Spaces:
Runtime error
Runtime error
File size: 1,325 Bytes
c859ce0 10054be c859ce0 |
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 |
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
MODEL_ID = "EmoCareAI/ChatPsychiatrist"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained(
MODEL_ID,
use_fast=False
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
quantization_config=bnb_config,
device_map="auto",
torch_dtype=torch.float16,
)
model.eval()
def generate_response(user_message: str) -> str:
system_prompt = """
You are ChatPsychiatrist.
Personality:
- Extremely warm, empathetic, and emotionally present
- Speaks in a flowing, reflective, conversational style
- Avoids clinical language
"""
prompt = f"""{system_prompt}
User: {user_message}
Assistant:"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=350,
temperature=0.85,
top_p=0.92,
repetition_penalty=1.05,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
return decoded.split("Assistant:")[-1].strip()
|