( model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True, ) stop_token_ids = [151329, 151336, 151338] sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids) inputs = tokenizer....
# 如果遇见 OOM 现象,建议减少max_model_len,或者增加tp_size max_model_len, tp_size = 131072, 1 model_name = "THUDM/glm-4-9b-chat" prompt = [{"role": "user", "content": "你好"}] tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) llm = LLM( model=mode...
from vllm import LLM, SamplingParams from modelscope import snapshot_download # GLM-4-9B-Chat max_model_len, tp_size = 131072, 1 model_name = snapshot_download("ZhipuAI/glm-4-9b-chat") prompt = '你好' tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) llm ...
model_name = "BAAI/bge-large-zh-v1.5" model_kwargs = {"device": "cpu"} encode_kwargs = {"normalize_embeddings": True} bgeEmbeddings = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) from langchain_community....
( model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True, ) stop_token_ids = [151329, 151336, 151338] sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids) inputs = tokenizer....
由于GQA相比MHA有更少的参数,把FFN的大小增加到10/3的hidden size来保持模型总参数基本不变。 3.Alignment SFT中,发现真实的人类prompt和交互比template-based的人造数据和模型生成的答案要好得多。 4.ChatGLM Techniques 在训练ChatGLM的路上,智谱总结了不少经验: LongAlign:《Longalign: A recipe for long ...
model_name="glm-4", ) 2、加载文档 这里特定领域用户的数据来源于一个示例的ordersample.csv文件,这个文件可以从我的github上获取:https://github.com/taoxibj/docs/blob/main/ordersample.csv 文件具体内容如下: 把orersample.csv下载到jupyter notebook当前ipynb文件目录,使用CSV文档加载器,加载文档内容: ...
per_device_train_batch_size:顾名思义batch_size gradient_accumulation_steps: 梯度累加,如果你的显存比较小,那可以把batch_size设置小一点,梯度累加增大一些。 logging_steps:多少步,输出一次log num_train_epochs:顾名思义epoch gradient_checkpointing:梯度检查,这个一旦开启,模型就必须执行model.enable_input_requi...
"hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 11008, "kv_channels": 128, "layer_norm_epsilon": 1e-06, "max_position_embeddings": 8192, "model_type": "qwen", "no_bias": true, "num_attention_heads": 16, ...
model_name_or_path: src/llamafactory/model/model/glm4-chat num_train_epochs: 2.0 optim: adamw_torch output_dir: saves/GLM-4-9B-Chat/lora/train_2024-06-24-23-30-00 packing: false per_device_eval_batch_size: 1 per_device_train_batch_size: 1 ...