Upload 2 files

app (1).py

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+import gradio as gr
+import torch, os
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import StoppingCriteria, TextIteratorStreamer
+from threading import Thread
+
+torch.set_num_threads(2)
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+# Loading the tokenizer and model from Hugging Face's model hub.
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it", use_auth_token=HF_TOKEN)
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", use_auth_token=HF_TOKEN)
+
+def count_tokens(text):
+    return len(tokenizer.tokenize(text))
+
+# Function to generate model predictions.
+def predict(message, history):
+
+    formatted_prompt = f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
+    model_inputs = tokenizer(formatted_prompt, return_tensors="pt")
+    
+    streamer = TextIteratorStreamer(tokenizer, timeout=120., skip_prompt=True, skip_special_tokens=True)
+    
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=2048 - count_tokens(formatted_prompt),
+        top_p=0.2,
+        top_k=20,
+        temperature=0.1,
+        repetition_penalty=2.0,
+        length_penalty=-0.5,
+        num_beams=1
+    )
+    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
+        yield partial_message
+
+# Setting up the Gradio chat interface.
+gr.ChatInterface(predict,
+                 title="Gemma 2b Instruct Chat",
+                 description=None
+                 ).launch()  # Launching the web interface.

requirements (2).txt

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+torch>=2.0
+transformers>=4.36.2
+gradio>=4.13.0
+sentencepiece