• 保存模型,并可选地将其推送到Hugging Face Hub 比如微调一个 Llama-3.1-8B model with Q-LoRA,yaml内容如下: # Model 参数model_name_or_path: Meta-Llama/Meta-Llama-3.1-8Btokenizer_name_or_path: Meta-Llama/Meta-Llama-3.1-8B-Instruct model_revision: maintorch_dtype: bfloat16 attn_implementa...
model_id: "meta-llama/Meta-Llama-3-70b" # Hugging Face model id dataset_path: "." # path to dataset max_seq_len: 3072 # 2048 # max sequence length for model and packing of the dataset # training parameters output_dir: "./llama-3-70b-hf-no-robot" # Temporary output directory for...
运行代码,这里是为了获得API URL和测试模型、TOKEN是否可用。注意有些模型是收费的,例如meta-llama/Meta-Llama-3-70B-Instruct,需要成为付费会员才能通过API访问,而网页版访问则没有这个限制。 Model requires a Pro subscription; check outhf.co/pricingto learn more. Make sure to include your HF token in yo...
model_id: "meta-llama/Meta-Llama-3-70b" # Hugging Face model id dataset_path: "." # path to dataset max_seq_len: 3072 # 2048 # max sequence length for model and packing of the dataset # training parameters output_dir: "./llama-3-70b-hf-no-robot" # Temporary output directory for...
model_id: "meta-llama/Meta-Llama-3-70b" # Hugging Face model id dataset_path: "." # path to dataset max_seq_len: 3072 # 2048 # max sequence length for model and packing of the dataset # training parameters output_dir: "./llama-3-70b-hf-no-robot" # Temporary output directory for...
model_id: "meta-llama/Meta-Llama-3-70b" # Hugging Face model id dataset_path: "." # path to dataset max_seq_len: 3072 # 2048 # max sequence length for model and packing of the dataset # training parameters output_dir: "./llama-3-70b-hf-no-robot" # Temporary output directory for...
model_id: "meta-llama/Meta-Llama-3-70b" # Hugging Face model id dataset_path: "." # path to dataset max_seq_len: 3072 # 2048 # max sequence length for model and packing of the dataset # training parameters output_dir: "./llama-3-70b-hf-no-robot" # Temporary output directory for...
第一步是安装 Hugging Face Libraries 以及 Pyroch,包括 trl、transformers 和 datasets 等库。trl 是建立在 transformers 和 datasets 基础上的一个新库,能让对开源大语言模型进行微调、RLHF 和对齐变得更容易。 代码语言:javascript 复制 # Install PytorchforFSDPandFA/SDPA%pip install"torch==2.2.2"tensorboard...
HuggingFaceTB/SmolLM2-360M-Instruct模型代表了紧凑语言模型领域的重大进步。它是SmolLM2系列的一部分,包括各种规模的模型,其中这个特定模型拥有3.6亿个参数。这个模型的独特之处在于它能够执行各种任务,包括遵循指令、知识和推理,同时又足够高效以在本地设备上运行。 由Loubna Ben Allal等专家领导的团队开发,该模型已经...
Learn how to simplify the creation of AI agents with Hugging Face's new Python library, smolagents, and follow along with a demo project to see it in action.