Update README.md

README.md

@@ -41,6 +41,33 @@ pipeline_tag: text-generation
 {Assistant}
 ```
 
+## Usage
+
+- Tested on A100 80GB
+- Our model can handle up to 10k input tokens, thanks to the `rope_scaling` option
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+tokenizer = AutoTokenizer.from_pretrained("upstage/llama-65b-instruct")
+model = AutoModelForCausalLM.from_pretrained(
+ "upstage/llama-65b-instruct",
+ device_map="auto",
+ torch_dtype=torch.float16,
+ load_in_8bit=True,
+ rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs
+)
+
+prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+del inputs["token_type_ids"]
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
+output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+```
+
 ## Hardware and Software
 
 * **Hardware**: We utilized an A100x8 * 4 for training our model