File size: 4,390 Bytes
cdcb203 |
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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
# Optimized prediction script for Hugging Face Inference Endpoints
# This version uses less memory and is optimized for smaller instances
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from cog import BasePredictor, Input
import logging
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class Predictor(BasePredictor):
def setup(self) -> None:
"""Load the DevOps SLM model into memory with optimizations"""
logger.info("Loading DevOps SLM model with memory optimizations...")
self.device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Using device: {self.device}")
# Load model with memory optimizations
self.model = AutoModelForCausalLM.from_pretrained(
"lakhera2023/devops-slm",
torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
device_map="auto" if self.device == "cuda" else None,
low_cpu_mem_usage=True,
trust_remote_code=True,
# Memory optimizations
use_cache=False, # Disable KV cache to save memory
attn_implementation="eager" # Use eager attention (less memory)
)
# Load tokenizer
self.tokenizer = AutoTokenizer.from_pretrained("lakhera2023/devops-slm")
# Set pad token
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
# Clear cache
if torch.cuda.is_available():
torch.cuda.empty_cache()
logger.info("DevOps SLM model loaded successfully with optimizations!")
def predict(
self,
prompt: str = Input(description="DevOps question or task prompt"),
max_tokens: int = Input(description="Maximum number of tokens to generate", default=150, ge=1, le=500),
temperature: float = Input(description="Sampling temperature", default=0.7, ge=0.1, le=2.0),
top_p: float = Input(description="Top-p sampling parameter", default=0.9, ge=0.1, le=1.0),
top_k: int = Input(description="Top-k sampling parameter", default=50, ge=1, le=100),
) -> str:
"""Generate DevOps response using the specialized model"""
try:
logger.info(f"Generating response for prompt: {prompt[:100]}...")
# Tokenize input with truncation to save memory
inputs = self.tokenizer([prompt], return_tensors="pt", truncation=True, max_length=256).to(self.device)
# Generate response with memory optimizations
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
do_sample=True,
top_p=top_p,
top_k=top_k,
pad_token_id=self.tokenizer.eos_token_id,
eos_token_id=self.tokenizer.eos_token_id,
repetition_penalty=1.1,
no_repeat_ngram_size=2,
early_stopping=True, # Stop early to save computation
use_cache=False, # Don't use KV cache
output_attentions=False, # Don't output attention weights
output_hidden_states=False # Don't output hidden states
)
# Decode response
full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the generated part
if prompt in full_response:
response = full_response.split(prompt)[-1].strip()
else:
response = full_response.strip()
# Clean up template artifacts
response = response.replace("<|im_start|>", "").replace("<|im_end|>", "").strip()
# Clear cache after generation
if torch.cuda.is_available():
torch.cuda.empty_cache()
logger.info(f"Generated response length: {len(response)}")
return response
except Exception as e:
logger.error(f"Error generating response: {e}")
return f"Error: {str(e)}" |