deploy and run LLM (large language models), including LLaMA, LLaMA2, Phi-2, Mixtral-MOE, and mamba-gpt, on the Raspberry Pi 5 8GB.
Create standalone with Splunk Free license
Splunk Free is the totally free version of Splunk software. The Free license lets you index up to 500 MB per day and will never expire.
Execute the following to bring up a Splunk Free standalone environment:
$ docker run --name so1 --hostname so1 -p 8000:8000
-e "SPLUNK_PASSWORD=<password>"
-e "SPLUNK_START_ARGS=--accept-license"
-e "SPLUNK_LICENSE_URI=Free"
-it splunk/splunk:latest
Splunk Free is the totally free version of Splunk software. The Free license lets you index up to 500 MB per day and will never expire.
Execute the following to bring up a Splunk Free standalone environment:
$ docker run --name so1 --hostname so1 -p 8000:8000
-e "SPLUNK_PASSWORD=<password>"
-e "SPLUNK_START_ARGS=--accept-license"
-e "SPLUNK_LICENSE_URI=Free"
-it splunk/splunk:latest
- Discusses the use of consumer graphics cards for fine-tuning large language models (LLMs)
- Compares consumer graphics cards, such as NVIDIA GeForce RTX Series GPUs, to data center and cloud computing GPUs
- Highlights the differences in GPU memory and price between consumer and data center GPUs
- Shares the author's experience using a GeForce 3090 RTX card with 24GB of GPU memory for fine-tuning LLMs
- Compares consumer graphics cards, such as NVIDIA GeForce RTX Series GPUs, to data center and cloud computing GPUs
- Highlights the differences in GPU memory and price between consumer and data center GPUs
- Shares the author's experience using a GeForce 3090 RTX card with 24GB of GPU memory for fine-tuning LLMs
