Comments (2)
I know that llama.cpp "might" be thread safe in these cases, but the comments over there are not clear to me.
Unfortunately that's the best answer available right now - LLamaSharp is as threadsafe as llama.cpp is, but it's unclear how threadsafe that actually is :(
would it be safe to load
LLamaWeights.LoadFromFile
only once and pass it to both implementations?
I haven't worked on the Kernel Memory part of LLamaSharp, @Oceania2018 may have a better answer than I do. I think the reason the weights are not shared is because a model can either be loaded in embedding mode (in which case it can't be used for inference) or in inference mode (in which case it can't be used for embedding).
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I've closed this issue since there's been no more activity and the question was answered, but feel free to re-open it if you want to know more :)
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Related Issues (20)
- 开局提问有几率会触发无限空行回复 HOT 3
- Unable to load SYCL compiled backend HOT 12
- LLamaSharp runtime binaries don't support Rosetta2 HOT 7
- Split the main package
- Make `LLamaSharp.semantic-kernel` depend on `Microsoft.SemanticKernel.Core` instead of `Microsoft.SemanticKernel`. HOT 1
- [Feature]: SemanticKernel FuctionCall HOT 3
- [BUG]: When using large models with the GPU the code crashes with cannot allocate kvcache HOT 13
- Llava DLL issue in Unity HOT 2
- [BUG]: When using the output IAsyncEnumerable<string> of session.ChatAsync() strings are not streamed into client HOT 5
- Semantic Kernel - Home Automation Sample HOT 4
- Phi-3-medium-128k-instruct - error due to tensor shape expected 245, got 243 HOT 1
- [BUG]: Offset and length were out of bounds
- Have an error then try run example "KernelMemorySaveAndLoad" HOT 1
- [Feature]: Expose implementation details of the KV Cache HOT 6
- Unable to Utilize Full CPU Capacity During Inference HOT 2
- [BUG]: Cannot load the backend on MACOS HOT 1
- [BUG]: qwen2 nvidia abnormal occurrence HOT 7
- [Feature]: AuthorRole can custom role labels be supported ?
- SEHException on Tokenize model. HOT 4
- Cannot figure out how to switch backend to OpenCL HOT 2
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