File size: 3,640 Bytes
82452fa |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from huggingface_hub import space
import time
# Full precision models for H200 70GB
MODELS = {
"Athena-R3X 8B": "Spestly/Athena-R3X-8B",
"Athena-R3X 4B": "Spestly/Athena-R3X-4B",
"Athena-R3 7B": "Spestly/Athena-R3-7B",
"Athena-3 3B": "Spestly/Athena-3-3B",
"Athena-3 7B": "Spestly/Athena-3-7B",
"Athena-3 14B": "Spestly/Athena-3-14B",
"Athena-2 1.5B": "Spestly/Athena-2-1.5B",
"Athena-1 3B": "Spestly/Athena-1-3B",
"Athena-1 7B": "Spestly/Athena-1-7B"
}
DEFAULT_MODEL = "Spestly/Athena-R3X-8B"
# GPU-accelerated function
@space.GPU
def load_model(model_name):
model_id = MODELS.get(model_name, DEFAULT_MODEL)
print(f"🚀 Loading {model_id} on H200 GPU...")
start_time = time.time()
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
low_cpu_mem_usage=True
)
load_time = time.time() - start_time
print(f"✅ Model loaded in {load_time:.2f} seconds")
print(f"GPU Memory Allocated: {torch.cuda.memory_allocated()/1e9:.2f}GB")
return model, tokenizer
@space.GPU
def generate_text(prompt, model_name, max_length=512, temperature=0.7):
try:
model, tokenizer = load_model(model_name)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
start_time = time.time()
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_length,
temperature=temperature,
do_sample=True,
top_p=0.9
)
generation_time = time.time() - start_time
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
stats = f"""
⚡ Generation completed in {generation_time:.2f}s
💾 GPU Memory: {torch.cuda.memory_allocated()/1e9:.2f}GB allocated
🌡️ Temperature: {temperature}
"""
return output_text, stats
except Exception as e:
return f"❌ Error: {str(e)}", ""
with gr.Blocks(title="Athena Playground") as demo:
gr.Markdown("""# 🚀 Athena Playground""")
with gr.Row():
with gr.Column(scale=1):
model_choice = gr.Dropdown(
label="Model",
choices=list(MODELS.keys()),
value="Athena-R3X 8B"
)
max_length = gr.Slider(32, 4096, value=512, label="Max Tokens")
temperature = gr.Slider(0.1, 2.0, value=0.7, label="Creativity")
gr.Markdown("**Note:** First load may take 1-2 minutes")
submit_btn = gr.Button("Generate", variant="primary")
with gr.Column(scale=3):
prompt = gr.Textbox(label="Your Prompt", lines=8, placeholder="Type your prompt here...")
output = gr.Textbox(label="Model Output", lines=12)
stats = gr.Textbox(label="Performance Stats", lines=3)
submit_btn.click(
generate_text,
inputs=[prompt, model_choice, max_length, temperature],
outputs=[output, stats]
)
gr.Examples(
examples=[
["Explain the transformer architecture like I'm five"],
["Write a poem about AI in the style of Shakespeare"],
["Generate Python code for a convolutional neural network"]
],
inputs=prompt
)
if __name__ == "__main__":
demo.launch() |