Modify step3 of RLHF: support using fined-tuned models from step 1 and 2. #50
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trained in step1, and reward model trained in step2. Due to limited memory with single GPU training, use dolly-v2-3b for base models in step 1 and 2.
goldmermaid
reviewed
Sep 10, 2023
| @@ -23,5 +23,6 @@ | |||
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| # run supervised finetuning | |||
| config = pykoi.RLHFConfig(base_model_path="elinas/llama-7b-hf-transformers-4.29", dataset_type="local_db") | |||
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update the code to
config = pykoi.RLHFConfig(base_model_path="databricks/dolly-v2-3b", dataset_type="local_db")
example/rlhf/demo_rw_finetuning.py
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| @@ -24,5 +24,6 @@ | |||
| # run reward model finetuning | |||
| # config = pykoi.RLHFConfig(dataset_type="local_db") | |||
| config = pykoi.RLHFConfig() | |||
| config.base_model_path = "databricks/dolly-v2-3b" | |||
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update to
config.reward_model_path = "databricks/dolly-v2-3b"
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In step3 of RLHF, support using policy model trained in step1, and reward model trained in step2.
Due to limited memory with single GPU training, use dolly-v2-3b for base models in step 1 and 2.