Update README.md

README.md

@@ -53,15 +53,67 @@ These side-by-side charts highlight mochaV2’s stronger performance in grammar,
 Getting started is easy! Just load the model and tokenizer like this:
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+import json
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM
+from huggingface_hub import hf_hub_download
+# HF repo containing your model
+repo_id = "theguywhosucks/mochaV2"
+# Download tokenizer files
+itos_file = hf_hub_download(repo_id, "itos.json")
+stoi_file = hf_hub_download(repo_id, "stoi.json")
+with open(stoi_file) as f:
+    stoi = json.load(f)
+with open(itos_file) as f:
+    itos = json.load(f)
+# Convert itos dict -> list if needed
+if isinstance(itos, dict):
+    itos = [itos[str(i)] for i in range(len(itos))]
+# Tokenizer
+class SimpleTokenizer:
+    def __init__(self, stoi, itos):
+        self.stoi = stoi
+        self.itos = itos
+        self.unk_token = "<unk>" if "<unk>" in stoi else itos[0]
+    def encode(self, text):
+        return [self.stoi.get(c, self.stoi.get(self.unk_token, 0)) for c in text]
+    def decode(self, ids):
+        return "".join([self.itos[i] if i < len(self.itos) else self.unk_token for i in ids])
+tokenizer = SimpleTokenizer(stoi, itos)
+# Load model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    torch_dtype=torch.float32,
+    trust_remote_code=True
+)
+model.to(device)
+model.eval()
+# Gradio function
+def complete_sentence(prompt, max_new_tokens=50, temperature=0.7):
+    input_ids = torch.tensor([tokenizer.encode(prompt)]).to(device)
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=temperature
+        )
+    return tokenizer.decode(outputs[0].tolist())
+# Launch Gradio app
+gr.Interface(
+    fn=complete_sentence,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Slider(10, 200, value=50, step=10, label="Max new tokens"),
+        gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature")
+    ],
+    outputs=gr.Textbox(label="Completed Text"),
+    title="Mocha Sentence Completion",
+    description="Enter a prompt and get AI completions from your model."
+).launch()
 
-tokenizer = AutoTokenizer.from_pretrained("path/to/mochaV2")
-model = AutoModelForCausalLM.from_pretrained("path/to/mochaV2")
-
-prompt = "The Eiffel Tower is"
-inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=50)
-print(tokenizer.decode(outputs[0]))
 ```
 
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