模型链接:https://modelscope.cn/models/OpenBuddy/openbuddy-llama2-13b-v8.1-fp16/summary 模型下载,load model,tokenizer import torch from modelscope import AutoConfig, AutoTokenizer, AutoModelForCausalLM model_id = 'OpenBuddy/openbuddy-llama2-13b-v8.1-fp16' model_config = AutoConfig.from_pretra...
LLama 2系列模型现已在ModelScope社区开源,包括:LLaMA-2-7B 模型链接:modelscope.cn/models/moLLaMA-2-7B-chat 模型链接:modelscope.cn/models/mo更多的LLaMa-2系列模型,社区上架中~~社区支持直接下载模型的repo. 通过如下代码,实现模型下载,以及load model, tokenizer: # ### Loading Model and Tokenizer model...
'interval': args.tb_interval } ] }, 'evaluation': { 'dataloader': { 'batch_size_per_gpu': args.batch_size, 'workers_per_gpu': 1, 'shuffle': False, 'drop_last': False, 'pin_memory': True }, 'metrics': [{ 'type': 'my_metric', 'vocab_size': tokenizer.vocab_size }] } ...
We compare the training loss of the Llama 2 family of models. We observe that after pretraining on 2T Tokens, the models still did not show any sign of saturation. Tokenizer. We use the same tokenizer as Llama 1; it employs a bytepair encoding (BPE) algorithm (Sennrich et al., 2016...
eos_token_id=tokenizer.eos_token_id, max_length=100, )forseqinsequences:print(f"Result:{seq['generated_text']}") This may produce output like the following: Result:deffibonacci(n):ifn ==0:return0elifn ==1:return1else:returnfibonacci(n-1) + fibonacci(n-2)deffibonacci_memo(n, memo=...
2. Loading LLaMA tokens and model weights. Note:“decapoda-research/llama-7b-hf” is not the official model weight. Decapoda Research has converted original model weights to work with Transformers. import transformers, torch from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig t...
tokenizer.add_tokens(["X1", "X2", "X3"]) tokenizer.save_pretrained("other_dir") sugarandgugu commented on Jan 20, 2025 sugarandgugu on Jan 20, 2025 你需要使用命令行激活你的环境,或者在ide里面比如pycharm和vscode里面,新建一个python程序。把上面的代码复制进去,运行即可。 hiyouga commented ...
The model emphasizes innovation, scalability, and simplicity, boasting several upgrades from its predecessor, LLama 2. This involves the following improvements:Better tokenizer for efficiency Use of grouped query attention (GQA) to make inferences faster Ability to handle longer sequences of up to 8,...
A new tokenizer, used to convert text into tokens, is more efficient, so prompts and responses take up 15% fewer tokens, meaning more text can fit into the context window. A new attention mechanism—the technique LLMs use to decide which words or phrases are essential for generating the ou...
Meta Llama 3 tokenizer to tokenize the data Checkpointer to read and write checkpoints Dataset component to load the dataset sh-4.2$ cat config_l3.1_8b_lora.yaml# Model Argumentsmodel:_component_:torchtune.models.llama3_1.lora_llama3_1_8b ...