Create huggingface.py

application/llm/huggingface.py

@@ -0,0 +1,31 @@
+from application.llm.base import BaseLLM
+
+class HuggingFaceLLM(BaseLLM):
+
+    def __init__(self, api_key, llm_name='Arc53/DocsGPT-7B'):
+        global hf
+
+        from langchain.llms import HuggingFacePipeline
+        from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+        tokenizer = AutoTokenizer.from_pretrained(llm_name)
+        model = AutoModelForCausalLM.from_pretrained(llm_name)
+        pipe = pipeline(
+            "text-generation", model=model,
+            tokenizer=tokenizer, max_new_tokens=2000,
+            device_map="auto", eos_token_id=tokenizer.eos_token_id
+        )
+        hf = HuggingFacePipeline(pipeline=pipe)
+
+    def gen(self, model, engine, messages, stream=False, **kwargs):
+        context = messages[0]['content']
+        user_question = messages[-1]['content']
+        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+
+        result = hf(prompt)
+
+        return result.content
+
+    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
+
+        raise NotImplementedError("HuggingFaceLLM Streaming is not implemented yet.")
+