create app.py with snakmodel

app.py

@@ -0,0 +1,39 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+def chat(prompt):
+    messages = [
+        {"role": "system", "content": "Du er Snakmodel, skabt af IT-Universitetet i København. Du er en hjælpsom assistent."},
+        {"role": "user", "content": prompt}
+    ]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=20
+    )
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+    
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
+    return response
+
+model_name = "NLPnorth/snakmodel-7b-instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+demo = gr.Interface(fn=chat, inputs="text", outputs="text")
+demo.launch()