By reducing model weights to a lower precision, the GGML and GPTQ models — two well-known quantized models — minimize model size and computational needs. HF models load on the GPU, which performs inference significantly more quickly than the CPU. Generally, the model is huge, and you ...
01-ai/Yi-VL-6B · Hugging Face 01-ai/Yi-34B-200K · Hugging Face ### Building the Next Generation of Open-Source and Bilingual LLMs 🤗 Hugging Face • 🤖 ModelScope • ✡️ WiseModel 👩🚀 Ask questions or discuss ideas on GitHub 👋 Join us on 👾 Discord or 💬...
使用优化的双编码器嵌入模型初始化检索器,并对文档库中的所有文档进行编码: from fastrag.retrievers import QuantizedBiEncoderRetriever model_id = "Intel/bge-small-en-v1.5-rag-int8-static" retriever = QuantizedBiEncoderRetriever(document_store=document_store, embedding_model=model_id) document_store.upda...
01-ai/Yi-VL-34B · Hugging Face Yi-VL-34B模型托管在Hugging Face上,是全球首个开源的340亿视觉语言模型,代表了人工智能领域的重大进展。它以其双语多模态能力脱颖而出,可以进行英文和中文的多轮文本-图像对话。该模型在图像理解方面表现出色,并在MMMU和CMMMU等基准测试中... 内容导读...
Load GPTQ-quantized models in Transformers using the backend AutoGPTQ library: import torch from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/Llama-2-7B-Chat-GPTQ") with torch.device("cuda"): model = AutoModelForCau...
You can check on the Hub if your favorite model has already been quantized. TheBloke, one of Hugging Face top contributors, has quantized a lot of models with AutoGPTQ and shared them on the Hugging Face Hub. We worked together to make sure that these repositories will work ou...
You can check on the Hub if your favorite model has already been quantized. TheBloke, one of Hugging Face top contributors, has quantized a lot of models with AutoGPTQ and shared them on the Hugging Face Hub. We worked together to make sure that these repositories will work ou...
Quantized Versions through bitsandbytes Using 8-bit precision (int8) # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/...
models. """ config_class = SiglipVisionConfig base_model_prefix = "siglip" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, SiglipVisionEmbeddings): width = self.config.hidden_size ...
内容: MaziyarPanahi/Meta-Llama-3-70B-Instruct-GGUF The GGUF and quantized models here are based on meta-llama/Meta-Llama-3-70B-Instruct model How to download You can download only the quants you need instead of cloning the entire repository as follows: huggingface-cli download MaziyarPanahi/...