Problem with embeddings model #31
Description
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