diff --git a/__init__.py b/__init__.py
index bf3580e..fc2a1e0 100644
--- a/__init__.py
+++ b/__init__.py
@@ -776,6 +776,7 @@ class ModelResolverExtension:
 
                     # Get the file path to hash
                     file_path = resolved_path if resolved_path else None
+                    file_location = ""
 
                     if not file_path and category:
                         # Try to find the file in the model directories using folder_paths
@@ -854,16 +855,26 @@ class ModelResolverExtension:
 
                     # Search CivitAI for the model using hash
                     if download_available and file_path and _os.path.exists(file_path):
+                        file_location = _os.path.dirname(file_path).replace("\\", "/")
+                        if file_location and not file_location.endswith("/"):
+                            file_location += "/"
                         try:
                             from .core.sources.civitai import (
                                 get_model_info_for_file,
                             )
 
                             result = get_model_info_for_file(file_path)
-                            if result and result.get("url"):
+                            if result and (
+                                result.get("url")
+                                or result.get("version_url")
+                                or result.get("from_metadata")
+                                or result.get("trained_words")
+                            ):
                                 return web.json_response(
                                     {
                                         "filename": filename,
+                                        "location": result.get("location")
+                                        or file_location,
                                         "url": result.get("url"),
                                         "version_url": result.get("version_url"),
                                         "model_id": result.get("model_id"),
@@ -874,6 +885,7 @@ class ModelResolverExtension:
                                         "version_id": result.get("version_id"),
                                         "version_name": result.get("version_name", ""),
                                         "sha256": result.get("sha256"),
+                                        "size": result.get("size"),
                                         "base_model": result.get("base_model"),
                                         "tags": result.get("tags", []),
                                         "trained_words": result.get(
@@ -904,8 +916,11 @@ class ModelResolverExtension:
                                 return web.json_response(
                                     {
                                         "url": result["url"],
+                                        "location": file_location,
                                         "model_name": result.get("name", clean_name),
                                         "version_id": result.get("version_id"),
+                                        "size": result.get("size"),
+                                        "tags": result.get("tags", []),
                                     }
                                 )
                         except Exception as e:
diff --git a/core/sources/civitai.py b/core/sources/civitai.py
index ef54121..1e26a09 100644
--- a/core/sources/civitai.py
+++ b/core/sources/civitai.py
@@ -1805,11 +1805,86 @@ def _read_model_metadata(metadata_path: str) -> Optional[Dict[str, Any]]:
         return None
 
 
+def _as_metadata_dict(value: Any) -> Dict[str, Any]:
+    return value if isinstance(value, dict) else {}
+
+
+def _as_metadata_list(value: Any) -> List[Any]:
+    if value is None:
+        return []
+    if isinstance(value, list):
+        return value
+    if isinstance(value, tuple):
+        return list(value)
+    return [value] if value else []
+
+
+def _first_metadata_value(*values: Any) -> Any:
+    for value in values:
+        if value is None:
+            continue
+        if isinstance(value, str) and not value.strip():
+            continue
+        if isinstance(value, (list, tuple, dict)) and not value:
+            continue
+        return value
+    return None
+
+
+def _metadata_size_to_bytes(value: Any) -> Optional[int]:
+    if value is None or value == "":
+        return None
+
+    try:
+        size = int(float(value))
+    except (TypeError, ValueError):
+        return None
+
+    return size if size >= 0 else None
+
+
+def _format_model_location(file_path: str) -> str:
+    if not file_path:
+        return ""
+
+    location = os.path.dirname(file_path).replace("\\", "/")
+    if location and not location.endswith("/"):
+        location += "/"
+    return location
+
+
+def _normalize_metadata_trained_words(*values: Any) -> List[str]:
+    words: List[str] = []
+    seen = set()
+
+    for value in values:
+        for item in _as_metadata_list(value):
+            if isinstance(item, dict):
+                item = (
+                    item.get("word")
+                    or item.get("name")
+                    or item.get("text")
+                    or item.get("value")
+                )
+            text = str(item or "").strip()
+            if not text:
+                continue
+            key = text.lower()
+            if key in seen:
+                continue
+            seen.add(key)
+            words.append(text)
+
+    return words
+
+
 def _metadata_to_model_info(metadata: Dict[str, Any]) -> Dict[str, Any]:
     """
     Convert metadata file format to model info format used by our extension.
     """
-    civitai_data = metadata.get("civitai") or {}
+    civitai_data = _as_metadata_dict(metadata.get("civitai"))
+    selected_version = _as_metadata_dict(metadata.get("selected_version"))
+    path_metadata = _as_metadata_dict(metadata.get("path_metadata"))
 
     # Extract images with metadata
     images = []
@@ -1840,14 +1915,21 @@ def _metadata_to_model_info(metadata: Dict[str, Any]) -> Dict[str, Any]:
                 }
             )
 
-    # Get trained words from CivitAI data
-    trained_words = civitai_data.get("trainedWords") or []
-    if isinstance(trained_words, str):
-        trained_words = [trained_words] if trained_words else []
+    # Get trained words from common sidecar shapes, including LoRA Manager metadata.
+    trained_words = _normalize_metadata_trained_words(
+        metadata.get("trained_words"),
+        metadata.get("trainedWords"),
+        civitai_data.get("trainedWords"),
+        selected_version.get("trained_words"),
+        selected_version.get("trainedWords"),
+        path_metadata.get("trained_words"),
+        path_metadata.get("trainedWords"),
+    )
 
     # Get model info
-    model_info = civitai_data.get("model") or {}
-    version_info = civitai_data
+    model_info = _as_metadata_dict(civitai_data.get("model"))
+    file_infos = _as_metadata_list(civitai_data.get("files"))
+    file_info = _as_metadata_dict(file_infos[0]) if file_infos else {}
 
     # Build model_id from CivitAI data
     model_id = civitai_data.get("modelId") or civitai_data.get("id")
@@ -1855,11 +1937,38 @@ def _metadata_to_model_info(metadata: Dict[str, Any]) -> Dict[str, Any]:
     return {
         "source": "metadata",
         "model_id": model_id,
-        "model_name": metadata.get("model_name") or metadata.get("file_name", ""),
+        "model_name": _first_metadata_value(
+            metadata.get("model_name"),
+            metadata.get("modelName"),
+            model_info.get("name"),
+            metadata.get("file_name"),
+        )
+        or "",
         "model_type": model_info.get("type", "") or civitai_data.get("type", ""),
         "version_id": civitai_data.get("id"),
-        "version_name": civitai_data.get("name", ""),
+        "version_name": _first_metadata_value(
+            metadata.get("version_name"),
+            metadata.get("versionName"),
+            selected_version.get("name"),
+            civitai_data.get("name"),
+        )
+        or "",
         "sha256": metadata.get("sha256", ""),
+        "size": _metadata_size_to_bytes(
+            _first_metadata_value(
+                metadata.get("size"),
+                metadata.get("file_size"),
+                metadata.get("fileSize"),
+                metadata.get("sizeBytes"),
+                path_metadata.get("size"),
+                path_metadata.get("file_size"),
+                path_metadata.get("fileSize"),
+                path_metadata.get("sizeBytes"),
+                (file_info.get("sizeKB") * 1024)
+                if file_info.get("sizeKB") is not None
+                else None,
+            )
+        ),
         "url": f"https://civitai.com/models/{civitai_data.get('modelId')}"
         if civitai_data.get("modelId")
         else None,
@@ -1867,14 +1976,32 @@ def _metadata_to_model_info(metadata: Dict[str, Any]) -> Dict[str, Any]:
         if model_id
         else None,
         "download_url": civitai_data.get("downloadUrl"),
-        "base_model": (metadata.get("base_model") or civitai_data.get("baseModel", "")),
-        "tags": metadata.get("tags") or [],
+        "base_model": _first_metadata_value(
+            metadata.get("base_model"),
+            metadata.get("baseModel"),
+            selected_version.get("base_model"),
+            selected_version.get("baseModel"),
+            civitai_data.get("baseModel"),
+        )
+        or "",
+        "tags": _as_metadata_list(metadata.get("tags") or model_info.get("tags")),
         "trained_words": trained_words,
         "images": images,
         "clip_skip": civitai_data.get("clipSkip"),
-        "description": metadata.get("model_description", "")
-        or civitai_data.get("description", ""),
-        "model_description": metadata.get("model_description", ""),
+        "description": _first_metadata_value(
+            metadata.get("modelDescription"),
+            metadata.get("model_description"),
+            metadata.get("description"),
+            model_info.get("description"),
+            civitai_data.get("description"),
+        )
+        or "",
+        "model_description": _first_metadata_value(
+            metadata.get("modelDescription"),
+            metadata.get("model_description"),
+            model_info.get("description"),
+        )
+        or "",
         "from_metadata": True,
     }
 
@@ -1903,7 +2030,15 @@ def get_model_info_for_file(
         metadata = _read_model_metadata(metadata_path)
         if metadata:
             log_info(f"Using metadata file for {file_path}")
-            return _metadata_to_model_info(metadata)
+            result = _metadata_to_model_info(metadata)
+            result["file_path"] = file_path
+            result["location"] = _format_model_location(file_path)
+            if not result.get("size"):
+                try:
+                    result["size"] = os.path.getsize(file_path)
+                except Exception:
+                    pass
+            return result
     else:
         # Debug: list all files in the same directory
         directory = os.path.dirname(file_path)
@@ -1919,4 +2054,14 @@ def get_model_info_for_file(
     if not file_hash:
         return None
 
-    return get_model_info_by_hash(file_hash, api_key)
+    result = get_model_info_by_hash(file_hash, api_key)
+    if result:
+        result["file_path"] = file_path
+        result["location"] = _format_model_location(file_path)
+        if not result.get("size"):
+            try:
+                result["size"] = os.path.getsize(file_path)
+            except Exception:
+                pass
+
+    return result
diff --git a/web/css/resolver-main.css b/web/css/resolver-main.css
index ab6d7f1..9d2fc43 100644
--- a/web/css/resolver-main.css
+++ b/web/css/resolver-main.css
@@ -3365,8 +3365,9 @@
 /* Info Area (type/base model tags) */
 .mr-info-area {
     display: flex;
+    flex-wrap: wrap;
     gap: 8px;
-    margin-bottom: 16px;
+    margin-bottom: 10px;
 }
 .mr-info-tag {
     display: inline-flex;
@@ -3386,6 +3387,37 @@
 .mr-info-tag.-basemodel-sdxl-turbo { background: rgba(33,150,243,0.2); color: #64B5F6; }
 .mr-info-tag.-basemodel-flux\.1 { background: rgba(255,152,0,0.2); color: #FFB74D; }
 .mr-info-tag.-basemodel-flux\.1-d { background: rgba(255,152,0,0.2); color: #FFB74D; }
+.mr-info-tags-row {
+    display: flex;
+    align-items: flex-start;
+    gap: 10px;
+    margin: 0 0 16px;
+}
+.mr-info-tags-label {
+    flex: 0 0 auto;
+    padding-top: 5px;
+    color: var(--mr-text-muted, #888);
+    font-size: 10px;
+    font-weight: 700;
+    letter-spacing: 0.04em;
+    text-transform: uppercase;
+}
+.mr-info-tags {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 6px;
+    min-width: 0;
+}
+.mr-info-model-tag {
+    max-width: 180px;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+    border: 1px solid rgba(255,255,255,0.08);
+    background: rgba(255,255,255,0.06);
+    color: var(--mr-text-muted, #c8c8c8);
+    font-size: 10px;
+}
 
 /* Info Table */
 .mr-info-table {
@@ -3413,6 +3445,12 @@
     color: var(--input-text, #e0e0e0);
     word-break: break-word;
 }
+.mr-info-location {
+    font-family: "Cascadia Mono", "Consolas", monospace;
+    font-size: 12px;
+    line-height: 1.45;
+    word-break: break-all;
+}
 .mr-info-trained-words-label {
     display: flex;
     align-items: center;
diff --git a/web/resolver/views/model_info_methods.js b/web/resolver/views/model_info_methods.js
index d8d2ff6..cde49e7 100644
--- a/web/resolver/views/model_info_methods.js
+++ b/web/resolver/views/model_info_methods.js
@@ -300,12 +300,20 @@ export const modelInfoMethods = {
                             <span class="mr-info-tag mr-info-type"></span>
                             <span class="mr-info-tag mr-info-basemodel"></span>
                         </div>
+                        <div class="mr-info-tags-row mr-hidden-initial">
+                            <span class="mr-info-tags-label">Tags</span>
+                            <div class="mr-info-tags"></div>
+                        </div>
                         <table class="mr-info-table">
                             <tbody>
                                 <tr class="mr-info-file-row">
                                     <td><span>File <span class="mr-tooltip-badge" data-tooltip="The model file name found locally or returned by CivitAI.">?</span></span></td>
                                     <td><span class="mr-info-file"></span></td>
                                 </tr>
+                                <tr class="mr-info-location-row mr-hidden-initial">
+                                    <td><span>Location <span class="mr-tooltip-badge" data-tooltip="Folder where this local model file is stored.">?</span></span></td>
+                                    <td><span class="mr-info-location"></span></td>
+                                </tr>
                                 <tr class="mr-info-hash-row">
                                     <td><span>Hash (sha256) <span class="mr-tooltip-badge" data-tooltip="Unique fingerprint of the local file. Model Resolver uses it to confirm the exact CivitAI version.">?</span></span></td>
                                     <td><span class="mr-info-hash"></span></td>
@@ -322,6 +330,10 @@ export const modelInfoMethods = {
                                     <td><span>Base Model <span class="mr-tooltip-badge" data-tooltip="Base model this resource was made for, for example SD1.5, SDXL or Flux.">?</span></span></td>
                                     <td><span class="mr-info-base-model"></span></td>
                                 </tr>
+                                <tr class="mr-info-size-row mr-hidden-initial">
+                                    <td><span>Size <span class="mr-tooltip-badge" data-tooltip="The local model file size, read from metadata or from the file on disk.">?</span></span></td>
+                                    <td><span class="mr-info-size"></span></td>
+                                </tr>
                                 <tr class="mr-info-trainedwords-row mr-hidden-initial">
                                     <td>
                                         <div class="mr-info-trained-words-label">
@@ -489,9 +501,162 @@ export const modelInfoMethods = {
             )];
         }
 
+        if (words && typeof words === 'object') {
+            return this.normalizeTrainedWords(
+                words.trained_words
+                    || words.trainedWords
+                    || words.words
+                    || words.values
+                    || []
+            );
+        }
+
         return [];
     },
 
+    formatInfoDialogLocation(data = {}) {
+        let location = data.location
+            || data.folder_path
+            || data.folderPath
+            || data.download_directory
+            || data.downloadDirectory
+            || data.directory
+            || '';
+
+        if (!location) {
+            const fullPath = data.resolved_path || data.resolvedPath || data.full_path || data.fullPath || data.path || '';
+            const normalizedFullPath = String(fullPath || '').replace(/\\/g, '/');
+            const lastSlash = normalizedFullPath.lastIndexOf('/');
+            if (lastSlash > 0) {
+                location = normalizedFullPath.slice(0, lastSlash + 1);
+            }
+        }
+
+        location = String(location || '').trim().replace(/\\/g, '/');
+        if (location && !location.endsWith('/')) {
+            location += '/';
+        }
+
+        return location;
+    },
+
+    normalizeInfoTags(tags) {
+        if (Array.isArray(tags)) {
+            const seen = new Set();
+            const result = [];
+            for (let tag of tags) {
+                if (tag && typeof tag === 'object') {
+                    tag = tag.name || tag.tag || tag.text || tag.value || '';
+                }
+                const text = String(tag || '').trim();
+                if (!text) continue;
+                const key = text.toLowerCase();
+                if (seen.has(key)) continue;
+                seen.add(key);
+                result.push(text);
+            }
+            return result;
+        }
+
+        if (typeof tags === 'string') {
+            return this.normalizeInfoTags(
+                tags
+                    .split(/[\n,|;]/)
+                    .map(tag => tag.trim())
+                    .filter(Boolean)
+            );
+        }
+
+        if (tags && typeof tags === 'object') {
+            return this.normalizeInfoTags(
+                tags.tags
+                    || tags.model_tags
+                    || tags.modelTags
+                    || tags.values
+                    || []
+            );
+        }
+
+        return [];
+    },
+
+    getInfoDialogTags(data = {}) {
+        const sources = [
+            data.tags,
+            data.model_tags,
+            data.modelTags,
+            data.civitai?.tags,
+            data.civitai?.model?.tags,
+            data.model?.tags,
+            data.metadata?.tags,
+            data.path_metadata?.tags
+        ];
+
+        return this.normalizeInfoTags(
+            sources.flatMap(source => this.normalizeInfoTags(source))
+        );
+    },
+
+    formatInfoDialogSize(data = {}) {
+        const rawSize = data.size
+            ?? data.file_size
+            ?? data.fileSize
+            ?? data.size_bytes
+            ?? data.sizeBytes
+            ?? data.file?.size
+            ?? data.file?.sizeBytes
+            ?? null;
+
+        if (rawSize === null || rawSize === undefined || rawSize === '') return '';
+        if (rawSize === 0 || rawSize === '0') return '0 B';
+
+        const numericSize = Number(rawSize);
+        if (Number.isFinite(numericSize)) {
+            return typeof this.formatBytes === 'function'
+                ? this.formatBytes(numericSize)
+                : `${numericSize} B`;
+        }
+
+        return String(rawSize);
+    },
+
+    getInfoDialogTrainedWords(data = {}) {
+        const sources = [
+            data.trained_words,
+            data.trainedWords,
+            data.civitai?.trainedWords,
+            data.civitai?.trained_words,
+            data.selected_version?.trainedWords,
+            data.selected_version?.trained_words,
+            data.modelVersion?.trainedWords,
+            data.modelVersion?.trained_words,
+            data.model_version?.trainedWords,
+            data.model_version?.trained_words,
+            data.version?.trainedWords,
+            data.version?.trained_words,
+            data.path_metadata?.trainedWords,
+            data.path_metadata?.trained_words,
+            data.metadata?.trainedWords,
+            data.metadata?.trained_words
+        ];
+
+        return this.normalizeTrainedWords(
+            sources.flatMap(source => this.normalizeTrainedWords(source))
+        );
+    },
+
+    setInfoTableRowVisible(row, visible) {
+        if (!row) return;
+        row.classList.toggle('mr-hidden-initial', !visible);
+        row.style.display = visible ? '' : 'none';
+    },
+
+    setInfoElementVisible(element, visible) {
+        if (!element) return;
+        element.classList.toggle('mr-hidden-initial', !visible);
+        element.style.display = visible ? '' : 'none';
+    },
+
     updateSelectedTrainedWordsSummary(dialog) {
         if (!dialog) return;
 
@@ -639,13 +804,19 @@ export const modelInfoMethods = {
      */
     async fetchModelInfoForDialog(loraName, modelData, dialog) {
         try {
+            const resolvedPath = modelData?.resolved_path
+                || modelData?.path
+                || modelData?.full_path
+                || modelData?.folder_path
+                || modelData?.download_directory
+                || '';
             const response = await api.fetchApi('/model_resolver/civitai-search', {
                 method: 'POST',
                 headers: { 'Content-Type': 'application/json' },
                 body: JSON.stringify({
                     filename: loraName,
                     category: modelData?.category || '',
-                    resolved_path: modelData?.resolved_path || ''
+                    resolved_path: resolvedPath
                 })
             });
 
@@ -706,12 +877,39 @@ export const modelInfoMethods = {
             }
         }
 
+        // Update top tags
+        const tagsRowEl = dialog.querySelector('.mr-info-tags-row');
+        const tagsEl = dialog.querySelector('.mr-info-tags');
+        if (tagsRowEl && tagsEl) {
+            const tags = this.getInfoDialogTags(data);
+            if (tags.length > 0) {
+                tagsEl.innerHTML = tags.map(tag => `
+                    <span class="mr-info-tag mr-info-model-tag" data-tooltip="${this.escapeHtml(tag)}">
+                        ${this.escapeHtml(tag)}
+                    </span>
+                `).join('');
+                this.setInfoElementVisible(tagsRowEl, true);
+                this.bindTooltips(tagsEl);
+            } else {
+                tagsEl.innerHTML = '';
+                this.setInfoElementVisible(tagsRowEl, false);
+            }
+        }
+
         // Update file
         const fileEl = dialog.querySelector('.mr-info-file');
         if (fileEl && data.filename) {
             fileEl.textContent = data.filename;
         }
 
+        // Update location
+        const locationEl = dialog.querySelector('.mr-info-location');
+        if (locationEl) {
+            const location = this.formatInfoDialogLocation(data);
+            locationEl.textContent = location;
+            this.setInfoTableRowVisible(locationEl.closest('tr'), Boolean(location));
+        }
+
         // Update hash
         const hashEl = dialog.querySelector('.mr-info-hash');
         if (hashEl) {
@@ -752,17 +950,22 @@ export const modelInfoMethods = {
             const baseModel = data.base_model || data.baseModel || '';
             baseModelRowEl.textContent = baseModel;
             const row = baseModelRowEl.closest('tr');
-            if (row && baseModel) {
-                row.style.display = '';
-            } else if (row) {
-                row.style.display = 'none';
-            }
+            this.setInfoTableRowVisible(row, Boolean(baseModel));
+        }
+
+        // Update file size row
+        const sizeEl = dialog.querySelector('.mr-info-size');
+        if (sizeEl) {
+            const sizeLabel = this.formatInfoDialogSize(data);
+            sizeEl.textContent = sizeLabel;
+            this.setInfoTableRowVisible(sizeEl.closest('tr'), Boolean(sizeLabel));
         }
 
         // Update trained words
         const trainedWordsEl = dialog.querySelector('.mr-info-trained-words');
         if (trainedWordsEl) {
-            const words = this.normalizeTrainedWords(data.trained_words || data.trainedWords || []);
+            const words = this.getInfoDialogTrainedWords(data);
+            const row = trainedWordsEl.closest('tr');
             if (words.length > 0) {
                 dialog._selectedTrainedWords = new Set();
                 trainedWordsEl.innerHTML = `<div class="mr-info-trained-words-list">${words.map(word => `
@@ -776,13 +979,11 @@ export const modelInfoMethods = {
                         ${this.escapeHtml(word)}
                     </button>
                 `).join('')}</div>`;
-                const row = trainedWordsEl.closest('tr');
-                if (row) row.style.display = '';
+                this.setInfoTableRowVisible(row, true);
                 this.bindTooltips(trainedWordsEl);
                 this.updateSelectedTrainedWordsSummary(dialog);
             } else {
-                const row = trainedWordsEl.closest('tr');
-                if (row) row.style.display = 'none';
+                this.setInfoTableRowVisible(row, false);
             }
         }
 
@@ -790,13 +991,12 @@ export const modelInfoMethods = {
         const clipSkipEl = dialog.querySelector('.mr-info-clip-skip');
         if (clipSkipEl) {
             const clipSkip = data.clip_skip || data.clipSkip;
+            const row = clipSkipEl.closest('tr');
             if (clipSkip && clipSkip !== 'None') {
                 clipSkipEl.textContent = clipSkip;
-                const row = clipSkipEl.closest('tr');
-                if (row) row.style.display = '';
+                this.setInfoTableRowVisible(row, true);
             } else {
-                const row = clipSkipEl.closest('tr');
-                if (row) row.style.display = 'none';
+                this.setInfoTableRowVisible(row, false);
             }
         }
 
@@ -823,9 +1023,7 @@ export const modelInfoMethods = {
                 descEl.classList.remove('is-expanded');
 
                 const shouldCollapse = textOnly.length > 520 || finalHtml.length > 900;
-                if (actionsEl) {
-                    actionsEl.style.display = shouldCollapse ? '' : 'none';
-                }
+                this.setInfoElementVisible(actionsEl, shouldCollapse);
                 if (toggleBtn) {
                     toggleBtn.textContent = 'Show more';
                 }
@@ -834,10 +1032,10 @@ export const modelInfoMethods = {
                 }
 
                 const row = descEl.closest('tr');
-                if (row) row.style.display = '';
+                this.setInfoTableRowVisible(row, true);
             } else {
                 const row = descEl.closest('tr');
-                if (row) row.style.display = 'none';
+                this.setInfoTableRowVisible(row, false);
             }
         }