init app

app.py

@@ -1,71 +1,73 @@
 import transformers
 import streamlit as st
 
-from transformers import AutoTokenizer, AutoModelWithLMHead
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "gpt2-large"
   
-tokenizer = AutoTokenizer.from_pretrained("gpt2-large")
-@st.cache
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+@st.cache_resource
 def load_model(model_name):
-    model = AutoModelWithLMHead.from_pretrained("gpt2-large")
+    model = AutoModelForCausalLM.from_pretrained(model_name)
     return model
 
-model = load_model("gpt2-large")
+model = load_model(checkpoint)
 
-def infer(input_ids, max_length, temperature, top_k, top_p):
+def infer(input_ids, max_tokens, temperature, top_k, top_p):
 
     output_sequences = model.generate(
         input_ids=input_ids,
-        max_length=max_length,
+        max_new_tokens=max_tokens,
         temperature=temperature,
         top_k=top_k,
         top_p=top_p,
         do_sample=True,
+        no_repeat_ngram_size=2,
+        early_stopping=True,
+        num_beams=4,
+        pad_token_id=tokenizer.eos_token_id,
         num_return_sequences=1
     )
 
     return output_sequences
-default_value = "See how a modern neural network auto-completes your text 🤗 This site, built by the Hugging Face team, lets you write a whole document directly from your browser, and you can trigger the Transformer anywhere using the Tab key. Its like having a smart machine that completes your thoughts 😀 Get started by typing a custom snippet, check out the repository, or try one of the examples. Have fun!"
 
+default_value = "We are building the world's first"
+    
 #prompts
-st.title("Write with Transformers 🦄")
-st.write("The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.")
+st.title("Write with vcGPT 🦄")
+st.write("This is a LLM that was fine-tuned on a dataset of investment memos to help you generate your next pitch.")
+
+sent = st.text_area("Text", default_value, height = 400)
 
-sent = st.text_area("Text", default_value, height = 275)
-max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30)
-temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
-top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
+max_tokens = st.sidebar.slider("Max Tokens", min_value = 32, max_value=512)
+temperature = st.sidebar.slider("Temperature", value = 0.8, min_value = 0.0, max_value=1.0, step=0.05)
+top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 4)
 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")
+encoded_prompt = tokenizer.encode(tokenizer.eos_token+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, temperature, top_k, top_p)
-
-
+output_sequences = infer(input_ids, max_tokens, temperature, top_k, 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)
+    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=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)) :]
+        sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True, skip_special_tokens=True)) :]
     )
 
     generated_sequences.append(total_sequence)
     print(total_sequence)
 
-
-st.write(generated_sequences[-1])
-
-
+st.write(generated_sequences[-1])