Update README.md

README.md

@@ -63,6 +63,32 @@ You can use this model just as any other HuggingFace models:
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-1.5B-world', trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-1.5B-world', trust_remote_code=True)
+
+model = model.cuda()
+prompt = "What is a large language model?"
+messages = [
+    {"role": "user", "content": "Who are you?"},
+    {"role": "assistant", "content": "I am a GPT-3 based model."},
+    {"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=1024,
+)
+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=False)[0]
+print(response)
 ```
 
 ## Training Details
@@ -85,7 +111,7 @@ This model is trained on the World v3 with a total of 3.119 trillion tokens.
 
 before conversion: ppl 4.13 acc 69.4% 
 
-after conversion: ppl 4.26 acc 68.8%
+after conversion: ppl 4.26 acc 68.8% (without apply temple)
 
 ## FAQ
 Q: safetensors metadata is none.