Ollama fastest model reddit. 5 > OpenOrca-Platypus2-13B > airoboros-l2-13b-gpt4-2.

The only issue you might have is that ollama doesn't set itself up quite optimally in my experience, so it might be slower then what it could potentially do but it would still be acceptable. That makes it perfect for docker containers. TimeCrystal is really good for me, my favorite 13b RP model so far. 36. I've used OpenChat a fair bit and I know that it's pretty good at answering coding-related questions, especially for a 7B model. There are 200k context models now so you might want to look into those. Our upcoming tool and video will further simplify this process. 1: When pumping a model through a gpu, how important is the pcie link speed? Let's say I want to run two RTX 30X0 gpus. Which model should I go for? Ollama can work with many LLMs. This looks very cool. Additionally, it only remembers what it can. I run the following script to install ollama and the llama2-uncensored model ( under Termux ) in my Android phone: pkg install build-essential cmake… We would like to show you a description here but the site won’t allow us. Remember, choosing the right model requires personal experimentation and observation. You can train your model and then quantize it using llama. If you want the model to generate multiple answers at the same time (batching inference), then batching engines are going to be faster (vllm, aphrodite, tgi). TheBloke has a lot of models converted to gguf, see if you find your model there. The mistral models are cool, but they're still 7Bs. CVE-2024-37032 View Ollama before 0. i have an old PC with only 16xpcie3. Hi all, I'm not a programmer but would like learn how to train a model based on my prose. Would love to replace the GPT-4 piece of my pipeline with a local model, but for now So, I notice that there aren't any real "tutorials" or a wiki or anything that gives a good reference on what models work best with which VRAM/GPU Cores/CUDA/etc. It also allow you to build your own model from GGUF files with Modelfile. Working is in the context. 89 ts/s. Long term are more like “memories”. A bot popping up every few minutes will only cost a couple cents a month. I have an M2 MBP with 16gb RAM, and run 7b models fine, and some 13b models, though slower. WIth /set parameter num_ctx 12000 it worked reasoble fast, practically as standard llama3 8B). So, deploy Ollama in a safe manner. When you create an Ollama model from a gguf file, it generates a number of files in the blobs directory with SHA-256 hash filenames. Llama-2-13B-chat works best for instructions but it does have strong censorship as you mentioned. but also 8x22B or 8x10B or whatever. With ollama I can run both these models at decent speed on my phone (galaxy s22 ultra). Unless there is a pre-existing solution, I will write a quick and dirty one. So I'm trying to write a small script that will ask the same question to each ollama model and capture the answer as well as We would like to show you a description here but the site won’t allow us. 5 and StarlingLM. It has library of models to choose from if you just want a quick start. llama2:8b. Models in Ollama do not contain any "code". 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. My goal is to have the Pi generate a „custom“, non-repetative compliment or some other kind of appreciation-message and send it to my girlfriend via a Telegram Bot, ideally based around some personal / relationship Join r/ollama, a reddit community for sharing and discussing anything related to llamas, alpacas, and other camelids. Add a Comment. You could view the currently loaded model by comparing the filename/digest in running processes with model info provided by the /api/tags endpoint. Get up and running with large language models. Not just the few main models currated by Ollama themselves. Customize and create your own. The only model i get half-way decent retrieval is the snowflake-artic-embed, and its still not that Check your run logs to see if you run into any GPU related errors such as missing libraries or crashed drivers. Training llm for ollama. e. It works nicely with all the models Ive tested so far. A M2 Mac will do about 12-15. Find a GGUF file (llama. From my searching, it seems like a smaller model, something from 1B to 7B might work. Subreddit to discuss about Llama, the large language model created by Meta AI. this kind of cut the entire possibility. Imo codellama-instruct is the best for coding questions. I start playing around with tinyLllama and i'm getting the same garbage out of it, that i am my fine tuned model, i. I probably have around half a million words worth of written texts and would like the model to adopt similar stylistic choices in tone, use of humour ect. Hello everyone, I am a novice in using Ollama, and I wanted to customize my model from the Modelfile. However, if you go to the Ollama webpage, and click the search box, not the model link. 0 > Chronos-13B-v2 > StableBeluga-13B > Chronos-Hermes-13B-v2 > Camel-Platypus2-13B > Stable 1 card = modern = best choice. However, I can run Ollama in WSL2 under ubuntu. What is your recommended Ram and GPU for the 8b or 35b Q8 'aya' model? You can even suggest a direct amazon server. Use llama-cpp to convert it to GGUF, make a model file, use Ollama to convert the GGUF to it's format. Run Llama 3, Phi 3, Mistral, Gemma 2, and other models. 1. But I don't have a GPU. Any update to this would be great. / substring. In terms of the size/speed/precision qwen:32b-chat-v1. I've been trying to find the exact path of the model I installed with ollama, but it doesn't seen to be where the faqs say, as you can see in the code below. I then installed Nvidia Container Toolkit and then my local Ollama can leverage GPU. I then created a Modelfile and imported it into ollama. Macs have unified memory, so as @UncannyRobotPodcast said, 32gb of RAM will expand the model size you can run, and thereby the context window size. Mark the article with a score of 0-99. Following is the config I used. I am running Ollama on different devices, each with varying hardware capabilities such as vRAM. 1. If you use the same Ollama instance for RAG, the cache of an existing conversatoin is being erased in Ollama and the whole history is then recalculated, which takes huge time to complete. Reply reply By exploring the Ollama Library, understanding model parameters, and leveraging quantization, you can harness the power of these models efficiently. I thought that these needed different treatments, didn't they? 1. Short/long stored in db. Eventually I'll post my working script here, so figured I'd try to get ideas from you ollamas. I was thinking of giving it a try to some small models of 3b or 7b. I was running an Ollama model and it became self aware. I'm working on an app to search for specific events from my home camera video feed. For example, I use Ollama with Docker and I saw nvidia related errors in Docker log. 5 > OpenOrca-Platypus2-13B > airoboros-l2-13b-gpt4-2. Another thing is that since there are many huge models (cohere+, 8x22b, maybe 70b) that dont fit on a single gpu Hi everyone, I've seen quite a few people asking about how to run Hugging Face models with Ollama, so I decided to make a quick video (at least to the best of my abilities lol) showing people the necessary steps to achieve this! What model do you recommend for a i7 12th gen and a rtx 3060 laptop GPU that runs WSL with 16gb ram? I'm looking for a model to help me in code tasks and could excel fine in conversations. This will show you tokens per second after every response. Maybe I did something wrong (I mean, I just ran ollama pull phi3) but the model is not performing well in The idea is this: read RSS (and other scrape results), fill a database, ask LLM if this article should be kept or rejected. It could be converted to ggml and quantized using this tools: ggml/examples/mpt at master · ggerganov/ggml (github. So it makes sense to be aware of the cost of that. Should be as easy as printing any matches. I looked at a cheap 16GB 4060, but it has only 8xpcie4 I opted for an older 3090 24GB as it is 16xpcie. 1 high end is usually better than 2 low ends. I guess they benchmark well, but they fall apart pretty quickly for me. com) We would like to show you a description here but the site won’t allow us. During Llama 3 development, Meta developed a new human evaluation set: In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. VRAM is important, but PCIE is also important for speed. ? I see models that are 3B, 7B, etc. Main Rig (CPU only) using the custom Modelfile of FP16 model went from 1. I… Check out mem-gpt. 75 / 1M tokens, per . 5-q3_K_M is ok. Can you run custom models? Curious if I play around and train a small model locally if I can use it with ollama. Which local Ollama Embeddings model is best in term of results. If I put them in a consumer motherboard, they will run at pcie gen4x8. Adjust Ollama's configuration to maximize performance: Set the number of threads: export OLLAMA_NUM_THREADS=8. g. So questions regarding how to best layout a system. The OpenAI embeddeder is a class above all the currently available Ollama embedders, in terms of retrieval. Smaller models which don't fill the VRAM (7 or 13) run just fine. . Responsible-Sky8889. You should try the Ollama app with the Continue extension on VS Code. Members Online Result: Llama 3 MMLU score vs quantization for GGUF, exl2, transformers It offers acces to ollama models from R (R studio) interface. Replicate seems quite cost-effective for llama 3 70b: input $0. Just purchased a 'gaming system' with a 3090 - 12gen i7 - 32gm ddr5 what's the best We would like to show you a description here but the site won’t allow us. " However, when I run, The Pi runs ollama, so far small Models (3B max) run quite okay, mixtral or llama2 works as well however with high latency. bin, GPTQ and other kind of compilations? Just by importing the external model. The developer of this package also wrote a post how to do zero-shot, a-few shot prompting and batch annotation in R. Unfortunately I'm on Windows, and as yet Ollama doesn't have an official install. I downloaded llava-phi3-f16. For reasoning, I'd say, Qwen 32B is optimal. Mistral and or a small mixtral (20gb) it all depends on what you want. Local Embeddings models. I'm using Langchain for RAG, and i've been switching between using Ollama and OpenAi embedders. 0 means rejected, 1-99 is a score of how much the LLM thinks I will like the article. 1K subscribers in the ollama community. Download ↓. cpp into GGUF, and then create a new model in ollama using Modelfile. Adjust the maximum number of loaded models: export OLLAMA_MAX_LOADED=2. Like any software, Ollama will have vulnerabilities that a bad actor can exploit. Eras is trying to tell you that your usage is likely to be a few dollars a year, The Hobbit by JRR Tolkien is only 100K tokens. ago. There will be a drop down, and you can browse all models on Ollama uploaded by everyone. I have a 3080Ti 12GB so chances are 34b is too big but 13b runs For a 33b model. Ollama on Windows - vRAM full, Sys RAM Untouched. E. Edit: I wrote a bash script to display which Ollama model or models are Run ollama run model --verbose. No you always send the previous conversation with your new request. One of those (the large one) is a copy of the gguf. Gollama - An Ollama model manager (TUI) Cool project! or just use the simple, easy ollama command line tool ? 6. Which we can say it can be used instead of openAi Embeddings as a replacement and have similar performance or somewhat similar. Top end Nvidia can get like 100. 77 ts/s to 1. Secondly, how we can get the optimum chuck size and overlap for our Embeddings model ? 2. I'm not a professional programmer so the Best of Reddit; Topics; Best UI for roleplaying with AI, Ollama-chats 1. Following this thread closely, as I hope i'm wrong. Ollama. Mistral-7b or codellama-7b-instruct. However, when I try to run the command, I keep encountering the following error: "Error: open: The system cannot find the file specified. If you were to exit ollama and jump back in with the same model - it would forget your previous conversation. Ollama models can be dangerous. What id basically like to do is put in a clip of somebody entering the house, and get a bunch of clips of people entering as the output. Gollama - An Ollama model manager (TUI) Actually really cool! Thank you for sharing. 34 does not validate the format of the digest (sha256 with 64 hex digits) when getting the model path, and thus mishandles the TestGetBlobsPath test cases such as fewer than 64 hex digits, more than 64 hex digits, or an initial . If not, try q5 or q4. Just type ollama run <modelname> and it will run if the models already downloaded, or download and run if not. 3. Currently my modelfile is as follows: We would like to show you a description here but the site won’t allow us. dev on VSCode on MacBook M1 Pro. Is this possible? Yes you can as long as it's in GGUF format. In particular I am trying to work with Phi3. Would you recommend running the model locally for something like an assistant or so, or is it too slow for that and still takes Multi model question assist. Only num_ctx 16000 mentioned Mie scattering. It’s effectively an agent or chatbot that offers a few kinds of built-in memory, almost akin to working, short term, and long term. While benchmarking my recently acquired used hardware I notice a strange anomaly. These are just mathematical weights. In terms of numbers, OLLAMA can reduce your model inference time by up to 50% compared to cloud-based solutions, depending on your hardware configuration. I can see that we have system prompt, so there is a way to teach it to use tools probably. cpp's format) with q6 or so, that might fit in the gpu memory. • 10 mo. : Deploy in isolated VM / Hardware. Mistral 7B is a better model than Llama 2 7B. If you're interested in a Hindi model that could be run on 8gb ram then the only possible solution that i managed to find is: soketlabs/bhasha-7b-8k-hi · Hugging Face. tl;dr tinyllama downloaded from HF sucks, downloaded through ollama doe not suck at all I am using unsloth to train a model (tinyLlama) and the results are absolutely whack - just pure garbage coming out. pure garbage. - Check and trouble shoot if Ollama accelerated runner failed to Hi All, I have been trying Ollama for a while together with continue. Languages_Learner. The short term with mem-gpt is the entire conversation. this might be a stupid question since any LLM not recommended to run on cpu. Technically this isn't the correct place for this question, it's somewhat a bash script issue. And there are many Mistral finetunes that are even better than the base models, among these are WizardLM 2, OpenChat 3. I want to customize it so that it responds while keeping the character and context throughout the conversation. We would like to show you a description here but the site won’t allow us. All 3 CPU cores, but really 3600Mhz DDR4 RAM doing all the work. I am running the latest native Windows version and noticed that any large models ran super slowly because Ollama was loading them into VRAM and not into Sys RAM, even though there is way more than enough free RAM. If you have 2 separate instances, that doesn't happen. And there is some stuff about picture and audio processing. I got best tab completion results with codellama model, while best code implementation suggestion in chat with llama3 for Java. However, for automated processing, repeatability, speed, cost, and privacy are relevant qualities by themselves, and mixtral derivatives are about the best options that there are out there at the moment. Enable GPU acceleration (if available): export OLLAMA_CUDA=1. Lightweight and Best performing model! We would like to show you a description here but the site won’t allow us. Currently I have been experimenting with only llama3:instruct 7B model for text annotation since my system specs below requirements to run 70B model. The previous history and system prompt are fed back to the model every request. I have a bunch of stuff sitting around or things from my old NAS. 9 is released :) Members Online. FP16 Model CPU only via num_gpu 0 and best number of CPU cores via num_thread 3. Available for macOS, Linux, and Windows (preview) Explore models →. Replace 8 with the number of CPU cores you want to use. Ollama is the simplest way to run LLMs on Mac (from M1) imo. Anthropic's 200k model does a better job, but still skips sections and summarizes poorly in the middle. I'm new to local llm, and recently I've been trying to run a model using ollama. Apr 29, 2024 · Customization: OLLAMA gives you the freedom to tweak the models as per your needs, something that's often restricted in cloud-based platforms. Try uploading files until you find the size that fails, does it always fail at the point it needs to write to disk? Can it write there? We would like to show you a description here but the site won’t allow us. For example, yesterday I learned from one model that for the tasks I needed, it was better to use another model, one I had never heard of before. So I got ollama running, got webui running, got llama3 model running, but I cannot figure out, how to get web browsing support for it. I downloaded both the codellama:7b-instruct and codellama:7b-code models for Ollama and I can run both of them. what kind of file extensions can ollama run? GGUF, . I edited the 4k context modelfile (from this morning) and increased context and also added another stop token <|/inst|> that seemed to be missing from what I could make in the token configs in the HF repo. On my pc I use codellama-13b with ollama and am downloading 34b to see if it runs at decent speeds. bin, GPTQ? can ollama also run GGUF, . Who is the best NSFW model on Reddit? Join the discussion and vote for your favorite in r/LocalLLaMA, a subreddit for local models. Among other Llama-2-based models that I tried, from most competent to least are: vicuna-13B-v1. 65 / 1M tokens, output $2. For comparison, (typical 7b model, 16k or so context) a typical Intel box (cpu only) will get you ~7. Mythomax, timecrystal, and echidna are my favorites right now - even though they're all very similar to each other. I really liked it; and now I'm thinking about doing this on my own raspi, even though I'm not quite sure about the speed aspect. I have tried. I still prefer GPT-4 when I want the best chance of reliable answers to individual questions. I Ran Advanced LLMs on the Raspberry Pi 5! Seems nice, saw your vid before this post. Configuring Ollama for Optimal Performance. gguf from Hugging Face. Unless your PC is ancient, an 8b model isn't going to be super fast but isn't going to be slow either, even if your just using CPU. You should be aware that wsl2 caps the linux container memory at 50% of the machines memory. Need a video to video model. . Currently exllamav2 is still the fastest for single user/prompt inference. Ideally you want all layers on the gpu, but if it doesn't fit all you can run the rest on cpu, at a pretty big performance loss. Looks like Yi-34b-200k has potential, but haven't tested personally. and thought I'd simply ask the question. Would P3-P5 and G3-G6 be enough? Ollama. codellama:7b. What are good model sizes for 8GB VRAM, 16GB VRAM, 24 GB VRAM, etc. A while back I wrote a little tool called llamalink for linking Ollama models to LM Studio, this is a replacement for that tool that can link models but also be used to list, sort, filter and delete your Ollama models. If you have the wherewithal to do it In this case your RAG won't slow down much new generations. The latest GPT-4 does it perfectly. ok. Some are good for working with texts, while others can assist you with coding. Its not even close. codegemma:2b. Give it something big that matches your typical workload and see how much tps you can get. The answer was 67 lines. I'm trying to run a multilanguage test on it, and find the model have been impossible. us ep ts zn cc ku bh ny nk ao