Repetition penalty llama reddit. 7B: Nous Hermes 2 SOLAR 10.
● Repetition penalty llama reddit 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. 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. 149K subscribers in the LocalLLaMA community. 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. do_sample=True top_p=1 top_k=12 temperature=0. Much higher and the penalty stops it from being able to end sentences (because . These are way better, and DRY prevents repetition way better without hurting the model. 0 coins. 05 typical_p=1. 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. 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. Pen. 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. How should I change the repetition penalty if my character keeps giving similar responses? Do I lower it? Coins. And this was using mirostat and high repetition penalty. 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. For Quality: NeverSleep/Noromaid-v0. 1 and no Repetition Penalty too and no problem, again, I could test only until 4K context. To avoid contamination, most of our human-written documents are taken. 9 top_p, 0 top_k, 1 typical_p, 0. is penalized) and soon loses all sense entirely. With adjustments to temperature and repetition penalty, the speed becomes 1. Along with a repetition penalty of about 1. 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. 5, repetition penalty to 1. 15 simple-proxy-for-tavern's default and ooba's LLaMA-Precise presets use Rep. 25, and start with 1. 1, Repetition Penalty at 1. 5 or so, and really goes wonky over 2. Any advice? comments sorted by Best Top New Controversial Q&A Add a Comment sh221B777 • Additional comment actions. com) LLaMA has been leaked on 4chan, above is a link to the github repo. Valheim Genshin Impact Minecraft Pokimane Halo Infinite Call of Duty: Warzone Path of Exile Hollow Knight: Silksong Escape from Tarkov Watch Dogs: Legion. 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. /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. cpp, special tokens like <s> and </s> are tokenized correctly. Someone on Reddit also said that Repetition penalty is also used, but I never tried messing with that in Mirostat. In the llama_sample_repetition_penalty function, we expect to penalize a token based upon how many times it is used. 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. Anyway, it seems to be a decently intelligent model based on the first part of that response, somewhat similar to Alpaca. This penalty is more of a bandaid fix than a good solution to preventing repetition; However, Mistral 7b models especially struggle without it. 05 MinP and all other samplers disabled, but Mirostat with low Tau also works. on 13B mistral based model mirostat 2 with repetition penalty 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. Repetition - How to reduce it? I've tried llama 2 13b, llama 1 13/33b models, loads of types. 5-mixtral-8x7b-GGUF Q4_K_M Repetition penalty makes no difference whatsoever. 5 is high enough that you very well might see stuff like this happen. --top_k 0 --top_p 1. I did a penalty range of about 1200-2000. Internet Culture (Viral) Amazing . tokenizer. ', do_sample=True, top_k=10, num_return_sequences=1, repetition_penalty=1. 10 repetition penalty over 1024 tokens. Any penalty calculation must track wanted, formulaic repitition imho. Share Add a Comment. 131K subscribers in the LocalLLaMA community. 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. Upped to Temperature 2. 466 votes, 198 comments. 18 with Repetition Penalty Slope 0! What is repetition penalty slope and how do I set this parameter within llama. The models that have LLaMa seems to take high temp well, but doesn't do well with repetition_penalty over 1. 21, 1. If you’re in a situation to run a 13B GGML version yourself, use Mirostat sampling (2, 5, and 0. All llama 2 models with stochastic sampling have this same issue. 07 Llama 3 has 8K context size, even fine tuned models don't work that well above 8K. Playing around with LZLV-70b 4QM, i am having a great time with the long form responses. 05) and DRY instead. Using it is very simple. Members Online Finetuned Miqu (Senku-70B) - EQ Bench 84. 0 (at the end of the Repetition Penalty Range). I've done a lot of testing with repetition penalty values 1. Special tokens. 1 as recommended here) Reddit's #1 spot for Pokémon GO™ discoveries and research. That's why I basically don't use repeat penalty, and I think that somehow crept back in with mirostat, even at penalty 1. 1B Llama model on 3 trillion tokens. 2-11B-Vision-Instruct · Issue about using "repetition_penalty" parameter in model. Make sure the repetition penalty range is set at 2048, this seems to remove repetition for me. 2 and that fixed it for one message. 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. This is done by dividing the token if it is above zero, and multiplying it by the penalty if it is below zero. The key is to disable top-P, top-K and user very low repetition penalty (around 1. Also excited for the updates, which Llama really needs. 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). 2 seems to be the magic number). 05 and no Repetition Penalty at all, and I did not have any weirdness at least through only 2~4K context. I'm hoping we get a lot of alpaca finetunes soon though, since it always works the best, imo. 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. 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. Also increase the repeated token penalty. /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. Conclusion: That's unusual. 15" or "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. Once you get a huge context going, the initial prompt processing takes a LONG time, but after that prompts are cached and its fast. 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. Then there are plethora of smaller models, with the honorary mention of Mistral 7B, performing absolutely amazing for its size. Important: Top P at 1. 17 Works best for me Reply 2 experts (default). 29, 1. always so damn satisfying to see, ha ha. 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. " 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). The sweet spot for responses is around 200 tokens. Reddit iOS Reddit Android Reddit Premium About Reddit Advertise Blog Careers Press. 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. Thanks. 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. Or it just doesn’t generate any text and the entire response is newlines. The model answers to the request just fine, but can't finish its response nevertheless. Temperature : 1. 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. Much less, and it keeps getting shorter; much more, and it tends to repeat itself like you see. 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. " It is a sequel to the first movie, "The Lord of the Rings: The Fellowship of the Ring. Saved searches Use saved searches to filter your results more quickly Welcome to Reddit's own amateur (ham) radio club. q4_0. eos_token_id, This is based on the 1 trillion token Checkpoint of tiny llama, there is not released chat version for the 1. 36 repetition_penalty=1. Sort by: You can think of it as a top-p with built-in repetition penalty. 1, and making the repetition penalty too high makes the answer nonsense. 1 rep pen, 1024 range and 0. 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 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. Response: I wrote a fantasy story about LOTR. 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. It uses RAG and local embeddings to provide better results and show sources. I haven't really gotten the AI Instructions to work very well, especially with Llama. 157K subscribers in the LocalLLaMA community. 3 (llama. 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. 15 and 1. Using codellama-13b-oasst-sft-v10. Premium Powerups Explore Gaming. 73 votes, 30 comments. 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. For answers that do generate, they are copied word for word from the given context. Try KoboldCPP with the GGUF model and see if it persists. 2: 428: October 14, 2024 Llama-2 7B-hf repeats context of question directly Mancer seems to be using mythomax GPTQ models. Well, that was the goal of inverse DPO, I suppose. Personally I run 0. For a more precise chat, use temp 0. Instructions for deployment on your own system can be found here: LLaMA Int8 ChatBot Guide v2 (rentry. 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). 7 slope which provides what our community agrees to be relatively decent results across most models. It's basically unusable from my testing. It's just a lightly modified Universal-Light preset with smoothing factor and repetition penalty added. I had to set both fairly high to get the best results. GGUF model, the setting `additive_repetition_penalty`, along with many other settings, all disappear. 99 temperature, 1. 50) Repetition Penalty : 1. 18" are the best, but in my experience it isn't. 7B is likely to loop in general. 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. gguf, Might have even better results by lowering 'repetition penalty' too. The prompt format is also fairly critical as well, I am actually having good luck with "novel style" raw prompting. decode(output[0], skip_special_tokens=True) output_text = Because you have your temperatures too low brothers. I'm thinking something like the function sqrt log(x) would help when generating long form outputs that have the potential for high repetition. 3 and even tried mirostat mode 1,2 on the kobold. 7B models are usually not as smart and good at reading „in between the lines” to my liking. . Loader is Exllama v2 HF. Catbox Link. The training has started on 2023-09-01. Takes about ~6-8GB RAM depending As for repetition on 70b: - REDUCE your repetition penalty. Repetition penalty 1. Adding a repetition_penalty of 1. 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. 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. Reddit . In my experience it's better than top-p for natural/creative output. I was looking through the sample settings for Llama. /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. 2023-08-19: After extensive testing, I've switched to Repetition Penalty 1. 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. This size keeps a good variety of interactions in the context. I prefer the Orca-Hashes prompt style over airoboros. Subreddit to discuss about Llama, the large language model created by Meta AI. 85), top_k 40, and top_p 0. Could anyone provide insights? 1 Like. When setting repetition penalty from 1. Takes about ~4-5GB RAM depending on context length. Then it did it again. works a dream. Top K at 0. (0. ggmlv3. 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. I have finally gotten it working okay, but only by turning up the repetition penalty to more than 1. 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. Both models have the slop that all models do, but it seems somehow more endearing when it comes from MM. I would be willing to improve the docs with a PR once I get this. shawwn/llama-dl: High-speed download of LLaMA, Facebook's 65B parameter GPT model (github. 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. I hope Meta addresses this for llama 3. For my settings, I keep my Min P at 0. bin -p "Act as a helpful Health IT consultant" -n -1. 7, repetition_penalty 1. 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. 2 across 15 different LLaMA (1) and Llama 2 models. Sure I could get a bit format The current implementation of rep pen in llama. We ask Much less repetitive. There is not a lot of difference from my experience. 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 the context template and instruct, I'm using the llama3 specific ones. 85 to produce the best results when combined with those other parameters. cpp? I've tried puffin and it really really wants to repeat itself. cpp is not just 1 or 2 percent faster; it's a whopping 28% faster than llama-cpp-python: 30. 25bpw is maybe too low for it to be usable 2. 2. I did try setting repetition penalty from about 1. Or check it out in the app stores TOPICS. 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. But with the default settings preset this and most other Posted by u/Enkay55 - 3 votes and 14 comments Min_p at 0. 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. 05 Minp, low temperature, mirostat with a tau of 1. Using LLaMA 13B 4bit running on an RTX 3080. 37, 1. 0 Just copy and paste that into a . The 128k context version is very useful for having large pdfs in context, which it can handle surprisingly well. 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. 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. 65bpw. It seems that you insist to kiss Elon's ass and tell everyone that his model is the best one. Internet Culture (Viral) Amazing Frequency penalty is like normal repetition penalty. So I upped the repetition tokens from 256 to 512 and it fixed it for one message, then it just carried on repeating itself. Not as good as 7B but miles better than 1. 1 samplers. 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. With adjustments to temperature and repetition Tried here with KoboldCPP - Temperature 1. 33 and repetition penalty at 1. 1. generate function. Just wondering if this is by design? interestingly, the repetition problem happened with `pygmalion-2-7b. But suffered from severe repetition (even within the same message) after ~15 messages. 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. 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. gguf` on the second message. Goal: Observing changes in output helps me understand how each parameter influences the model’s responses. 6, Min-P at 0. Repetition penalty application in proportion to historical token frequency. However, one point I'm concerned about is the EOS token <|im_end|> being part of the prompt template: . Like a lot higher. 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. For 30b though, like WizardLM uncensored 30b, it's gotta be GPTQ and even then the speed isn't great (RTX 3090). 5 in most areas! However, that was back when llama-2 was fairly new. 0, Min-P at 0. Q4_K_S. 1 -s 42 -m llama-2-13b-chat. OTarumi July 1, 2023, 12:59am 3. 5/hr on vast. (2048 for original LLaMA, 4096 for Llama 2, or higher with extended context - but not hundreds of thousands of tokens). 18, range 0 (full context). ? The default sequence breakers should do the trick already. 1764705882352942 (1/0. 05-1. Valheim; Genshin Impact; Subreddit to discuss about Llama, the large language model created by Meta AI. For immediate help and problem solving, please join us at There are 3 nodejs libraries for llama. This is Llama. 1. cpp (locally typical sampling and mirostat) which I haven't tried yet. main: build = 938 (c574bdd) main: seed = 42 Confused about Takes over ~2GB RAM and tested on my 3GB 32-bit phone via llama. 7B: Nous Hermes Mistral 7B DPO. /main -ins -t 6 -ngl 10 --color -c 2048 --temp 0. 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. I had to increase repetition penalty otherwise it's prone to get stuck in a thought loop. Also as others have noted 2. 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. Keskar et al. 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. 10. 03 or so. 0. I think it is caused by the "<|image|>" token whose id is 128256, and meta-llama/Llama-3. I find it incredible that such a small open-source model outperforms gpt-3. Roleplay instruct mode preset: Showed personality and wrote extremely well, much better than I'd expect from a 7B or even 13B. But yes, it really depends on the model. This further confirms that existing llama models are severely under trained. Repetition penalty between 1 and 1. Get the Reddit app Scan this QR code to download the app now. 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. I’d say you should proofread a bunch of your model’s outputs and lower the rep penalty if you do. 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. Transformers parameters like epsilon_cutoff, eta_cutoff, and encoder_repetition_penalty can be used. cpp on Termux. I generally agree, although what they recommend is what I've referred to as "LLaMA-Precise. 1B model only, settings: repetition_penalty=1. , 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. Repetition penalty is something greatly misunderstood. 🤗Transformers. I've just finished a lot of testing with various repetition penalty settings: KoboldAI by default uses Rep. 4bpw might do better if you can fit it in 24GB. And magically, the repetition was gone again. Yi runs HOT. 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. 1 or greater has solved infinite newline generation, but does not get me full answers. The best base models at each size right now are Llama 3 8b, Yi 1. I see many people struggle to find a sweet spot for LLama 3. 01 temp, 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. Using --repeat_penalty 1. ai The output is at least as good as davinci. 1) and the repetition and sudden loss of articles/punctuation issues just vanish. 33 votes, 46 comments. 10, Rep. perplexity. If the repetition penalty is high, the model could end up writing something weird like “ the largest country in the America”. LLaMA +sampling +penalty Figure 1: Detectors for machine-generated text are often (Reddit, Poetry), and knowledge of specific media (Books, Reviews). People sometimes say "1. 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. 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. Llama 3 prefers lower temperature and repetition penalty. I'm not sure if this setting is more important for low bpw models, or if 2x gain is considered consistent for 4. 8 with 0. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. I have tried token forcing, beam search, repetition penalty - nothing solves the problem; I tried other prompt formats. and with a temperature so close to 1, all it's really doing is repetition penalty and top_p. It helps fight against llama2's tendency to repeat itself, and gives diverse responses with each regeneration. Draft model r/LocalLLaMA • HuggingChat, the open-source alternative to ChatGPT from HuggingFace just released a new websearch feature. These two are different beasts compared to poor Llama-2. 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. Just consider that, depending on repetition penalty settings, what's already part of the context will affect what tokens will be output. I think some early results are using bad repetition penalty and/or temperature settings. MM does this much less often. At this point I usually have hundreds of generations from the model. Repetition penalty range also makes no difference. So I've been using llama-cpp-python's server: python3 -m llama_cpp. 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. Also, set repetition penalty to 1. 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. I use Contrastive Search with a slightly increased repetition penalty. 7B. Im not super familiar with LMstudio but things such as temperature, repetition penalty, and correct system prompt and such can make a huge difference. 5, (the higher the temperature the more creative the model) depending on your tests, which works best. With Mistral and Llama-3, I think we barely have any objective data about samplers. They also added a couple other sampling methods to llama. sh and then do "docker compose up --build" to start it with new parameters. 1B, almost on par with Llama 1 7B models. $1. 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). This remains the same with repetition_penalty=1. 5 (exl2) or 1. Check your presets and sampler order, especially Temperature, Mirostat (if enabled), Repetition Penalty and the sampler values. Reply jackfood2004 Interesting question that pops here quite often, rarely at least with the most obvious answer: lift the repetition penalty (round 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. Repetition in the Yi models can be eliminated with the right samplers. 18 turned out to be the best across the board. It will beat all llama-1 finetunes easily, except orca possibly. 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". Gaming. 7 oobabooga's text-generation-webui default simple-1 preset uses Rep. Llama. As a model I use upstage 70b. cpp recently add tail-free sampling with the --tfs arg. 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. So, I've noticed that Llama repeats the same phrase multiple times despite the AI instruction saying to avoid repetition. 27 votes, 18 comments. 95 --temp 0. It seems like this is much more prone to repetition than GPT-3 was. 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. The settings show when I have no model loaded. :) Parasitic really outdid himself with that one. cpp when streaming, since you can start reading right away. I did search around reddit and Google for a while, and couldn't find any comprehensive explanation of the various samplers. 7 were good for me. 2-1. Adding a I switched up the repetition penalty from 1. 4 + repetition penalty range 2048 solved that problem for me. cpp directly, but with the following benefits: More samplers. 15, 1. 'The TinyLlama project aims to pretrain a 1. 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. 05 to 1. Related topics Topic Replies Views Activity; Loading pre-trained models with AddedTokens. 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. 4 Likes. 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. Can't get 34B to run locally so far, but am using an online version (https://labs. 1, 1. After that there is a repetition penalty parameter, which I set to 1. 5, eos_token_id=tokenizer. ai/). 7B: Nous Hermes 2 SOLAR 10. As far as I understand, it was trained on about twice the data that llama 2 was trained on. 37! are 1. I was unsure tbh, but since in my opinion llama. Not claiming that it's perfect, but it works well for me. e. " Get the Reddit app Scan this QR code to download the app now. Use min-P (around 0. 2 and anything less than 2. Works on my laptop with 8GB RAM. I tried using llama_HF with those quants to add the correct tokenizer back in, but I got a garbled mess as a result. 0 --tfs 0. By using the transformers Llama tokenizer with llama. txt file and name it whatever you want and put it in the presets folder in the Oobabooga install directory. It's just the same things over and over and over again. I have used GPT-3 as a base model. 1 to 1. Stop doing the same old mistake of cranking it way up every time you see some repetition. 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. I am open to sampler suggestions here myself. reReddit: Top posts of February 12, 2023. 12, depending on whether the model repeats too much, then increase the penalty. However, I haven’t come across a similar mathematical description for the repetition_penalty in LLaMA-2 (including its research paper). Yes Exllama is much faster but the speed is ok with llama. Can't be that all combinations cause these issues for you with LLaMA (1) models. Apologies if this is well known on the sub. 37 (Also good results but !not as good as with 1. true. min_p 0, top_k 20, repetition penalty 1. Internet Culture (Viral) Amazing; Animals & Pets Subreddit to discuss about Llama, the large language model created by Meta AI. 10 to about 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. Min P to 0. 2ish. It's not really necessarily documented in the commandline what this is doing, so one has to read the code to find this out. 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. 172K subscribers in the LocalLLaMA community. 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. I wouldn't expect llama 3 70b performance, but it absolutely obliterates the 8b model. My go-to SillyTavern sampler settings if anyone is interested. Terms & Policies My KoboldCPP Settings Using Code Llama That Are Giving Me Great Results . then I use the continue command to finish the response. 02). 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. I Yep, that Llama 2 repetition issue is a terrible problem and makes these newer models useless for chat/RP. 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. cpp) Approach: I experiment with one parameter at a time — temperature, num_beams, top_k, top_p, repetition_penalty, no_repeat_ngram_size. more control for min-p top-k and repetition penalty are useful, especially if you can save a per-model default (i. 15 repetition_penalty, 75 top_k, 0. It complements the regular repetition penalty, which targets single token repetitions, by mitigating repetitions of token sequences and breaking loops. It is called "The Lord of the Rings: The Battle of the Five Armies. uses ChatML format That looks like an emerging standard and I saw surprisingly good results with that in my latest model test/comparison. Sometimes it is necessary though, like for Mistral 7b models. 03. 1 Note that one hang-up I had is llama. 1, smoothing at 0. Deterministic preset, so temperature and top_k don't apply - it always picks the most probable token. The defaults we use for this are 1. 18, and 1. Mixtral, MythoMax and TieFighter are good, but I really feel like this is a step up. Frustrating to see such excellent writing ruined by the extreme repetition. Prompt: instruction: Write a fantasy story about LOTR. Try at least 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. I'm also getting constant repetition of very long sentences with dolphin-2. 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. 7 --repeat_penalty 1. View community ranking In the Top 5% of largest communities on Reddit. Presence penalty makes it choose less used tokens. Q5_K_M. However after a while now i am beginning to notice "AI styled writing" I tried pumping up the temperature to 1. 18, Rep. If you are playing on 6B however it will break if you set repetition penalty over 1. - Repetition Penalty should be used lightly, if at all, (1. I'd just start changing variables, using different models and presets. Most presets have repetition_penalty set to a value somewhere between 1. As soon as I load any . 12 top_p, typical_p 1, length penalty 1. Like in your example applying viruses to everything. 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). cpp "main" program does (like grammar and many others). 20, but I find that lowering this to around 1. 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. Slope 0 pipeline, or model. 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. 5, num_tokens_to_generate = 100. Generation parameters preset: LLaMA-Precise (temp 0. 5s. 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. Yeah, the model is batshit crazy. 9s vs 39. 25, especially trying out 1. 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). As far as I know, the EOS token doesn't get special treatment so it is affected by repetition penalty like any other token. Slope 0. <|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. / Goes into repeat loops that repetition penalty couldn't fix. I almost never use it now, instead set a Min_P of 0,2-0,32. Amount generation: 128 Tokens Context Size: 1124 (If you have enough VRAM increase the value if not lower it!!. 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. - Some models are less capable of answering specific questions, or talk on specific themes. 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). (2019)’s repetition penalty when avail-able. 18 since everyone says that's the magic number. cpp If the repetition penalty gets too high, the AI gets nonsensical. Since then, I figured Repetition Penalty is kind of redundant and model breaking when it's >1,15. Sports. The benefit of this over straight llama chat is that it is uncensored (it doesn’t refuse requests). 6 temp and 0. It's set up to launch the 7b llama model, but you can edit launch parameters in run. 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. It's like a traumatized person who can only think bad/nasty things. Using silly tavern, change the repetition penalty to 1. 08 still keeps repetitiveness under control in most cases, while generating vastly longer outputs for many prompts. 7 top p. I disable traditional repetition penalties, while others leave a small presence penalty of 1. From my experience, a rep penalty of 1. 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. 1). After ~30 messages, fell into a repetition loop. 05 (and repetition penalty range at 3x the token limit). eeifqeltspndyobqlehhndnzfhacqajvdhjrqqbyfqcblqkwj