Comments (1)
Training/finetuning isn't something that you can do with LLamaSharp at the moment (I'm not sure how you're even able to do it with llama.cpp itself).
It sounds to me like you may not need training though, if you want to provide documents with extra context to the LLM then you might want to look into using RAG through semantic kernel.
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Related Issues (20)
- CentOS x86_64 Failed Loading 'libllama.so' HOT 4
- System.TypeInitializationException: 'The type initializer for 'LLama.Native.NativeApi' threw an exception.' HOT 12
- How do I continously print the answer word for word when using document ingestion with kernel memory? HOT 1
- How to rebuild LLamaSharp backends HOT 3
- Namespace should be consistent HOT 2
- Mamba HOT 11
- Android Backend HOT 5
- [Feature] Allow async model loading and cancellation
- [CI] Add more unit test to ensure the the outputs are reasonable HOT 3
- Take multiple chat templates into account
- [Feature]: Support for Function Calling or Tools HOT 4
- [BUG]: DefragThreshold default is not matching llama.cpp and probably not intended HOT 6
- [BUG]: Answer stop abruptly after contextsize, even with limiting prompt size HOT 1
- [BUG]: Linux cuda version detection could be incorrect HOT 2
- [BUG]: WSL2 has problem running LLamaSharp with cuda11
- Add unit test about long context HOT 3
- Add debug mode of LLamaSharp
- How to better provide system information for LLMs HOT 3
- LLAVA Configuration HOT 4
- [Feature]: 不同的LLM模型,代码要以怎样的方式融合到项目里 HOT 3
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