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from fastapi import FastAPI
from pydantic import BaseModel
import requests
from ctransformers import AutoModelForCausalLM
llms = {
"TinyLLama 1b 4_K_M 2048": {
"nctx": 2048,
"file": "tinyllama-1.1b-chat-v0.3.Q4_K_M.gguf",
"prefix": "### Human:",
"suffix": "### Assistant:"
},
"TinyLLama 1b OpenOrca 4_K_M 2048": {
"nctx": 2048,
"file": "tinyllama-1.1b-1t-openorca.Q4_K_M.gguf",
"prefix": "<|im_start|>system You are a helpfull assistant<|im_end|><|im_start|>user",
"suffix": "<|im_end|><|im_start|>assistant"
},
"OpenLLama 3b 4_K_M 196k": {
"nctx": 80000,
"file": "open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf",
"prefix": "### HUMAN:",
"suffix": "### RESPONSE:"
},
"Phi-2 2.7b 4_K_M 2048": {
"nctx": 2048,
"file": "phi-2.Q4_K_M.gguf",
"prefix": "Instruct:",
"suffix": "Output:"
},
"Mixtral MOE 7bx2 4_K_M 32K": {
"nctx": 32000,
"file": "mixtral_7bx2_moe.Q4_K_M.gguf",
"prefix": "",
"suffix": ""
},
"Stable Zephyr 3b 4_K_M 4096": {
"nctx": 4096,
"file": "stablelm-zephyr-3b.Q4_K_M.gguf",
"prefix": "<|user|>",
"suffix": "<|endoftext|><|assistant|>"
}
}
#Pydantic object
class validation(BaseModel):
prompt: str
llm: str
#Fast API
app = FastAPI()
@app.post("/llm_on_cpu")
async def stream(item: validation):
model = llms[item.llm]
prefix=model['prefix']
suffix=model['suffix']
nctx = model['nctx'] if 'nctx' in item.keys() else 1024
max_tokens = model['max_tokens'] if 'max_tokens' in item.keys() else 512
user="""
{prompt}"""
model = Llama(model_path="./"+model['file'], n_ctx=model['nctx'], verbose=False, n_threads=8)
prompt = f"{prefix}{user.replace('{prompt}', item.prompt)}{suffix}"
return llm(prompt, max_tokens=max_tokens) |