LM格式强制正则表达式生成¶
使用lm-format-enforcer通过LlamaIndex生成结构化数据。
通过lm-format-enforcer，您可以通过强制LLM输出所需的标记来确保输出结构的正确性。 当您使用容量较低的模型（例如当前的开源模型）时，这将特别有帮助，否则模型将难以生成符合所需输出模式的有效输出。
lm-format-enforcer支持正则表达式和JSON模式，本演示重点介绍正则表达式。有关JSON模式+Pydantic的信息，请参阅示例Pydantic程序笔记本。
如果您在colab上打开这个笔记本，您可能需要安装LlamaIndex 🦙。
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%pip install llama-index-llms-llama-cpp
%pip install llama-index-llms-llama-cpp
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!pip install llama-index lm-format-enforcer llama-cpp-python
!pip install llama-index lm-format-enforcer llama-cpp-python
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import lmformatenforcer
import re
from llama_index.core.prompts.lmformatenforcer_utils import (
activate_lm_format_enforcer,
build_lm_format_enforcer_function,
)
import lmformatenforcer
import re
from llama_index.core.prompts.lmformatenforcer_utils import (
activate_lm_format_enforcer,
build_lm_format_enforcer_function,
)
定义输出格式
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regex = r'"Hello, my name is (?P<name>[a-zA-Z]*)\. I was born in (?P<hometown>[a-zA-Z]*). Nice to meet you!"'
regex = r'"Hello, my name is (?P[a-zA-Z]*)\. I was born in (?P[a-zA-Z]*). Nice to meet you!"'
创建模型。在这个演示中，我们使用LlamaCPP作为LLM，但也支持HuggingFaceLLM。
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from llama_index.llms.llama_cpp import LlamaCPP
llm = LlamaCPP()
from llama_index.llms.llama_cpp import LlamaCPP
llm = LlamaCPP()
llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from /mnt/wsl/PHYSICALDRIVE1p3/llama_index/models/llama-2-13b-chat.Q4_0.gguf (version GGUF V2 (latest)) llama_model_loader: - tensor 0: token_embd.weight q4_0 [ 5120, 32000, 1, 1 ] llama_model_loader: - tensor 1: blk.0.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 2: blk.0.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 3: blk.0.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 4: blk.0.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 5: blk.0.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 6: blk.0.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 7: blk.0.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 8: blk.0.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 9: blk.0.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 10: blk.1.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 11: blk.1.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 12: blk.1.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 13: blk.1.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 14: blk.1.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 15: blk.1.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 16: blk.1.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 17: blk.1.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 18: blk.1.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 19: blk.10.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 20: blk.10.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 21: blk.10.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 22: blk.10.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 23: blk.10.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 24: blk.10.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 25: blk.10.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 26: blk.10.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 27: blk.10.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 28: blk.11.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 29: blk.11.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 30: blk.11.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 31: blk.11.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 32: blk.11.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 33: blk.11.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 34: blk.11.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 35: blk.11.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 36: blk.11.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 37: blk.12.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 38: blk.12.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 39: blk.12.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 40: blk.12.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 41: blk.12.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 42: blk.12.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 43: blk.12.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 44: blk.12.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 45: blk.12.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 46: blk.13.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 47: blk.13.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 48: blk.13.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 49: blk.13.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 50: blk.13.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 51: blk.13.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 52: blk.13.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 53: blk.13.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 54: blk.13.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 55: blk.14.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 56: blk.14.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 57: blk.14.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 58: blk.14.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 59: blk.14.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 60: blk.14.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 61: blk.14.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 62: blk.14.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 63: blk.14.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 64: blk.15.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 65: blk.15.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 66: blk.2.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 67: blk.2.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 68: blk.2.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 69: blk.2.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 70: blk.2.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 71: blk.2.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 72: blk.2.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 73: blk.2.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 74: blk.2.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 75: blk.3.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 76: blk.3.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 77: blk.3.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 78: blk.3.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 79: blk.3.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 80: blk.3.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 81: blk.3.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 82: blk.3.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 83: blk.3.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 84: blk.4.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 85: blk.4.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 86: blk.4.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 87: blk.4.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 88: blk.4.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 89: blk.4.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 90: blk.4.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 91: blk.4.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 92: blk.4.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 93: blk.5.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 94: blk.5.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 95: blk.5.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 96: blk.5.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 97: blk.5.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 98: blk.5.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 99: blk.5.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 100: blk.5.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 101: blk.5.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 102: blk.6.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 103: blk.6.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 104: blk.6.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 105: blk.6.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 106: blk.6.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 107: blk.6.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 108: blk.6.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 109: blk.6.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 110: blk.6.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 111: blk.7.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 112: blk.7.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 113: blk.7.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 114: blk.7.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 115: blk.7.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 116: blk.7.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 117: blk.7.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 118: blk.7.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 119: blk.7.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 120: blk.8.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 121: blk.8.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 122: blk.8.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 123: blk.8.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 124: blk.8.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 125: blk.8.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 126: blk.8.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 127: blk.8.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 128: blk.8.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 129: blk.9.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 130: blk.9.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 131: blk.9.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 132: blk.9.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 133: blk.9.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 134: blk.9.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 135: blk.9.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 136: blk.9.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 137: blk.9.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 138: blk.15.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 139: blk.15.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 140: blk.15.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 141: blk.15.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 142: blk.15.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 143: blk.15.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 144: blk.15.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 145: blk.16.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 146: blk.16.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 147: blk.16.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 148: blk.16.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 149: blk.16.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 150: blk.16.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 151: blk.16.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 152: blk.16.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 153: blk.16.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 154: blk.17.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 155: blk.17.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 156: blk.17.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 157: blk.17.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 158: blk.17.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 159: blk.17.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 160: blk.17.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 161: blk.17.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 162: blk.17.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 163: blk.18.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 164: blk.18.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 165: blk.18.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 166: blk.18.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - 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tensor 349: blk.38.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 350: blk.38.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 351: blk.38.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 352: blk.38.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 353: blk.39.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 354: blk.39.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 355: blk.39.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 356: blk.39.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 357: blk.39.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 358: blk.39.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 359: blk.39.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 360: blk.39.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 361: blk.39.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 362: output_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - kv 0: general.architecture str llama_model_loader: - kv 1: general.name str llama_model_loader: - kv 2: llama.context_length u32 llama_model_loader: - kv 3: llama.embedding_length u32 llama_model_loader: - kv 4: llama.block_count u32 llama_model_loader: - kv 5: llama.feed_forward_length u32 llama_model_loader: - kv 6: llama.rope.dimension_count u32 llama_model_loader: - kv 7: llama.attention.head_count u32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 llama_model_loader: - kv 10: general.file_type u32 llama_model_loader: - kv 11: tokenizer.ggml.model str llama_model_loader: - kv 12: tokenizer.ggml.tokens arr llama_model_loader: - kv 13: tokenizer.ggml.scores arr llama_model_loader: - kv 14: tokenizer.ggml.token_type arr llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 llama_model_loader: - kv 18: general.quantization_version u32 llama_model_loader: - type f32: 81 tensors llama_model_loader: - type q4_0: 281 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_print_meta: format = GGUF V2 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 4096 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 40 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: n_ff = 13824 llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: model type = 13B llm_load_print_meta: model ftype = mostly Q4_0 llm_load_print_meta: model params = 13.02 B llm_load_print_meta: model size = 6.86 GiB (4.53 BPW) llm_load_print_meta: general.name = LLaMA v2 llm_load_print_meta: BOS token = 1 '<s>' llm_load_print_meta: EOS token = 2 '</s>' llm_load_print_meta: UNK token = 0 '<unk>' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0.12 MB llm_load_tensors: mem required = 7024.01 MB ................................................................................................... llama_new_context_with_model: n_ctx = 3900 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_new_context_with_model: kv self size = 3046.88 MB llama_new_context_with_model: compute buffer total size = 348.18 MB AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
激活格式强制器并以所需的正则表达式格式运行LLM获取结构化输出。只要我们在with activate_lm_format_enforcer(...)块内，LLM将会输出所需的格式。
如果我们使用了lmformatenforcer.JsonSchemaParser和一个JSON模式，我们将会得到JSON输出。
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regex_parser = lmformatenforcer.RegexParser(regex)
lm_format_enforcer_fn = build_lm_format_enforcer_function(llm, regex_parser)
with activate_lm_format_enforcer(llm, lm_format_enforcer_fn):
output = llm.complete(
"Here is a way to present myself, if my name was John and I born in Boston: "
)
regex_parser = lmformatenforcer.RegexParser(regex)
lm_format_enforcer_fn = build_lm_format_enforcer_function(llm, regex_parser)
with activate_lm_format_enforcer(llm, lm_format_enforcer_fn):
output = llm.complete(
"Here is a way to present myself, if my name was John and I born in Boston: "
)
llama_print_timings: load time = 2709.44 ms llama_print_timings: sample time = 7.26 ms / 22 runs ( 0.33 ms per token, 3031.56 tokens per second) llama_print_timings: prompt eval time = 2709.40 ms / 21 tokens ( 129.02 ms per token, 7.75 tokens per second) llama_print_timings: eval time = 3047.28 ms / 21 runs ( 145.11 ms per token, 6.89 tokens per second) llama_print_timings: total time = 5965.41 ms
输出是一个字符串，根据正则表达式，我们可以解析并从中提取参数。
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print(output)
print(re.match(regex, output.text).groupdict())
print(output)
print(re.match(regex, output.text).groupdict())
"Hello, my name is John. I was born in Boston, Nice to meet you!"
{'name': 'John', 'hometown': 'Boston'}