add more useage examples

docs/source/en/model_doc/dbrx.md

@@ -36,14 +36,63 @@ This model was contributed by [eitan-turok](https://huggingface.co/eitanturok) a
 
 ## Usage Examples
 
-The `generate()` method can be used to generate text using DBRX.
+The `generate()` method can be used to generate text using DBRX. You can generate using the standard attention implementation, flash-attention, and the PyTorch scaled dot product attention. The last two attention implementations give speed ups.
 
 ```python
 from transformers import DbrxForCausalLM, AutoTokenizer
 import torch
 
 tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct", token="YOUR_HF_TOKEN")
-model = DbrxForCausalLM.from_pretrained("databricks/dbrx-instruct", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True, token="YOUR_HF_TOKEN")
+model = DbrxForCausalLM.from_pretrained(
+    "databricks/dbrx-instruct",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    token="YOUR_HF_TOKEN",
+    )
+
+input_text = "What does it take to build a great LLM?"
+messages = [{"role": "user", "content": input_text}]
+input_ids = tokenizer.apply_chat_template(messages, return_dict=True, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=200)
+print(tokenizer.decode(outputs[0]))
+```
+
+If you have flash-attention installed (`pip install flash-attn`), it is possible to generate faster. (The HuggingFace documentation for flash-attention can be found [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2).)
+```python
+from transformers import DbrxForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct", token="YOUR_HF_TOKEN")
+model = DbrxForCausalLM.from_pretrained(
+    "databricks/dbrx-instruct",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    token="YOUR_HF_TOKEN",
+    attn_implementation="flash_attention_2",
+    )
+
+input_text = "What does it take to build a great LLM?"
+messages = [{"role": "user", "content": input_text}]
+input_ids = tokenizer.apply_chat_template(messages, return_dict=True, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=200)
+print(tokenizer.decode(outputs[0]))
+```
+
+You can also generate faster using the PyTorch scaled dot product attention. (The HuggingFace documentation for scaled dot product attention can be found [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#pytorch-scaled-dot-product-attention).)
+```python
+from transformers import DbrxForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct", token="YOUR_HF_TOKEN")
+model = DbrxForCausalLM.from_pretrained(
+    "databricks/dbrx-instruct",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    token="YOUR_HF_TOKEN",
+    attn_implementation="sdpa",
+    )
 
 input_text = "What does it take to build a great LLM?"
 messages = [{"role": "user", "content": input_text}]