testing gpt2 large

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
+  
+model_name = "gpt2-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+@st.cache
+def load_model(model_name):
+    model = AutoModelWithLMHead.from_pretrained(model_name)
+    return model
+
+model = load_model(model_name)
+
+def infer(input_ids, **generator_args):
+
+    output_sequences = model.generate(
+        input_ids,generator_args
+    )
+
+    return output_sequences
+default_value = "See how a modern neural network auto-completes your text 🤗 Have fun!"
+
+#prompts
+st.title("Text Extension")
+st.write("Some other texts, like instructions...")
+
+sent = st.text_area("Text", default_value, height = 275)
+max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=50)
+temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
+num_return_sequences = st.sidebar.slider("Num Return Sequences", min_value = 1, max_value=4, value = 1)
+num_beams = st.sidebar.slider("Num Beams", min_value = 4, max_value=6, value = 4)
+top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
+top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9)
+
+encoded_prompt = tokenizer.encode(sent, add_special_tokens=False, return_tensors="pt")
+if encoded_prompt.size()[-1] == 0:
+    input_ids = None
+else:
+    input_ids = encoded_prompt
+
+
+output_sequences = infer(input_ids, max_length=max_length,num_return_sequences=num_return_sequences,
+                        num_beams=num_beams,
+                        temperature=temperature, top_k=top_k, top_p=top_p)
+
+for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
+    print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
+    generated_sequences = generated_sequence.tolist()
+
+    # Decode text
+    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
+
+    # Remove all text after the stop token
+    #text = text[: text.find(args.stop_token) if args.stop_token else None]
+
+    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
+    total_sequence = (
+        sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
+    )
+
+    generated_sequences.append(total_sequence)
+    print(total_sequence)
+
+
+st.write(generated_sequences[-1])

requirements.txt

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+transformers
+streamlit
+torch