add usage example

README.md

@@ -35,6 +35,40 @@ The results (on the human eval benchmark) are on par with other open-source mode
 
 It still underperforms compared to other models like CodeLLama (53%) chat gpt 4 (82) or wizard coder (73.2), but these model are more than 30 times bigger.
 
+## Usage
+You can download and use the model like so:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+        "jinaai/starcoder-1b-textbook", device_map='auto'
+    )
+
+tokenizer = AutoTokenizer.from_pretrained("jinaai/starcoder-1b-textbook")
+
+prompt = '''
+def unique(l: list):
+    """Return sorted unique elements in a list
+    >>> unique([5, 3, 5, 2, 3, 3, 9, 0, 123])
+    [0, 2, 3, 5, 9, 123]
+    """
+'''
+
+inputs = tokenizer(prompt.rstrip(), return_tensors="pt").to("cuda")
+
+generation_output = model.generate(
+    **inputs,
+    max_new_tokens=128,
+    eos_token_id=tokenizer.eos_token_id,
+    return_dict_in_generate=True,
+)
+
+s = generation_output.sequences[0]
+output = tokenizer.decode(s, skip_special_tokens=True)
+
+print(output)
+```
+
 ## Finetuning details
 
 We did full parameter fine-tuning and used a Nvidia a40 for 12 hours using a batch size of 128 and a micro-batch size of 8.