Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+BASE = "openlm-research/open_llama_3b"   # base of your LoRA
+LORA = "GilbertAkham/openlm-llama-lora-codetrans"   # your LoRA on HF
+
+tokenizer = AutoTokenizer.from_pretrained(BASE)
+model = AutoModelForCausalLM.from_pretrained(
+    BASE,
+    load_in_8bit=True,          # fits on T4/low VRAM
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(model, LORA)
+model.eval()
+
+def chat_fn(prompt, max_new_tokens=256):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        out = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=0.3,
+            top_p=0.9
+        )
+    return tokenizer.decode(out[0], skip_special_tokens=True)
+
+demo = gr.Interface(
+    fn=chat_fn,
+    inputs=[gr.Textbox(lines=6, label="Prompt"), gr.Slider(16,1024,256,label="Max new tokens")],
+    outputs="text",
+    title="openlm-llama-LoRA codetrans",
+)
+
+demo.launch(share=True)   # share=True gives you a free public link