Finetune LoRA adapters for popular models using axolotl styled declarative configs
Model | GPU Requirements |
---|---|
Llama 3.1 70B | 4x L40S (Recommended) |
Llama 3.1 8B | 1-2x A10G |
keda
environment:
get_job_status
function. The function returns the status of the job as QUEUED
, PROCESSING
, COMPLETED
, or FAILED
.
tensorkube-keda-train-bucket
. All your training lora adapters will reside here. We construct adapter id from your job-id
and the type of gpus used for training so your adapter urls would look like this:-
s3://<bucket-name>/lora-adapter/<job_name>/<job_id>
fine-tuning-job
and job-id unique_id
, trained on 4
gpu of type l40s
--env default
flag in the secret creation command.lorax
library.
tensorkube list deployments
.curl
. This will query the base model without any adapters.