feat(llama-lora): add display names and defaults; add peft; rebuild static\n\n- UI: add baseName/loraName fields and send to backend; show names in results\n- Defaults: base=openlm-research/open_llama_3b, adapter=HGKo/openllama3b-lora-adapter\n- Backend deps: add peft to requirements.txt\n- Frontend: build CRA and sync artifacts to top-level static/

api.py

@@ -46,6 +46,12 @@ from flask_login import (
     login_fresh,
 )
 
+try:
+    # PEFT for LoRA adapters (optional)
+    from peft import PeftModel
+except Exception as _e:
+    PeftModel = None
+
 # Fix for SQLite3 version compatibility with ChromaDB
 try:
     import pysqlite3
@@ -779,6 +785,48 @@ def yolo_detect():
     result = process_yolo(image)
     return jsonify(result)
 
+# --- HF CausalLM + LoRA helper caches ---
+hf_tokenizers = {}
+hf_base_models = {}
+hf_lora_models = {}
+
+def _preferred_dtype():
+    try:
+        return torch.float16 if torch.cuda.is_available() else torch.float32
+    except Exception:
+        return torch.float32
+
+def load_hf_base_and_tokenizer(base_id: str, tok_id: str = None):
+    """Load and cache base CausalLM and tokenizer. tok_id can point to adapter repo to use its tokenizer."""
+    global hf_tokenizers, hf_base_models
+    tok_key = tok_id or base_id
+    if tok_key not in hf_tokenizers:
+        hf_tokenizers[tok_key] = AutoTokenizer.from_pretrained(tok_key, use_fast=True)
+    if base_id not in hf_base_models:
+        hf_base_models[base_id] = AutoModelForCausalLM.from_pretrained(
+            base_id,
+            torch_dtype=_preferred_dtype(),
+            use_safetensors=False,
+        ).to(device)
+    return hf_tokenizers[tok_key], hf_base_models[base_id]
+
+def load_hf_lora_model(base_id: str, adapter_id: str):
+    """Load and cache a PEFT-wrapped LoRA model from base + adapter. Returns model or raises if PEFT unavailable."""
+    if PeftModel is None:
+        raise RuntimeError("PEFT (peft) is not installed; cannot load LoRA adapter")
+    key = f"{base_id}::{adapter_id}"
+    if key in hf_lora_models:
+        return hf_lora_models[key]
+    # Create a fresh base to avoid mutating the cached base used for non-LoRA runs
+    base = AutoModelForCausalLM.from_pretrained(
+        base_id,
+        torch_dtype=_preferred_dtype(),
+        use_safetensors=False,
+    ).to(device)
+    lora_model = PeftModel.from_pretrained(base, adapter_id).eval().to(device)
+    hf_lora_models[key] = lora_model
+    return lora_model
+
 @app.route('/api/detect/detr', methods=['POST'])
 @require_auth()
 def detr_detect():
@@ -1395,16 +1443,26 @@ def start_llama_lora_compare():
                     text = res.get('choices', [{}])[0].get('text', '').strip()
                     # Ensure we don't return an empty base output
                     base_output = text or "[empty]"
+                elif '/' in base_model_id:
+                    # HF Transformers path (e.g., openlm-research/open_llama_3b)
+                    tok_id = lora_path or base_model_id  # user expects tokenizer from adapter repo when provided
+                    tokenizer_hf, base_model_hf = load_hf_base_and_tokenizer(base_model_id, tok_id)
+                    inputs = tokenizer_hf(full_prompt, return_tensors='pt').to(device)
+                    with torch.no_grad():
+                        out = base_model_hf.generate(**inputs, max_new_tokens=128, temperature=0.7, top_p=0.9)
+                    text = tokenizer_hf.decode(out[0], skip_special_tokens=True)
+                    if text.startswith(full_prompt):
+                        stripped = text[len(full_prompt):].strip()
+                        text = stripped if stripped else text
+                    base_output = text or "[empty]"
                 elif llm_model is not None and llm_tokenizer is not None:
                     inputs = llm_tokenizer(full_prompt, return_tensors='pt').to(device)
                     with torch.no_grad():
                         out = llm_model.generate(**inputs, max_new_tokens=128, temperature=0.7, top_p=0.9)
                     text = llm_tokenizer.decode(out[0], skip_special_tokens=True)
-                    # Some models echo the prompt; only strip if there's meaningful content after
                     if text.startswith(full_prompt):
                         stripped = text[len(full_prompt):].strip()
                         text = stripped if stripped else text
-                    # Provide a non-empty fallback
                     base_output = text or "[empty]"
                 else:
                     base_output = f"[mock] Base response for: {full_prompt[:80]}..."
@@ -1413,12 +1471,20 @@ def start_llama_lora_compare():
             base_latency = int((time.time() - start_base) * 1000)
             lora_add_message(session_id, f"Base inference done in {base_latency} ms")
 
-            # Run LoRA inference (mock unless PEFT is integrated)
+            # Run LoRA inference (prefer HF+PEFT when adapter provided)
             start_lora = time.time()
             try:
-                if lora_path and llm_model is not None and llm_tokenizer is not None:
-                    # Placeholder: in real integration, load LoRA via PEFT and run generate
-                    lora_output = f"[mock-lora:{lora_path}] {base_output}"
+                if lora_path and '/' in base_model_id:
+                    tokenizer_hf, _ = load_hf_base_and_tokenizer(base_model_id, lora_path)
+                    lora_model = load_hf_lora_model(base_model_id, lora_path)
+                    inputs = tokenizer_hf(full_prompt, return_tensors='pt').to(device)
+                    with torch.no_grad():
+                        out = lora_model.generate(**inputs, max_new_tokens=128, temperature=0.7, top_p=0.9)
+                    text = tokenizer_hf.decode(out[0], skip_special_tokens=True)
+                    if text.startswith(full_prompt):
+                        stripped = text[len(full_prompt):].strip()
+                        text = stripped if stripped else text
+                    lora_output = text or "[empty]"
                 else:
                     lora_output = f"[mock] LoRA response (no adapter) for: {full_prompt[:80]}..."
             except Exception as e:

frontend/src/components/LlamaLoraCompare.js

@@ -4,8 +4,10 @@ import { Box, Paper, Typography, Grid, TextField, Button, CircularProgress, Divi
 const LlamaLoraCompare = () => {
   const [prompt, setPrompt] = useState('Describe the image concisely.');
   const [imageFile, setImageFile] = useState(null);
-  const [baseModel, setBaseModel] = useState('gguf:tinyllama-q4km');
-  const [loraPath, setLoraPath] = useState('');
+  const [baseModel, setBaseModel] = useState('openlm-research/open_llama_3b');
+  const [loraPath, setLoraPath] = useState('HGKo/openllama3b-lora-adapter');
+  const [baseName, setBaseName] = useState('Base Model');
+  const [loraName, setLoraName] = useState('LoRA Adapter');
   const [results, setResults] = useState(null);
   const [isProcessing, setIsProcessing] = useState(false);
   const [logs, setLogs] = useState([]);
@@ -33,7 +35,9 @@ const LlamaLoraCompare = () => {
     const formData = new FormData();
     formData.append('prompt', prompt);
     formData.append('baseModel', baseModel);
+    if (baseName) formData.append('baseName', baseName);
     if (loraPath) formData.append('loraPath', loraPath);
+    if (loraName) formData.append('loraName', loraName);
     if (imageFile) formData.append('image', imageFile);
 
     try {
@@ -96,7 +100,9 @@ const LlamaLoraCompare = () => {
         <Grid item xs={12} md={6}>
           <Box display="flex" flexDirection="column" gridGap={12}>
             <TextField label="Base model id" value={baseModel} onChange={(e) => setBaseModel(e.target.value)} variant="outlined" fullWidth />
+            <TextField label="Base model name (display)" value={baseName} onChange={(e) => setBaseName(e.target.value)} variant="outlined" fullWidth />
             <TextField label="LoRA path/ID (optional)" value={loraPath} onChange={(e) => setLoraPath(e.target.value)} variant="outlined" fullWidth />
+            <TextField label="LoRA adapter name (display, optional)" value={loraName} onChange={(e) => setLoraName(e.target.value)} variant="outlined" fullWidth />
             <input type="file" accept="image/*" onChange={(e) => setImageFile(e.target.files?.[0] || null)} />
             <Button color="primary" variant="contained" onClick={handleStart} disabled={isProcessing}>
               {isProcessing ? <CircularProgress size={22} /> : 'Run Comparison'}
@@ -130,9 +136,9 @@ const LlamaLoraCompare = () => {
             <Box>
               <Typography variant="subtitle2">Prompt</Typography>
               <Box style={{ whiteSpace: 'pre-wrap', marginBottom: 8 }}>{results.prompt}</Box>
-              <Typography variant="subtitle2">Base Output</Typography>
+              <Typography variant="subtitle2">{baseName || 'Base Output'}</Typography>
               <Box style={{ whiteSpace: 'pre-wrap', marginBottom: 8 }}>{results.base?.output || '-'}</Box>
-              <Typography variant="subtitle2">LoRA Output</Typography>
+              <Typography variant="subtitle2">{loraName || 'LoRA Output'}</Typography>
               <Box style={{ whiteSpace: 'pre-wrap', marginBottom: 8 }}>{results.lora?.output || '-'}</Box>
               <Typography variant="subtitle2">Latency</Typography>
               <Box>{`base: ${results.base?.latency_ms || 0} ms | lora: ${results.lora?.latency_ms || 0} ms`}</Box>

requirements.txt

@@ -52,6 +52,9 @@ langchain-openai>=0.1.16
 langchain-community>=0.2.6
 langchain-experimental>=0.0.60
 
+# PEFT for LoRA
+peft>=0.10.0
+
 # llama.cpp bindings for loading local GGUF (quantized Q4) models — optional.
 # Removed from default install to avoid long native builds on Spaces.
 # llama-cpp-python>=0.2.90

static/asset-manifest.json

@@ -1,8 +1,8 @@
 {
   "files": {
     "main.css": "/static/css/main.3b4eede1.chunk.css",
-    "main.js": "/static/js/main.2c1002fc.chunk.js",
-    "main.js.map": "/static/js/main.2c1002fc.chunk.js.map",
+    "main.js": "/static/js/main.be1003f9.chunk.js",
+    "main.js.map": "/static/js/main.be1003f9.chunk.js.map",
     "runtime-main.js": "/static/js/runtime-main.1d79dee9.js",
     "runtime-main.js.map": "/static/js/runtime-main.1d79dee9.js.map",
     "static/js/2.933cba9a.chunk.js": "/static/js/2.933cba9a.chunk.js",
@@ -10,7 +10,7 @@
     "static/js/3.10abbce4.chunk.js": "/static/js/3.10abbce4.chunk.js",
     "static/js/3.10abbce4.chunk.js.map": "/static/js/3.10abbce4.chunk.js.map",
     "index.html": "/index.html",
-    "precache-manifest.b122c3ac2cc3b905017a2fe31e239c0f.js": "/precache-manifest.b122c3ac2cc3b905017a2fe31e239c0f.js",
+    "precache-manifest.e677d30bd91056af61222e81dd6b30d3.js": "/precache-manifest.e677d30bd91056af61222e81dd6b30d3.js",
     "service-worker.js": "/service-worker.js",
     "static/css/main.3b4eede1.chunk.css.map": "/static/css/main.3b4eede1.chunk.css.map",
     "static/js/2.933cba9a.chunk.js.LICENSE.txt": "/static/js/2.933cba9a.chunk.js.LICENSE.txt"
@@ -19,6 +19,6 @@
     "static/js/runtime-main.1d79dee9.js",
     "static/js/2.933cba9a.chunk.js",
     "static/css/main.3b4eede1.chunk.css",
-    "static/js/main.2c1002fc.chunk.js"
+    "static/js/main.be1003f9.chunk.js"
   ]
 }

static/index.html

@@ -1 +1 @@
-<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Multi-Model Object Detection Demo"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>Vision Web App</title><link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"/><link href="/static/css/main.3b4eede1.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function r(r){for(var n,i,a=r[0],c=r[1],l=r[2],f=0,s=[];f<a.length;f++)i=a[f],Object.prototype.hasOwnProperty.call(o,i)&&o[i]&&s.push(o[i][0]),o[i]=0;for(n in c)Object.prototype.hasOwnProperty.call(c,n)&&(e[n]=c[n]);for(p&&p(r);s.length;)s.shift()();return u.push.apply(u,l||[]),t()}function t(){for(var e,r=0;r<u.length;r++){for(var t=u[r],n=!0,a=1;a<t.length;a++){var c=t[a];0!==o[c]&&(n=!1)}n&&(u.splice(r--,1),e=i(i.s=t[0]))}return e}var n={},o={1:0},u=[];function i(r){if(n[r])return n[r].exports;var t=n[r]={i:r,l:!1,exports:{}};return e[r].call(t.exports,t,t.exports,i),t.l=!0,t.exports}i.e=function(e){var r=[],t=o[e];if(0!==t)if(t)r.push(t[2]);else{var n=new Promise((function(r,n){t=o[e]=[r,n]}));r.push(t[2]=n);var u,a=document.createElement("script");a.charset="utf-8",a.timeout=120,i.nc&&a.setAttribute("nonce",i.nc),a.src=function(e){return i.p+"static/js/"+({}[e]||e)+"."+{3:"10abbce4"}[e]+".chunk.js"}(e);var c=new Error;u=function(r){a.onerror=a.onload=null,clearTimeout(l);var t=o[e];if(0!==t){if(t){var n=r&&("load"===r.type?"missing":r.type),u=r&&r.target&&r.target.src;c.message="Loading chunk "+e+" failed.\n("+n+": "+u+")",c.name="ChunkLoadError",c.type=n,c.request=u,t[1](c)}o[e]=void 0}};var l=setTimeout((function(){u({type:"timeout",target:a})}),12e4);a.onerror=a.onload=u,document.head.appendChild(a)}return Promise.all(r)},i.m=e,i.c=n,i.d=function(e,r,t){i.o(e,r)||Object.defineProperty(e,r,{enumerable:!0,get:t})},i.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.t=function(e,r){if(1&r&&(e=i(e)),8&r)return e;if(4&r&&"object"==typeof e&&e&&e.__esModule)return e;var t=Object.create(null);if(i.r(t),Object.defineProperty(t,"default",{enumerable:!0,value:e}),2&r&&"string"!=typeof e)for(var n in e)i.d(t,n,function(r){return e[r]}.bind(null,n));return t},i.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(r,"a",r),r},i.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},i.p="/",i.oe=function(e){throw console.error(e),e};var a=this["webpackJsonpvision-web-app"]=this["webpackJsonpvision-web-app"]||[],c=a.push.bind(a);a.push=r,a=a.slice();for(var l=0;l<a.length;l++)r(a[l]);var p=c;t()}([])</script><script src="/static/js/2.933cba9a.chunk.js"></script><script src="/static/js/main.2c1002fc.chunk.js"></script></body></html>
+<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Multi-Model Object Detection Demo"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>Vision Web App</title><link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"/><link href="/static/css/main.3b4eede1.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function r(r){for(var n,i,a=r[0],c=r[1],l=r[2],f=0,s=[];f<a.length;f++)i=a[f],Object.prototype.hasOwnProperty.call(o,i)&&o[i]&&s.push(o[i][0]),o[i]=0;for(n in c)Object.prototype.hasOwnProperty.call(c,n)&&(e[n]=c[n]);for(p&&p(r);s.length;)s.shift()();return u.push.apply(u,l||[]),t()}function t(){for(var e,r=0;r<u.length;r++){for(var t=u[r],n=!0,a=1;a<t.length;a++){var c=t[a];0!==o[c]&&(n=!1)}n&&(u.splice(r--,1),e=i(i.s=t[0]))}return e}var n={},o={1:0},u=[];function i(r){if(n[r])return n[r].exports;var t=n[r]={i:r,l:!1,exports:{}};return e[r].call(t.exports,t,t.exports,i),t.l=!0,t.exports}i.e=function(e){var r=[],t=o[e];if(0!==t)if(t)r.push(t[2]);else{var n=new Promise((function(r,n){t=o[e]=[r,n]}));r.push(t[2]=n);var u,a=document.createElement("script");a.charset="utf-8",a.timeout=120,i.nc&&a.setAttribute("nonce",i.nc),a.src=function(e){return i.p+"static/js/"+({}[e]||e)+"."+{3:"10abbce4"}[e]+".chunk.js"}(e);var c=new Error;u=function(r){a.onerror=a.onload=null,clearTimeout(l);var t=o[e];if(0!==t){if(t){var n=r&&("load"===r.type?"missing":r.type),u=r&&r.target&&r.target.src;c.message="Loading chunk "+e+" failed.\n("+n+": "+u+")",c.name="ChunkLoadError",c.type=n,c.request=u,t[1](c)}o[e]=void 0}};var l=setTimeout((function(){u({type:"timeout",target:a})}),12e4);a.onerror=a.onload=u,document.head.appendChild(a)}return Promise.all(r)},i.m=e,i.c=n,i.d=function(e,r,t){i.o(e,r)||Object.defineProperty(e,r,{enumerable:!0,get:t})},i.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.t=function(e,r){if(1&r&&(e=i(e)),8&r)return e;if(4&r&&"object"==typeof e&&e&&e.__esModule)return e;var t=Object.create(null);if(i.r(t),Object.defineProperty(t,"default",{enumerable:!0,value:e}),2&r&&"string"!=typeof e)for(var n in e)i.d(t,n,function(r){return e[r]}.bind(null,n));return t},i.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(r,"a",r),r},i.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},i.p="/",i.oe=function(e){throw console.error(e),e};var a=this["webpackJsonpvision-web-app"]=this["webpackJsonpvision-web-app"]||[],c=a.push.bind(a);a.push=r,a=a.slice();for(var l=0;l<a.length;l++)r(a[l]);var p=c;t()}([])</script><script src="/static/js/2.933cba9a.chunk.js"></script><script src="/static/js/main.be1003f9.chunk.js"></script></body></html>

static/{precache-manifest.b122c3ac2cc3b905017a2fe31e239c0f.js → precache-manifest.e677d30bd91056af61222e81dd6b30d3.js}

@@ -1,10 +1,10 @@
 self.__precacheManifest = (self.__precacheManifest || []).concat([
   {
-    "revision": "f1bad2b23dad084bbe2b9f32933cd096",
+    "revision": "345dc438ab4c3d30cb74185f014bf900",
     "url": "/index.html"
   },
   {
-    "revision": "f780ff4dddfa2065259b",
+    "revision": "c644b449a961b4302219",
     "url": "/static/css/main.3b4eede1.chunk.css"
   },
   {
@@ -20,8 +20,8 @@ self.__precacheManifest = (self.__precacheManifest || []).concat([
     "url": "/static/js/3.10abbce4.chunk.js"
   },
   {
-    "revision": "f780ff4dddfa2065259b",
-    "url": "/static/js/main.2c1002fc.chunk.js"
+    "revision": "c644b449a961b4302219",
+    "url": "/static/js/main.be1003f9.chunk.js"
   },
   {
     "revision": "154c4b0ed16fd7552969",

static/service-worker.js

@@ -14,7 +14,7 @@
 importScripts("https://storage.googleapis.com/workbox-cdn/releases/4.3.1/workbox-sw.js");
 
 importScripts(
-  "/precache-manifest.b122c3ac2cc3b905017a2fe31e239c0f.js"
+  "/precache-manifest.e677d30bd91056af61222e81dd6b30d3.js"
 );
 
 self.addEventListener('message', (event) => {

static/static/js/{main.2c1002fc.chunk.js → main.be1003f9.chunk.js}

@@ -1,2 +1,2 @@
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