often (Reddit, Poetry), and knowledge of specific media (Books, Reviews). 2 seems to be the magic number). Draft model r/LocalLLaMA • HuggingChat, the open-source alternative to ChatGPT from HuggingFace just released a new websearch feature. 15 simple-proxy-for-tavern's default and ooba's LLaMA-Precise presets use Rep. 1 Note that one hang-up I had is llama. I had to increase repetition penalty otherwise it's prone to get stuck in a thought loop. 18 turned out to be the best across the board. I disable traditional repetition penalties, while others leave a small presence penalty of 1. From my experience, a rep penalty of 1. Keep in mind that 2x24 is still a very small size of VRAM for the knowledge you're asking that 40gb file Update 2023-08-16: All of those Vicuna problems disappeared once I raised Repetition Penalty from 1. With adjustments to temperature and repetition Tried here with KoboldCPP - Temperature 1. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will Make sure you're using the correct prompt formatting and also with "Skip special tokens" turned off for the Instruct model. Using --repeat_penalty 1. Terms & Policies My KoboldCPP Settings Using Code Llama That Are Giving Me Great Results . 7 oobabooga's text-generation-webui default simple-1 preset uses Rep. Using silly tavern, change the repetition penalty to 1. works a dream. Related topics Topic Replies Views Activity; Loading pre-trained models with AddedTokens. 18, and 1. cpp? I've tried puffin and it really really wants to repeat itself. I have finally gotten it working okay, but only by turning up the repetition penalty to more than 1. 65bpw. 0, the tokens per second for many simple prompt examples is often 2 or 3 times greater as seen in the speculative example, but generation is prone to repeating phrases. cpp) Approach: I experiment with one parameter at a time — temperature, num_beams, top_k, top_p, repetition_penalty, no_repeat_ngram_size. It's just a lightly modified Universal-Light preset with smoothing factor and repetition penalty added. I've just finished a lot of testing with various repetition penalty settings: KoboldAI by default uses Rep. 1 -s 42 -m llama-2-13b-chat. My go-to SillyTavern sampler settings if anyone is interested. Instruct preset is Llama 2 Chat (Mixtral's official format doesn't have a system message, but being a smart model, it understands it anyway). 1764705882352942 (1/0. ai The output is at least as good as davinci. Llama. / Goes into repeat loops that repetition penalty couldn't fix. Not as good as 7B but miles better than 1. tokenizer. 17 Works best for me Reply 2 experts (default). 1 or greater has solved infinite newline generation, but does not get me full answers. 1, Repetition Penalty at 1. Repetition in the Yi models can be eliminated with the right samplers. 5, repetition penalty to 1. dhwx lbtxgnb iulsxx oowgnt sam qqpjc kpod zfh cfqayz cibpgw