Comments (3)
Thanks for your interest! We haven't tested the current implementation on fewer than 8 GPUs for this particular instruction-following setup. But there are several things you may try
- low precision training, e.g., with bnb
- quantized training
- LoRA or other adapters
There are also recent approaches that combine several paradigms above such as QLoRA.
We're working on some code that would make this training much easier, but we don't have a timeline for that yet.
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May I know if there is any updates? Thanks a lot.
from alpaca_farm.
Hi,
We don't have an updated timeline for this, sorry about that - it's not currently on our near-term roadmap. We'd be happy to accept any PRs that implement this, but until then closing this issue as it's getting stale.
from alpaca_farm.
Related Issues (20)
- Question about KL term HOT 1
- Reproducibility of pretuned reward model
- Use with Llama-2-70b-hf? HOT 4
- KeyError: 'llama' in /recover_model_weights.py HOT 2
- Confusing detail preference mapping HOT 1
- recover_model_weights.py gives WARNING:root:Your base LLaMA checkpoint is converted with transformers==4.27.0.dev0
- Huge memory demand of recover_model_weights.py?
- RewardModel.from_pretrained() loads redundant weights (incurs extra ~30GB of RAM)
- Why use FSDP instead of Deepspeed? HOT 2
- lower cuda version? HOT 1
- integrity_check error with sft10k
- score with reward model HOT 2
- RecursionError: maximum recursion depth exceeded
- BaseAnnotator.__init__() got an unexpected keyword argument 'other_keys_to_keep' HOT 4
- PairwiseAutoAnnotator always "Annotating 0 examples with gpt4_3" HOT 1
- Repeated Deprecation Error HOT 3
- Error downloading pre-trained weights
- Using pretrained models HOT 3
- Inquiry Regarding Supervised Fine-Tuning with AlpacaFarm Framework for Pythia Models HOT 1
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