Upload app (1).py

app (1).py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+from threading import Thread
+
+torch.set_default_device("cuda")
+
+# Loading the tokenizer and model from Hugging Face's model hub.
+tokenizer = AutoTokenizer.from_pretrained(
+    "mlabonne/phixtral-2x2_8",
+    trust_remote_code=True
+)
+model = AutoModelForCausalLM.from_pretrained(
+    "mlabonne/phixtral-2x2_8",
+    torch_dtype="auto",
+    load_in_8bit=True,
+    trust_remote_code=True
+)
+
+# Defining a custom stopping criteria class for the model's text generation.
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [50256, 50295]  # IDs of tokens where the generation should stop.
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:  # Checking if the last generated token is a stop token.
+                return True
+        return False
+
+
+# Function to generate model predictions.
+def predict(message, history):
+    history_transformer_format = history + [[message, ""]]
+    stop = StopOnTokens()
+
+    # Formatting the input for the model.
+    system_prompt = "<|im_start|>system\nYou are Phixtral, a helpful AI assistant.<|im_end|>"
+    messages = system_prompt + "".join(["".join(["\n<|im_start|>user\n" + item[0], "<|im_end|>\n<|im_start|>assistant\n" + item[1]]) for item in history_transformer_format])
+    print(messages)
+    input_ids = tokenizer([messages], return_tensors="pt").to('cuda')
+    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        input_ids,
+        streamer=streamer,
+        max_new_tokens=1024,
+        do_sample=True,
+        top_p=0.95,
+        top_k=50,
+        temperature=0.7,
+        num_beams=1,
+        stopping_criteria=StoppingCriteriaList([stop])
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()  # Starting the generation in a separate thread.
+    partial_message = ""
+    for new_token in streamer:
+        partial_message += new_token
+        if '<|im_end|>' in partial_message:  # Breaking the loop if the stop token is generated.
+            break
+        yield partial_message
+
+
+# Setting up the Gradio chat interface.
+gr.ChatInterface(predict,
+                 description="""
+                 <center><img src="https://i.imgur.com/CJSeIGg.png" width="33%"></center>\n\n
+                 Chat with [mlabonne/phixtral-2x2_8](https://huggingface.co/mlabonne/phixtral-2x2_8), the first Mixture of Experts made by merging two fine-tuned [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) models.
+                 This small model (4.46B param) is good for various tasks, such as programming, dialogues, story writing, and more.\n\n
+                 ❤️ If you like this work, please follow me on [Hugging Face](https://huggingface.co/mlabonne) and [Twitter](https://twitter.com/maximelabonne).
+                 """,
+                 examples=[
+                     'Can you solve the equation 2x + 3 = 11 for x?',
+                     'Write an epic poem about Ancient Rome.',
+                     'Who was the first person to walk on the Moon?',
+                     'Use a list comprehension to create a list of squares for numbers from 1 to 10.',
+                     'Recommend some popular science fiction books.',
+                     'Can you write a short story about a time-traveling detective?'
+                 ],
+                 theme=gr.themes.Soft(primary_hue="orange"),
+                 ).launch()