1、LlamaModel 1、初始化方法 2、 input_embeddings: 3、 注意力掩码: 3.1 输入 3.2 过程 4、forward 4.1 处理默认参数 4.2 输入数据处理 4.3 位置编码(positional embedding) 4.4 输入嵌入 4.5 自注意力掩码 4.6 Gradient Checkpoint 4.7 解码器层的循环迭代部分 2、LlamaDecoderLayer 1、初始化方法 2、forward...
[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]...
请使用llm install model-name的格式,在命令行安装插件。例如: 复制 llm install llm-gpt4all 1. 接着,您可以使用命令llm models list,查看所有可用的远程或已安装的模型。如下列表所示,其中还包含了每个型号的简要信息。 您可以通过使用以下语法,向本地LLM发送查询请求: 复制 llm -m the-model-name "Your que...
temperature: float = 0.6,top_p: float = 0.9,) -> str:llm = Replicate (model=model,model_kwargs={"temperature": temperature,"top_p": top_p, "max_new_tokens": 1000} return llm (prompt)def chat_completion (messages: List [Dict],model = DEFAULT_MODEL,temperature: float = 0.6,to...
Ollama 是一个基于 Go 语言的本地大语言模型运行框架,类 docker 产品(支持 list,pull,push,run 等命令),事实上它保留了 Docker 的操作习惯,支持上传大语言模型仓库 (有 deepseek、llama 2,mistral,qwen 等模型,你也可以自定义模型上传)。 在管理模型的同时,它还提供了一些 Api 接口,让你能够像调用 OpenAI ...
Breaking change Proposed change Updates the builtin list of models for Ollama from: https://ollama.com/library Type of change Dependency upgrade Bugfix (non-breaking change which fixes an iss...
model = torch.load("Meta-Llama-3-8B/consolidated.00.pth")print(json.dumps(list(model.keys())[:20], indent=4))["tok_embeddings.weight","layers.0.attention.wq.weight","layers.0.attention.wk.weight","layers.0.attention.wv.weight","layers.0.attention.wo.weight","layers.0.feed_forward....
create Create a model from a Modelfile show Show information for a model run Run a model pull Pull a model from a registry push Push a model to a registry list List models cp Copy a model rm Remove a model help...
typing import Any, List, Optionalfrom langchain.callbacks.manager import CallbackManagerForLLMRunfrom transformers import AutoTokenizer, AutoModelForCausalLM, RagTokenizer, RagRetriever, RagSequenceForGenerationimport torchclass LLaMA3_LLM(LLM): tokenizer: AutoTokenizer = None model: AutoModelFor...
model = torch.load ("Meta-Llama-3-8B/consolidated.00.pth")print (json.dumps (list (model.keys ())[:20], indent=4)) ["tok_embeddings.weight","layers.0.attention.wq.weight","layers.0.attention.wk.weight","layers.0.attention...