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from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
import prompt_style
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# **************************************************
# import transformers
# import torch
# pipeline = transformers.pipeline(
# "text-generation",
# model=model_id,
# model_kwargs={"torch_dtype": torch.bfloat16},
# device_map="auto",
# )
def generate_1(item: Item):
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
messages,
max_new_tokens=item.max_new_tokens,
eos_token_id=terminators,
do_sample=True,
temperature=item.temperature,
top_p=item.top_p,
)
return outputs[0]["generated_text"][-1]
# **************************************************
client = InferenceClient(model_id)
class Item(BaseModel):
prompt: str
history: list
system_prompt: str
temperature: float = 0.6
max_new_tokens: int = 1024
top_p: float = 0.95
seed : int = 42
app = FastAPI()
def format_prompt(item: Item):
messages = [
{"role": "system", "content": prompt_style.data},
]
for it in item.history:
messages.append[{"role" : "user", "content": it[0]}]
messages.append[{"role" : "assistant", "content": it[1]}]
return messages
def generate(item: Item):
temperature = float(item.temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(item.top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=item.max_new_tokens,
top_p=top_p,
repetition_penalty=item.repetition_penalty,
do_sample=True,
seed=item.seed,
)
formatted_prompt = format_prompt(item)
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto",)
input_ids = tokenizer.apply_chat_template(formatted_prompt, add_generation_prompt=True, return_tensors="pt").to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(input_ids, eos_token_id=terminators, do_sample=True, **generate_kwargs)
response = outputs[0][input_ids.shape[-1]:]
return tokenizer.decode(response, skip_special_tokens=True)
# stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
# output = ""
# for response in stream:
# output += response.token.text
# return output
@app.post("/generate/")
async def generate_text(item: Item):
ans = generate(item)
return {"response": ans} |