diff --git a/tests/test-model-random.cpp b/tests/test-model-random.cpp
index 218cfcb82b..3b636039b4 100644
--- a/tests/test-model-random.cpp
+++ b/tests/test-model-random.cpp
@@ -7,6 +7,7 @@
 #include <algorithm>
 #include <cstdint>
 #include <cstdio>
+#include <queue>
 #include <random>
 #include <utility>
 // NOTE: the llm_arch enum is in the private API
@@ -895,13 +896,68 @@ static void permute_from_ids(uint8_t * array, size_t elem_size, const std::vecto
     memcpy(array, tmp.data(), ids.size() * elem_size);
 }
 
-static void shuffle_batch(struct llama_batch & batch, std::mt19937 & rng) {
-    std::vector<int32_t> ids(batch.n_tokens);
-    for (int32_t i = 0; i < batch.n_tokens; ++i) {
-        ids[i] = i;
+static std::vector<int32_t> random_merge_ids(std::vector<std::queue<int32_t>> & ids_per_seq, std::mt19937 & rng) {
+    size_t total_size = 0;
+    for (const auto & v : ids_per_seq) {
+        total_size += v.size();
     }
 
-    std::shuffle(ids.begin(), ids.end(), rng);
+    std::vector<int32_t> ids;
+    ids.reserve(total_size);
+
+    for (size_t i = 1; i <= total_size; ++i) {
+        // need weighted random selection
+        std::uniform_int_distribution<int32_t> rand(0, total_size - i);
+        int32_t rand_id = rand(rng);
+
+        // find out in which seq set this would belong
+        for (size_t j = 0; j < ids_per_seq.size(); ++j) {
+            if (rand_id < (int32_t) ids_per_seq[j].size()) {
+                ids.push_back(ids_per_seq[j].front());
+                ids_per_seq[j].pop();
+                break;
+            }
+            rand_id -= ids_per_seq[j].size();
+        }
+    }
+
+    return ids;
+}
+
+// shuffle across sequences but not within seqences
+static void shuffle_batch(struct llama_batch & batch, std::mt19937 & rng) {
+    std::vector<std::set<llama_seq_id>> seq_sets;
+    std::vector<std::queue<int32_t>> ids_per_seq;
+
+    for (int32_t i = 0; i < batch.n_tokens; ++i) {
+        int32_t seq_set_id = -1;
+        for (size_t s = 0; s < seq_sets.size(); ++s) {
+            for (int j = 0; j < batch.n_seq_id[i]; ++j) {
+                if (seq_sets[s].find(batch.seq_id[i][j]) != seq_sets[s].end()) {
+                    // any match, to avoid shuffling between dependent sets
+                    seq_set_id = s;
+                    break;
+                }
+            }
+        }
+
+        if (seq_set_id < 0) {
+            seq_sets.push_back({});
+            ids_per_seq.push_back({});
+            seq_set_id = seq_sets.size() - 1;
+        }
+        std::set<llama_seq_id> & seq_set = seq_sets[seq_set_id];
+        for (int j = 0; j < batch.n_seq_id[i]; ++j) {
+            // make sure the set contains all relevant seq_ids
+            seq_set.insert(batch.seq_id[i][j]);
+        }
+
+        ids_per_seq[seq_set_id].push(i);
+    }
+
+    std::vector<int32_t> ids = random_merge_ids(ids_per_seq, rng);
+
+    GGML_ASSERT(ids.size() == (size_t) batch.n_tokens);
 
     if (batch.token) {
         permute_from_ids((uint8_t *) batch.token, sizeof(*batch.token), ids);
@@ -991,6 +1047,8 @@ int main(int argc, char ** argv) {
                     ref_params.n_ubatch = 1;
                     ref_params.n_ctx = n_seq_len;
                     ref_params.n_seq_max = 1;
+                    ref_params.type_k = GGML_TYPE_F32;
+                    ref_params.type_v = GGML_TYPE_F32;
 
                     llama_context * ref_ctx = llama_init_from_model(model, ref_params);
 
@@ -1006,10 +1064,8 @@ int main(int argc, char ** argv) {
 
                 for (bool shuffle : { false, true }) {
 
-                    // skip shuffling the batch for non-recurrent models
-                    // (simple splits don't handle shuffled batches correctly)
-                    // FIXME: remove this
-                    if (shuffle && !llama_model_is_recurrent(model)) {
+                    // can't really shuffle a single sequence with itself
+                    if (shuffle && n_seq_max == 1) {
                         continue;
                     }
 
@@ -1022,6 +1078,9 @@ int main(int argc, char ** argv) {
                         ctx_params.n_seq_max = n_seq_max;
                         ctx_params.n_ubatch = n_ubatch;
                         ctx_params.n_batch = n_batch;
+                        // TODO: remove once F16 is fixed on ARM
+                        ctx_params.type_k = GGML_TYPE_F32;
+                        ctx_params.type_v = GGML_TYPE_F32;
 
                         llama_context * ctx = llama_init_from_model(model, ctx_params);