Spaces:
Running
Running
File size: 1,912 Bytes
74cb225 |
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 44 45 46 47 48 49 50 51 52 53 54 55 56 |
import sys
import os
abs_path = os.getcwd()
sys.path.append(abs_path) # Adds higher directory to python modules path.
from transformers import AutoModelForCausalLM, AutoTokenizer
from outlines import models, generate
from pydantic import BaseModel
schema = """
{
"title": "Modules",
"type": "object",
"properties": {
"background": {"type": "boolean"},
"command": {"type": "boolean"},
"suggesstion": {"type": "boolean"},
"goal": {"type": "boolean"},
"examples": {"type": "boolean"},
"constraints": {"type": "boolean"},
"workflow": {"type": "boolean"},
"output_format": {"type": "boolean"},
"skills": {"type": "boolean"},
"style": {"type": "boolean"},
"initialization": {"type": "boolean"}
},
"required": ["background", "command", "suggesstion", "goal", "examples", "constraints", "workflow", "output_format", "skills", "style", "initialization"]
}
"""
class Generator:
def __init__(self, model_path, device):
self.llm = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code = True).to(device)
self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code = True)
self.llm = self.llm.eval()
self.model = models.Transformers(self.llm, self.tokenizer)
pass
def generate_response(self, messages):
g = generate.text(self.model)
prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
response = g(prompt)
return response
def json_response(self, messages):
g = generate.json(self.model, schema)
prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
response = g(prompt)
return response |