由于更好的泛化能力,它实现了针对指令调整的 LLM 和多模态 LLM 的出色量化性能。除了 AWQ,我们还实现了 TinyChat,这是一个针对 4 位端上 LLM/VLM 的高效且灵活的推理框架。通过内核融合和平台感知的权重打包,TinyChat 在桌面和移动 GPU 上均比 Huggingface FP16 实现快 3 倍以上。它还使 70B Llama-2 模型...
sagemaker-huggingface-llm.md sasha-luccioni-interview.md sb3.md sc2-instruct.md scalable-data-inspection.md sd3.md sd_distillation.md sdxl_jax.md sdxl_lora_advanced_script.md sdxl_ort_inference.md searching-the-hub.md segmoe.md sempre-health-eap-case-study.md sentence-t...
Download the model from huggingface and prepare the model for inference Run the model for example as below Open the mini-forge prompt, activate the llm-sycl environment and enable oneAPI enviroment as below "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 List the sycl devices ...
huggingface/optimum-quantoPublic NotificationsYou must be signed in to change notification settings Fork58 Star796 main 20Branches 19Tags Code README Apache-2.0 license Optimum Quanto 🤗 Optimum Quanto is a pytorch quantization backend foroptimum. ...
从huggingface_hub导入快照下载model_name = "google/gemma-2-2b-it" # 我们想要量化的模型methods = [ 'Q4_K_S' , 'Q4_K_M' ] # 用于量化的方法base_model = "./original_model_gemma2-2b/" # FP16 GGUF 模型的存储位置quantized_path = "./quantized_model_gemma2-2b/" # 量化的 GGUF ...
从huggingface_hub导入快照下载 model_name = "google/gemma-2-2b-it" # 我们想要量化的模型 methods = [ 'Q4_K_S' , 'Q4_K_M' ] # 用于量化的方法 base_model = "./original_model_gemma2-2b/" # FP16 GGUF 模型的存储位置 quantized_path = "./quantized_model_gemma2-2b/" # 量化...
Next, we'll want to install the open source HuggingFace Hub library so that we can use its API to download the Granite Model files. bin/pip install huggingface_hub Next, either save the following script to a file and run it, or simply start a Python3 session and run it there. ...
TheBloke/Mixtral-8x7B-Instruct-v0.1-AWQkeeps outputting nothing (is mentioned in huggingface discussionshere Is there anyone having faced and resolved such a problem? I know it may not be directly related to vLLM. And is there anyone having tested a quantized Mixtral model with vLLM well?
While quantization remains an extremely dynamic field as the HuggingFace model quantizers and hardware vendors alike strive for fewer bits, better accuracy, and energy efficiency. It’s far more subtle than just a number of bits though – there’s substantial complexity and a mix of all sorts ...
pip install -q -U transformers peft accelerate optimum pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu117/ 运行: from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig model_id = "facebook/opt-125m" tokenizer = AutoTokenizer.from...