初始化项目，由ModelHub XC社区提供模型

Model: verseAI/databricks-dolly-v2-3b
Source: Original Platform

tester.py

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+import pathlib
+
+import torch
+from transformers import pipeline
+
+from langchain import PromptTemplate, LLMChain
+from langchain.llms import HuggingFacePipeline
+
+def getText():
+    s = '''
+    A US climber has died on his way to scale Mount Everest on Monday, according to an expedition organizer.
+
+“Jonathan Sugarman died at Camp 2 after he began to feel unwell,” Pasang Sherpa told CNN on Tuesday.
+
+Seattle-based Sugarman was part of an expedition arranged by Washington state-based International Mountain Guides (IMG) with Beyul Adventure handling the local logistics.
+
+Sherpa added that “his body remains at Camp 2 with the rest of the climbing team.”
+
+This comes after Nepal has issued permits for a record 463 climbers by April 26, for this spring season’s expeditions to Mount Everest.
+
+Following Sugarman’s death, the Embassy of the United States issued a statement. “We can confirm Dr. Jonathan Sugarman passed away while climbing Mt. Everest Monday May 1,” it said. “Our deepest sympathies go out to his family and friends.
+
+“The Embassy is in contact with Dr. Sugarman’s family and with local authorities. Out of respect for the family’s privacy, we cannot comment further,” read a statement sent to CNN by an Embassy spokesperson.
+'''
+    
+    return s
+
+def mainSimple():
+    print("\nIn main simple...")
+    # print(pathlib.Path().resolve())
+    # generate_text_pipline = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
+    # generate_text_pipline = pipeline(model="verseAI/databricks-dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
+    workingDir = pathlib.Path().resolve()
+    generate_text_pipline = pipeline(model=workingDir, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True)
+    inputText = getText()
+    resp = generate_text_pipline(inputText)
+    respStr = resp[0]["generated_text"] 
+    
+    print(f'\nInput: {inputText}')
+    print(f'\nResponse: {respStr}')
+    
+    print("\nAll Done!\n")
+
+def getInstrAndContext():
+    context = '''George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman,
+and Founding Father who served as the first president of the United States from 1789 to 1797.'''
+    # instr = '''When was George Washington president?'''
+    instr = '''What do you think?'''
+
+    return instr, context
+
+def mainContext():
+    print("\nIn main context...")
+    workingDir = pathlib.Path().resolve()
+    generate_text_pipline = pipeline(model=workingDir, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True)
+
+    # template for an instrution with no input
+    prompt = PromptTemplate(
+        input_variables=["instruction"],
+        template="{instruction}")
+
+    # template for an instruction with input
+    prompt_with_context = PromptTemplate(
+        input_variables=["instruction", "context"],
+        template="{instruction}\n\nInput:\n{context}")
+
+    hf_pipeline = HuggingFacePipeline(pipeline=generate_text_pipline)
+
+    llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
+    llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)
+
+    instr, context = getInstrAndContext()
+    resp = ''
+    
+    if(context and not context.isspace()):
+        resp = llm_context_chain.predict(instruction=instr, context=context).lstrip()
+    else:
+        resp = llm_chain.predict(instruction=instr).lstrip()
+
+    print(f'\nInput-Context: {context}')
+    print(f'\nInput-Instr: {instr}')
+    print(f'\nResponse: {resp}')
+    
+    print("\nAll Done!\n") 
+
+if __name__ == "__main__":
+    mainContext()