Comments (2)
HI there - we've actually worked with the OSI folks to update definitions.
You can see the details here: https://hackmd.io/@opensourceinitiative/osaid-0-0-6#
Cheers..
So, you want to update the definition and not your license, right? Llama is not open source, we have no access of every aspect. Sorry not to sorry.
from llama.
HI there - we've actually worked with the OSI folks to update definitions.
You can see the details here: https://hackmd.io/@opensourceinitiative/osaid-0-0-6#
Cheers..
from llama.
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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