Update README.md

README.md

@@ -42,14 +42,14 @@ You can use OLMo with the standard HuggingFace transformers library:
 from transformers import AutoModelForCausalLM, AutoTokenizer
 olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Instruct")
 tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Instruct")
-message = ["Language modeling is "]
+message = ["Who would win in a fight - a dinosaur or a cow named Moo Moo?"]
 inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
 # optional verifying cuda
 # inputs = {k: v.to('cuda') for k,v in inputs.items()}
 # olmo = olmo.to('cuda')
 response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
 print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
->> 'Language modeling is  a key component of any text-based application, but its effectiveness...'
+>> 'This is a fun and imaginative question! Let’s break it down...'
 ```
 
 For faster performance, you can quantize the model using the following method:
@@ -78,6 +78,31 @@ out = list_repo_refs("allenai/Olmo-3-7B-Instruct")
 branches = [b.name for b in out.branches]
 ```
 
+## Chat template
+
+## Default System Message
+The default system prompt for this model is:
+```
+<|im_start|>system
+You are a helpful function-calling AI assistant. 
+You do not currently have access to any functions. <functions></functions><|im_end|>
+```
+
+## Chat Format
+
+The chat template for this model is formatted as:
+```
+<|im_start|>system
+You are a helpful function-calling AI assistant. 
+You do not currently have access to any functions. <functions></functions><|im_end|>
+<|im_start|>user
+Who would win in a fight - a dinosaur or a cow named Moo Moo?<|im_end|>
+<|im_start|>assistant
+This is a fun and imaginative question! Let’s break it down...
+Moo Moo the cow would certinaly win.
+<|endoftext|>
+```
+
 ### Model Description
 
 - **Developed by:** Allen Institute for AI (Ai2)