S

copy_main.py

@@ -0,0 +1,57 @@
+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from huggingface_hub import InferenceClient
+import uvicorn
+from typing import Generator
+import json  # Asegúrate de que esta línea esté al principio del archivo
+
+app = FastAPI()
+
+# Initialize the InferenceClient with your model
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+class Item(BaseModel):
+    prompt: str
+    history: list
+    system_prompt: str
+    temperature: float = 0.8
+    max_new_tokens: int = 9000
+    top_p: float = 0.15
+    repetition_penalty: float = 1.0
+
+def format_prompt(message, history):
+    prompt = "<s>"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def generate_stream(item: Item) -> Generator[bytes, None, None]:
+    formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
+    generate_kwargs = {
+        "temperature": item.temperature,
+        "max_new_tokens": item.max_new_tokens,
+        "top_p": item.top_p,
+        "repetition_penalty": item.repetition_penalty,
+        "do_sample": True,
+        "seed": 42,  # Adjust or omit the seed as needed
+    }
+
+    # Stream the response from the InferenceClient
+    for response in client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True):
+        # This assumes 'details=True' gives you a structure where you can access the text like this
+        chunk = {
+            "text": response.token.text,
+            "complete": response.generated_text is not None  # Adjust based on how you detect completion
+        }
+        yield json.dumps(chunk).encode("utf-8") + b"\n"
+
+@app.post("/generate/")
+async def generate_text(item: Item):
+    # Stream response back to the client
+    return StreamingResponse(generate_stream(item), media_type="application/x-ndjson")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)

main.py

@@ -20,13 +20,16 @@ class Item(BaseModel):
     top_p: float = 0.15
     repetition_penalty: float = 1.0
 
-def format_prompt(message, history):
-    prompt = "<s>"
-    for user_prompt, bot_response in history:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-    prompt += f"[INST] {message} [/INST]"
-    return prompt
+def format_prompt(current_prompt, history):
+    formatted_history = "<s>"
+    for entry in history:
+        if entry["role"] == "user":
+            formatted_history += f"[USER] {entry['content']} [/USER]"
+        elif entry["role"] == "assistant":
+            formatted_history += f"[ASSISTANT] {entry['content']} [/ASSISTANT]"
+    formatted_history += f"[USER] {current_prompt} [/USER]</s>"
+    return formatted_history
+
 
 def generate_stream(item: Item) -> Generator[bytes, None, None]:
     formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)