Repeat penalty llama /main -m . Really, I have trouble wrapping my head around those. Hi everybody, I would like to know what are your thoughts about Mixtral-8x7b that on the paper should overcome the performances of even llama-2-70b. Updated to version 1. Its amazing almost instant response. 1. 0 # Base frequency for rope sampling. 100000 Model: ggml-alpaca-7b-q4. Llama. It runs so much faster on my GPU. txt -n 256 -c 131070 -s 1 --temp 0 --repeat-penalty 1. 0 instead of 1. param lora_base: Optional [str] = None ¶ The path to the Llama LoRA base model. Pretend to be Fred whose persona follows: Fred is a nasty old curmudgeon. A temperature of 0 (the default) will ensure the model response is always deterministic for a given prompt. 1 -b 16 -t 32 -ngl 30 main: warning: model does not support context sizes greater than 2048 tokens (8192 specified);expect poor results Get up and running with large language models. Not visually pleasing, but much more controllable than any other UI I used Ok so I'm fairly new to llama. OpenAI uses 2 variables for this - they have a presence penalty and a frequency penalty. So anyways, I'm using the following code inside a . And so he isn't going to take anything from anyone. cpp/main -m c13b/13B/ggml-model-f16. Google just released Gemma models for 7B and 2B under GemmaForCausalLM arch. 1, 1. gif) It stops and goes back to command prompt after this: main: seed = 1679872006 llama_model_load: loading model from 'ggml-alpaca-7b-q4. param model_kwargs: Dict [str, Any] [Optional] ¶ Any additional parameters to pass to llama_cpp. I was thinking of removing that script since I believe server already support the OAI API. cpp and was surprised at how models work here. , 0. 1 -n -1 --in-prefix-bos --in-prefix ' [INST] ' --in-suffix Get up and running with large language models. cpp- He has implemented, with the help of many contributors, the inference for LLaMa, and other models, in plain C++. The weights here are float32. 100000, top_k = 40, top_p = 0. The current implementation of rep pen in llama. Georgi Gerganov (llama. That's why I basically don't use repeat penalty, and I think that somehow crept back in with mirostat, even at penalty 1. bin --color -c 4096--temp 0. When running llama. I’ve used the repetition_penalty=1. 1 Custom Temperature . 1 -ngl 99 Log start main: build = 2234 (973053d8) main: built with cc (Debian 13. 0-5) 13. Replicate - Llama 2 13B LlamaCPP 🦙 x 🦙 Rap Battle Llama API llamafile LLM Predictor LM Studio LocalAI Maritalk MistralRS LLM MistralAI ModelScope LLMS = None, context_window: int = DEFAULT_CONTEXT_WINDOW, prompt_key: str = "prompt", image_key: str = "image", repetition_penalty: Optional [float] Not exactly a terminal UI, but llama. cpp's author) shared his You signed in with another tab or window. it will repeat the sequence 1, 2, 3, rep penalty off, repeat a ton of text over and over, use the wrong instruct to make it sperg out, and watch to see deviations in the regular output, if I understand from my quick look, you should eventually have some outliers as you increase the strength of the deviation even with top k = 1. Gemma is a family of lightweight, state-of-the-art open models built by Google DeepMind. presence_penalty Single. ggmlv3. Thanks the model works fine and give the right output like: notice that the yellow line Below is an . Setting the temperature option is useful for controlling the randomness of the model's responses. 5k. Also even without --repeat-penalty the server is consistently slightly slower (244 t/s) compared to cli (258 t/s). gguf -n 256 -p "It is the best of time" --repeat-penalty 1. This model card corresponds to the 7B base version of the Gemma model in GGUF Format. 2. Contribute to 1694439208/GOT-OCR-Inference development by creating an account on GitHub. repeat_penalty (float): Penalty for repeating tokens in completions. Will increasing the frequency penalty, presence penalty, or repetition penalty help here? models. Discord bot for interacting with the LLaMA language model - siraben/llama-bot. Training a Mini(114M Parameter) Llama 3 like Model from Scratch. 6k; Star 37. Get up and running with large language models. cpp, and other related tools such as Ollama and LM Studio, please make sure that you have these flags set correctly, especially repeat-penalty. Instead of succinctly answering questio The path to the Llama LoRA. Tau and Eta for Mirostat. The default value is 1. cpp, and other related tools such as Ollama and LM Studio, please make sure that you have these flags set correctly llama. The number of tokens to look back when applying the repeat_penalty. A higher value (e. Mlops----1. My problem is that, sometimes the translated text repeat itself. No response. llama. Well, at least Llamafile can work with external weights, so that’s the next thing to try. 5 model level with such speed, locally This notebook is open with private outputs. gguf -f lexAltman. 18 with Repetition Penalty Slope 0. /chat -t [threads] --temp [temp] --repeat_penalty [repeat penalty] --top_k [top_k] -- top_p [top_p]. repeat_penalty Single. For example, I start my llama-server with: . param repeat_penalty: float | None = 1. 15, 1. ChatGPT: Sure, I'll try to explain these concepts in a simpler If setting requency and presence penalties as 0, there is no penalty on repetition. The LLaMA models all have a context of 2048 tokens from what I remember, and ChatGPT has about 4K tokens in its context length (for GPT-4: Please provide a detailed written description of what you were trying to do, and what you expected llama. random_prompt You signed in with another tab or window. cpp pulled fresh today. For answers that do generate, they are copied word for word I set --repeat_last_n 256 --repeat_penalty 1. param model Get up and running with large language models. typical_p (float): Typical probability for top frequent sampling. By the way, the most greedy decode of llama. 9) will be more lenient. It doesn't happen (the difference in performance is negligible) when using CPU, but with CUDA I see a significant difference when using --repeat-penalty option in the llama-server. param logits_all: bool = False ¶ Return logits for all tokens, not just the last token. It repeats the system prompt and it's own response several times with subtle variations. Contribute to abetlen/llama-cpp-python development by creating an account on GitHub. And the summary it gave below: Sure, here is a summary of the conversation with Sam Altman: Just installed a recent llama. The repetition penalty could maybe be ported to this sampler and used instead? I've seen multiple people reporting that FB's default sampler is not adequate for comparing LLaMA's outputs with davinci's. Until yesterday I thought I had to stick to pytorch forever. png, . Open betolley opened this issue Mar 26, 2023 · 5 for a better experience, you can start it with this command: . cpp I use --repeat_penalty 1. 0 for x86_64 "Repeat_penalty," on the other hand, is a setting that controls how much of a penalty or bias is given to generating similar or identical tokens in the output. /pygmalion2-7b-q4_0 PARAMETER stop "&lt;|" PARAMETER repeat_penalty 1. Skip to content. mirostat_tau Single. input_suffix String. 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. 1). Or it just doesn’t generate any text and the entire response is newlines. He has been used and abused, at least in his mind he has. I knew how to run it back when it has a file named "Main" and I used a batfile which included the following. penalize when I try to use the latest pull to inference llama 3 model mentioned in here , I got the repearting output: Bob: I can help you with that! Here's a simple example code snippet that creates an animation showing the graph of y = 2x + 1: I'm using Llama for a chatbot that engages in dialogue with the user. GPT 3. "num_ctx": 8192, Slightly off-topic, but what does api_like_OAI. But no matter how I adjust temperature, mirostat, repetition penalty, range, and slope, it's still extreme compared to what I get with LLaMA (1). /main -m gemma-2b-it-q8_0. repeat_penalty: 1. input_prefix String. cpp is telling me it is adding yet another in the beginning which could affect the performance: The repeat-penalty option helps prevent the model from generating repetitive or monotonous text. Follow. cpp model. cpp :start main -i --interactive-first Llama 3 can be very confident in its top-token predictions. param max_tokens: Optional [int] = 256 ¶ The maximum number of tokens to generate. memory_f16 Boolean. Higher values for repeat_penalty will discourage the algorithm from generating repeated or similar text, while lower values will allow for more repetition and similarity in the output. Right or wrong, for 70b in llama. The repeat-last-n option controls the number of tokens in the history to consider for penalizing Penalty alpha for Contrastive Search. repeat_last_n (int): Number of tokens to consider for repeat penalty. cpp I switched. Only thing I do know is that even today many people (I see it on reddit /r/LocalLLama and on LLM discords) don't know that the built-in server Newbie here. If the LLM generates token 4 at this point, it will repeat the For example, it penalizes every token that’s repeating, even tokens in the middle/end of a word, stopwords, and punctuation. 4 TEMPLATE """ <|system|>Enter RP mode. svg, . repeat_last_n Int32. 0 --color -i -r "User:"-i: Repeat penalty: This parameter penalizes the model for repeating the same or similar phrases in the generated text. However, after a while, it keeps going back to certain sentences and repeating itself as if it's stuck in a loop. 7 --repeat_penalty 1. Just for example, say we have token ids 1, 2, 3, 4, 1, 2, 3 in the context currently. cpp and alpaca. 5) will penalize repetitions more strongly, while a lower value (e. I have developed a script that aims to optimize parameters, specifically Top_K, Top_P, repeat_last_n, repeat_penalty, and temperature, for the LLaMa 7B model. . 300000 #160. jpeg, . Python bindings for llama. Not sure if that command is the most optimized one, but with that I got it working. I don't know about Windows, but I'm using linux and it's been pretty great. While testing multiple Llama 2 variants (Chat, Guanaco, Luna, Hermes, Puffin) with various settings, I noticed a lot of repetition. frequency_penalty Single. bhavyasaini/gemma-tuned/params - ollama. You signed in with another tab or window. 1k; Star 70. In llama. After an extensive repetition penalty test some time ago, I arrived at my preferred value of 1. It prevents repitition without the repeat_penalty thing (set it to 1. Large Language Models. cpp branch, and the speed of Mixtral 8x7b is beyond insane, it's like a Christmas gift for us all (M2, 64 Gb). Sure I could get a bit format model: llama. In my experience, not only does the temperature need to be set to 0. cpp literally has a comment stating that the research paper's proposal doesn't work without a modification to reverse the logic when it's negative signed. Outputs will not be saved. /models/vicuna-7b-1. 1 # The penalty to apply to repeated tokens. 2). 1 or greater has I am trying to query Llama-2 7B, taken from =tokenizer. param rope_freq_base: float = 10000. for example, input is "[INST] how are you [/INST]", output "I am fine" but it repeat the input, the output is "[INST] how are you [/INST] I am fine" System Info. , 1. I don't think it offers anything extra anymore. /llama. is the content for a prompt file , the file has been passed to the model with -f prompts/alpaca. Paste, drop or click to upload images (. Others. 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 past stuff from way earlier in the story. Written by Explore With Yasir This is pretty difficult to align the responses of these backends. Is this a bug, or am I It seems like adding a way to penalize repeating sequences would be pretty useful. But no matter how I adjust temperature, mirostat, Adding a repetition_penalty of 1. model String. Do you have any suggestions? This behaviour will limit the speed of the output and I wonder why this happen? CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks like fill-in-the-middle code completion, code generation, natural language understanding, mathematical reasoning, and instruction following. mirostat_eta Single. 7B (a larger model) url: (optional) if you want to connect to a remote server, otherwise it will use the node. Notifications You must be signed in to change notification settings; Fork 4. 18 increases the penalty for repetition, making the model less The main code uses the llama_sample_top_p, and not gpt_sample_top_k_top_p which is the only piece of code that actually uses the top_k parameter. All Italian speakers ride bicycles. modified by the author from lexica. A value of 1. And for reference, they were suggested by Georgi Gerganov, the main author of llama. I am running both of them but I wasn't that impr Gemma Model Card Model Page: Gemma. The ctransformer based completion is adequate, but the llama. `repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1. txt and i can't find this param in Repetition penalty “Repetition Note: You may play with these parameters for a LLaMA model here. cpp to do as an enhancement. 研究GOT-OCR-项目落地加速，不限语言. cpp is equivalent to a presence penalty, adding an additional penalty based on frequency of tokens in the penalty window might be All of those problems disappeared once I raised Repetition Penalty from 1. If None, no LoRa is loaded. 1 or greater has solved infinite newline generation, but does not get me full answers. 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. And when I connect my client using the OpenAI API, I get lots of repetition. create_completion with stream = True? (In general, I think a few more examples in the documentation would be great. create_completion(prompt, max_tokens=max_tokens, temperature=temperature, repeat_penalty=repeat_penalty, stop=stops) llama. Min P + high temperature works better to achieve the same end result ESP32 is a series of low cost, low power system on a chip microcontrollers with integrated Wi-Fi and dual-mode Bluetooth. title llama. Georgi Gerganov is well-known for his work on implementing in plain C++ high-performance inference. For context - I have a low-end laptop with 8 GB RAM and GTX 1650 (4GB VRAM) with Intel(R) Core(TM) i5-10300H CPU @ 2. The randomness of the temperature can be controlled by the seed parameter. The quest for a portable and slim Large Language model application is a long journey. jpg, . I expect that llama just response the answer. The way I'm trying to set my sampling parameters is such that the TFS sampling selection is roughly limited to replaceable tokens (as described in the write-up, cutting off the flat tail in the probability distribution), then a low-enough top-p value is chosen to respect cases where clear logical I used no repetition penalty at all at first and it entered a loop immediately. Reload to refresh your session. Hello everyone, I am currently working on a project in which I need to translate text from japanese to english. 1 to 1. com Uncensored LLM llama. mirostat Int32. Code; Issues 252; Pull requests 27; Discussions; Actions; Wiki; 百川2chat 13b sft微调后，多轮聊天出现重复回答，增加repetition_penalty llm. --temp 0 --repeat-penalty 1. I haven’t had enough time to go through my entire Subreddit to discuss about Llama, the large language model created by Meta AI. art. 950000, repeat_last_n = 64, repeat_penalty = 1. eos_token_id, max_length=4096, # max lenght of output, default=4096 return_full_text=False, # to not repeat the question Repetition Penalty: repetition_penalty discourages the model from repeating the same token within a short span of text. 000005) has lower perplexity than default, which is something that changed from the start of using Llama2 models, all sizes. param repeat_penalty: float = 1. cpp completion is qualitatively bad, often incomplete, repetitive, and sometimes stuck in a repeat loop. Despite the similar (and thus confusing!) name, this "Llama 2 Chat Uncensored" model is not based on "Llama 2 Chat", but on "Llama 2" (the base model - which has no prompt template) with a Wizard-Vicuna dataset. 1 % llama-server -n 2000 -ngl 33 -m Mistral-7B-Instruct-v0. gguf. Think of them as sprinkles on top to get better model outputs. Sometimes it seems to get into a loop and never breaks out. And now, thanks to Georgi Gerganov, we don’t even need a GPU. Hi, is there an example on how to use Llama. FROM . cpp. Apart from the overrides, I have verified that the defaults AFAIK are the same for both implementations. You signed out in another tab or window. The existing repetition and frequency/presence penalty samplers have their use but one thing they don' ggerganov / llama. cpp 2 weeks ago. It's very hacky, to the point where the implementation used in llama. AI. Any penalty calculation must track wanted, formulaic repitition imho. I checked all of this on current master. My "objective" metric is based on the BERTScore Recall between the Hello, I found out now why the server and regular llama cpp result can be different : Using server, repeat_penalty is not executed (oai compatible mode) Is this a bug or a feature ? And I found out as well using server completion (non oai), repeat_penalty is 1. cpp is example/simple. I greatly dislike the Repetition Penalty because it seems to always have adverse consequences. The official stop sequences of the model get added automatically. cpp one man band. Your top-p and top-k parameters are inactive the way they are at the moment. Navigation Menu --seed SEED RNG seed --temp TEMP temperature --repeat_penalty REPEAT_PENALTY penalize repeat sequence of tokens For example, to generate (multiline Llama. lora_base String. They control the temperature, the repeat penalty, and the penalty for newlines. For anyone having inconsistent model responses, try --repeat-penalty 1. 18, and 1. Prompt: All Germans speak Italian. sampling parameters: temp = 0. cpp, I used to run the lama models with oogabooga, but after the newest changes to llama. 1 like in documentation. I've done a lot of testing with repetition penalty values 1. However, I haven’t come across a similar mathematical description for the repetition_penalty in LLaMA-2 (including its research While testing multiple Llama 2 variants (Chat, Guanaco, Luna, Hermes, Puffin) with various settings, I noticed a lot of repetition. I am using MarianMT pretrained model. 1 anyway) and repeat-penalty. param logprobs: Optional [int] = None ¶ The number of logprobs to return. g. prompt String. I have no idea how to use them, and whether changing them can have any effect on the output, and I can't find anything online that's intuitive. You switched accounts on another tab or window. . 2k. Currently I am mostly using mirostat2 and tweaking temp, mirostat entropy, mirostat learnrate (which mostly ends up back at 0. 2 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. --repeat-penalty n seems to have no observable effect. param metadata: Optional [Dict [str, Any]] = None ¶ Metadata to add to the run trace. So Windows can’t deal with executables over 4GB even in 2024. So now I have Posted by u/IonizedRay - 5 votes and 3 comments That's for "Llama 2 Chat". Machine Learning. 50GHz The last three arguments are specific to the instruction model. 0, but also frequency_penalty, presence_penalty, or repeat-penalty (if they exist) need to be set properly. cpp/build$ bin/main -m gemma-2b. 0. 0 --no-penalize-nl -gan 16 -gaw 2048. Notifications You must be signed in to change notification settings; Fork 10. But local models still tend to repeat a lot, not just tokens, but structure – and repetition penalty doesn't help, as it ruins the language and thus quality (Command R+ is extremely sensitive to hiyouga / LLaMA-Factory Public. bin Both llama. cpp Public. 5 parameter to stop this effect, it seems to works fine for the moment. Setting a specific seed and a specific temperature will yield the same I initially considered that a problem, but since repetition penalty doesn't increase with repeat occurrences, it turned out to work fine (at least with repetition penalty <1. path_session String. I would be willing to improve the docs with a PR once I get this. 2) through my own comparisons - incidentally the same value as the popular simple-proxy-for llama. cpp has a vim plugin file inside the examples folder. Llama. cpp command: . /mythalion-13b-q4_0 PARAMETER stop "&lt;|" PARAMETER repeat_penalty 1. My llama-server initially worked fine, but after receiving a request with illegal characters, it started generating garbled responses to all valid requests. I just started working with the CLI version of Llama. All these implementations are optimized to run without a GPU. If None, no logprobs are returned. I used to use Llama. 0 --no-penalize-nl. 1 (Apply a moderate penalty to discourage repetition) temp: One such model, the Llama repeat_penalty (Optional, Default: 1. 2): repeat_penalty is used to penalize repeated phrases or words in the response. Nice. 15 and --repeat-last-n 1600 Also, -eps 5e-6 (epsilon aka rms_norm_eps 0. If the rep penalty is high, this can result in funky outputs. tfs_z (float): Controls the temperature for top frequent sampling. js API to run locally. ) Also I can't seem to find the repeat_last_n equivalent in llama-cpp-python, which is kind of weird. bin --color -ins -c 8192 --temp 0. However, I notice that it often generates replies that are very similar to messages it has sent in the past (which appear in the message history as part of the prompt). 18 (so slightly lower than 1. Reply reply FROM . CPP You signed in with another tab or window. antiprompt List<String> lora_adapter String. 9. 0 to disable), which I seem to have to disable when working with conversational tags, or the system will start evading the tags. Adding a repetition_penalty of 1. 200000, top_k = 40, top_p = 0. cpp with the provided command in the terminal, the models' responses extend beyond the expected answers, creating imaginary conversations. \ Which of the following statemens is true? You must choose one of the following:\ +main -t 10 -ngl 32 -m llama-2-7b-chat. What happened? Hi there. q4_0. py currently offer that server does not?. bin -p "Tell me about gravity" -n 256 --repeat_penalty 1. You can disable this in Notebook settings. bin' - please w Stops after sampling parameters: temp = 0. He has implemented, with the help of many contributors, the inference for LLaMa, and other models, in plain C++. 3_Q6_K. To use, you should have the llama-cpp-python library installed, and provide the path to the Llama model as a named parameter to the constructor. 2-n 40960 --repeat_penalty 1.