Problem with embeddings model

[filtercodes]

I'm trying to run an embedding model mxbai-embed-large-v1-f16.gguf on iOS. Have copied it to the device and the model loads with logs:

llm_test1.app/mxbai-embed-large-v1-f16.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = bert
llama_model_loader: - kv   1:                               general.name str              = mxbai-embed-large-v1
llama_model_loader: - kv   2:                           bert.block_count u32              = 24
llama_model_loader: - kv   3:                        bert.context_length u32              = 512
llama_model_loader: - kv   4:                      bert.embedding_length u32              = 1024
llama_model_loader: - kv   5:                   bert.feed_forward_length u32              = 4096
llama_model_loader: - kv   6:                  bert.attention.head_count u32              = 16
llama_model_loader: - kv   7:          bert.attention.layer_norm_epsilon f32              = 0.000000
llama_model_loader: - kv   8:                          general.file_type u32              = 1
llama_model_loader: - kv   9:                      bert.attention.causal bool             = false
llama_model_loader: - kv  10:                          bert.pooling_type u32              = 2
llama_model_loader: - kv  11:            tokenizer.ggml.token_type_count u32              = 2
llama_model_loader: - kv  12:                tokenizer.ggml.bos_token_id u32              = 101
llama_model_loader: - kv  13:                tokenizer.ggml.eos_token_id u32              = 102
llama_model_loader: - kv  14:                       tokenizer.ggml.model str              = bert
llama_model_loader: - kv  15:                      tokenizer.ggml.tokens arr[str,30522]   = ["[PAD]", "[unused0]", "[unused1]", "...
llama_model_loader: - kv  16:                      tokenizer.ggml.scores arr[f32,30522]   = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  17:                  tokenizer.ggml.token_type arr[i32,30522]   = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  18:            tokenizer.ggml.unknown_token_id u32              = 100
llama_model_loader: - kv  19:          tokenizer.ggml.seperator_token_id u32              = 102
llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  21:                tokenizer.ggml.cls_token_id u32              = 101
llama_model_loader: - kv  22:               tokenizer.ggml.mask_token_id u32              = 103
llama_model_loader: - type  f32:  243 tensors
llama_model_loader: - type  f16:  146 tensors
llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
llm_load_vocab: special tokens cache size = 5
llm_load_vocab: token to piece cache size = 0.2032 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = bert
llm_load_print_meta: vocab type       = WPM
llm_load_print_meta: n_vocab          = 30522
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 512
llm_load_print_meta: n_embd           = 1024
llm_load_print_meta: n_layer          = 24
llm_load_print_meta: n_head           = 16
llm_load_print_meta: n_head_kv        = 16
llm_load_print_meta: n_rot            = 64
llm_load_print_meta: n_swa            = 0
llm_load_print_meta: n_embd_head_k    = 64
llm_load_print_meta: n_embd_head_v    = 64
llm_load_print_meta: n_gqa            = 1
llm_load_print_meta: n_embd_k_gqa     = 1024
llm_load_print_meta: n_embd_v_gqa     = 1024
llm_load_print_meta: f_norm_eps       = 1.0e-12
llm_load_print_meta: f_norm_rms_eps   = 0.0e+00
llm_load_print_meta: f_clamp_kqv      = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale    = 0.0e+00
llm_load_print_meta: n_ff             = 4096
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 0
llm_load_print_meta: pooling type     = 2
llm_load_print_meta: rope type        = 2
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn  = 512
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = 335M
llm_load_print_meta: model ftype      = F16
llm_load_print_meta: model params     = 334.09 M
llm_load_print_meta: model size       = 637.85 MiB (16.02 BPW) 
llm_load_print_meta: general.name     = mxbai-embed-large-v1
llm_load_print_meta: BOS token        = 101 '[CLS]'
llm_load_print_meta: EOS token        = 102 '[SEP]'
llm_load_print_meta: UNK token        = 100 '[UNK]'
llm_load_print_meta: SEP token        = 102 '[SEP]'
llm_load_print_meta: PAD token        = 0 '[PAD]'
llm_load_print_meta: CLS token        = 101 '[CLS]'
llm_load_print_meta: MASK token       = 103 '[MASK]'
llm_load_print_meta: LF token         = 0 '[PAD]'
llm_load_print_meta: EOG token        = 102 '[SEP]'
llm_load_print_meta: max token length = 21
llm_load_tensors: ggml ctx size =    0.16 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/25 layers to GPU
llm_load_tensors:        CPU buffer size =   637.85 MiB
Grammar: llama_new_context_with_model: n_ctx      = 2048
llama_new_context_with_model: n_batch    = 2048
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base  = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:        CPU KV buffer size =   192.00 MiB
llama_new_context_with_model: KV self size  =  192.00 MiB, K (f16):   96.00 MiB, V (f16):   96.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.12 MiB
llama_new_context_with_model:        CPU compute buffer size =    25.00 MiB
llama_new_context_with_model: graph nodes  = 848
llama_new_context_with_model: graph splits = 337
AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | 
Logits inited.

But after passing the prompt it returns an error:

llama.cpp:17219: strcmp(res->name, "result_output") == 0 && "missing result_output tensor"
" UserInfo={NSLocalizedDescription=GGML_ASSERT:

I tried to set params.embedding to true. Not sure is there anything else I'm missing here