Update app.py

app.py

@@ -1,12 +1,54 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import transformers
+from transformers import AutoTokenizer,GenerationConfig
+import torch
+from peft import PeftModel
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+#client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+from util.llama_rope_scaled_monkey_patch import replace_llama_rope_with_scaled_rope
+        replace_llama_rope_with_scaled_rope()
+base_model = "Neko-Institute-of-Science/LLaMA-65B-HF"
+lora_weights = "adapter_config.json"
+model = transformers.AutoModelForCausalLM.from_pretrained(
+                            base_model,
+                            torch_dtype=torch.float16,
+                            cache_dir=cache_dir,
+                            device_map="auto",
+                    )
 
+model = PeftModel.from_pretrained(
+                    model,
+                    lora_weights,
+                    device_map="auto",
+                    cache_dir=cache_dir,
+                    torch_dtype=torch.float16,
+                )
+tokenizer =  AutoTokenizer.from_pretrained(base_model,use_fast=False,cache_dir=cache_dir)
+tokenizer.pad_token = tokenizer.unk_token
+model.eval()
+PROMPT_DICT = {
+    "prompt_input": (
+        "Below is an instruction that describes a task, paired with further context. "
+        "Write a response that appropriately completes the request.\n\n"
+        "Instruction:\n{instruction}\n\n Input:\n{input}\n\n Response:"
+    ),
+    "prompt_no_input": (
+        "Below is an instruction that describes a task. "
+        "Write a response that appropriately completes the request.\n\n"
+        "Instruction:\n{instruction}\n\nResponse:"
+    ),
+}
 
+def generate_prompt(instruction, input=None):
+    if input:
+        return PROMPT_DICT["prompt_input"].format(instruction=instruction,input=input)
+    else:
+        return PROMPT_DICT["prompt_no_input"].format(instruction=instruction)
+        
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -15,29 +57,55 @@ def respond(
     temperature,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    ins_f = generate_prompt(instruction,input)
+    inputs  =  tokenizer(ins_f, return_tensors="pt")
+    input_ids = inputs["input_ids"].cuda()
+    generation_config = GenerationConfig(
+            temperature=0.1,
+            top_p=0.75,
+            top_k=40,
+            do_sample=True,
+            num_beams=1,
+            max_new_tokens = 512
+        )
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # Without streaming
+    with torch.no_grad():
+        generation_output = model.generate(
+                input_ids=input_ids,
+                generation_config=generation_config,
+                return_dict_in_generate=True,
+                output_scores=False,
+                max_new_tokens=max_new_tokens,
+            )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    response = output.split("Response:")[1].strip()
+    yield response
+    
+    #messages = [{"role": "system", "content": system_message}]
 
-    messages.append({"role": "user", "content": message})
+    #for val in history:
+    #    if val[0]:
+    #        messages.append({"role": "user", "content": val[0]})
+    #    if val[1]:
+    #        messages.append({"role": "assistant", "content": val[1]})
 
-    response = ""
+    # messages.append({"role": "user", "content": message})
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    #response = ""
 
-        response += token
-        yield response
+    #for message in client.chat_completion(
+    #    messages,
+    #    max_tokens=max_tokens,
+    #    stream=True,
+    #    temperature=temperature,
+    #    top_p=top_p,
+    #):
+    #    token = message.choices[0].delta.content
+
+    #    response += token
+    #    yield response
 
 
 """