File size: 1,156 Bytes
9f4e457
ac58288
9f4e457
27d8de0
9f4e457
27d8de0
 
 
2d88192
27d8de0
9f4e457
27d8de0
9f4e457
27d8de0
 
9f4e457
27d8de0
 
9f4e457
 
ac58288
 
 
 
 
 
 
 
 
 
 
 
 
 
27d8de0
9f4e457
27d8de0
bdf7da6
9f4e457
27d8de0
9f4e457
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
from fastapi import FastAPI, Form
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from ctransformers import AutoModelForCausalLM

#Model loading
llm = AutoModelForCausalLM.from_pretrained("zephyr-7b-beta.Q4_K_S.gguf",
model_type='mistral',
max_new_tokens = 256,
threads = 3,
)
   

#Pydantic object
class validation(BaseModel):
    prompt: str
    
#Fast API
app = FastAPI()

# Set up CORS
origins = [
    "http://localhost",           # Replace with the address of your Flutter web app
    "http://localhost:55345",     # Add the port used by your Flutter web app
]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

#Zephyr completion
@app.post("/llm_on_cpu")
async def stream(item: validation):
    system_prompt = 'Your name is Maff, a mentor who teaches people how to make money. Follow these guideline- Keep your sentences short.'
    E_INST = "</s>"
    user, assistant = "<|user|>", "<|assistant|>"
    prompt = f"{system_prompt}{E_INST}\n{user}\n{item.prompt.strip()}{E_INST}\n{assistant}\n"
    return llm(prompt)