File size: 2,853 Bytes
0af2a1b | 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 | from pathlib import Path
import gradio as gr
import spaces
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
BASE_MODEL = "Qwen/Qwen2.5-Coder-3B-Instruct"
ADAPTER_DIR = Path(__file__).resolve().parent
def load_model():
tokenizer = AutoTokenizer.from_pretrained(ADAPTER_DIR)
use_cuda = torch.cuda.is_available()
dtype = torch.float16 if use_cuda else torch.float32
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
torch_dtype=dtype,
low_cpu_mem_usage=True,
)
if use_cuda:
# ZeroGPU requires the model to be placed on CUDA during app startup.
base_model = base_model.to("cuda")
else:
base_model = base_model.to("cpu")
model = PeftModel.from_pretrained(base_model, ADAPTER_DIR)
model.eval()
return model, tokenizer
model, tokenizer = load_model()
def gpu_duration(prompt: str, max_new_tokens: int, temperature: float) -> int:
# Allow time for long requests while keeping typical calls in the fast queue.
return min(120, max(30, 15 + int(max_new_tokens) // 4))
@spaces.GPU(duration=gpu_duration)
@torch.inference_mode()
def predict(prompt: str, max_new_tokens: int, temperature: float) -> str:
if not prompt.strip():
raise gr.Error("Enter a prompt before requesting an inference.")
inputs = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
)
inputs = inputs.to(model.get_input_embeddings().weight.device)
generation_options = {
"max_new_tokens": int(max_new_tokens),
"eos_token_id": tokenizer.eos_token_id,
"pad_token_id": tokenizer.pad_token_id or tokenizer.eos_token_id,
"do_sample": temperature > 0,
}
if temperature > 0:
generation_options["temperature"] = temperature
output = model.generate(**inputs, **generation_options)
generated_tokens = output[0, inputs["input_ids"].shape[1] :]
return tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
demo = gr.Interface(
fn=predict,
inputs=[
gr.Textbox(label="Prompt", lines=5, placeholder="Ask for help writing or explaining code..."),
gr.Slider(minimum=16, maximum=512, value=128, step=16, label="Maximum new tokens"),
gr.Slider(minimum=0, maximum=1.5, value=0.7, step=0.1, label="Temperature (0 = deterministic)"),
],
outputs=gr.Textbox(label="Model response", lines=10),
title="GRPO Qwen2.5 Coder",
description="Qwen2.5-Coder-3B-Instruct fine-tuned with a GRPO LoRA adapter.",
api_name="predict",
)
if __name__ == "__main__":
demo.launch()
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