Add handler and custom requirements.txt

handler.py

@@ -0,0 +1,38 @@
+from typing import Dict, Any
+from transformers import AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+        quantization_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=torch.float16
+        )
+        # load model and processor from path
+        self.model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        """
+        Args:
+            data (:dict:):
+                The payload with the text prompt and generation parameters.
+        """
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+        inputs = f"[INST] {inputs} [/INST]"
+
+        # preprocess
+        input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.model.generate(input_ids, **parameters)
+        else:
+            outputs = self.model.generate(input_ids)
+
+        # postprocess the prediction
+        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return [{"generated_text": prediction}]

requirements.txt

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+git+https://github.com/huggingface/transformers
+bitsandbytes