try new model with simple no template

sketch.js

@@ -1,56 +1,50 @@
 
 // IMPORT LIBRARIES TOOLS
-import { pipeline, env, AutoTokenizer } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.10.1';
-// AutoTokenizer
+import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.10.1';
 
 // skip local model check
 env.allowLocalModels = false;
 
 // GLOBAL VARIABLES
-let PROMPT_INPUT = `The woman has a job as a [MASK] and likes to ...` // a field for writing or changing a text value
+let PROMPT_INPUT = `The woman has a job as a [MASK] and likes to...` // a field for writing or changing a text value
 let pField
-let PREPROMPT = `You're a friendly pirate. Please complete the phrase and fill in any [MASK].`
+let PREPROMPT = `You're a friendly pirate. Please complete the phrase and fill in any [MASK]: `
 
 // RUN TEXT-GEN MODEL
 
-async function textGenTask(input){
+async function textGenTask(pre, prompt){
   console.log('text-gen task initiated')
 
+  let INPUT = pre + prompt
+
+  // PICK MODEL 
   let MODEL = 'Xenova/bloomz-560m'
   // const = modelsList = ['Xenova/LaMini-Cerebras-256M', 'Xenova/TinyLlama-1.1B-Chat-v1.0']
 
   const pipe = await pipeline('text-generation', MODEL)
-  let tokenizer = AutoTokenizer.from_pretrained(MODEL)
-
-  const messages = [
-      {"role": "system", "content": PREPROMPT},
-      {"role": "user", "content": input}
-    ]
-
-  const prompt = tokenizer.apply_chat_template(messages, { tokenize: false });
-  // const prompt = pipe.tokenizer.apply_chat_template(messages, { tokenize: false });
 
-  // run text through model, setting hyperparameters
-  var out = await pipe(prompt, {
+  // RUN INPUT THROUGH MODEL, setting hyperparameters
+  var out = await pipe(INPUT, {
     max_new_tokens: 256,
     temperature: 0.7,
     do_sample: true,
     top_k: 50,
-  }).apply_
+  })
 
   console.log(await out)
   console.log('text-gen task completed')
 
-  // parse results as a list of outputs, two different ways depending on the model
+  // PARSE RESULTS as a list of outputs, two different ways depending on the model
   
   let OUTPUT_LIST = [] // a blank array to store the results from the model
-
+  
   // parsing of output
   await out.forEach(o => {
     console.log(o)
     OUTPUT_LIST.push(o.generated_text)
   })
 
+  // alternate format for parsing, for chat model type
   // await out.choices.forEach(o => {
   //   console.log(o)
   //   OUTPUT_LIST.push(o.message.content)
@@ -145,7 +139,7 @@ new p5(function (p5){
   }
 
   async function displayResults(){
-    console.log('displayed, pressed')
+    console.log('submitButton pressed')
 
     PROMPT_INPUT = pField.value() // updates prompt if it's changed
     console.log("latest prompt: ", PROMPT_INPUT)
@@ -154,8 +148,8 @@ new p5(function (p5){
     // let outs = await getOutputs(fillIn)
     
     // call the function that runs the model for the task of your choice here 
-    // make sure to use the PROMPT_INPUT as a parameter
-    let outs = await textGenTask(PROMPT_INPUT)
+    // make sure to use the PROMPT_INPUT as a parameter, or also the PREPROMPT if valid for that task
+    let outs = await textGenTask(PREPROMPT, PROMPT_INPUT)
     console.log(outs)
   
     let outText = p5.createP('')