Update README.md

README.md

@@ -52,6 +52,28 @@ The model was trained using the following setup:
 
 For more detailed training information, please refer to Section 3.4 and Appendix F of the DCLM paper.
 
+
+## Quickstart
+First install open_lm
+```
+pip install git+https://github.com/mlfoundations/open_lm.git
+```
+
+Then you can load the model using HF's Auto classes as follows:
+```python
+from open_lm.hf import *
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("TRI-ML/DCLM-1B-IT")
+model = AutoModelForCausalLM.from_pretrained("TRI-ML/DCLM-1B")
+
+inputs = tokenizer(["Machine learning is"], return_tensors="pt")
+gen_kwargs = {"max_new_tokens": 50, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.1}
+output = model.generate(inputs['input_ids'], **gen_kwargs)
+output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
+print(output)
+```
+
+
 ## Evaluation
 Here are the evaluation results for DCLM-1B models on various tasks (using [llm-foundry](https://github.com/mosaicml/llm-foundry) eval suite)