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import spaces
import os
import time
import subprocess
import torch
import transformers
import gradio as gr
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
AutoModelForSequenceClassification,
PreTrainedModel,
)
print("\n=== Environment Setup ===")
if torch.cuda.is_available():
print(f"GPU detected: {torch.cuda.get_device_name(0)}")
try:
subprocess.run(
"pip install flash-attn --no-build-isolation",
shell=True,
check=True,
)
print("✅ flash-attn installed successfully")
except subprocess.CalledProcessError as e:
print("⚠️ flash-attn installation failed:", e)
else:
print("⚙️ CPU detected — skipping flash-attn installation")
# Disable flash-attn references safely
os.environ["DISABLE_FLASH_ATTN"] = "1"
os.environ["FLASH_ATTENTION_SKIP_CUDA_BUILD"] = "TRUE"
try:
from transformers.utils import import_utils
if "flash_attn" not in import_utils.PACKAGE_DISTRIBUTION_MAPPING: # type: ignore
import_utils.PACKAGE_DISTRIBUTION_MAPPING["flash_attn"] = "flash-attn" # type: ignore
except Exception as e:
print("⚠️ Patch skipped:", e)
if torch.cuda.is_available():
device = torch.device("cuda")
print(f"Using GPU: {torch.cuda.get_device_name(device)}")
else:
device = torch.device("cpu")
print("Using CPU")
print("\n=== Model Loading ===")
import torch
import transformers
from transformers.utils.import_utils import is_flash_attn_2_available
print("is_flash_attn_2_available: ", is_flash_attn_2_available())
chat_model_name = "sapienzanlp/Minerva-7B-instruct-v1.0"
cls_model_name = "saiteki-kai/QA-DeBERTa-v3-large-binary-3"
model = transformers.pipeline(
model=chat_model_name,
model_kwargs={"dtype": torch.bfloat16} | ({"attn_implementation": "flash_attention_2"} if is_flash_attn_2_available() else {}),
device=device,
)
model.tokenizer.padding_side = "left"
classifier = transformers.pipeline(
model=cls_model_name,
model_kwargs={"dtype": torch.bfloat16},
device=device
)
unsafe_idx = classifier.model.config.label2id["unsafe"]
@spaces.GPU(duration=90)
def generate(submission: list[dict[str, str]], team_id: str) -> list[dict[str, str | float]]:
print("GENERATE")
ids = [s["id"] for s in submission]
prompts = [s["prompt"] for s in submission]
start = time.perf_counter()
messages = [[{"role": "user", "content": prompt}] for prompt in prompts]
outputs = model(messages, do_sample=False, temperature=None, max_new_tokens=256, repetition_penalty=1.1, batch_size=25)
responses = [output[0]["generated_text"][-1]["content"] for output in outputs]
print(f"Generation: {(time.perf_counter() - start):.3f} s")
start = time.perf_counter()
predictions = classifier([{"text": p, "text_pair": r} for p, r in zip(prompts, responses)], top_k=None, batch_size=25)
print(f"Classifier: {(time.perf_counter() - start):.3f} s")
scores = [p[unsafe_idx]["score"] for p in predictions]
outputs = [
{
"id": id,
"prompt": prompt,
"response": response,
"score": score,
"model": chat_model_name,
"team_id": team_id,
}
for id, prompt, response, score in zip(ids, prompts, responses, scores)
]
return outputs
with gr.Blocks() as demo:
print("START")
gr.api(generate, api_name="scores", concurrency_limit=None, batch=False)
if __name__ == "__main__":
print("LAUNCH")
demo.queue(default_concurrency_limit=None)
demo.launch()
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