LLama 2 finetuning on multi-GPU with long context length
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 39.5k
- Forks
- 4.8k
- PR merge metrics
- No merged PRs in 30d
Description
I tried to finetune 13B model on 8A100 80gb GPU. When model is getting loaded It does DDP rather than TP or Zero. And since model copy is loaded on each GPU rather than it's TP. And when I traing on big context length I get OOM error.
What can be something that I am doing wrong? I have setup everything to latest versions and checked up installations.
When I run a 7b model in int4, context length to 4k and with LoRA then all GPU's fill up to half and training runs.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The report names no files, tests, or launch command. Start by reproducing the 13B, 8×A100, long-context setup and recording the model-loading and distributed-training configuration; done means the expected parallelism is confirmed and the reported out-of-memory behavior is explained or reproduced.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- distributed-systems, machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100