Delete mistral_model.py

mistral_model.py

@@ -1,85 +0,0 @@
-import warnings
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-from transformers.models.mistral.modeling_mistral import MistralForCausalLM
-from transformers.models.llama.tokenization_llama_fast import LlamaTokenizerFast
-
-model_name = "mistralai/Mistral-7B-Instruct-v0.2"
-
-quantization_config = BitsAndBytesConfig(load_in_4bit=True)
-model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
-
-from langchain.llms.base import LLM
-from langchain.callbacks.manager import CallbackManagerForLLMRun
-from typing import Optional, List, Mapping, Any
-
-class CustomLLMMistral(LLM):
-    model: MistralForCausalLM
-    tokenizer: LlamaTokenizerFast
-
-    @property
-    def _llm_type(self) -> str:
-        return "custom"
-
-    def _call(self, prompt: str, stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None) -> str:
-
-        messages = [
-         {"role": "user", "content": prompt},
-        ]
-
-        encodeds = self.tokenizer.apply_chat_template(messages, return_tensors="pt")
-        model_inputs = encodeds.to(self.model.device)
-
-        generated_ids = self.model.generate(model_inputs, max_new_tokens=512, do_sample=True, pad_token_id=tokenizer.eos_token_id, top_k=4, temperature=0.7)
-        decoded = self.tokenizer.batch_decode(generated_ids)
-
-        output = decoded[0].split("[/INST]")[1].replace("</s>", "").strip()
-
-        if stop is not None:
-          for word in stop:
-            output = output.split(word)[0].strip()
-
-        while not output.endswith("```"):
-          output += "`"
-
-        return output
-
-    @property
-    def _identifying_params(self) -> Mapping[str, Any]:
-        return {"model": self.model}
-
-llm = CustomLLMMistral(model=model, tokenizer=tokenizer)
-
-import numexpr as ne
-from langchain.tools import WikipediaQueryRun, BaseTool
-from langchain_community.utilities import WikipediaAPIWrapper
-
-wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=2500))
-
-print(wikipedia.run("Deep Learning"))
-
-
-wikipedia_tool = Tool(
-    name="wikipedia",
-    description="Never search for more than one concept at a single step. If you need to compare two concepts, search for each one individually. Syntax: string with a simple concept",
-    func=wikipedia.run
-)
-
-class Calculator(BaseTool):
-    name = "calculator"
-    description = "Use this tool for math operations. It requires numexpr syntax. Use it always you need to solve any math operation. Be sure syntax is correct."
-
-    def _run(self, expression: str):
-      try:
-        return ne.evaluate(expression).item()
-      except Exception:
-        return "This is not a numexpr valid syntax. Try a different syntax."
-
-    def _arun(self, radius: int):
-        raise NotImplementedError("This tool does not support async")
-
-calculator_tool = Calculator()
-
-calculator_tool.run("2+3")