Comments (3)
The custom CUDA kernels we use for BLOOM were only tested on A100s. I deactivated the mandatory build of these extensions with this commit .
Can you try again?
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What type of GPU do you have?
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@OlivierDehaene It's a server with several NVIDIA RTX A6000s.
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Related Issues (20)
- Shared volume using mountpoint-s3, permissions issues HOT 5
- Planned/Potential of significant work
- Suport for InternVL-Chat-V1-5 HOT 1
- Support for ReFT
- Python client: Extra slash in base_uri leads to failures in chat endpoint
- The TGI loading model consumes all available gpus memory
- Process hangs in local run HOT 1
- Out of Memory Errors When Running text-generation-benchmark Despite Compliant Batch Token Limit HOT 3
- TGI crashes with complex json schemas provided as grammar without any information (on debug/trace level) HOT 1
- Canno launch with error exllamav2_kernels not installed. HOT 4
- Failing to start a TGI pod with 2 or more GPUs. Sharding fails.
- Unable to stop TGI after serving models HOT 1
- Do I need to additionally apply an inference template?
- UserWarning: You are using a Backend <class 'text_generation_server.utils.dist.FakeGroup'> as a ProcessGroup. This usage is deprecated since PyTorch 2.0
- Serverless inference API endpoints fails to return logprobs via chat completions
- 404 for Multi-modal docs
- The quantized llama-3-8b-instruct-awq with TGI 1.4 can handle fewer batch requests than the standard llama-3-8b-instruct with TGI 1.4 on the same RTX 3090 with 24GB VRAM.
- Add `stop_regex` parameter to `/generate` HOT 1
- Add `grammar` to chat/completions endpoint / Messages API
- Add Intel Arc iGPU support (Meteor Lake)
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