初始化项目，由ModelHub XC社区提供模型

Model: AI-ModelScope/Vicuna-7B
Source: Original Platform

ms_wrapper.py

@@ -0,0 +1,77 @@
+import os
+from typing import Any, Dict, Union
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
+
+from modelscope.models.base import Model, TorchModel
+from modelscope.models.builder import MODELS
+from modelscope.pipelines.base import Pipeline
+from modelscope.pipelines.builder import PIPELINES
+from modelscope.pipelines.nlp.text_generation_pipeline import \
+    TextGenerationPipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.logger import get_logger
+
+Vicuna_PROMPT_FORMAT = "### Human:\n{prompt} \n ### Assistant:\n"
+
+@PIPELINES.register_module(Tasks.text_generation,
+                           module_name='Vicuna7b-text-generation-pipe')
+class Vicuna7bTextGenerationPipeline(TextGenerationPipeline):
+    def __init__(self, model: Union[Model, str], *args, **kwargs):
+        model = Vicuna7bTextGeneration(model) if isinstance(model,
+                                                            str) else model
+        super().__init__(model=model, **kwargs)
+
+    def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
+        return inputs
+
+    def _sanitize_parameters(self, **pipeline_parameters):
+        return {}, pipeline_parameters, {}
+
+    # define the forward pass
+    def forward(self, inputs: Dict, **forward_params) -> Dict[str, Any]:
+        return self.model(inputs, **forward_params)
+
+    # format the outputs from pipeline
+    def postprocess(self, input, **kwargs) -> Dict[str, Any]:
+        return input
+
+
+@MODELS.register_module(Tasks.text_generation, module_name='Vicuna7b')
+class Vicuna7bTextGeneration(TorchModel):
+    def __init__(self, model_dir=None, *args, **kwargs):
+        super().__init__(model_dir, *args, **kwargs)
+        self.logger = get_logger()
+        # loading tokenizer
+        self.tokenizer = LlamaTokenizer.from_pretrained(model_dir,
+                                                       use_fast=False)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_dir,
+            low_cpu_mem_usage=True,
+            device_map="auto",
+            torch_dtype=torch.float16)
+        self.model = self.model.eval()
+
+    def forward(self, input: Dict, *args, **kwargs) -> Dict[str, Any]:
+        output = {}
+        res = self.infer(input, **kwargs)
+        output['text'] = res
+        return output
+
+    def quantize(self, bits: int):
+        self.model = self.model.quantize(bits)
+        return self
+
+    def infer(self, input, max_new_tokens=1024, **kwargs):
+        kwargs['max_new_tokens'] = max_new_tokens
+        device = self.model.device
+        input = Vicuna_PROMPT_FORMAT.format(prompt=input)
+        input_ids = self.tokenizer(input,
+                                   return_tensors="pt").input_ids.to(device)
+        output_ids = self.model.generate(input_ids, **kwargs)
+        output_ids = output_ids[0][len(input_ids[0]):]
+
+        outputs = self.tokenizer.decode(output_ids,
+                                        skip_special_tokens=True).strip()
+        return outputs