Update README.md

README.md

@@ -27,7 +27,7 @@ By leveraging a pre-sparsified model's structure, you can efficiently fine-tune
 
 ### Running the model
 
-This model has not been fine-tuned for instruction-following but may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse).
+This model may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse).
 
 ```python
 # pip install transformers accelerate
@@ -37,7 +37,7 @@ tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-pruned50-retra
 model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-pruned50-retrained-ultrachat", device_map="auto")
 
 input_text = "Write me a poem about Machine Learning."
-input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+input_ids = tokenizer.apply_chat_template(input_text, add_generation_prompt=True, return_tensors="pt").to("cuda")
 
 outputs = model.generate(**input_ids)
 print(tokenizer.decode(outputs[0]))
@@ -47,7 +47,7 @@ print(tokenizer.decode(outputs[0]))
 
 Model evaluation metrics and results.
 
-| Benchmark                                      | Metric        | Llama-2-7b  | Llama-2-7b-pruned50-retrained-ultrachat |
+| Benchmark                                      | Metric        | Llama-2-7b-ultrachat  | Llama-2-7b-pruned50-retrained-ultrachat |
 |------------------------------------------------|---------------|-------------|-------------------------------|
 | [MMLU](https://arxiv.org/abs/2009.03300)       | 5-shot, top-1 | xxxx        | xxxx                          |
 | [HellaSwag](https://arxiv.org/abs/1905.07830)  | 0-shot        | xxxx        | xxxx                          |