Just a couple of environment variables and arguments, simply do the following:
export VLLM_LOGGING_LEVEL=DEBUG
export VLLM_DEBUG_LOG_API_SERVER_RESPONSE=TRUE
vllm serve \
--enable-log-requests \
--enable-log-outputs 2>&1 | tee vllm_server.log
That’s it, Enjoy!
Source:
Simply run curl -fsSL https://lmstudio.ai/install.sh | bash
After you install, don’t forget to source your .bashrc file via source .bashrc so that you can run the lms command.
To bring it up, run lms daemon up
After it’s up, you can download a model using lms get, like this:
lms get MODEL_LINK@MODEL_QUANTIZATION
e.g.
lms get https://lmstudio.ai/models/nvidia/nemotron-3-nano-omni@q8_0
To load a model into memory:
lms load "nemotron-3-nano-omni" --context-length 128000
To start the server:
lms server start --port 1234
That’s it, Enjoy!
Sources:
I needed to add a web search functionality to my models in LM Studio, so I simply had to add the Tavily MCP Server configuration to LM studio, along with my API key of course.
You simply click on Edit msp.json after clicking + Install on the top right-hand side:
then replace the values there with the following:
{ "mcpServers": { "tavily-remote": { "command": "npx", "args": [ "-y", "mcp-remote", "https://mcp.tavily.com/mcp/?tavilyApiKey=tvly-prod-THE_REST_OF_YOUR_API_KEY" ] } }}
Click on Save and you are good to go!
Don’t forget to enable it :), Enjoy!
Source:
Simply run:
cat input_file.json | jq 'length'
For example:
{ "entity_id_01": { "field_a": "value_a", "field_b": "value_b", "field_c": "This is a generic feedback message" }, "entity_id_02": { "field_a": "value_a", "field_b": "value_b", "field_c": "This is a generic feedback message" }}
Will return 2!
That’s it, Enjoy!
Sources:
Simply run it as follows:
OLLAMA_DEBUG=1 ollama serve
Note: You might want to export another variable to show the ollama serve command where the models are saved in case you receive a 404 about model not available, this happened to me for some reason on Linux only.
In this case, just export these two variables and then run the command:
export OLLAMA_MODELS=/usr/share/ollama/.ollama/modelsexport OLLAMA_DEBUG=1ollama serve
That’s it, Enjoy!
Just Some Techie Notes! 