Added EOS toke stuff increased new token limit and added QOL features to frontent

models/blenderbot.py

@@ -23,6 +23,6 @@ def generate(input_text):
   input_ids = tokenizer.encode(input_text, return_tensors="pt")
   
   # Generate output using the model
-  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
+  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)
   
   return tokenizer.decode(output_ids[0], skip_special_tokens=True)

models/fast.py

@@ -11,6 +11,6 @@ def load():
 
 def generate(input_text):
   input_ids = tokenizer.encode(input_text, return_tensors="pt")
-  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
+  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)
   
   return tokenizer.decode(output_ids[0], skip_special_tokens=True)

models/gpt2.py

@@ -16,6 +16,6 @@ def generate(input_text):
   attention_mask = tf.ones_like(input_ids)
   
   # Generate output using the model
-  output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100)
+  output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)
   
   return tokenizer.decode(output_ids[0], skip_special_tokens=True)

models/hermes.py

@@ -13,6 +13,6 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
   
 def generate(messages):
   gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
-  output_ids = model.generate(**gen_input, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100)
+  output_ids = model.generate(**gen_input, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)
   
   return tokenizer.decode(output_ids[0], skip_special_tokens=True)

models/llama2.py

@@ -11,6 +11,6 @@ def load():
 
 def generate(input_text):
   input_ids = tokenizer.encode(input_text, return_tensors="pt")
-  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
+  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)
   
   return tokenizer.decode(output_ids[0], skip_special_tokens=True)

models/llama3.py

@@ -11,6 +11,6 @@ def load():
 
 def generate(input_text):
   input_ids = tokenizer.encode(input_text, return_tensors="pt")
-  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
+  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)
   
   return tokenizer.decode(output_ids[0], skip_special_tokens=True)

models/llamatiny.py

@@ -11,6 +11,6 @@ def load():
 
 def generate(input_text):
   input_ids = tokenizer.encode(input_text, return_tensors="pt")
-  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
+  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)
   
   return tokenizer.decode(output_ids[0], skip_special_tokens=True)

models/mamba.py

@@ -11,6 +11,6 @@ def load():
 
 def generate(input_text):
   input_ids = tokenizer.encode(input_text, return_tensors="pt")
-  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
+  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100, eos_token_id=tokenizer.eos_token_id)
   
   return tokenizer.decode(output_ids[0], skip_special_tokens=True)

models/tinystories.py

@@ -11,6 +11,12 @@ def load():
 
 def generate(input_text):
   input_ids = tokenizer.encode(input_text, return_tensors="pt")
-  output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
+  output_ids = model.generate(
+    input_ids,
+    no_repeat_ngram_size=2,
+    max_new_tokens=200,
+    eos_token_id=tokenizer.eos_token_id,
+    temperature=0.2
+  )
   
   return tokenizer.decode(output_ids[0], skip_special_tokens=True)

templates/index.html

@@ -74,12 +74,23 @@
     const apiUrl = `https://beveledcube-bevelapi.hf.space/api`;
     const sendPromptButton = document.getElementById("send-prompt");
     const responseContainer = document.getElementById("responses");
+    let promptInput = document.getElementById("prompt")
+
+    sendPromptButton.addEventListener("click", () => sendPrompt());
+    promptInput.addEventListener("keydown", (event) => {
+      if (event.key === "Enter") {
+        // Prevent the default action if needed (e.g., form submission)
+        event.preventDefault();
+        sendPrompt()
+      }
+    });
 
-    sendPromptButton.addEventListener("click", async () => {
+    function sendPrompt() {
       console.log("Sending prompt")
 
       const responseElement = document.createElement("div");
-      const requestData = { prompt: getValue("prompt") };
+      const requestData = { prompt: promptInput.value };
+      promptInput.value = "";
 
       responseElement.classList.add("response-container");
 
@@ -114,8 +125,7 @@
         .catch(error => {
           console.error("Error:", error.message);
         });
-
-    });
+    }
 
     function getValue(elementId) {
       return document.getElementById(elementId).value;