当use_cache=True时,会返回这个参数,可选参数。 inputs_embeds:直接传入嵌入表示而不是input_ids,形状为(batch_size, sequence_length, hidden_size)的torch.FloatTensor,可选参数。 use_cache:是否使用缓存加速解码的布尔值,当设置为True时,past_key_values的键值状态将被返回,用于加速解码,可选参数。 output_att...
use_cache 是一个布尔值,表示是否使用缓存。 首先调用 extract_key_value 方法从 hidden 中提取注意力计算所需的键、值和权重,得到 receptance, key, value, state。 如果state 不为None,则提取 state 中的状态信息,准备传递给注意力计算函数 rwkv_linear_attention。 调用rwkv_linear_attention 函数计算注意力并...
1.1-use_cacheis most of the case set toTrueon all model configs, therefore it will pass this logic: transformers/src/transformers/models/llama/modeling_llama.py Line 1004 in2788f8d ifuse_cache: and the model will create a non-Nonepast_key_values. ...
type_vocab_size: 类型词汇表的大小。 use_cache: 是否使用缓存。 vocab_size: 词汇表的大小。 上述选项只是一些常见的选项,具体的选项和对应的值会因不同的模型类型和架构而有所不同。除了上述字段之外,config.json文件还可能包含其他特定于模型的配置信息。在加载模型时,可以使用AutoConfig.from_pretrained()方法...
"layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "position_embedding_type": "absolute", "transformers_version": "4.8.1", "type_vocab_size": 2, "use_cache": true, "vocab...
"intermediate_size":3072,"layer_norm_eps":1e-12,"max_position_embeddings":512,"model_type":"bert","num_attention_heads":12,"num_hidden_layers":12,"pad_token_id":0,"position_embedding_type":"absolute","transformers_version":"4.3.3","type_vocab_size":2,"use_cache":true,"vocab_...
1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "position_embedding_type": "absolute", "transformers_version": "4.6.0.dev0", "type_vocab_size": 2, "use_cache": true, "vocab_size": 30522...
"hidden_size":4096,"inner_hidden_size":16384,"layernorm_epsilon":1e-05,"max_sequence_length":2048,"model_type":"chatglm","num_attention_heads":32,"num_layers":28,"position_encoding_2d": true,"torch_dtype":"float16","transformers_version":"4.23.1","use_cache": true,"vocab_size"...
185 if "use_cache" in inspect.signature(model_forward).parameters.keys():186 model_inputs["use_cache"] = False--> 187 return self.model(**model_inputs) File ~\miniconda3\envs\npu-infer\Lib\site-packages\optimum\modeling_base.py:92, in OptimizedModel.__call__...
from_pretrained(model_id, device_map=device) # use static cache, enabling automatically torch compile with fullgraph and reduce-overhead model.generation_config.max_length = 250 # big enough to avoid recompilation model.generation_config.max_new_tokens = None # would take precedence over max_...