26 lines
1.4 KiB
Markdown
26 lines
1.4 KiB
Markdown
1. Download + install [vLLM](https://docs.vllm.ai/en/latest/getting_started/installation.html)
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2. Launch a vLLM **OpenAI-compatible** API server using [the official vLLM documentation](https://docs.vllm.ai/en/latest/getting_started/quickstart.html)
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For example, if we want to use the model `dolphin-2.2.1-mistral-7b` from [HuggingFace](https://huggingface.co/ehartford/dolphin-2.2.1-mistral-7b), we would run:
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```sh
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python -m vllm.entrypoints.openai.api_server \
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--model ehartford/dolphin-2.2.1-mistral-7b
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```
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vLLM will automatically download the model (if it's not already downloaded) and store it in your [HuggingFace cache directory](https://huggingface.co/docs/datasets/cache).
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In your terminal where you're running MemGPT, run `memgpt configure` to set the default backend for MemGPT to point at vLLM:
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```
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# if you are running vLLM locally, the default IP address + port will be http://localhost:8000
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? Select LLM inference provider: local
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? Select LLM backend (select 'openai' if you have an OpenAI compatible proxy): vllm
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? Enter default endpoint: http://localhost:8000
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? Enter HuggingFace model tag (e.g. ehartford/dolphin-2.2.1-mistral-7b): ehartford/dolphin-2.2.1-mistral-7b
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...
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```
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If you have an existing agent that you want to move to the vLLM backend, add extra flags to `memgpt run`:
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```sh
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memgpt run --agent your_agent --model-endpoint-type vllm --model-endpoint http://localhost:8000 --model ehartford/dolphin-2.2.1-mistral-7b
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```
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