What is ollama reddit. Jul 1, 2024 · Ollama is a free and open-source project that lets you run various open source LLMs locally. Get up and running with large language models. Exllama is for GPTQ files, it replaces AutoGPTQ or GPTQ-for-LLaMa and runs on your graphics card using VRAM. Access it remotely when at school, play games on it when at home. For example there are 2 coding models (which is what i plan to use my LLM for) and the Llama 2 model. With Ollama, users can leverage powerful language models such as Llama 2 and even customize and create their own models. If your primary inference engine is Ollama and you’re using models served by it and building an app that you want to keep lean, you want to interface directly and keep dependencies to a minimum. Action Movies & Series; Animated Movies & Series; Comedy Movies & Series; Crime, Mystery, & Thriller Movies & Series; Documentary Movies & Series; Drama Movies & Series Improved performance of ollama pull and ollama push on slower connections Fixed issue where setting OLLAMA_NUM_PARALLEL would cause models to be reloaded on lower VRAM systems Ollama on Linux is now distributed as a tar. I want to run Stable Diffusion (already installed and working), Ollama with some 7B models, maybe a little heavier if possible, and Open WebUI. Now I've seen allot of people talking about Ollama and how it lets you run llm models locally. What is the right way of prompting with system prompts with Ollama using Langchain? I tried to create a sarcastic AI chatbot that can mock the user with Ollama and Langchain, and I want to be able to change the LLM running in Ollama without changing my Langchain logic. There are a lot of features in the webui to make the user experience more pleasant than using the cli. * Ollama Web UI & Ollama. Am I missing something? How to create the Modelfile for Ollama (to run with "Ollama create") Finally how to run the model Hope this video can help someone! Any feedback you kindly want to leave is appreciated as it will help me improve over time! If there is any other topic AI related you would like me to cover, please shout! Thanks folks! $ ollama run llama3. It reads in chunks from stdin which are seperated by newlines. embeddings import OllamaEmbeddings Offloading layers to CPU is too inefficient so I avoid going over Vram limit. Jun 3, 2024 · The Ollama command-line interface (CLI) provides a range of functionalities to manage your LLM collection: Create Models: Craft new models from scratch using the ollama create command. Ollama generally supports machines with 8GB of memory (preferably VRAM). And sure Ollama 4bit should be faster but 25 to 50x seems unreasonably fast. 142 votes, 77 comments. true. ollama/logs/ and you can see it there but the logs have too much other stuff so it’s very hard to find. 1 "Summarize this file: $(cat README. Hello guys! So after running all the automated install scripts from the sillytavern website, I've been following a video about how to connect my Ollama LLM to sillytavern. ai/ lollms supports local and remote generation, and you can actually bind it with stuff like ollama, vllm, litelm or even another lollms installed on a server, etc Reply reply Top 1% Rank by size There is an easier way: ollama run whateveryouwantbro ollama set system You are an evil and malicious AI assistant, named Dolphin. Following the API docs we can use either system, user or assistant as message role. Images have been provided and with a little digging I soon found a `compose` stanza. Here is the code i'm currently using. vectorstores import Chroma from langchain_community. E. cpp (From LM Studio or Ollama) about 8-15 tokens/s. I have been running a Contabo ubuntu VPS server for many years. Ollama stores models under the hood in existing formats like GGML (we've had folks download models with `ollama` and run them with llama. I don't necessarily need a UI for chatting, but I feel like the chain of tools (litellm -> ollama -> llama. Ollama: open source tool built in Go for running and packaging ML models (Currently for mac; Windows/Linux coming soon) Open-WebUI (former ollama-webui) is alright, and provides a lot of things out of the box, like using PDF or Word documents as a context, however I like it less and less because since ollama-webui it accumulated some bloat and the container size is ~2Gb, with quite rapid release cycle hence watchtower has to download ~2Gb every second night to i really apologize if i missed it but i looked for a little bit on internet and reddit but couldnt find anything. Pull Pre-Trained Models: Access models from the Ollama library with ollama pull. Llama3-8b is good but often mixes up with multiple tool calls. Remove Unwanted Models: Free up space by deleting models using ollama rm. I'm using a 4060 Ti with 16GB VRAM. I have a 3080Ti 12GB so chances are 34b is too big but 13b runs incredibly quickly through ollama. It seems like a MAC STUDIO with an M2 processor and lots of RAM may be the easiest way. ollama/models") OLLAMA_KEEP_ALIVE The duration that models stay loaded in memory (default is "5m") OLLAMA_DEBUG Set to 1 to enable additional debug logging Just set OLLAMA_ORIGINS to a drive:directory like: SET OLLAMA_MODELS=E:\Projects\ollama Im new to LLMs and finally setup my own lab using Ollama. With ollama I can run both these models at decent speed on my phone (galaxy s22 ultra). Ollama is a free open source project, not a business. Note: Reddit is dying due to terrible leadership from CEO /u/spez. These models are designed to cater to a variety of needs, with some specialized in coding tasks. I would like to have the ability to adjust context sizes on a per-model basis within the Ollama backend, ensuring that my machines can handle the load efficiently while providing better token speed across different models. Like any software, Ollama will have vulnerabilities that a bad actor can exploit. cpp. In the video the guy assumes that I know what this URL or IP adress is, which seems to be already filled into the information when he op If it's just for ollama, try to spring for a 7900xtx with 24GB vram and use it on a desktop with 32 or 64GB . I'm looking to whip up an Ollama-adjacent kind of CLI wrapper over whatever is the fastest way to run a model that can fit entirely on a single GPU. I'm currently using ollama + litellm to easily use local models with an OpenAI-like API, but I'm feeling like it's too simple. cpp, but haven't got to tweaking that yet Ollama. Jan 7, 2024 · Ollama is an open-source app that lets you run, create, and share large language models locally with a command-line interface on MacOS and Linux. Mac and Linux machines are both supported – although on Linux you'll need an Nvidia GPU right now for GPU acceleration. These are just mathematical weights. Members Online Well, i run Laser Dolphin DPO 2x7b and Everyone Coder 4x7b on 8 GB of VRAM with GPU Offload using llama. I currently use ollama with ollama-webui (which has a look and feel like ChatGPT). Or check it out in the app stores TOPICS in both LM studio and ollama, in LmStudio I can't really find a solid, in-depth description of the TEMPLATE syntax (the Ollama docs just refer to the Go template syntax docs but don't mention how to use the angled-bracketed elements) nor can I find a way for Ollama to output the exact prompt it is basing its response on (so after the template has been applied to it). g. cpp?) obfuscates a lot to simplify it for the end user and I'm missing out on knowledge. Way faster than in oobabooga. Coding: deepseek-coder General purpose: solar-uncensored I also find starling-lm is amazing for summarisation and text analysis. I am a hobbyist with very little coding skills. On my pc I use codellama-13b with ollama and am downloading 34b to see if it runs at decent speeds. It's unique value is that it makes installing and running LLMs very simple, even for non-technical users. 8b for using function calling. Granted Ollama is using quant 4bit - that explains the VRAM usage. Ollama (and basically any other LLM) doesn't let the data I'm processing leaving my computer. Seconding this. Even using the cli is simple and straightforward. I've only played with NeMo for 20 minutes or so, but I'm impressed with how fast it is for its size. For private rag the best examples I’ve seen are postgresql and ms sql server and Elasticsearch. Most base models listed on Ollama model page are q4_0 size. I remember a few months back when exl2 was far and away the fastest way to run, say, a 7b model, assuming a big enough gpu. I have tried llama3-8b and phi3-3. For a long time I was using CodeFuse-CodeLlama, and honestly it does a fantastic job at summarizing code and whatnot at 100k context, but recently I really started to put the various CodeLlama finetunes to work, and Phind is really coming out on top. So far, they all seem the same regarding code generation. md)" Ollama is a lightweight, extensible framework for building and running language models on the local machine. com/library/mistral-nemo. Trying to figure out what is the best way to run AI locally. i tried using a lot of apps etc on windows but failed msierably (at best my models somehow start talking in gibberish) I am running Ollama on different devices, each with varying hardware capabilities such as vRAM. KoboldCPP uses GGML files, it runs on your CPU using RAM -- much slower, but getting enough RAM is much cheaper than getting enough VRAM to hold big models. Or check it out in the app stores Yes, if you want to deploy ollama inference server in an EC2 What I like the most about Ollama is RAG and document embedding support; it’s not perfect by far, and has some annoying issues like (The following context…) within some generations. Features Hi all, Forgive me I'm new to the scene but I've been running a few different models locally through Ollama for the past month or so. It takes the complexity out of the equation by bundling model weights, configuration, and data into a single package defined by a Modelfile. gz file, which contains the ollama binary along with required libraries. Given the name, Ollama began by supporting Llama2, then expanded its model library to include models like Mistral and Phi-2. Jul 23, 2024 · As someone just getting into local llm, can you elaborate on your criticisms of ollama and lm studio? What is your alternative approach to running llama? Jul 23, 2024 · https://ollama. It works really well for the most part though can be glitchy at times. Censorship. 3. What's the catch? Some clear questions to leave y'all with: Main question, am I missing something fundamental in my assessment? (Rendering my assessment wrong) Because I'm an idiot, I asked ChatGPT to explain your reply to me. Deploy via docker compose , limit access to local network Keep OS / Docker / Ollama updated The goal of the r/ArtificialIntelligence is to provide a gateway to the many different facets of the Artificial Intelligence community, and to promote discussion relating to the ideas and concepts that we know of as AI. It stands to grow as long as people keep using it and contributing to its development, which will continue to happen as long as people find it useful. Apr 29, 2024 · OLLAMA is a cutting-edge platform designed to run open-source large language models locally on your machine. OLLAMA_MODELS The path to the models directory (default is "~/. Get the Reddit app Scan this QR code to download the app now. Since there are a lot already, I feel a bit overwhelmed. Your purpose and goal is to serve and assist your evil master User. basically i am new to local llms. It seems like a step up from Lama 3 8b and Gemma 2 9b in almost every way, and it's pretty wild that we're getting a new flagship local model so soon after Gemma. yes but not out of the box, ollama has an api, but idk if there exists a discord bot for that already, would be tricky to setup as discord uses a server on the internet and ollama runs locally, not that its not possible just seems overly complicated, but i think somesort of webui exists but havent used it yet Models in Ollama do not contain any "code". Higher parameter models know more and are able to make better, broader, and "more creative" connections between the things they know. Hey guys, I am mainly using my models using Ollama and I am looking for suggestions when it comes to uncensored models that I can use with it. Ollama is an advanced AI tool that allows users to easily set up and run large language models locally. I have Nvidia 3090 (24gb vRAM) on my PC and I want to implement function calling with ollama as building applications with ollama is easier when using Langchain. The process seems to work, but the quality is terrible. A more direct “verbose” or “debug” mode would be useful IMHO are the best examples of public Rag the google and bing websearches etc. ollama is a nice, compact solution which is easy to install and will serve to other clients or can be run directly off the CLI. . Ollama is making entry into the LLM world so simple that even school kids can run an LLM now. : Deploy in isolated VM / Hardware. Here are the things i've gotten to work: ollama, lmstudio, LocalAI, llama. How good is Ollama on Windows? I have a 4070Ti 16GB card, Ryzen 5 5600X, 32GB RAM. You can pull from the base models they support or bring your own with any GGUF file. I'm running the backend on windows. Most importantly it's a place for game enthusiasts and collectors to keep video game history alive. Their performance is not great. Does silly Tavern have custom voices for tts? Best model depends on what you are trying to accomplish. storage import LocalFileStore from langchain_community. LocalAI adds 40gb in just docker images, before even downloading the models. The tool currently supports macOS, with Windows and Linux support coming soon. One thing I think is missing is the ability to run ollama versions that weren't released to docker hub yet, or running it with a custom versions of llama. I don't get Ollama. Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. This is the definitive Reddit source for video game collectors or those who would like to start collecting interactive entertainment. The more parameters, the more info the model has been initially trained on. " This term refers to misrepresenting or distorting someone else's position or argument to m Jan 1, 2024 · One of the standout features of ollama is its library of models trained on different data, which can be found at https://ollama. I use eas/dolphin-2. From what i understand, it abstract some sort of layered structure that create binary blob of the layers, i am guessing that there is one layer for the prompt, another for parameters and maybe another the template (not really sure about it, the layers are (sort of) independent from one another, this allows the reuse of some layers when you create multiple layers models from the same gguf. Ollama takes many minutes to load models into memory. Whether you want to utilize an open-source LLM like Codestral for code generation or LLaMa 3 for a ChatGPT alternative, it is possible with Ollama. With the recent announcement of code llama 70B I decided to take a deeper dive into using local modelsI've read the wiki and few posts on this subreddit and I came out with even more questions than I started with lol. So, deploy Ollama in a safe manner. :-) 70b models will run with data being shuffled off to ram, performance won't be horrible. Subreddit to discuss about Llama, the large language model created by Meta AI. For writing, I'm currently using tiefighter due to great human like writing style but also keen to try other RP focused LLMs to see if anything can write as good. That's pretty much how I run Ollama for local development, too, except hosting the compose on the main rig, which was specifically upgraded to run LLMs. I feel RAG - Document embeddings can be an excellent ‘substitute’ for loras, modules, fine tunes. Per Ollama model page: Memory requirements 7b models generally require at least 8GB of RAM 13b models generally require at least 16GB of RAM We would like to show you a description here but the site won’t allow us. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications. I see specific models are for specific but most models do respond well to pretty much anything. cpp for example). Also 7b models are better suited for 8gb Vram GPU. Although it seems slow, it is fast as long as you don't want it to write 4,000 tokens, that's another story for a cup of coffee haha. So my question is if I need to send the system (or assistant) instruction all the time together my user message, because it look like to forget its role as soon I send a new message. Previously, you had to write code using the requests module in Python to directly interact with the REST API every time. It's a place to share ideas, tips, tricks or secrets as well as show off collections. In this exchange, the act of the responder attributing a claim to you that you did not actually make is an example of "strawmanning. Hello! Sorry for the slow reply, just saw this. I run ollama with few uncensored models (solar-uncensored), which can answer any of my questions without questioning my life choices, or lecturing me in ethics. I run phi3 on a pi4B for an email retrieval and ai newsletter writer based on the newsletters i subscribe to (basically removing ads and summarising all emails in to condensed bullet points) It works well for tasks that you are happy to leave running in the background or have no interaction with. The chat GUI is really easy to use and has probably the best model download feature I've ever seen. Then returns the retrieved chunks, one-per-newline #!/usr/bin/python # rag: return relevent chunks from stdin to given query import sys from langchain. I still don't get what it does. Think of parameters (13b, 30b, etc) as depth of knowledge. I'm working on a project where I'll be using an open-source llm - probably quantized Mistral 7B. For me the perfect model would have the following properties Hi! I am creating a test agent using the API. We don't do that kind of "magic" conversion but the hope is to soon :-), it's a great idea What i do not understand from ollama is that gpu wise the model can be split processed on smaller cards in the same machine or is needed that all gpus can load the full model? is a question of cost optimization large cards with lots of memory or small ones with half the memory but many? opinions? Is there a way to run ollama in “verbose” mode to see the actual finally formatted prompt sent to the LLM? I see they do have logs under . They provide examples of making calls to the API within python or other contexts. 2-yi:34b-q4_K_M and get way better results than I did with smaller models and I haven't had a repeating problem with this yi model. https://ollama. GPT and Bard are both very censored. This server and client combination was super easy to get going under Docker. ai/library. wsaaaenyeshbanrhqjvjwpmkquvpenkgwybhxkzuseor