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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import gradio as gr
import time
# βββ Modell βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
MODEL_ID = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
MAX_TOKENS = 32768
DEFAULT_TEMP = 0.6
DEFAULT_SYSTEM = "You are Qwen Coder Boosted, an expert coding assistant. Generate clean, working code. Always show complete files with [FILE:filename] markers."
print("π Lade Modell...")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
print("β
Modell geladen!")
# βββ GPU-Funktion (nur hier ist echte GPU) ββββββββββββββββββββββββββββββ
@spaces.GPU(duration=120)
def generate_response(messages, temperature, max_tokens):
"""Generate a response from the model. GPU is real inside this function."""
text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
do_sample=True,
top_p=0.9,
repetition_penalty=1.05,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(
outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True
)
return response
# βββ Chat-Logik ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def chat(message, history, system_prompt, temperature):
"""Handle a chat turn. Builds messages, calls GPU function, returns updated history."""
if not message.strip():
return history
# Build messages array from history
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
# Generate (this triggers @spaces.GPU)
response = generate_response(messages, temperature, MAX_TOKENS)
# Update history
history.append((message, response))
return history
# βββ UI ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
with gr.Blocks(
theme=gr.themes.Soft(primary_hue="blue", secondary_hue="indigo"),
title="Qwen Coder Boosted",
) as demo:
gr.Markdown("""
# π Qwen Coder Boosted
**Qwen3-Coder-30B-A3B (4-bit) auf Zero GPU** β 48 GB shared VRAM, PRO-Quota
""")
with gr.Row():
with gr.Column(scale=1):
system_prompt = gr.Textbox(
label="βοΈ System Prompt",
value=DEFAULT_SYSTEM,
lines=3,
)
temperature = gr.Slider(
label="π‘οΈ Temperature",
minimum=0.1,
maximum=1.5,
value=DEFAULT_TEMP,
step=0.05,
)
clear_btn = gr.Button("ποΈ Chat leeren", variant="secondary")
with gr.Column(scale=3):
chatbot = gr.Chatbot(
label="π¬ Chat",
height=550,
bubble_full_width=False,
show_copy_button=True,
)
with gr.Row():
msg = gr.Textbox(
label="Deine Nachricht",
placeholder="Schreib einen Prompt... z.B. 'Baue eine To-Do-App mit HTML/CSS/JS'",
scale=8,
container=True,
)
submit = gr.Button("π€ Senden", variant="primary", scale=1)
gr.Markdown("""
---
### π‘ Info
- **Modell:** Qwen3-Coder-30B-A3B (4-bit, ~15 GB VRAM)
- **Zero GPU:** 48 GB shared, PRO = 40 Min/Tag inklusive
- **Max Tokens:** 32K (hardcodiert)
- **Temperature:** 0.6 (Standard fΓΌr Coding)
""")
# Events
submit.click(
fn=chat,
inputs=[msg, chatbot, system_prompt, temperature],
outputs=[chatbot],
).then(
fn=lambda: "",
outputs=[msg],
)
msg.submit(
fn=chat,
inputs=[msg, chatbot, system_prompt, temperature],
outputs=[chatbot],
).then(
fn=lambda: "",
outputs=[msg],
)
clear_btn.click(fn=lambda: None, outputs=[chatbot])
if __name__ == "__main__":
demo.launch()
|