Create app.py

app.py

@@ -0,0 +1,46 @@
+import os
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "futurehouse/ether0"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.float32
+)
+
+@spaces.GPU
+def chat_fn(prompt, max_tokens=512):
+    max_tokens = min(int(max_tokens), 32_000)
+    messages = [{"role": "user", "content": prompt}]
+    chat_prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = tokenizer(chat_prompt, return_tensors="pt").to(model.device)
+    
+    # Generate with proper parameters
+    outputs = model.generate(
+        **inputs, 
+        max_new_tokens=max_tokens,
+        do_sample=True,
+        temperature=0.1,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    # Decode only the new tokens (not the input)
+    generated_text = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
+    return generated_text
+
+gr.Interface(
+    fn=chat_fn,
+    inputs=[
+        gr.Textbox(label="prompt"),
+        gr.Number(label="max_tokens", value=512, precision=0)
+    ],
+    outputs="text",
+    title="Ether0"
+).launch(ssr_mode=False)