convert.py : add consolidated.safetensors for mixtral 8x22b

[slaren]

The architecture seems to be the same as mixtral 8x7b, however the data is in consolidated.safetensors. Adding this file to convert.py seems to be enough to make it work.

main: build = 2618 (bcb1678a)
main: built with cc (Ubuntu 12.3.0-9ubuntu2) 12.3.0 for x86_64-linux-gnu
main: seed  = 1712752654
llama_model_loader: loaded meta data with 20 key-value pairs and 563 tensors from mixtral2-q4_0.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              = llama
llama_model_loader: - kv   1:                               general.name str              = tmp
llama_model_loader: - kv   2:                           llama.vocab_size u32              = 32000
llama_model_loader: - kv   3:                       llama.context_length u32              = 32768
llama_model_loader: - kv   4:                     llama.embedding_length u32              = 6144
llama_model_loader: - kv   5:                          llama.block_count u32              = 56
llama_model_loader: - kv   6:                  llama.feed_forward_length u32              = 16384
llama_model_loader: - kv   7:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   8:                 llama.attention.head_count u32              = 48
llama_model_loader: - kv   9:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  10:                         llama.expert_count u32              = 8
llama_model_loader: - kv  11:                    llama.expert_used_count u32              = 2
llama_model_loader: - kv  12:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  13:                       llama.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  14:                          general.file_type u32              = 2
llama_model_loader: - kv  15:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  16:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  17:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  18:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  19:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  113 tensors
llama_model_loader: - type  f16:   56 tensors
llama_model_loader: - type q4_0:  281 tensors
llama_model_loader: - type q8_0:  112 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = llama
llm_load_print_meta: vocab type       = SPM
llm_load_print_meta: n_vocab          = 32000
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: n_ctx_train      = 32768
llm_load_print_meta: n_embd           = 6144
llm_load_print_meta: n_head           = 48
llm_load_print_meta: n_head_kv        = 8
llm_load_print_meta: n_layer          = 56
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_embd_head_k    = 128
llm_load_print_meta: n_embd_head_v    = 128
llm_load_print_meta: n_gqa            = 6
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       = 0.0e+00
llm_load_print_meta: f_norm_rms_eps   = 1.0e-05
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             = 16384
llm_load_print_meta: n_expert         = 8
llm_load_print_meta: n_expert_used    = 2
llm_load_print_meta: causal attn      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 0
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx  = 32768
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: model type       = ?B
llm_load_print_meta: model ftype      = Q4_0
llm_load_print_meta: model params     = 140.62 B
llm_load_print_meta: model size       = 74.05 GiB (4.52 BPW)
llm_load_print_meta: general.name     = tmp
llm_load_print_meta: BOS token        = 1 '<s>'
llm_load_print_meta: EOS token        = 2 '</s>'
llm_load_print_meta: UNK token        = 0 '<unk>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 3090 Ti, compute capability 8.6, VMM: yes
llm_load_tensors: ggml ctx size =    0.77 MiB
llm_load_tensors: offloading 16 repeating layers to GPU
llm_load_tensors: offloaded 16/57 layers to GPU
llm_load_tensors:        CPU buffer size = 55089.18 MiB
llm_load_tensors:      CUDA0 buffer size = 21590.25 MiB
....................................................................................................
llama_new_context_with_model: n_ctx      = 512
llama_new_context_with_model: n_batch    = 512
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: freq_base  = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:  CUDA_Host KV buffer size =    80.00 MiB
llama_kv_cache_init:      CUDA0 KV buffer size =    32.00 MiB
llama_new_context_with_model: KV self size  =  112.00 MiB, K (f16):   56.00 MiB, V (f16):   56.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.49 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =   604.25 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =    13.01 MiB
llama_new_context_with_model: graph nodes  = 2750
llama_new_context_with_model: graph splits = 484

system_info: n_threads = 16 / 32 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 |
perplexity: tokenizing the input ..
perplexity: tokenization took 495.064 ms
perplexity: calculating perplexity over 642 chunks, n_ctx=512, batch_size=512, n_seq=1
perplexity: 12.67 seconds per pass - ETA 2 hours 15.57 minutes
[1]2.7213,[2]3.2070,[3]3.7790,[4]3.3997,[5]2.8887,[6]2.6979,[7]2.5652,[8]2.5075,[9]2.5407,[10]2.5108,[11]2.5205,[12]2.5158,[13]2.5843,[14]2.6868,[15]2.6940,[16]2.7038,[17]2.7709,[18]2.7684,[19]2.8067,[20]2.9213,[21]2.9347,[22]2.8439,[23]2.8122,

[phymbert]

Fixes #6580

[slaren]

The goal here is not to add full support for mixtral 8x22b, it looks to be working, but there may be other changes that I am missing. This is only meant to be a small change to the convert script to allow people to try it.

[phymbert]

We might populate model type:

llm_load_print_meta: model type = ?B

[rankaiyx]

We might populate model type:

llm_load_print_meta: model type = ?B

8x22B
or 176B?