Create app.py

app.py

@@ -0,0 +1,47 @@
+from transformers import BloomTokenizerFast, BloomModel
+import torch
+
+mname = "bigscience/bloom-1b3"
+tokenizer = BloomTokenizerFast.from_pretrained(mname, use_cache=True)
+model = BloomModel.from_pretrained(mname, use_cache=True)
+
+def take_last_tokens(inputs, note_history, history):
+    """Filter the last 256 tokens"""
+    if inputs['input_ids'].shape[1] > 256:
+        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-256:].tolist()])
+        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-256:].tolist()])
+        note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
+        history = history[1:]
+    return inputs, note_history, history
+
+def add_note_to_history(note, note_history):
+    """Add a note to the historical information"""
+    note_history.append(note)
+    note_history = '</s> <s>'.join(note_history)
+    return [note_history]
+
+def chat(message, history):
+    history = history or []
+    if history: 
+        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
+    else:
+        history_useful = []
+    history_useful = add_note_to_history(message, history_useful)
+    inputs = tokenizer(history_useful, return_tensors="pt")
+    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
+    reply_ids = model.generate(**inputs)
+    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
+    history_useful = add_note_to_history(response, history_useful)
+    list_history = history_useful[0].split('</s> <s>')
+    history.append((list_history[-2], list_history[-1]))
+    return history, history
+
+gr.Interface(
+    fn=chat,
+    theme="huggingface",
+    css=".footer {display:none !important}",
+    inputs=["text", "state"],
+    outputs=["message", "state"],
+    title="Bloom 1b3 chat",
+    allow_flagging="never",
+    ).launch()