MaziyarPanahi

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Mixtral-8x22B-Instruct-v0.1-GGUF

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Downloaded the Q5_K_M model but can't seem to get it running with llama-cpp-python. Anyone got any idea how to fix this? The error I'm receiving is as follows:

ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 3 CUDA devices:
Device 0: NVIDIA A100-SXM4-40GB, compute capability 8.0, VMM: yes
Device 1: NVIDIA A100-SXM4-40GB, compute capability 8.0, VMM: yes
Device 2: NVIDIA A100-SXM4-40GB, compute capability 8.0, VMM: yes
llama_model_loader: loaded meta data with 29 key-value pairs and 150 tensors from /scicore/home/meinlsch/ballan0000/LLMs/gguf/mixtral_8x22b/Mixtral-8x22B-Instruct-v0.1.Q5_K_M-
00001-of-00004.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 = models--mistralai--Mixtral-8x22B-Inst...
llama_model_loader: - kv 2: llama.block_count u32 = 56
llama_model_loader: - kv 3: llama.context_length u32 = 65536
llama_model_loader: - kv 4: llama.embedding_length u32 = 6144
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 16384
llama_model_loader: - kv 6: llama.attention.head_count u32 = 48
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
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: general.file_type u32 = 17
llama_model_loader: - kv 13: llama.vocab_size u32 = 32768
llama_model_loader: - kv 14: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 15: tokenizer.ggml.model str = llama
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,32768] = ["", "", "", "[INST]", "[...
llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,32768] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,32768] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {{bos_token}}{% for message in messag...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - kv 26: split.no u16 = 0
llama_model_loader: - kv 27: split.count u16 = 4
llama_model_loader: - kv 28: split.tensors.count i32 = 563
llama_model_loader: - type f32: 29 tensors
llama_model_loader: - type f16: 15 tensors
llama_model_loader: - type q8_0: 30 tensors
llama_model_loader: - type q5_K: 67 tensors
llama_model_loader: - type q6_K: 9 tensors
llm_load_vocab: mismatch in special tokens definition ( 1027/32768 vs 259/32768 ).
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 = 32768
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 65536
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: n_ff = 16384
llm_load_print_meta: n_expert = 8
llm_load_print_meta: n_expert_used = 2
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 = 65536
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = ?B
llm_load_print_meta: model ftype = Q5_K - Medium
llm_load_print_meta: model params = 37.76 B
llm_load_print_meta: model size = 25.14 GiB (5.72 BPW)
llm_load_print_meta: general.name = models--mistralai--Mixtral-8x22B-Instruct-v0.1
llm_load_print_meta: BOS token = 1 ''
llm_load_print_meta: EOS token = 2 ''
llm_load_print_meta: UNK token = 0 ''
llm_load_print_meta: LF token = 781 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.23 MiB
llama_model_load: error loading model: create_tensor: tensor 'output_norm.weight' not found
llama_load_model_from_file: failed to load model