diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index d98a090c32..7e0b17a7c1 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -257,6 +257,21 @@ set(LLAMA_TEST_NAME test-mtmd-c-api)
 llama_build_and_test(test-mtmd-c-api.c)
 target_link_libraries(${LLAMA_TEST_NAME} PRIVATE mtmd)
 
+# GGUF model data fetcher library for tests that need real model metadata
+# Only compile when cpp-httplib has SSL support (CPPHTTPLIB_OPENSSL_SUPPORT)
+if (TARGET cpp-httplib)
+    get_target_property(_cpp_httplib_defs cpp-httplib INTERFACE_COMPILE_DEFINITIONS)
+    if (_cpp_httplib_defs MATCHES "CPPHTTPLIB_OPENSSL_SUPPORT")
+        add_library(gguf-model-data STATIC gguf-model-data.cpp)
+        target_link_libraries(gguf-model-data PRIVATE common cpp-httplib)
+        target_include_directories(gguf-model-data PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
+
+        add_executable(test-gguf-model-data test-gguf-model-data.cpp)
+        target_link_libraries(test-gguf-model-data PRIVATE gguf-model-data common)
+        llama_test(test-gguf-model-data LABEL "model")
+    endif()
+endif()
+
 # dummy executable - not installed
 get_filename_component(TEST_TARGET test-c.c NAME_WE)
 add_executable(${TEST_TARGET} test-c.c)
diff --git a/tests/gguf-model-data.cpp b/tests/gguf-model-data.cpp
new file mode 100644
index 0000000000..3bc82c88da
--- /dev/null
+++ b/tests/gguf-model-data.cpp
@@ -0,0 +1,613 @@
+// GGUF binary parser adapted from the huggingface/gguf package.
+// Reference: https://github.com/huggingface/huggingface.js
+
+#include "gguf-model-data.h"
+
+#include "common.h"
+#include "gguf.h"
+
+#include <algorithm>
+#include <cstdio>
+#include <cstring>
+#include <filesystem>
+#include <fstream>
+
+#include "http.h"
+#define JSON_ASSERT GGML_ASSERT
+#include <nlohmann/json.hpp>
+
+// Equivalent of RangeView
+struct gguf_buf_reader {
+    const char * data;
+    size_t       size;
+    size_t       pos;
+
+    gguf_buf_reader(const std::vector<char> & buf) : data(buf.data()), size(buf.size()), pos(0) {}
+
+    bool has_n_bytes(size_t n) const {
+        return pos + n <= size;
+    }
+
+    template <typename T>
+    bool read_val(T & out) {
+        if (!has_n_bytes(sizeof(T))) {
+            return false;
+        }
+        memcpy(&out, data + pos, sizeof(T));
+        pos += sizeof(T);
+        return true;
+    }
+
+    bool read_str(std::string & out) {
+        uint64_t len;
+        if (!read_val(len)) {
+            return false;
+        }
+        if (!has_n_bytes((size_t)len)) {
+            return false;
+        }
+        out.assign(data + pos, (size_t)len);
+        pos += (size_t)len;
+        return true;
+    }
+
+    bool skip(size_t n) {
+        if (!has_n_bytes(n)) {
+            return false;
+        }
+        pos += n;
+        return true;
+    }
+};
+
+static size_t gguf_val_type_size(int32_t vtype) {
+    switch (vtype) {
+        case GGUF_TYPE_UINT8:   return 1;
+        case GGUF_TYPE_INT8:    return 1;
+        case GGUF_TYPE_UINT16:  return 2;
+        case GGUF_TYPE_INT16:   return 2;
+        case GGUF_TYPE_UINT32:  return 4;
+        case GGUF_TYPE_INT32:   return 4;
+        case GGUF_TYPE_FLOAT32: return 4;
+        case GGUF_TYPE_BOOL:    return 1;
+        case GGUF_TYPE_UINT64:  return 8;
+        case GGUF_TYPE_INT64:   return 8;
+        case GGUF_TYPE_FLOAT64: return 8;
+        default:                return 0; // string/array handled separately
+    }
+}
+
+// Equivalent of readMetadataValue(), skips unused values rather than storing
+static bool gguf_skip_value(gguf_buf_reader & r, int32_t vtype) {
+    if (vtype == GGUF_TYPE_STRING) {
+        std::string tmp;
+        return r.read_str(tmp);
+    }
+    if (vtype == GGUF_TYPE_ARRAY) {
+        int32_t elem_type;
+        uint64_t count;
+        if (!r.read_val(elem_type)) {
+            return false;
+        }
+        if (!r.read_val(count)) {
+            return false;
+        }
+        if (elem_type == GGUF_TYPE_STRING) {
+            for (uint64_t i = 0; i < count; i++) {
+                std::string tmp;
+                if (!r.read_str(tmp)) {
+                    return false;
+                }
+            }
+            return true;
+        }
+        if (elem_type == GGUF_TYPE_ARRAY) {
+            // nested arrays - recurse
+            for (uint64_t i = 0; i < count; i++) {
+                if (!gguf_skip_value(r, GGUF_TYPE_ARRAY)) {
+                    return false;
+                }
+            }
+            return true;
+        }
+        size_t elem_sz = gguf_val_type_size(elem_type);
+        if (elem_sz == 0) {
+            return false;
+        }
+        return r.skip((size_t)count * elem_sz);
+    }
+    size_t sz = gguf_val_type_size(vtype);
+    if (sz == 0) {
+        return false;
+    }
+    return r.skip(sz);
+}
+
+static bool gguf_read_uint32_val(gguf_buf_reader & r, int32_t vtype, uint32_t & out) {
+    if (vtype == GGUF_TYPE_UINT8) {
+        uint8_t v;
+        if (!r.read_val(v)) {
+            return false;
+        }
+        out = v;
+        return true;
+    }
+    if (vtype == GGUF_TYPE_INT8) {
+        int8_t v;
+        if (!r.read_val(v)) {
+            return false;
+        }
+        out = (uint32_t)v;
+        return true;
+    }
+    if (vtype == GGUF_TYPE_UINT16) {
+        uint16_t v;
+        if (!r.read_val(v)) {
+            return false;
+        }
+        out = v;
+        return true;
+    }
+    if (vtype == GGUF_TYPE_INT16) {
+        int16_t v;
+        if (!r.read_val(v)) {
+            return false;
+        }
+        out = (uint32_t)v;
+        return true;
+    }
+    if (vtype == GGUF_TYPE_UINT32) {
+        uint32_t v;
+        if (!r.read_val(v)) {
+            return false;
+        }
+        out = v;
+        return true;
+    }
+    if (vtype == GGUF_TYPE_INT32) {
+        int32_t v;
+        if (!r.read_val(v)) {
+            return false;
+        }
+        out = (uint32_t)v;
+        return true;
+    }
+    if (vtype == GGUF_TYPE_UINT64) {
+        uint64_t v;
+        if (!r.read_val(v)) {
+            return false;
+        }
+        out = (uint32_t)v;
+        return true;
+    }
+    if (vtype == GGUF_TYPE_INT64) {
+        int64_t v;
+        if (!r.read_val(v)) {
+            return false;
+        }
+        out = (uint32_t)v;
+        return true;
+    }
+    return false;
+}
+
+// Follows the same header -> KV -> tensor parsing sequence as gguf() huggingface/gguf
+static std::optional<gguf_remote_model> gguf_parse_meta(const std::vector<char> & buf) {
+    gguf_buf_reader r(buf);
+
+    // Header: magic(4) + version(4) + tensor_count(8) + kv_count(8) = 24 bytes minimum
+    uint32_t magic_raw;
+    if (!r.read_val(magic_raw)) {
+        return std::nullopt;
+    }
+    if (memcmp(&magic_raw, "GGUF", 4) != 0) {
+        fprintf(stderr, "gguf_parse_meta: invalid magic\n");
+        return std::nullopt;
+    }
+
+    uint32_t version;
+    if (!r.read_val(version)) {
+        return std::nullopt;
+    }
+    if (version < 2 || version > 3) {
+        fprintf(stderr, "gguf_parse_meta: unsupported version %u\n", version);
+        return std::nullopt;
+    }
+
+    int64_t tensor_count_raw;
+    int64_t kv_count_raw;
+    if (!r.read_val(tensor_count_raw)) {
+        return std::nullopt;
+    }
+    if (!r.read_val(kv_count_raw)) {
+        return std::nullopt;
+    }
+
+    uint64_t tensor_count = (uint64_t)tensor_count_raw;
+    uint64_t kv_count     = (uint64_t)kv_count_raw;
+
+    gguf_remote_model model;
+
+    std::string arch_prefix;
+
+    // Parse KV pairs
+    for (uint64_t i = 0; i < kv_count; i++) {
+        std::string key;
+        if (!r.read_str(key)) {
+            return std::nullopt;
+        }
+
+        int32_t vtype;
+        if (!r.read_val(vtype)) {
+            return std::nullopt;
+        }
+
+        if (key == "general.architecture" && vtype == GGUF_TYPE_STRING) {
+            if (!r.read_str(model.architecture)) {
+                return std::nullopt;
+            }
+            arch_prefix = model.architecture + ".";
+            continue;
+        }
+
+        // Extract split.count for proper handling of split files
+        if (key == "split.count") {
+            uint32_t v;
+            if (!gguf_read_uint32_val(r, vtype, v)) {
+                return std::nullopt;
+            }
+            model.n_split = (uint16_t)v;
+            continue;
+        }
+
+        // Extract split.tensors.count so we can verify we have all tensors
+        if (key == "split.tensors.count") {
+            uint32_t v;
+            if (!gguf_read_uint32_val(r, vtype, v)) {
+                return std::nullopt;
+            }
+            model.n_split_tensors = v;
+            continue;
+        }
+
+        if (!arch_prefix.empty()) {
+            uint32_t * target = nullptr;
+
+            if      (key == arch_prefix + "embedding_length")         { target = &model.n_embd; }
+            else if (key == arch_prefix + "feed_forward_length")      { target = &model.n_ff; }
+            else if (key == arch_prefix + "block_count")              { target = &model.n_layer; }
+            else if (key == arch_prefix + "attention.head_count")     { target = &model.n_head; }
+            else if (key == arch_prefix + "attention.head_count_kv")  { target = &model.n_head_kv; }
+            else if (key == arch_prefix + "expert_count")             { target = &model.n_expert; }
+            else if (key == arch_prefix + "attention.key_length")     { target = &model.n_embd_head_k; }
+            else if (key == arch_prefix + "attention.value_length")   { target = &model.n_embd_head_v; }
+
+            if (target) {
+                if (!gguf_read_uint32_val(r, vtype, *target)) {
+                    return std::nullopt;
+                }
+                continue;
+            }
+        }
+
+        if (!gguf_skip_value(r, vtype)) {
+            return std::nullopt;
+        }
+    }
+
+    // Parse tensor info entries
+    model.tensors.reserve((size_t)tensor_count);
+    for (uint64_t i = 0; i < tensor_count; i++) {
+        gguf_remote_tensor t;
+
+        if (!r.read_str(t.name)) {
+            return std::nullopt;
+        }
+        if (!r.read_val(t.n_dims)) {
+            return std::nullopt;
+        }
+
+        if (t.n_dims > 4) {
+            fprintf(stderr, "gguf_parse_meta: tensor '%s' has %u dims (max 4)\n", t.name.c_str(), t.n_dims);
+            return std::nullopt;
+        }
+
+        for (uint32_t d = 0; d < t.n_dims; d++) {
+            if (!r.read_val(t.ne[d])) {
+                return std::nullopt;
+            }
+        }
+
+        int32_t type_raw;
+        if (!r.read_val(type_raw)) {
+            return std::nullopt;
+        }
+        t.type = (ggml_type)type_raw;
+
+        uint64_t offset;
+        if (!r.read_val(offset)) {
+            return std::nullopt;
+        }
+
+        // Infer n_vocab from token_embd.weight
+        if (t.name == "token_embd.weight") {
+            model.n_vocab = (uint32_t)t.ne[1];
+        }
+
+        model.tensors.push_back(std::move(t));
+    }
+
+    return model;
+}
+
+// cache handling for local download
+static std::string get_default_cache_dir() {
+    return fs_get_cache_directory() + "gguf-headers/";
+}
+
+static std::string sanitize_for_path(const std::string & s) {
+    std::string out = s;
+    for (char & c : out) {
+        if (c == '/' || c == '\\' || c == ':') {
+            c = '_';
+        }
+    }
+    return out;
+}
+
+static bool read_file(const std::string & path, std::vector<char> & out) {
+    std::ifstream f(path, std::ios::binary | std::ios::ate);
+    if (!f.good()) {
+        return false;
+    }
+    auto sz = f.tellg();
+    if (sz <= 0) {
+        return false;
+    }
+    out.resize((size_t)sz);
+    f.seekg(0);
+    f.read(out.data(), sz);
+    return f.good();
+}
+
+static bool write_file(const std::string & path, const std::vector<char> & data) {
+    std::ofstream f(path, std::ios::binary | std::ios::trunc);
+    if (!f.good()) {
+        return false;
+    }
+    f.write(data.data(), (std::streamsize)data.size());
+    return f.good();
+}
+
+// HuggingFace file auto-detection and HTTP download
+static std::pair<long, std::vector<char>> gguf_http_get(
+        const std::string & url,
+        const httplib::Headers & headers = {},
+        int timeout_sec = 60) {
+    try {
+        auto [cli, parts] = common_http_client(url);
+
+        if (timeout_sec > 0) {
+            cli.set_read_timeout(timeout_sec, 0);
+            cli.set_write_timeout(timeout_sec, 0);
+        }
+        cli.set_connection_timeout(30, 0);
+
+        std::vector<char> body;
+        auto res = cli.Get(parts.path, headers,
+            [&](const char * data, size_t len) {
+                body.insert(body.end(), data, data + len);
+                return true;
+            }, nullptr);
+
+        if (!res) {
+            fprintf(stderr, "gguf_fetch: HTTP request failed for %s (error %d)\n",
+                    url.c_str(), (int)res.error());
+            return {-1, {}};
+        }
+        return {res->status, std::move(body)};
+    } catch (const std::exception & e) {
+        fprintf(stderr, "gguf_fetch: HTTP error: %s\n", e.what());
+        return {-1, {}};
+    }
+}
+
+// Find the filename for given repo/quant.
+// For split models, returns the first shard (the one containing "00001-of-")
+// split_prefix is set to the portion before "-00001-of-XXXXX.gguf" when a split file is found
+static std::string detect_gguf_filename(const std::string & repo, const std::string & quant,
+                                        std::string & split_prefix) {
+    split_prefix.clear();
+    std::string api_url = "https://huggingface.co/api/models/" + repo;
+
+    auto [code, body] = gguf_http_get(api_url, {}, 30);
+    if (code != 200 || body.empty()) {
+        fprintf(stderr, "gguf_fetch: failed to query HF API for %s (HTTP %ld)\n", repo.c_str(), code);
+        return "";
+    }
+
+    nlohmann::json j;
+    try {
+        j = nlohmann::json::parse(body.begin(), body.end());
+    } catch (...) {
+        fprintf(stderr, "gguf_fetch: failed to parse HF API response\n");
+        return "";
+    }
+
+    if (!j.contains("siblings") || !j["siblings"].is_array()) {
+        fprintf(stderr, "gguf_fetch: unexpected HF API response format\n");
+        return "";
+    }
+
+    std::vector<std::string> matches;
+    std::string quant_upper = quant;
+    for (char & c : quant_upper) { c = (char)toupper(c); }
+
+    for (const auto & sibling : j["siblings"]) {
+        if (!sibling.contains("rfilename")) { continue; }
+        std::string fname = sibling["rfilename"].get<std::string>();
+        if (fname.size() < 5 || fname.substr(fname.size() - 5) != ".gguf") {
+            continue;
+        }
+
+        std::string fname_upper = fname;
+        for (char & c : fname_upper) { c = (char)toupper(c); }
+        if (fname_upper.find(quant_upper) != std::string::npos) {
+            matches.push_back(fname);
+        }
+    }
+
+    if (matches.empty()) {
+        fprintf(stderr, "gguf_fetch: no .gguf files matching '%s' in %s\n", quant.c_str(), repo.c_str());
+        return "";
+    }
+
+    std::sort(matches.begin(), matches.end());
+
+    // Prefer non-split, non-supplementary file
+    for (const auto & m : matches) {
+        if (m.find("-of-") == std::string::npos && m.find("mmproj") == std::string::npos) {
+            return m;
+        }
+    }
+
+    // Return the first shard (00001-of-) and extract the prefix
+    for (const auto & m : matches) {
+        auto pos = m.find("-00001-of-");
+        if (pos != std::string::npos) {
+            split_prefix = m.substr(0, pos);
+            return m;
+        }
+    }
+
+    return matches[0];
+}
+
+static std::optional<gguf_remote_model> fetch_and_parse(
+        const std::string & repo,
+        const std::string & filename,
+        const std::string & cache_path) {
+    std::string url = "https://huggingface.co/" + repo + "/resolve/main/" + filename;
+
+    // Progressive download inspired by RangeView.fetchChunk()
+    // Start at 2MB, double each time, cap at 64MB
+    size_t chunk_size = 2 * 1024 * 1024;
+    const size_t max_chunk = 64 * 1024 * 1024;
+
+    while (chunk_size <= max_chunk) {
+        fprintf(stderr, "gguf_fetch: downloading %zu bytes from %s\n", chunk_size, filename.c_str());
+
+        char range_buf[64];
+        snprintf(range_buf, sizeof(range_buf), "bytes=0-%zu", chunk_size - 1);
+        httplib::Headers headers = {{"Range", range_buf}};
+
+        auto [code, body] = gguf_http_get(url, headers, 120);
+        if (code != 200 && code != 206) {
+            fprintf(stderr, "gguf_fetch: HTTP %ld fetching %s\n", code, url.c_str());
+            return std::nullopt;
+        }
+
+        if (body.empty()) {
+            fprintf(stderr, "gguf_fetch: empty response\n");
+            return std::nullopt;
+        }
+
+        auto result = gguf_parse_meta(body);
+        if (result.has_value()) {
+            write_file(cache_path, body);
+            return result;
+        }
+
+        if (code == 200) {
+            fprintf(stderr, "gguf_fetch: server returned full response but metadata parse failed\n");
+            return std::nullopt;
+        }
+
+        // Parse failed, try larger chunk
+        chunk_size *= 2;
+    }
+
+    fprintf(stderr, "gguf_fetch: metadata exceeds 64MB, giving up\n");
+    return std::nullopt;
+}
+
+// Try cache first, then fetch and parse a single GGUF shard.
+static std::optional<gguf_remote_model> fetch_or_cached(
+        const std::string & repo,
+        const std::string & filename,
+        const std::string & cdir,
+        const std::string & repo_part) {
+    std::string fname_part = sanitize_for_path(filename);
+    std::string cache_path = cdir + "/" + repo_part + "--" + fname_part + ".partial";
+
+    {
+        std::vector<char> cached;
+        if (std::filesystem::exists(cache_path) && read_file(cache_path, cached)) {
+            auto result = gguf_parse_meta(cached);
+            if (result.has_value()) {
+                fprintf(stderr, "gguf_fetch: loaded from cache: %s\n", cache_path.c_str());
+                return result;
+            }
+        }
+    }
+
+    fs_create_directory_with_parents(cdir);
+    return fetch_and_parse(repo, filename, cache_path);
+}
+
+std::optional<gguf_remote_model> gguf_fetch_model_meta(
+        const std::string & repo,
+        const std::string & quant,
+        const std::string & cache_dir) {
+    std::string cdir = cache_dir.empty() ? get_default_cache_dir() : cache_dir;
+    std::string repo_part = sanitize_for_path(repo);
+
+    std::string split_prefix;
+    std::string filename = detect_gguf_filename(repo, quant, split_prefix);
+    if (filename.empty()) {
+        return std::nullopt;
+    }
+
+    auto model_opt = fetch_or_cached(repo, filename, cdir, repo_part);
+    if (!model_opt.has_value()) {
+        fprintf(stderr, "gguf_fetch: failed to fetch %s\n", filename.c_str());
+        return std::nullopt;
+    }
+
+    auto & model = model_opt.value();
+
+    // If the model is split across multiple files we need to fetch the remaining shards metadata
+    if (model.n_split > 1) {
+        if (split_prefix.empty()) {
+            fprintf(stderr, "gguf_fetch: model reports %u splits but filename has no split pattern\n", model.n_split);
+            return std::nullopt;
+        }
+
+        fprintf(stderr, "gguf_fetch: split model with %u shards, fetching remaining %u...\n",
+                model.n_split, model.n_split - 1);
+
+        for (int i = 2; i <= model.n_split; i++) {
+            char num_buf[6], total_buf[6];
+            snprintf(num_buf,   sizeof(num_buf),   "%05d", i);
+            snprintf(total_buf, sizeof(total_buf), "%05d", (int)model.n_split);
+            std::string shard_name = split_prefix + "-" + num_buf + "-of-" + total_buf + ".gguf";
+
+            auto shard = fetch_or_cached(repo, shard_name, cdir, repo_part);
+            if (!shard.has_value()) {
+                fprintf(stderr, "gguf_fetch: failed to fetch shard %d: %s\n", i, shard_name.c_str());
+                return std::nullopt;
+            }
+
+            model.tensors.insert(model.tensors.end(),
+                std::make_move_iterator(shard->tensors.begin()),
+                std::make_move_iterator(shard->tensors.end()));
+        }
+
+        if (model.n_split_tensors > 0 && model.tensors.size() != model.n_split_tensors) {
+            fprintf(stderr, "gguf_fetch: WARNING: expected %u tensors from split.tensors.count, got %zu\n",
+                    model.n_split_tensors, model.tensors.size());
+        }
+    }
+
+    return model_opt;
+}
diff --git a/tests/gguf-model-data.h b/tests/gguf-model-data.h
new file mode 100644
index 0000000000..ed433791ad
--- /dev/null
+++ b/tests/gguf-model-data.h
@@ -0,0 +1,42 @@
+#pragma once
+
+#include "ggml.h"
+
+#include <cstdint>
+#include <optional>
+#include <string>
+#include <vector>
+
+struct gguf_remote_tensor {
+    std::string  name;
+    ggml_type    type    = GGML_TYPE_F32;
+    int64_t      ne[4]   = {1, 1, 1, 1};  // dimensions, unused dims = 1
+    uint32_t     n_dims  = 0;
+};
+
+struct gguf_remote_model {
+    // Selected KV metadata
+    std::string architecture;               // general.architecture
+    uint32_t    n_embd          = 0;        // <arch>.embedding_length
+    uint32_t    n_ff            = 0;        // <arch>.feed_forward_length
+    uint32_t    n_vocab         = 0;        // inferred from token_embd.weight ne[1]
+    uint32_t    n_layer         = 0;        // <arch>.block_count
+    uint32_t    n_head          = 0;        // <arch>.attention.head_count
+    uint32_t    n_head_kv       = 0;        // <arch>.attention.head_count_kv
+    uint32_t    n_expert        = 0;        // <arch>.expert_count (0 if absent)
+    uint32_t    n_embd_head_k   = 0;        // <arch>.attention.key_length
+    uint32_t    n_embd_head_v   = 0;        // <arch>.attention.value_length
+    uint16_t    n_split         = 0;        // split.count (0 = not split)
+    uint32_t    n_split_tensors = 0;        // split.tensors.count (0 if not split)
+
+    std::vector<gguf_remote_tensor> tensors;
+};
+
+// Fetch model metadata from HuggingFace with local caching.
+// repo: e.g., "ggml-org/Qwen3-32B-GGUF"
+// quant: e.g., "Q8_0" -- auto-detects filename (including first shard of split models)
+// Returns nullopt if download fails or network is unavailable.
+std::optional<gguf_remote_model> gguf_fetch_model_meta(
+    const std::string & repo,
+    const std::string & quant = "Q8_0",
+    const std::string & cache_dir = "");  // empty = default
diff --git a/tests/test-gguf-model-data.cpp b/tests/test-gguf-model-data.cpp
new file mode 100644
index 0000000000..cc0174961d
--- /dev/null
+++ b/tests/test-gguf-model-data.cpp
@@ -0,0 +1,121 @@
+#include "gguf-model-data.h"
+
+#include <cstdio>
+
+#define TEST_ASSERT(cond, msg) \
+    do { \
+        if (!(cond)) { \
+            fprintf(stderr, "FAIL: %s (line %d): %s\n", #cond, __LINE__, msg); \
+            return 1; \
+        } \
+    } while (0)
+
+int main() {
+    fprintf(stderr, "=== test-gguf-model-data ===\n");
+
+    // Fetch Qwen3-0.6B Q8_0 metadata
+    auto result = gguf_fetch_model_meta("ggml-org/Qwen3-0.6B-GGUF", "Q8_0");
+
+    if (!result.has_value()) {
+        fprintf(stderr, "SKIP: could not fetch model metadata (no network or HTTP disabled)\n");
+        return 0;
+    }
+
+    const auto & model = result.value();
+
+    fprintf(stderr, "Architecture:  %s\n", model.architecture.c_str());
+    fprintf(stderr, "n_embd:        %u\n", model.n_embd);
+    fprintf(stderr, "n_ff:          %u\n", model.n_ff);
+    fprintf(stderr, "n_vocab:       %u\n", model.n_vocab);
+    fprintf(stderr, "n_layer:       %u\n", model.n_layer);
+    fprintf(stderr, "n_head:        %u\n", model.n_head);
+    fprintf(stderr, "n_head_kv:     %u\n", model.n_head_kv);
+    fprintf(stderr, "n_expert:      %u\n", model.n_expert);
+    fprintf(stderr, "n_embd_head_k: %u\n", model.n_embd_head_k);
+    fprintf(stderr, "n_embd_head_v: %u\n", model.n_embd_head_v);
+    fprintf(stderr, "tensors:       %zu\n", model.tensors.size());
+
+    // Verify architecture
+    TEST_ASSERT(model.architecture == "qwen3", "expected architecture 'qwen3'");
+
+    // Verify key dimensions (Qwen3-0.6B)
+    TEST_ASSERT(model.n_layer == 28, "expected n_layer == 28");
+    TEST_ASSERT(model.n_embd == 1024, "expected n_embd == 1024");
+    TEST_ASSERT(model.n_head == 16, "expected n_head == 16");
+    TEST_ASSERT(model.n_head_kv == 8, "expected n_head_kv == 8");
+    TEST_ASSERT(model.n_expert == 0, "expected n_expert == 0 (not MoE)");
+    TEST_ASSERT(model.n_vocab == 151936, "expected n_vocab == 151936");
+
+    // Verify tensor count
+    TEST_ASSERT(model.tensors.size() == 311, "expected tensor count == 311");
+
+    // Verify known tensor names exist
+    bool found_attn_q = false;
+    bool found_token_embd = false;
+    bool found_output_norm = false;
+    for (const auto & t : model.tensors) {
+        if (t.name == "blk.0.attn_q.weight") {
+            found_attn_q = true;
+        }
+        if (t.name == "token_embd.weight") {
+            found_token_embd = true;
+        }
+        if (t.name == "output_norm.weight") {
+            found_output_norm = true;
+        }
+    }
+    TEST_ASSERT(found_attn_q, "expected tensor 'blk.0.attn_q.weight'");
+    TEST_ASSERT(found_token_embd, "expected tensor 'token_embd.weight'");
+    TEST_ASSERT(found_output_norm, "expected tensor 'output_norm.weight'");
+
+    // Verify token_embd.weight shape
+    for (const auto & t : model.tensors) {
+        if (t.name == "token_embd.weight") {
+            TEST_ASSERT(t.ne[0] == 1024, "expected token_embd.weight ne[0] == 1024");
+            TEST_ASSERT(t.n_dims == 2, "expected token_embd.weight to be 2D");
+            break;
+        }
+    }
+
+    // Test that second call uses cache (just call again, it should work)
+    auto result2 = gguf_fetch_model_meta("ggml-org/Qwen3-0.6B-GGUF", "Q8_0");
+    TEST_ASSERT(result2.has_value(), "cached fetch should succeed");
+    TEST_ASSERT(result2->tensors.size() == model.tensors.size(), "cached result should match");
+
+    // Test a split MoE model without specifying quant (should default to Q8_0)
+    auto result3 = gguf_fetch_model_meta("ggml-org/GLM-4.6V-GGUF");
+    if (!result3.has_value()) {
+        fprintf(stderr, "SKIP: could not fetch GLM-4.6V metadata (no network?)\n");
+        return 0;
+    }
+    const auto & model3 = result3.value();
+
+    fprintf(stderr, "Architecture:  %s\n", model3.architecture.c_str());
+    fprintf(stderr, "n_embd:        %u\n", model3.n_embd);
+    fprintf(stderr, "n_ff:          %u\n", model3.n_ff);
+    fprintf(stderr, "n_vocab:       %u\n", model3.n_vocab);
+    fprintf(stderr, "n_layer:       %u\n", model3.n_layer);
+    fprintf(stderr, "n_head:        %u\n", model3.n_head);
+    fprintf(stderr, "n_head_kv:     %u\n", model3.n_head_kv);
+    fprintf(stderr, "n_expert:      %u\n", model3.n_expert);
+    fprintf(stderr, "n_embd_head_k: %u\n", model3.n_embd_head_k);
+    fprintf(stderr, "n_embd_head_v: %u\n", model3.n_embd_head_v);
+    fprintf(stderr, "tensors:       %zu\n", model3.tensors.size());
+
+    // Verify architecture
+    TEST_ASSERT(model3.architecture == "glm4moe", "expected architecture 'glm4moe'");
+
+    // Verify key dimensions (GLM-4.6V)
+    TEST_ASSERT(model3.n_layer == 46, "expected n_layer == 46");
+    TEST_ASSERT(model3.n_embd == 4096, "expected n_embd == 4096");
+    TEST_ASSERT(model3.n_head == 96, "expected n_head == 96");
+    TEST_ASSERT(model3.n_head_kv == 8, "expected n_head_kv == 8");
+    TEST_ASSERT(model3.n_expert == 128, "expected n_expert == 128 (MoE)");
+    TEST_ASSERT(model3.n_vocab == 151552, "expected n_vocab == 151552");
+
+    // Verify tensor count
+    TEST_ASSERT(model3.tensors.size() == 780, "expected tensor count == 780");
+
+    fprintf(stderr, "=== ALL TESTS PASSED ===\n");
+    return 0;
+}