Update README.md

README.md

@@ -37,4 +37,26 @@ eval_tokenizer = AutoTokenizer.from_pretrained(
 )
 ```
 Now load the QLoRA adapter from the appropriate checkpoint directory
-```
+```
+from peft import PeftModel
+
+ft_model = PeftModel.from_pretrained(base_model, "mistral-viggo-finetune/checkpoint-950")
+```
+Let's try the same eval_prompt and thus model_input as above, and see if the new finetuned model performs better.
+```
+eval_prompt = """Given a target sentence construct the underlying meaning representation of the input sentence as a single function with attributes and attribute values.
+This function should describe the target string accurately and the function must be one of the following ['inform', 'request', 'give_opinion', 'confirm', 'verify_attribute', 'suggest', 'request_explanation', 'recommend', 'request_attribute'].
+The attributes must be one of the following: ['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating', 'genres', 'player_perspective', 'has_multiplayer', 'platforms', 'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier']
+
+### Target sentence:
+Earlier, you stated that you didn't have strong feelings about PlayStation's Little Big Adventure. Is your opinion true for all games which don't have multiplayer?
+
+### Meaning representation:
+"""
+
+model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda")
+
+ft_model.eval()
+with torch.no_grad():
+    print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
+```