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import subprocess
import gradio as gr
import spaces
MODEL_REPO = "HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive"
MODEL_FILE = "Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf"
MMPROJ_FILE = "mmproj-Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-f16.gguf"
MODEL_PATH = os.path.join("/tmp", MODEL_FILE)
MMPROJ_PATH = os.path.join("/tmp", MMPROJ_FILE)
server_process = None
LLAMA_DIR = "/tmp/llama"
LLAMA_SERVER = os.path.join(LLAMA_DIR, "llama-server")
LLAMA_RELEASE = "b10182"
LLAMA_ARCHIVE = f"llama.cpp-{LLAMA_RELEASE}-cuda-12.8-amd64.tar.gz"
LLAMA_URL = f"https://github.com/ai-dock/llama.cpp-cuda/releases/download/{LLAMA_RELEASE}/{LLAMA_ARCHIVE}"
def download_llama():
if os.path.exists(LLAMA_SERVER):
return
os.makedirs(LLAMA_DIR, exist_ok=True)
archive = os.path.join("/tmp", LLAMA_ARCHIVE)
subprocess.run(
["wget", "-q", "--show-progress", "-O", archive, LLAMA_URL],
check=True,
)
subprocess.run(
["tar", "-xzf", archive, "-C", LLAMA_DIR, "--strip-components=1"],
check=True,
)
if not os.path.exists(LLAMA_SERVER):
raise RuntimeError(
f"llama-server was not found after extraction: {LLAMA_SERVER}"
)
os.chmod(LLAMA_SERVER, 0o755)
def download_models():
from huggingface_hub import hf_hub_download
if not os.path.exists(MODEL_PATH):
hf_hub_download(
repo_id=MODEL_REPO,
filename=MODEL_FILE,
local_dir="/tmp",
)
if not os.path.exists(MMPROJ_PATH):
hf_hub_download(
repo_id=MODEL_REPO,
filename=MMPROJ_FILE,
local_dir="/tmp",
)
@spaces.GPU
def generate(prompt, max_tokens, temperature):
global server_process
download_llama()
download_models()
if server_process is None or server_process.poll() is not None:
cmd = [
LLAMA_SERVER,
"-m", MODEL_PATH,
"--mmproj", MMPROJ_PATH,
"--host", "127.0.0.1",
"--port", "8080",
"-c", "32768",
"-n", "4096",
"--n-gpu-layers", "99",
"-t", "2",
]
server_env = os.environ.copy()
server_env["LD_LIBRARY_PATH"] = (
LLAMA_DIR
+ ":"
+ server_env.get("LD_LIBRARY_PATH", "")
)
server_process = subprocess.Popen(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
env=server_env,
)
import time
import requests
for _ in range(120):
try:
r = requests.get("http://127.0.0.1:8080/health", timeout=1)
if r.ok:
break
except Exception:
pass
time.sleep(1)
else:
raise RuntimeError("llama-server did not become ready.")
response = requests.post(
"http://127.0.0.1:8080/completion",
json={
"prompt": prompt,
"n_predict": int(max_tokens),
"temperature": float(temperature),
"stream": False,
},
timeout=600,
)
response.raise_for_status()
return response.json().get("content", "")
with gr.Blocks(title="Gemma-4-E4B Uncensored") as demo:
gr.Markdown("# Gemma-4-E4B-Uncensored-HauhauCS-Aggressive")
prompt = gr.Textbox(
label="Prompt",
placeholder="Enter your prompt...",
lines=8,
)
with gr.Row():
max_tokens = gr.Slider(
minimum=64,
maximum=4096,
value=1024,
step=64,
label="Max tokens",
)
temperature = gr.Slider(
minimum=0.0,
maximum=2.0,
value=0.7,
step=0.05,
label="Temperature",
)
output = gr.Textbox(
label="Response",
lines=20,
)
generate_button = gr.Button("Generate", variant="primary")
generate_button.click(
generate,
inputs=[prompt, max_tokens, temperature],
outputs=output,
)
if __name__ == "__main__":
demo.launch(ssr_mode=False)
|