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handler1.py

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+# Code to inference Hermes with HF Transformers
+# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
+
+import torch
+#from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import LlamaTokenizer, MixtralForCausalLM
+import bitsandbytes, flash_attn
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.tokenizer = LlamaTokenizer.from_pretrained(path, trust_remote_code=True)
+        self.model = MixtralForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.float16,
+            device_map="auto",
+            load_in_8bit=False,
+            load_in_4bit=True,
+            use_flash_attention_2=True
+            )
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        sys_prompt=data["prompt"]
+        list=data["inputs"]
+        prompt=f"<|im_start|>system\n{sys_prompt}.<|im_end|>\n"
+        for item in list:
+            if item["role"]=="assistant":
+                content=item["content"]
+                prompt+=f"<|im_start|>assistant\n{content}<|im_end|>\n"
+            else:
+                content=item["content"]
+                prompt+=f"<|im_start|>user\n{content}<|im_end|>\n"
+        prompt+="<|im_start|>assistant\n"
+
+        #for chat in prompts:
+            #print(chat)
+        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
+        generated_ids = self.model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=self.tokenizer.eos_token_id)
+        response = self.tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
+        print(f"Response: {response}")
+
+        """
+        encodeds = self.tokenizer.encode(prompt, return_tensors="pt")
+        model_inputs = encodeds.to(device)
+        self.model.to(device)
+        generated_ids = self.model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
+        decoded = self.tokenizer.decode(generated_ids[0])
+        return decoded
+        """
+

requirements.txt

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+pytorch
+transformers
+bitsandbytes
+sentencepiece
+protobuf
+flash-attn