Comments (2)
That example is a quick demo to demonstrate how the model output looks like, l and for simplicity, we don't show the full implementation of Self-RAG, as noted in README :) Update: I made the note bold to avoid confusion.
Currently, the full implementation multi-step generations for pre-given retrieved documents are at run_long_form_static.py. We are also planning to release the refactored implementations with memory-efficient dynamic retrieval -- our original implementations require 100+GB RAM at inference time and we wanted to reduce the computational requirements.
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Great! I will study the py file. Thank you for your reply.
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Related Issues (20)
- How can I get initial input file for generator?
- model issues
- Processed Input Dataset and Flan-3B Critic Generated Dataset
- Reproducing Self-RAG
- accuracy metric HOT 3
- About parameter `max_depth` HOT 2
- Doesn't the generator need to call the retriever when training the model?
- The critic model will generate different type of token when I use run_reward_vllm.py to generate tokens HOT 1
- some problem with run_long_form_static.py
- Data formatting to call the retriever
- Question Regarding Formula Error in Your Paper
- FactScore Inference Fails with KeyError: 'original_splitted_sentences' HOT 1
- Incorrect setup of Learning Rate Scheduler HOT 6
- dependency HOT 1
- torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: -9) local_rank: 0 (pid: 14447) of binary: HOT 2
- CUDA Memory is not enough
- Max_logprobs and logprobs value
- How to curate the preceding sentences? and Can you inform the distribution of IsUse token (1~5)?
- About bio eval
- question about multi content reference HOT 1
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