Llama 2 采用了 Llama 1 的大部分预训练设置和模型架构。他们使用标准的Transformer架构，应用RMSNorm进行...
1. 解析输入参数,这些参数包括模型的大小(对应的是模型参数的数量),输入目录(包含LLaMA模型的权重和tokenizer的数据),输出目录(将转换后的模型保存到的位置),以及安全序列化选项。 2. 在主函数中,根据解析到的参数调用`write_model`函数。该函数将从输入目录中加载LLaMA模型的权重,并进行转换,最后保存到输出目录中。
grad(): print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0],skip_...
tokenizer和LLaMA一致,BPE,数字分成单个的digit,总共32k的词表(这里也许能看到一点就是LLaMA 2还是关注英文,其他语言不会有太大的提升) We use the same tokenizer as Llama 1; it employs a bytepair encoding (BPE) algorithm using the implementation from SentencePiece. As with Llama 1, we split all numb...
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. from typing import Optional import fire from llama import Llama def main( ckpt_dir: str, tokenizer_path: str, temperature: float = 0.6, top_p: float = 0.9, max_seq_len: int ...
The special tokens absolutely should not be tokenized unconditionally, since that could be a security issue in online services. But the tokenizer should have an option to do so. The simplest would be to just add a parameter equivalent to bool tokenize_special_tokens to llama_tokenize. Then we...
(cd ${TARGET_FOLDER} && md5sum -c tokenizer_checklist.chk) 再然后就获取每个模型对应多少个文件,文件数为SHARD+1个。 for m in ${MODEL_SIZE//,/ } do if [[ $m == "7B" ]]; then SHARD=0 MODEL_PATH="llama-2-7b" elif [[ $m == "7B-chat" ]]; then ...
LlamaTokenizer model_id = "/home/model_zoo/LLM/llama2/Llama-2-7b-hf/" tokenizer = LlamaTokenizer.from_pretrained(model_id) model = LlamaForCausalLM.from_pretrained(model_id, load_in_8bit=True, device_map='auto', torch_dtype=torch.float16) test_prompt = """ Summarize this dialog: A...
pipeline = pipeline(task="text-generation", model=model, tokenizer=tokenizer)## clearing out GPU memorydel model del tokenizer del pipeline gc.collect() torch.cuda.empty_cache() Run Code Online (Sandbox Code Playgroud) 还有其他人经历过吗?任何见解或指导将非常感激。
('meta-llama/lama-2-7b-hf',load_in_low_bit="nf4",# According to the QLoRA paper, using "nf4" could yield better model quality than "int4"optimize_model=False,torch_dtype=torch.bfloat16,modules_to_not_convert=["lm_head"],)model=model.to('xpu')tokenizer=LlamaTokenizer....