Comments (3)
Once there is support in HF transformers, then it should be relatively straightforward to port into vLLM. It seems there aren't any efforts from searching the transformers issues/PRs
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attention
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I started a paid bounty to close these issues. Already over $200 of support.
https://x.com/natolambert/status/1814735390877884823
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Related Issues (20)
- Create speculative decode dynamic parallel strategy HOT 1
- [Bug]: CUDA out of memory for llama3.1 70gb gptq, while in llama3 70gb gptq doesn't HOT 1
- [Feature]: continuous batching for vllm.LLM HOT 3
- [Bug]: Using LLM Engine to infer the MiniCPM-V-2_6 model, the result is wrong HOT 1
- [Bug]: vllm.engine.async_llm_engine.AsyncEngineDeadError: Background loop has errored already. HOT 2
- [Bug]: `gemma-2-27b-it-GGUF`: `Architecture gemma2 not supported` HOT 5
- [RFC]: Encoder/decoder models & feature compatibility HOT 3
- [Usage]: how to use LLM class with AsyncLLMEngine HOT 2
- [Installation]: git clone cutlass fails HOT 7
- [Misc]: Improving VLLM KVCACHE Transfer Efficiency with NCCL P2P Communication HOT 1
- [Feature]: Support block manager v2 for chunked prefill HOT 3
- [Bug]: Phi-3-vision: ERROR 08-09 11:41:40 async_llm_engine.py:56] RuntimeError: stack expects each tensor to be equal size, but got [1933, 4096] at entry 0 and [2509, 4096] at entry 1 HOT 14
- [Bug]: Tensor Parallel > 1 causes desc_act=True GPTQ models to give bad output on ROCm
- [Usage]: Getting empty text using llm.generate of mixtral-8X7b-Instruct AWQ model HOT 1
- [Bug]: prefill/prefix FP8 triton kernel for opt-125m - an illegal memory access was encountered
- [Performance]: vllm inference in CPU instance has generation < 10 tokens / second HOT 2
- [Bug]: LLaMa 3.1 8B/70B/405B all behave poorly and differently using completions API as compared to good chat API HOT 16
- [Bug]: some questions regarding the usage of NCCL allreduce/broadcast/allgather/send/recv in VLLM using pycomm and torch's distributed. HOT 4
- [Bug]: `facebook/chameleon-30b` triggers assertion error while loading weights HOT 2
- [Bug]: Successfully deployed embedding model 'gte-Qwen2-7B-instruct', but got "TypeError: 'async for' requires an object with __aiter__ method, got coroutine" when calling it HOT 3
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