Comments (1)
@glample - want to add this to your paper? :)
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Related Issues (20)
- ValidationError: Input validation error: `inputs` must have less than 4096 tokens. Given: 4545
- Too long for pending a review for huggingface model
- ### System Info HOT 1
- Architecture
- Agnostic Atheist AI not Normal HOT 14
- Discussing a potential bias in Llama2-Chat that can lead to content safety issues
- download.sh didn't work well HOT 3
- parameter count of Llama2-70B and Llama2-13B
- Change the name of openai to closeai and change the project name to openai.
- Error: llama runner process no longer running: 3221225785
- [Generation, Question] Why does the `seed` have to be the same in different processors (`Llama.build`)?
- how can i evaluate mathematic datasets like GSM8K?
- Test Tokenizer gives Incorrect padding error
- No response from request to access models
- how to download this model HOT 1
- Providing SHA-256 hashes
- This PR will implement code for reproducing results in the following paper:
- Unable to access the Hugging Face Llama-3 model repo
- [Parallel MD5] Accelerating `download.sh`
- LLaMA3 supports an 8K token context length. When continuously pretraining with proprietary data, the majority of the text data is significantly shorter than 8K tokens, resulting in a substantial amount of padding. To enhance training efficiency and effectiveness, it is necessary to merge multiple short texts into a longer text, with the length remaining below 8K tokens. However, the question arises: how should these short texts be combined into a single training sequence? Should they be separated by delimiters, or should an approach involving masking be used during the pretraining process?
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