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  • How to Uninstall models? : r ollama - Reddit
    That's really the worst To get rid of the model I needed on install Ollama again and then run "ollama rm llama2" It should be transparent where it installs - so I can remove it later Meh
  • Critical RCE Vulnerability Discovered in Ollama AI . . . - Reddit
    The "--name ollama" flag assigns the container the name "ollama " In what IP will that open port be available? The port will be available on the IP address of the host machine where the Docker container is running If the host machine has a public IP address, the port will be accessible from the internet
  • Ollama iOS mobile app (open source) : r LocalLLaMA - Reddit
    OLLAMA_HOST=your ip address here ollama serve Ollama will run and bind to that IP instead of localhost and the Ollama server can be accessed on your local network (ex: within your house)
  • CPU only performance : r ollama - Reddit
    Run ollama run model --verbose This will show you tokens per second after every response Give it something big that matches your typical workload and see how much tps you can get For comparison, (typical 7b model, 16k or so context) a typical Intel box (cpu only) will get you ~7 A M2 Mac will do about 12-15 Top end Nvidia can get like 100
  • Why ollama faster than LMStudio? : r LocalLLaMA - Reddit
    I just tested the Mistral 7b instruct v0 2, in both LM studio and ollama, in LmStudio they only allow the gguf format so i knew it was working on CPU hence so slow (Took around 90 minutes to generate which generates in 2 minutes in gemini)
  • Why should I use Ollama when there is ChatGPT and Bard? : r ollama - Reddit
    For me Ollama provides basically three benefits: Working with sensitive data I'm working in the bank and being able to use LLM for data processing without exposing the data to any third-parties is the only way to do it Ollama (and basically any other LLM) doesn't let the data I'm processing leaving my computer Censorship
  • Ollama only uses dedicated video memory : r ollama - Reddit
    Ollama only uses dedicated video memory I bought a Blade18 which has a 4090 mobile that has BOTH dedicated and shared memory video memory access However, it seems like Ollama only uses the dedicated pool Would it be possible to improve performance by accessing the shared memory pool as well? Question to the nvidia subreddit to ask about the card:
  • Training a model with my own data : r LocalLLaMA - Reddit
    I'm using ollama to run my models I want to use the mistral model, but create a lora to act as an assistant that primarily references data I've supplied during training This data will include things like test procedures, diagnostics help, and general process flows for what to do in different scenarios
  • Multiple GPUs supported? : r ollama - Reddit
    Multiple GPU's supported? I’m running Ollama on an ubuntu server with an AMD Threadripper CPU and a single GeForce 4070 I have 2 more PCI slots and was wondering if there was any advantage adding additional GPUs Does Ollama even support that and if so do they need to be identical GPUs???





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