初始化项目，由ModelHub XC社区提供模型

Model: AI-ModelScope/falcon-7b-instruct
Source: Original Platform

ms_wrapper.py

@@ -0,0 +1,75 @@
+import os
+from typing import Any, Dict, Union
+
+import torch
+import transformers
+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.utils.constant import Tasks
+from modelscope.utils.logger import get_logger
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+if 'CUDA_VISIBLE_DEVICES' not in os.environ:
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+@PIPELINES.register_module(
+    Tasks.text_generation,
+    module_name='falcon-7b-instruct-text-generation-pipe')
+class falcon7binstructTextGenerationPipeline(Pipeline):
+    def __init__(self, model: Union[Model, str], *args, **kwargs):
+        model = falcon7binstructTextGeneration(model) if isinstance(
+            model, str) else model
+        super().__init__(model=model, **kwargs)
+
+    def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
+        return inputs
+
+    # define the forward pass
+    def forward(self, inputs: Dict, **forward_params) -> Dict[str, Any]:
+        return self.model(inputs)
+
+    # format the outputs from pipeline
+    def postprocess(self, input, **kwargs) -> Dict[str, Any]:
+        return input
+
+
+@MODELS.register_module(Tasks.text_generation,
+                        module_name='falcon-7b-instruct')
+class falcon7binstructTextGeneration(TorchModel):
+    def __init__(self, model_dir=None, *args, **kwargs):
+        super().__init__(model_dir, *args, **kwargs)
+        self.logger = get_logger()
+        # loading tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
+        self.pipeline = transformers.pipeline(
+            "text-generation",
+            model=model_dir,
+            tokenizer=self.tokenizer,
+            torch_dtype=torch.bfloat16,
+            trust_remote_code=True,
+            device_map="auto",
+        )
+
+    def forward(self, input: Dict) -> Dict[str, Any]:
+        output = {}
+        res = self.infer(input)
+        output['text'] = res
+        return output
+
+    def quantize(self, bits: int):
+        self.model = self.model.quantize(bits)
+        return self
+
+    def infer(self, input):
+        sequences = self.pipeline(
+                input,
+                max_length=200,
+                do_sample=True,
+                top_k=10,
+                num_return_sequences=1,
+                eos_token_id=self.tokenizer.eos_token_id,
+            )
+        return sequences