Comments (3)
Ok @REIGN12 I believe I have fixed the issue! Please look at the updated instructions at https://github.com/tatsu-lab/alpaca_farm#downloading-pre-tuned-alpacafarm-models.
In particular, first download the sft10k
model, and afterwards downloading the reward models (to the same parent directory) should work.
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I think for people easy to use your reward model, the following hack may save the day:
- change the config.json in hf repo: change the backbone_name_or_path to any public llama weight(diff) huggingface repo
- change the logic in common.py the function get_pretrained_model_name_with_model_name_or_path, to better handle this logic
Just some of my thoughts! Hope you guys may fix this bug soon! ❤️
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Ah sorry about this issue! Taking a look into it now
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Related Issues (20)
- [Bug] Error importing is_deepspeed_zero3_enabled HOT 1
- 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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