from_pretrained(model_path) 这部分代码从本地路径 "./model_llama2" 加载一个预训练的分词器和因果语言模型。这通常是在你已经训练或微调过一个模型并保存到这个路径的情况下使用。 4. 初始化文本生成管道 generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda...
bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.bfloat16,)# Load the tokenizer and model from the fine-tuned model directorymodel_path="/content/drive/MyDrive/Machine Learning/model_llama3"model=Auto
MODEL_PATH="llama-2-7b"elif[[$m=="7B-chat"]];thenSHARD=0 MODEL_PATH="llama-2-7b-chat"elif[[$m=="13B"]];thenSHARD=1 MODEL_PATH="llama-2-13b"elif[[$m=="13B-chat"]];thenSHARD=1 MODEL_PATH="llama-2-13b-chat"elif[[$m=="70B"]];thenSHARD=7 MODEL_PATH="llama-2-70b"eli...
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer model_path = "LinkSoul/Chinese-Llama-2-7b" tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_path).half().cuda() streamer = TextStreamer(tokenizer, ski...
llm = LlamaCpp(model_path="./models/llama-7b.ggmlv3.q4_0.bin") embeddings = LlamaCppEmbeddings(model_path="models/llama-7b.ggmlv3.q4_0.bin") llm_chain = LLMChain(llm=llm, prompt=prompt) st.title("📄 Document...
llm=LlamaCpp(model_path="./models/llama-7b.ggmlv3.q4_0.bin")embeddings=LlamaCppEmbeddings(model_path="models/llama-7b.ggmlv3.q4_0.bin")llm_chain=LLMChain(llm=llm,prompt=prompt)st.title("📄 Document Conversation 🤖")uploaded_file=st.file_uploader("Upload an article",type="txt")if...
model_path=model_file, temperature=0.1, n_gpu_layers=0, n_batch=256, callback_manager=callback_manager, verbose=True, ) defllm_start(question:str): """ Ask LLM a question """ globalllm, template prompt=PromptTemplate(template=template, input_variables=["question"]) ...
MEGATRON_PATCH_PATH=$2 设置开源Megatron的代码路径。 MODEL_SIZE=$3 模型结构参数量级:8B、70B。 BATCH_SIZE=$4 每卡训练一次迭代样本数:4或8。 GLOBAL_BATCH_SIZE=$5 全局Batch Size。 LR=$6 学习率:1e-5或5e-5。 MIN_LR=$7 最小学习率:1e-6或5e-6。 SEQ_LEN=$8 序列长度。 PAD_LEN=${...
model_path="llama-2-7b.Q4_K_M.gguf", n_gpu_layers=n_gpu_layers, n_batch=n_batch, callback_manager=callback_manager, verbose=True,#Verbose is required to pass to the callback manager) llm_chain= LLMChain(prompt=prompt, llm=llm) ...
embeddings = LlamaCppEmbeddings(model_path="models/llama-7b.ggmlv3.q4_0.bin") llm_chain = LLMChain(llm=llm, prompt=prompt) st.title("📄 Document Conversation 🤖") uploaded_file = st.file_uploader("Upload an article", type="txt") ...