I’m currently shopping around for something a bit faster than ollama and because I could not get it to use a different context and output length, which seems to be a known and long ignored issue. Somehow everything I’ve tried so far did miss one or more critical features, like:
- “Hot” model replacement, so loading and unloading models on demand
- Function calling
- Support of most models
- OpenAI API compatibility (to work well with Open WebUI)
I’d be happy about any recommendations!
Yeah, but there are many open issues on GitHub related to these settings not working right. I’m using the API, and just couldn’t get it to work. I used a request to generate a json file, and it never generated one longer than about 500 lines. With the same model on vllm, it worked instantly and generated about 2000 lines
Are you using a tiny model (1.5B-7B parameters)? ollama pulls 4bit quant by default. It looks like vllm does not used quantized models by default so this is likely the difference. Tiny models are impacted more by quantization
I have no problems with changing num_ctx or num_predict
It was multiple models, mainly 32-70B
Can you try setting the
num_ctxandnum_predictusing a Modelfile with ollama? https://github.com/ollama/ollama/blob/main/docs/modelfile.md#parameterI’ve read about this method in the GitHub issues, but to me it seemed impractical to have different models just to change the context size, and that was the point I started looking for alternatives
You can overwrite the model by using the same name instead of creating one with a new name if it bothers you. Either way there is no duplication of the llm model file