switch from inference to pipeline

README.md

@@ -7,7 +7,6 @@ sdk: static
 pinned: false
 models:
 - meta-llama/Meta-Llama-3-70B-Instruct
-# - meta-llama/Meta-Llama-3-70B-Instruct
 # - Xenova/detr-resnet-50
 # - Xenova/gpt2
 # - Xenova/bloom-560m
@@ -15,7 +14,7 @@ models:
 # - Xenova/gpt-3.5-turbo
 # - Xenova/llama-68m
 # - Xenova/LaMini-Flan-T5-783M
-- mistralai/Mistral-7B-Instruct-v0.2
+# - mistralai/Mistral-7B-Instruct-v0.2
 # - meta-llama/Meta-Llama-3-8B
 
 ---

sketch.js

@@ -1,8 +1,10 @@
-// import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';
-import { HfInference } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected]/+esm';
-const inference = new HfInference();
+// connect to API via module
 
-// let pipe = await pipeline('text-generation', 'mistralai/Mistral-7B-Instruct-v0.2');
+import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.10.1';
+// import { HfInference } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected]/+esm';
+// const inference = new HfInference();
+
+// PIPELINE MODELS
 // models('Xenova/gpt2', 'Xenova/gpt-3.5-turbo', 'mistralai/Mistral-7B-Instruct-v0.2', 'Xenova/llama-68m', 'meta-llama/Meta-Llama-3-8B', 'Xenova/bloom-560m', 'Xenova/distilgpt2')
 // list of models by task: 'https://huggingface.co/docs/transformers.js/index#supported-tasksmodels'
 
@@ -12,22 +14,33 @@ const inference = new HfInference();
 
 ///////// VARIABLES 
 
+// establish global variables
 
+let PROMPT,
+  PREPROMPT,
+  promptResult,
+  submitButton,
+  addButton,
+  promptInput,
+  modelDisplay,
+  modelResult;
 
-let PROMPT, PREPROMPT, promptResult, submitButton, addButton, promptInput, inputValues, modelDisplay, modelResult
+// pick a model (see list of models)
+// INFERENCE MODELS
+// let MODELNAME = "mistralai/Mistral-7B-Instruct-v0.2";
+// models('Xenova/gpt2', 'Xenova/gpt-3.5-turbo', 'mistralai/Mistral-7B-Instruct-v0.2', 'Xenova/llama-68m', "meta-llama/Meta-Llama-3-70B-Instruct", 'meta-llama/Meta-Llama-3-8B', 'Xenova/bloom-560m', 'Xenova/distilgpt2', "meta-llama/Meta-Llama-3-70B-Instruct")
 
 // const detector = await pipeline('text-generation', 'meta-llama/Meta-Llama-3-8B', 'Xenova/LaMini-Flan-T5-783M');
 
+
 let blankArray = []
 
-let MODELNAME = 'mistralai/Mistral-7B-Instruct-v0.2'
-// let MODELNAME = "meta-llama/Meta-Llama-3-8B-Instruct"
-// models('Xenova/gpt2', 'Xenova/gpt-3.5-turbo', 'mistralai/Mistral-7B-Instruct-v0.2', 'Xenova/llama-68m', "meta-llama/Meta-Llama-3-70B-Instruct", 'meta-llama/Meta-Llama-3-8B', 'Xenova/bloom-560m', 'Xenova/distilgpt2', "meta-llama/Meta-Llama-3-70B-Instruct")
 
 ///// p5 STUFF
 
+// create an instance of the p5 class as a workspace for all your p5.js code
 
-new p5(function(p5){
+new p5(function (p5) {
     p5.setup = function(){
         console.log('p5 loaded')
         p5.noCanvas()
@@ -180,13 +193,39 @@ new p5(function(p5){
 
 
 async function runModel(PREPROMPT, PROMPT){
-    // Chat completion API
-    const out = await inference.chatCompletion({    
-        model: MODELNAME,
-        messages: [{ role: "user", content: PREPROMPT + PROMPT }],
-        max_tokens: 100
-    });
+    // // Chat completion API
+
+    // inference API version, not working in spaces
+    // const out = await inference.chatCompletion({    
+    //     model: MODELNAME,
+    //     messages: [{ role: "user", content: PREPROMPT + PROMPT }],
+    //     max_tokens: 100
+    // });
+
+    // console.log(out)
+
+    // // modelResult = await out.messages[0].content
+
+    // var modelResult = await out.choices[0].message.content
+    // // var modelResult = await out[0].generated_text
+    // console.log(modelResult);
+
+    // return modelResult
+
+    // pipeline/transformers version TEST
+    let pipe = await pipeline('text-generation', 'meta-llama/Meta-Llama-3-70B-Instruct');
+
+    out = pipe((PREPROMPT, PROMPT), num_return_sequences=3, return_full_text=false)
     
+    console.log(out)
+
+    var modelResult = await out.generated_text
+    console.log(modelResult)
+
+    return modelResult
+
+}
+
     //inference.fill_mask({
     // let out = await pipe(PREPROMPT + PROMPT)
     // let out = await pipe(PREPROMPT + PROMPT, {
@@ -198,16 +237,7 @@ async function runModel(PREPROMPT, PROMPT){
     //     // num_beams: 2,
     //     num_return_sequences: 1
     // });
-    console.log(out)
-
-    // modelResult = await out.messages[0].content
-
-    var modelResult = await out.choices[0].message.content
-    // var modelResult = await out[0].generated_text
-    console.log(modelResult);
 
-    return modelResult
-}
 
 
 // Reference the elements that we will need
@@ -222,6 +252,7 @@ async function runModel(PREPROMPT, PROMPT){
 // status.textContent = 'Loading model...';
 // const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
 
+
 // status.textContent = 'Ready';
 
 // example.addEventListener('click', (e) => {

style.css

@@ -99,8 +99,6 @@ canvas {
 
 input {
     margin: 1rem;
-    size: 200;
-    /* position: relative; */
     font-size: 12pt;
 }
 
@@ -108,7 +106,6 @@ button {
     float: left;
     font-size: 16px;
     margin: 1rem;
-    /* size: 200; */
     padding: 1rem 2rem;
     cursor: pointer;
     border-radius: 4px;