Repetition penalty llama reddit. 3 and even tried mirostat mode 1,2 on the kobold.

Repetition penalty llama reddit Is there something I can do in the settings? Get the Reddit app Scan this QR code to download the app now. Apologies if this is well known on the sub. Higher values penalize words that have similar embeddings. 95), it cuts down repetition without having to up the rep penalty. They don't really take into account a phrase, only words (or technically, tokens). 15" or "1. I want to be better at it as my application for LLaMa revolves around the use of large amounts of text. 25, any more and it gets crazy or deterministic, especially if you up the temperature. I think the repetition penalty is severely outdated, and should be replaced. Q4_K_S. bin -p "Act as a helpful Health IT consultant" -n -1 Confused about temperature, top_k, top_p, repetition_penalty, frequency_penalty, presence_penalty? /r/StableDiffusion is back open after the protest of Reddit killing open Get the Reddit app Scan this QR code to download the app now. Repetition Penalty 2. using mistral also allows me to detect relations between those entities. cpp If the repetition penalty gets too high, the AI gets nonsensical. What's more I don't know who is experiencing repetition issues or not since, there hasn't been a post for 26 days Nous-Hermes-Llama-2 13B GGUF model with repetition seeming to still being somewhat The repetition penalty works like temperature but in a selective manner. 9 top_p, 0 top_k, 1 typical_p, 0. After ~30 messages, fell into a repetition loop. KoboldAI instead uses a group of 3 values, what we call "Repetition Penalty", a "Repetition Penalty Slope" and a "Repetition Penalty Range". 80 Repetition Penalty Range 2048 Repetition Penalty Slope 0. txt file and name it whatever you want and put it in the presets folder in the Oobabooga install directory. Frustrating to see such excellent writing ruined by the extreme repetition. The lower the value, the 73 votes, 30 comments. 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 the chat. 1 Note that one hang-up I had is llama. Quadratic, even by itself I keep under . Since then, I figured Repetition Penalty is kind of redundant and model breaking when it's >1,15. I do believe it was targeted at older 7B and 3B models. They also added a couple other sampling methods to llama. This size keeps a good variety of interactions in the context. model Nous-Capybara-limarpv3-34B (made my own exl2 quant for it, yeepee) and noticed that I started encountering repetition issues which I haven’t had previously. Using codellama-13b-oasst-sft-v10. Just consider that, depending on repetition penalty settings, what's already part of the context will affect what tokens will be output. For the context template and instruct, I'm using the llama3 specific ones. Try KoboldCPP with the GGUF model and see if it persists. Therefore, a repetition penalty would start punishing writing these tags correctly, thus destroying the conversation format. Works on my laptop with 8GB RAM. Everything else is at the default values for me. 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. Thank you!! Can you recommend parameter settings for AI chat partner purposes, e. 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. 2 and that fixed it for one message. Any penalty calculation must track wanted, formulaic repitition imho. Related topics Topic Replies Views Activity; Loading pre-trained models with AddedTokens. 131K subscribers in the LocalLLaMA community. 1 or greater has solved infinite newline generation, but does not get me full answers. How does this work and what is a good mental model for the scale? The docs do seem to not make it more clear: `repeat_penalty`: Control the repetition of token sequences in the generated text I switched up the repetition penalty from 1. Repetition penalty is something greatly misunderstood. Then it did it again. It causes tokens to be less likely to be picked if they had been picked recently. 02000 Repetition Penalty Presence 0. Much less, and it keeps getting shorter; much more, and it tends to repeat itself like you see. r/LocalLLaMA • HuggingChat, the open-source alternative to ChatGPT from HuggingFace just released a new websearch feature. Not claiming that it's perfect, but it works well for me. I've been kind of toying with the idea of an "inverse repetition penalty" for a while, or you could call it an "infrequency" 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. g. 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 More samplers. I found the pygmalion presets usually a good balancing start, and TabbyAPI: added speculative ngram, skew sampling, and repetition decay controls. 10. 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. generate(**model_input, max_new_tokens=200, repetition_penalty=2. 1, Repetition Penalty Tokens: 256, Prompt Batch Size: 1024, Mirostat: true, Mirostat LR: 0. Most presets have repetition_penalty set to a value somewhere between 1. 1, and making the repetition penalty too high makes the answer nonsense. 1 -s 42 -m llama-2-13b-chat. 1 Repetition Penalty Range = Context size Smoothing Factor: 3. 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 Adding a repetition_penalty of 1. However, that was back when llama-2 was fairly new. 7B: Nous Hermes Mistral 7B DPO. It rerolls the results until it gets something more "rich". 05 (and repetition penalty range at 3x the token limit). 02 Repetition Penalty Range: 1024 MinP: 0. 🤗Transformers. Then again, I'm using mostly 70b models. 2000 requests per day for 10$/monthly to such capable model as Llama 3 70b is a very good bargain. But nothing in this world was going to stop that bot from saying the words 2 experts (default). cpp doesn't interpret a top_k of 0 as "unlimited", so I ended up setting it to 160 for creative Get the Reddit app Scan this QR code to download the app now. Pen. Draft model Get the Reddit app Scan this QR code to download the app now. ggmlv3. 0, Min-P at 0. Repetition penalty 1. 03 or so. 1 and no Repetition Penalty too and no problem, again, I could test only until 4K context. Also as others have noted 2. The Streaming example for hundreds of consecutive prompts with the same history shows how this can be used much more effectively. 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. I see many people struggle to find a sweet spot for LLama 3. It's just normal content. I'm also getting constant repetition of very long sentences with dolphin-2. 7B. As a model I use upstage 70b. 25 range, otherwise it does tend to repeat itself. 05-1. I did try setting repetition penalty from 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. 5-mixtral-8x7b-GGUF Q4_K_M Repetition penalty makes no difference whatsoever. Transformers parameters like epsilon_cutoff, eta_cutoff, and encoder_repetition_penalty can be used. Presence penalty makes it choose less used tokens. Looking forward to try the new rep penalty and new smoothing that is being cooked up. 95 repetition penalty 1. 000 Tail Free Sampling 0. Sometimes it is necessary though, like for Mistral 7b models. I'm not sure if this setting is more important for low bpw models, or if 2x gain is considered consistent for 4. It comes with presets that set all the parameters for good generation. 0)[0], skip_special_tokens=True)) Subreddit to discuss about Llama, the large language model created by Meta AI. These two are different beasts compared to poor Llama-2. By using the transformers Llama tokenizer with llama. . With Mistral and Llama-3, I think we barely have any objective data about samplers. 1 Get the Reddit app Scan this QR code to download the app now. 33B/35B and 70B don't need Mirostat, as its variability is already plenty not to repeat itself too much. cpp server, but 1 is more likely to be a neutral factor while 0 is something like maximally incentivize repeating. 1) and the repetition and sudden Get the Reddit app Scan this QR code to download the app now. I'm fairly sure the repetition penalty of 1. - Some models are less capable of answering specific questions, or talk on specific themes. 9 top_k: 20 repetition_penalty: 1. 8 with 0. You can soft prompt make adjustments like tweak the temperature and repetition penalty via sliders, make characters for chat and role-playing and much more. 15, 1. Takes about ~6-8GB RAM depending Another member of the community did a lot of testing and found a repetition penalty of 1/0. 05 to 1. Using it Subreddit to discuss about Llama, the large language model created by Meta AI. 1, and tokens maxed out, but Repetition Penalty Range: Defines the range of tokens to which the repetition penalty is applied. But with the default settings preset this and most other parameters won't work. 1B, almost on par with Llama 1 7B models. Repetition penalty range also makes no difference. There is something called RoPE, which you can set when loading the model (alpha, rope base and compress are the rope settings), and this is a way to "stretch" the context a bit ESP32 is a series of low cost, low power system on a chip microcontrollers with integrated Wi-Fi and dual-mode Bluetooth. 7B models are usually not as smart and good at reading „in between the lines” to my liking. 33 and repetition penalty at 1. Min_p at 0. For Llama 2, that's 4096. I don't believe changing any of the temp, rep penalty will work in ST as its open router. on 13B mistral based model mirostat 2 with I made a repo that is meant to serve as a sort of database for prompts that work with LLaMA. It uses RAG and local embeddings to provide better results and show sources. The model answers to the request just fine, but can't finish its response nevertheless. The sweet spot for responses is around 200 tokens. This subreddit is private Subreddit to discuss about Llama, the large language model created by Meta AI. Frequency Penalty: Decreases the likelihood of repeated words, promoting a wider variety of terms(i think). Upped to Temperature 2. *My specs: RTX 3060 12 GB, 64 GB RAM, some i7 CPU Thanks. 95 --temp 0. 12, depending on whether the model repeats too much, then increase the penalty. 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. I suspect the top_k is largely redundant, too, and with a temperature so close to 1, all it's really doing is repetition penalty and top_p. 85), top_k 40, and top_p 0. 2, the model starts to spam some annoying tokens: em dash Is an interface to load the model do inference an even training. 5 34B, Cohere Command R 34B, Llama 3 70B, and Cohere Command R+ 103B Reply reply Great-Investigator30 Subreddit to discuss about Llama, the large language model created by Meta AI. I just want to know if anybody has a lot of experience or knows how superbooga works. Also excited for the updates, which Llama really needs. Repetition in the Yi models can be eliminated with the right samplers. 05 typical_p=1. max_new_tokens =512, temperature=temperature, 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. 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. Maybe it's because using 10. Someone on Reddit also said that Repetition penalty is also used, but I never tried messing with that in Mirostat. As for presence penalty, I'm not really familiar with it, and I don't know if all models will support it. While there are out there others solutions than LLM or this specific model ex : Gliner or slim-ner. 1 Currently testing this with 7B models (looks promising but needs more testing): Dynatemp: 0. Also found that typ_p is like min_P, just counts Otherwise it's likely a sampling issue or a context/format issue, I don't know the exact process with FastAPI but set the sampling settings to: temperature: 0. I used no repetition penalty at all at first and it entered a loop immediately. Or check it out in the app stores repetition_penalty 1. Saved searches Use saved searches to filter your results more quickly Get the Reddit app Scan this QR code to download the app now. I noticed that eventually the responses it generates start to have repetitive sentences in them. 20, but I find that lowering this to around 1. gguf` on the second message. uses ChatML format That looks like an emerging standard and I saw surprisingly good results with that in my latest model test/comparison. 7B: Nous Hermes 2 SOLAR 10. 65bpw. 15 This is the simple-1 and is widely regarded to Subreddit to discuss about Llama, the large language model created by Meta AI. /main -ins -t 6 -ngl 10 --color -c 2048 --temp 0. 1 as recommended here) Chat Parameters: defaults (max_new_tokens: 200, Maximum prompt size in tokens: 2048) Reddit's #1 spot for Pokémon GO™ discoveries and research. Also, set repetition penalty to 1. -i should be the interactive mode, in our case we have this as a WebUI / API. 7B model you will need to have the slider much closer to 2 on repetition penalty and if your playing with a generic model like the ones on the main menu you also need to give it a longer introduction including some example responses tge AI should do. 6T tokens and 8k ctxlen, and now it can do more than just code. Repetition penalty application in proportion to historical token frequency. The models that have 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. I'm literally working through a bug right now because even with repetition penalty for some reason my summarizer just got caught in a loop and generated the same token over and over until it crashed. 4 - 0. Characters started repeating entire phrases such as “tapping fingers against the table’s Interesting question that pops here quite often, rarely at least with the most obvious answer: lift the repetition penalty (round 1. 16B parameter model trained on 1. I've Pretty much every sampler was explained here on reddit and randomly all over the net. 1, Mirostat Entropy: 5, MLock Get the Reddit app Scan this QR code to download the app now. I prefer the Orca-Hashes prompt style over airoboros. Because you have your temperatures too low brothers. Reply reply Phrase Repetition Penalty (PRP) Originally intended to be called Magic Mode, PRP is a new and exclusive preset option. $1. Please keep posted images SFW. 18, and 1. This can quickly derail the conversation when the initial prompt, world and character definitions are lost - that's usually the most important information at the beginning and the one which gets removed from the context first. Now supports multi-swipe mode. q4_0. pull down the list at the top. 5/hr on vast. no repetition penalty) and the LLM not being trained for such long outputs. 1 debugmode blasbatcheize 64 Chat settings: max tokens 1024 temperature 1 amount to gen 512 top k 45 top p . 1764705882352942, 'encoder_repetition_penalty': 1. 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. 3 and even tried mirostat mode 1,2 on the kobold. 05 MinP and all other samplers disabled, but Mirostat with low Tau also works. cpp recently add tail-free sampling with the --tfs arg. Haven't tried Hermes yet. Google MakerSuite: added custom API URL control. 2 across 15 different LLaMA (1) and Llama 2 models. cpp, special tokens like <s> and </s> are tokenized correctly. decode(output[0], (0. It seems like this is much more prone to repetition than GPT-3 was. Yi runs HOT. Or check it out in the app stores Instead of analyzing past context like Repetition Penalty, Here are some factors that may The current implementation of rep pen in llama. These are way better, and DRY prevents repetition way better without hurting the model. I checked the website. 97, disable-bos-token: true I haven't pushed the context much past ~20K so far but I have it set to 64k, and it seems like I should be able to get 40-70k in 48G vram based on reports. I've tried tweaking various settings like disabling mirostat, adjusting temperature, repetition penalty, instruction mode, etc However, none of these seem to rectify the issue. 0. Please read the sidebar rules and Get the Reddit app Scan this QR code to download the app now. You can think of it as a top-p with built-in repetition penalty. 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. I almost never use it now, instead set a Min_P of 0,2-0,32. This About repetition penalty, I agree. 9, min_p 0, top_k 20, repetition penalty 1. If I remember correctly, Mirostat was made to be something like a "multipackage solution" to repetition and "boringness". 85 to produce the best results when combined with those other parameters. It's set up to launch the 7b llama model, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from 2023-08-19: After extensive testing, I've switched to Repetition Penalty 1. Try the 70b version with a higher repetition penalty. No one uses it that high. 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. The prompt format is also fairly critical as well, I am actually having good luck with "novel style" raw prompting. Any advice on how to deal with repetition? After about 20 messages Llama70B via Open Router just starts to reply with the same message whatever I do. If you put it min_p: 0. 5 is better all around regardless of what very specific tests will tell you about Llama. Or check it out in the app stores Whatever was the reason to put LLAma-precise as the default, makes ooba a verbatim parrot - the same regenerated question will give you the same verbatim answer even if it is a question that has many interpretations. When setting repetition penalty from 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. I did a penalty range of about 1200-2000. 18 turned out to be the best across the board. That was without repetition penalty, but when I tried it For a more precise chat, use temp 0. 03. As soon as I load any . 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. But suffered from severe repetition (even within the same message) after ~15 messages. GGUF model, the setting `additive_repetition_penalty`, along with many other settings, all disappear. 1764705882352942 (1/0. For Quality: NeverSleep/Noromaid-v0. pipeline, or model. Aside from that, do you know of a list of general DRY sequence breakers I can paste into Ooba that works for most model The formula provided is as below. e. I'd just start Yep, that Llama 2 repetition issue is a terrible problem and makes these newer models useless for chat/RP. I had to set both fairly high to get the best results. true. 02 Repetition Penalty Frequency 0. Add the bos token, skip special tokens and activate text streaming is checked, auto 466 votes, 198 comments. temperature or repetition_penalty? I know I should play around with myself, but maybe you found some sweet spot already. Q5_K_M. If you really want to know what they do - it'll be better if you google it instead of asking here. do_sample=True top_p=1 top_k=12 temperature=0. People sometimes say "1. Yes, exactly, unless your UI is smart enough to refactor the context, the AI will forget older stuff once the context limit is reached. My Repetition Penalty is at 1 - Keep an eye on that bastard, because it can ruin the output. Please share your tips, tricks, and workflows for using this software to create your AI art. If the repetition penalty is too high, most models get trapped and just send "safe" or broken responses. You can see GPTQ is completely broken for this model :/ Goes into repeat loops that repetition penalty couldn't fix. So I upped the repetition tokens from 256 to 512 and it fixed it for one message, then it just carried on repeating itself. 0 repetition_penalty: 1 - Repetition Penalty should be used lightly, if at all, (1. :) Parasitic really outdid himself with that one. Repetition penalty between 1 and 1. load the dataset and generate 5 shot prompts: 🆕 Update 2023-08-16: All of those Vicuna problems disappeared once I raised Repetition Penalty from 1. I've done a lot of testing with repetition penalty values 1. I generally agree, although what they recommend is what I've referred to as "LLaMA-Precise. 5 Pro access or introduces censorship That's unusual. KoboldCpp: added repetition penalty slope control. This is Welcome to the unofficial ComfyUI subreddit. 01 temp, 0. 4-Mixtral-Instruct-8x7b-Zloss-GGUF (Q5_K_M) Starcoder+ seems extremely promising. As far as I know, the EOS token doesn't get special treatment so it is affected by repetition penalty like any other token. Check it out here ! Skip to main content. I don't know, maybe your parameters are not correct for code generation? Maybe your repetition penalty is too low? I used text-genteration webui by Oobabooga. " As for repetition on 70b: - REDUCE your repetition penalty. 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. 'repetition_penalty': 1. 5, (the higher the temperature the more creative the model) depending on your tests, which works best. 18 with Repetition Penalty Slope 0! Also fixed MythoMax-L2-13B's "started talking/acting as User" issue as well. I did search around reddit and Google for a while, and couldn't find any comprehensive explanation of the various samplers. Increase the repetition penalty, I guess? And be sure to only use MinP, and bump it up some. 0, 'top_k': 40, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation 27 votes, 18 comments. Could anyone provide insights? 1 Like. 157K subscribers in the LocalLLaMA community. 1, Repetition Penalty at 1. Also increase the repeated token penalty. encoder_repetition_penalty: 1 top_k: 0 min_length: 0 no_repeat_ngram_size: 0 num_beams: 1 penalty_alpha: 0 Llama. gguf, I accidently discovered these settings are working great because I accidently used it with other settings and I kept getting garbage code that would not compile until I realized I was not using these settings. g gpt4-x-alpaca-13b-native-4bit-128g cuda doesn't work out of the box on alpaca/llama. Color we don't have, I assume it just changes the color of the output which in our case is customized in the UI where possible. cpp (that's what I know about) that would not count these tags towards repitition. Subreddit to discuss about Llama, the large language model created by Meta AI. Takes about ~4-5GB RAM depending on context length. This is essential for using the llama-2 chat models, as well as other fine-tunes like Vicuna. However after a while now i am beginning to notice "AI styled writing" I tried pumping up the temperature to 1. vs Divine Intellect presets temperature: 1. All llama 2 models with stochastic sampling have this same issue. Use min-P (around 0. I have used GPT-3 as a base model. I'll keep this site in mind in case Google restricts Gemini 1. 4bpw might do better if you can fit it in 24GB. Important: Top P at 1. 18, range 0 (full context). 18 top_k: 1. Since you're using completely different inference software, it's either a problem with the Llama 2 base or a fundamental I was looking through the sample settings for Llama. What parameters for temperature, top_p , min_p,repetition penalty etc are you using? However, I haven’t come across a similar mathematical description for the repetition_penalty in LLaMA-2 (including its research paper). What's worse, the only weapon against it (repetition penalty) distorts language structure, affecting the output quality. 2, you can go as low as 0. 1 Reply reply More replies. The issue I have is the default penalty sampler (is it even called this way) doesn't help preventing That's more related to the naive generation settings (i. --top_k 0 --top_p 1. 3 temp and still get meaningful output. I'm using Mistral 7B with llama. I am looking to figure out how to stop this? I've tried different repetition penalty settings to no avail. Removed deprecated models from Perplexity I recently tried to improve Llama2's ability to speak german, totally failed, but got me into benchmarking language capabilities. 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. Top K at 0. Welcome to Destiny Reddit! This sub is for discussing Bungie's Destiny 2 and its predecessor, Destiny. 10 repetition penalty over 1024 tokens. Top 1% The settings show when I have no model loaded. is penalized) and soon loses all sense entirely. Like it will say the same things at the end of each response. I am open to sampler suggestions here myself. 915 Phrase Repetition Penalty Aggressive Preamble set to [ Style: chat, complex, sensory, visceral, role-play ] Nothing in "Banned Tokens" Try at least 0. Yup. 7 were good for me. MM does this much less often. I've been using a 70B model for a while (MiquMaid 70B IQ3XXS). 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. Welcome to Reddit's own amateur (ham) radio club. Added new models for Cohere and MistralAI. 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. tokenizer. 05 Minp, low temperature, mirostat with a tau of 1. It's just the same things over and over and over again. Keep in mind that 2x24 is still a very small size of VRAM for the knowledge you're asking that 40gb file Runtime: Llama 7B Chat on KoboldCPP Custom settings for runtime: threads 6 contextsize 2048 stream smartcontext usemirostat 2 0. 14 repetition_penalty: 1. 08 still keeps repetitiveness under control in most cases, while generating vastly longer outputs for many prompts. 2 seems to be the magic number). Using temp/min_P combo is also more likely to require repetition penalty, IME. . 05) and DRY instead. The main downside is that on low temps AI gets fixated on some ideas and you get much less variation on "retry". If it's also so bad for LLaMA (1) models, though, maybe some other settings are also at play. We are currently private in protest of Reddit's poor management and decisions related to third party platforms Yesterday I tried TheBloke/Llama-2-13B-chat-GPTQ with Exllama loader and gives good answers with low wait time, but after a while starts repeating itself. My go-to SillyTavern sampler settings if anyone is interested. If the character says something at the beginning or end of two consecutive messages, it is almost guaranteed to include that in all following ones. 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 Subreddit to discuss about Llama, the large language model created by Meta AI. Get the Reddit app Scan this QR code to download the app now. cpp). (Model I use, e. KoboldAI instead uses a The existing repetition and frequency/presence penalty samplers have their use but one thing they don't really help with is stopping the LLM from repeating a sequence of tokens it's already generated or from the prompt. 7B Fim-Kuro-Lotus chat RP model and I don't have use rep penalty like when using instruct models. 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. After that there is a repetition penalty parameter, which I set to 1. 5 or so, and really goes wonky over 2. However, one point I'm concerned about is the EOS token <|im_end|> being part of the prompt template: . 18, Range 2048, Slope 0 (same settings I'm curious to find out if that helps alleviate the annoying Llama 2 repetition/looping There was also temporarily an issue with the copy&paste-able Custom Stopping Strings because of Reddit's annoying formatting. I use Contrastive Search with a slightly increased repetition penalty. 0 --tfs 0. 2ish. "Repetition penalty slope" becomes a very important setting later on. And this was using mirostat and high repetition penalty. 1 to 1. Check your presets and sampler order, especially Temperature, Mirostat (if enabled), Repetition Penalty and the sampler values. mu[j] -> mu[j] - c[j] * alpha_frequency - float(c[j] > 0) * alpha_presence However, I haven’t come Get the Reddit app Scan this QR code to download the app now. 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. Not as good as 7B but miles better than 1. These settings are just using a preset (Liminal Drift), with the only change being the temp set to . Greedy sampling selects the token the model finds most probable, and anything else 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. cpp to extract entities from text. It My intuitive take was that 0 would be the default/unimpacted sampling in llama. 7, repetition_penalty 1. 25, and start with 1. (ft_model. I've just finished a lot of testing with various repetition penalty settings: KoboldAI by default uses Rep. The Silph Road is a grassroots network of It tends to be verbose and occasionally produces nonsensical outputs, reminiscent of smaller models that overreach in their attempts to sound intelligent and fall short. 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. 0 If anyone has suggestions or tips for settings with smoothing factor, please let me know. 2. Or check it out in the app stores &nbsp; Frequency penalty is like normal repetition penalty. cpp (locally typical sampling and mirostat) which I haven't tried yet. Catbox Link. I've had good results in the 1. It complements the regular repetition penalty, which targets single token repetitions, by mitigating repetitions of token sequences and breaking loops. Encoder Penalty: Adjusts the likelihood of words based on their encoding. For answers that do generate, they are copied word for word from the given context. Generation parameters preset: LLaMA-Precise (temp 0. Once you get a huge context going, the initial prompt processing takes a LONG time, but after that prompts are cached and its fast. 1 is more than enough for most cases. 7 top_p: 0. ( like repetition penalty), but I've only found the remark on the generation length limited to 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. The ESP32 series employs either a Tensilica Xtensa LX6, Xtensa LX7 or a RiscV processor, and both dual-core and single-core variations are available. 6 Repetition Penalty: 1. ai The output is at least as good as davinci. If you’re in a situation to run a 13B GGML version yourself, use Mirostat sampling (2, 5, and 0. LLaMa seems to take high temp well, but doesn't do well with repetition_penalty over 1. 6, Min-P at 0. Additionally seems to help: - Make a very compact bot character description, using W++ - Include example chats in It's full of repititions of the character tags. Anyway, it seems to be a decently intelligent model based on the first part of that response, somewhat similar to Alpaca. I also use typ_P (. The reason is that "a" has appeared so many times, the repetition penalty has reduces its probability of appearing below a threshold and now it has to produce something else. 7 --repeat_penalty 1. Conclusion: Repetition often happens when the AI thinks it is either the only fitting response, or there are no other more fitting responses left. It's just a lightly modified Universal-Light preset with smoothing factor and repetition penalty added. Those generations are still good after 300+ prompts. Deterministic preset, so temperature and top_k don't apply - it always picks the most probable token. 2: 428: 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. 7 Maybe that's the reason why I don't encounter the dreaded Llama 2 repetition/looping issues with this model - it doesn't mimic as easily as other models do, making it more resistant to that problem Tried here with KoboldCPP - Temperature 1. Make sure you're using the correct prompt formatting and also with "Skip special tokens" turned off for the Instruct model. I think some early results are using bad repetition penalty and/or temperature settings. 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). I'm thinking something like the function sqrt log(x) would help when generating long I'm using llama-13b finetunes to write stories and when I crack up the rep_penalty to 1. We would like to show you a description here but the site won’t allow us. Loader is Exllama v2 HF. 10, Rep. 10 to about 1. 36 repetition_penalty=1. 31 top_p: 0. 5, repetition penalty to 1. In Text completion presets, set the temperature between 1 and 2. 25bpw is maybe too low for it to be usable 2. 25, especially trying out 1. I tried using llama_HF with those quants to add the correct tokenizer back in, but I got a garbled mess as a result. Much higher and the penalty stops it from being able to end sentences (because . Haven't found much on how to use sacreBLEU with LLMs except this, that's why I share my approach, applicable to any language-pair not only english->german, maybe useful to some of you. 05 and no Repetition Penalty at all, and I did not have any weirdness at least through only 2~4K context. 0 Just copy and paste that into a . I have finally gotten it working okay, but only by turning up the repetition penalty to more than 1. 5 0. Like a lot higher. It doesn't do what people think it does. I'm sure this can find a market. 18 since everyone says that's the magic number. Using repetition penalty 1. Just wondering if this is by design? interestingly, the repetition problem happened with `pygmalion-2-7b. 1. Stop doing the same old mistake of cranking it way up every time you see some repetition. 33 votes, 46 comments. The key is to disable top-P, top-K and user very low repetition penalty (around 1. If you are playing with a 2. Slope 0. The current implementation of rep pen in llama. But yes, it really depends on the model. Repetition Penalty: 1. 02). There is nothing in llama. I'm hoping we get a lot of alpaca finetunes soon though, since it always works the best, imo. Can you guys help me either use Superbooga effectively or any other ways that can help the LLaMa process >100000 characters of text. cpp on Termux. For any good model, repetition penalty (and even more frequence penalty) should degrade performance That because (at least in my use the following search parameters to narrow your results: subreddit:subreddit find submissions in "subreddit" author:username find submissions by "username" I disable traditional repetition penalties, while others leave a small presence penalty of 1. 5 is the main reason of your issue. Or check it out in the app stores &nbsp; Subreddit to discuss about Llama, the large language model created by Meta AI. 99-. 1 samplers. If you are wondering what Amateur Radio is about, it's basically a two way radio service where licensed operators Mancer seems to be using mythomax GPTQ models. The best base models at each size right now are Llama 3 8b, Yi 1. GPT-3. Roleplay instruct mode preset: Showed personality and wrote extremely well, much better than I'd expect from a 7B or even 13B. Increasing the token range of repetition penalty and setting the slope to a value like Mixtral, MythoMax and TieFighter are good, but I really feel like this is a step up. 1, 1. Repetition Penalty is the Repetition Penalty in the KoboldAI Lite settings or once again in the UI of your choosing. View community ranking In the Top 5% of largest communities on Reddit. Both models have the slop that all models do, but it seems somehow more endearing when it It's worth mentioning, bigger context means higher RAM/VRAM requirement. In my experience it's better than top-p for natural/creative output. 1, smoothing at 0. Repetition penalty is a a very sensitive slider, especially in longer conversations. (2048 for original LLaMA, 4096 for Llama 2, or higher Subreddit to discuss about Llama, the large language model created by Meta AI. I'm running LLaMA-65B on a single A100 80GB with 8bit quantization. The tools and models supported are growing week by week is like the A1111 of the SD world but for LLMs. 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. Min P to 0. 08, mirostat_mode: 2, mirostat_tau: 6, encoder_repetition_penalty: 0. 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. 0 repetition_penalty: 1. top_p 0. Nah, you Just Need to tamper a bit more with the specific parameters, especially temperature and repetition penalty. Special tokens. If setting requency and presence penalties as 0, there is no penalty on repetition. Personally I run 0. It helps fight against llama2's tendency to repeat itself, and gives diverse responses with each regeneration. 18" are the best, but in my experience it isn't. Playing with repetition_penalty, encoder_repetition_penalty and no_repeat_ngram_size doesn't prevent that, at best makes the model change a word or two of the pharase or phrases it is stuck on For my settings, I keep my Min P at 0. Testing was done with TheBloke's q3_k_s ggml version. 2 - 1. Takes over ~2GB RAM and tested on my 3GB 32-bit phone via llama. 17 top_k: 49. Top K:40, Repetition Penalty: 1. Enabled image inlining for Gemini Flash. I have tried token forcing, beam search, repetition penalty - nothing solves the problem; I tried other prompt formats. This remains the same with repetition_penalty=1. Many assume that if a model likes to repeat phrases, upping the repetition penalty will stop that but that's not how the models work. Playing around with LZLV-70b 4QM, i am having a great time with the long form responses. 15 and 1. 172K subscribers in the LocalLLaMA community. msbua ufys iyexp rbitgx pgjt rzcf xtwrql jekqc vmrokk sdn