f_commit

mistral_model

@@ -0,0 +1,85 @@
+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")