Comments (2)
To minimize MTL's memory usage, you can put embedding on cpu memory by setting cpu_embedding=True
when calling from_pretrained
or load_low_bit
. Qwen's embedding is about 1GB.
from bigdl.
we can close it, issue can not be reproduced again
from bigdl.
Related Issues (20)
- Qwen1.5-4b and Qwen1.5-7b model cannot be loaded correctly in ipex-llm version 20240522 HOT 9
- [inference]: fine tuned model fails to do inferencing HOT 1
- ModuleNotFoundError: No module named 'ipex_llm.vllm.xpu' while using docker and installation HOT 1
- [integration]: merging bfloat16 model failed HOT 2
- all-in-one with version 2.1.0b1 failed HOT 3
- need an easy way to roll back driver installs HOT 3
- all-in-one benchmark llama-3-8b-instruct issue with version 2.1.0b1 HOT 3
- about test 3 Gpu with ipex HOT 1
- install issue HOT 2
- bug for inference qwen1.5-7b-chat with SYM_INT4 on Windows platform HOT 3
- Evaluation on if MiniCPM-2B-sft-bf16 need model based optimization on ipex-llm HOT 1
- finetune chatGLM6B using LoRA on arc HOT 3
- XEON and MAX with Kernel 5.15 configuration HOT 1
- transformers 4.38.1 gives bad llama3 performance on MTL iGPU HOT 2
- phi3 medium - garbage output in webui or generated by ollama HOT 12
- TypeError: llama_model_forward_4_36() got an unexpected keyword argument 'cache_position' during inference by TinyLlama-1.1B-Chat-v1.0 HOT 4
- Ollama codestral model produces nonsensical output on PVC HOT 3
- [Feature Request] Provide IPEX-LLM as an executable to install in Windows HOT 4
- Could ipex-llm support llama3 or the other llm QLora finetune? HOT 4
- CPU finetune can't work HOT 4
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from bigdl.