quickstart steps:

README.md

@@ -5,6 +5,55 @@ language: en
 # Fork of sosier's [nanoGPT - Character-Level Shakespeare](https://huggingface.co/sosier/nanoGPT-shakespeare-char-tied-weights)
 **This is a fork of [sosier/nanoGPT-shakespear-char-tied-weights](https://huggingface.co/sosier/nanoGPT-shakespeare-char-tied-weights) for demonstration purposes.**
 
+## Quickstart
+Load model:
+```python
+from transformers import AutoModel
+model = AutoModel.from_pretrained("n8cha/nanoGPT-shakespeare-char", trust_remote_code=True)
+```
+
+Setup inference:
+```python
+import torch
+
+class CharTokenizer:
+    def __init__(self):
+        self.token_map = {'\n': 0, ' ': 1, '!': 2, '$': 3, '&': 4, "'": 5, ',': 6, '-': 7, '.': 8, '3': 9, ':': 10, ';': 11, '?': 12, 'A': 13, 'B': 14, 'C': 15, 'D': 16, 'E': 17, 'F': 18, 'G': 19, 'H': 20, 'I': 21, 'J': 22, 'K': 23, 'L': 24, 'M': 25, 'N': 26, 'O': 27, 'P': 28, 'Q': 29, 'R': 30, 'S': 31, 'T': 32, 'U': 33, 'V': 34, 'W': 35, 'X': 36, 'Y': 37, 'Z': 38, 'a': 39, 'b': 40, 'c': 41, 'd': 42, 'e': 43, 'f': 44, 'g': 45, 'h': 46, 'i': 47, 'j': 48, 'k': 49, 'l': 50, 'm': 51, 'n': 52, 'o': 53, 'p': 54, 'q': 55, 'r': 56, 's': 57, 't': 58, 'u': 59, 'v': 60, 'w': 61, 'x': 62, 'y': 63, 'z': 64}
+        self.rev_map = {v: k for k, v in self.token_map.items()}
+
+    def encode(self, text):
+        try:
+            return [self.token_map[c] for c in text]
+        except KeyError as e:
+            raise ValueError(f"Character not in vocabulary: {e.args[0]}")
+
+    def decode(self, tokens):
+        try:
+            return ''.join(self.rev_map[t] for t in tokens)
+        except KeyError as e:
+            raise ValueError(f"Token not in vocabulary: {e.args[0]}")
+
+tokenizer = CharTokenizer()
+
+def generate(prompt):
+    prompt_encoded = tokenizer.encode(prompt)
+    x = (torch.tensor(prompt_encoded, dtype=torch.long, device="cpu")[None, ...])
+    with torch.no_grad():
+        y = model.generate(
+            x,
+            max_new_tokens=1000,
+            temperature=0.8,
+            top_k=200
+        )
+        return tokenizer.decode(y[0].tolist())
+```
+
+Run inference:
+```
+response = generate("O Romeo, Romeo, ")
+print(response)
+```
+
 Below is the original README.
 
 ---