Update generate example to llama 3

examples/generate.py

@@ -2,28 +2,36 @@
 from transformers import AutoTokenizer, TextStreamer
 
 
-quant_path = "TheBloke/Mistral-7B-Instruct-v0.2-AWQ"
+quant_path = "casperhansen/llama-3-8b-instruct-awq"
 
 # Load model
 model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
 tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
 streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
 
-# Convert prompt to tokens
-prompt_template = "[INST] {prompt} [/INST]"
-
 prompt = "You're standing on the surface of the Earth. "\
         "You walk one mile south, one mile west and one mile north. "\
         "You end up exactly where you started. Where are you?"
 
-tokens = tokenizer(
-    prompt_template.format(prompt=prompt), 
-    return_tensors='pt'
-).input_ids.cuda()
+chat = [
+    {"role": "system", "content": "You are a concise assistant that helps answer questions."},
+    {"role": "user", "content": prompt},
+]
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+tokens = tokenizer.apply_chat_template(
+    chat,
+    return_tensors="pt"
+).cuda()
 
 # Generate output
 generation_output = model.generate(
     tokens, 
     streamer=streamer,
-    max_new_tokens=512
-)
+    max_new_tokens=64,
+    eos_token_id=terminators
+)