laurids-reichardt commented 10 months ago

Prerequisites

Please answer the following questions for yourself before submitting an issue.

[x] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
[x] I carefully followed the README.md.
[x] I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
[x] I reviewed the Discussions, and have a new bug or useful enhancement to share.

Expected Behavior

Before ec893798b7a2a803466cc8f063051499ec3d96f7 llama.cpp was able to load and run CodeLlama 34B 4_K_M via Metal on 32 GB Apple M1 Max.

Output for 45855b3f1c7bdd0320aa632334d0b3e8965c26c4:

❯ ./main --model models/TheBloke/Phind-CodeLlama-34B-v2-GGUF/phind-codellama-34b-v2.Q4_K_M.gguf --seed 1 --ctx_size 8192 -n 16 --ignore-eos -t 8 --no-mmap --mlock --prompt "The meaning of life" -b 128
Log start
main: warning: changing RoPE frequency base to 0 (default 10000.0)
main: warning: scaling RoPE frequency by 0 (default 1.0)
main: build = 1282 (45855b3)
main: built with Apple clang version 15.0.0 (clang-1500.0.40.1) for arm64-apple-darwin23.0.0
main: seed  = 1
llama_model_loader: loaded meta data with 17 key-value pairs and 435 tensors from models/TheBloke/Phind-CodeLlama-34B-v2-GGUF/phind-codellama-34b-v2.Q4_K_M.gguf (version GGUF V2 (latest))
llama_model_loader: - tensor    0:                token_embd.weight q4_K     [  8192, 32000,     1,     1 ]
llama_model_loader: - tensor    1:              blk.0.attn_q.weight q4_K     [  8192,  8192,     1,     1 ]
...
llama_model_loader: - tensor  433:               output_norm.weight f32      [  8192,     1,     1,     1 ]
llama_model_loader: - tensor  434:                    output.weight q6_K     [  8192, 32000,     1,     1 ]
llama_model_loader: - kv   0:                       general.architecture str
llama_model_loader: - kv   1:                               general.name str
llama_model_loader: - kv   2:                       llama.context_length u32
llama_model_loader: - kv   3:                     llama.embedding_length u32
llama_model_loader: - kv   4:                          llama.block_count u32
llama_model_loader: - kv   5:                  llama.feed_forward_length u32
llama_model_loader: - kv   6:                 llama.rope.dimension_count u32
llama_model_loader: - kv   7:                 llama.attention.head_count u32
llama_model_loader: - kv   8:              llama.attention.head_count_kv u32
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32
llama_model_loader: - kv  10:                       llama.rope.freq_base f32
llama_model_loader: - kv  11:                          general.file_type u32
llama_model_loader: - kv  12:                       tokenizer.ggml.model str
llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr
llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr
llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr
llama_model_loader: - kv  16:               general.quantization_version u32
llama_model_loader: - type  f32:   97 tensors
llama_model_loader: - type q4_K:  289 tensors
llama_model_loader: - type q6_K:   49 tensors
llm_load_print_meta: format         = GGUF V2 (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    = 16384
llm_load_print_meta: n_ctx          = 8192
llm_load_print_meta: n_embd         = 8192
llm_load_print_meta: n_head         = 64
llm_load_print_meta: n_head_kv      = 8
llm_load_print_meta: n_layer        = 48
llm_load_print_meta: n_rot          = 128
llm_load_print_meta: n_gqa          = 8
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: n_ff           = 22016
llm_load_print_meta: freq_base      = 1000000.0
llm_load_print_meta: freq_scale     = 1
llm_load_print_meta: model type     = 34B
llm_load_print_meta: model ftype    = mostly Q4_K - Medium
llm_load_print_meta: model params   = 33.74 B
llm_load_print_meta: model size     = 18.83 GiB (4.79 BPW)
llm_load_print_meta: general.name   = LLaMA
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>'
llm_load_tensors: ggml ctx size = 19282.62 MB
llm_load_tensors: mem required  = 19282.62 MB (+ 1536.00 MB per state)
...................................................................................................
llama_new_context_with_model: kv self size  = 1536.00 MB
llama_new_context_with_model: compute buffer total size =  270.47 MB
llama_new_context_with_model: max tensor size =   205.08 MB
ggml_metal_add_buffer: allocated 'data            ' buffer, size = 16384.00 MB, offs =            0
ggml_metal_add_buffer: allocated 'data            ' buffer, size =  3103.72 MB, offs =  16964812800, (19488.34 / 21845.34)
ggml_metal_add_buffer: allocated 'eval            ' buffer, size =     1.48 MB, (19489.83 / 21845.34)
ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1538.00 MB, (21027.83 / 21845.34)
ggml_metal_add_buffer: allocated 'alloc           ' buffer, size =   269.02 MB, (21296.84 / 21845.34)

system_info: n_threads = 8 / 10 | 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 |
sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000
generate: n_ctx = 8192, n_batch = 128, n_predict = 16, n_keep = 0

 The meaning of life or how to program a computer to find the answer

The question "what
llama_print_timings:        load time =  5625.76 ms
llama_print_timings:      sample time =    12.89 ms /    16 runs   (    0.81 ms per token,  1241.46 tokens per second)
llama_print_timings: prompt eval time =   354.44 ms /     5 tokens (   70.89 ms per token,    14.11 tokens per second)
llama_print_timings:        eval time =  1096.89 ms /    15 runs   (   73.13 ms per token,    13.68 tokens per second)
llama_print_timings:       total time =  1486.03 ms
ggml_metal_free: deallocating
Log end

Current Behavior

Output since ec893798b7a2a803466cc8f063051499ec3d96f7:

❯ ./main --model models/TheBloke/Phind-CodeLlama-34B-v2-GGUF/phind-codellama-34b-v2.Q4_K_M.gguf --seed 1 --ctx_size 8192 -n 16 --ignore-eos -t 8 --no-mmap --mlock --prompt "The meaning of life" -b 128
Log start
main: warning: changing RoPE frequency base to 0 (default 10000.0)
main: warning: scaling RoPE frequency by 0 (default 1.0)
main: build = 1283 (ec89379)
main: built with Apple clang version 15.0.0 (clang-1500.0.40.1) for arm64-apple-darwin23.0.0
main: seed  = 1
llama_model_loader: loaded meta data with 17 key-value pairs and 435 tensors from models/TheBloke/Phind-CodeLlama-34B-v2-GGUF/phind-codellama-34b-v2.Q4_K_M.gguf (version GGUF V2 (latest))
llama_model_loader: - tensor    0:                token_embd.weight q4_K     [  8192, 32000,     1,     1 ]
llama_model_loader: - tensor    1:              blk.0.attn_q.weight q4_K     [  8192,  8192,     1,     1 ]
...
llama_model_loader: - tensor  433:               output_norm.weight f32      [  8192,     1,     1,     1 ]
llama_model_loader: - tensor  434:                    output.weight q6_K     [  8192, 32000,     1,     1 ]
llama_model_loader: - kv   0:                       general.architecture str
llama_model_loader: - kv   1:                               general.name str
llama_model_loader: - kv   2:                       llama.context_length u32
llama_model_loader: - kv   3:                     llama.embedding_length u32
llama_model_loader: - kv   4:                          llama.block_count u32
llama_model_loader: - kv   5:                  llama.feed_forward_length u32
llama_model_loader: - kv   6:                 llama.rope.dimension_count u32
llama_model_loader: - kv   7:                 llama.attention.head_count u32
llama_model_loader: - kv   8:              llama.attention.head_count_kv u32
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32
llama_model_loader: - kv  10:                       llama.rope.freq_base f32
llama_model_loader: - kv  11:                          general.file_type u32
llama_model_loader: - kv  12:                       tokenizer.ggml.model str
llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr
llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr
llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr
llama_model_loader: - kv  16:               general.quantization_version u32
llama_model_loader: - type  f32:   97 tensors
llama_model_loader: - type q4_K:  289 tensors
llama_model_loader: - type q6_K:   49 tensors
llm_load_print_meta: format         = GGUF V2 (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    = 16384
llm_load_print_meta: n_ctx          = 8192
llm_load_print_meta: n_embd         = 8192
llm_load_print_meta: n_head         = 64
llm_load_print_meta: n_head_kv      = 8
llm_load_print_meta: n_layer        = 48
llm_load_print_meta: n_rot          = 128
llm_load_print_meta: n_gqa          = 8
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: n_ff           = 22016
llm_load_print_meta: freq_base      = 1000000.0
llm_load_print_meta: freq_scale     = 1
llm_load_print_meta: model type     = 34B
llm_load_print_meta: model ftype    = mostly Q4_K - Medium
llm_load_print_meta: model params   = 33.74 B
llm_load_print_meta: model size     = 18.83 GiB (4.79 BPW)
llm_load_print_meta: general.name   = LLaMA
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>'
llm_load_tensors: ggml ctx size = 19282.62 MB
llm_load_tensors: mem required  = 19282.62 MB (+ 1536.00 MB per state)
...................................................................................................
llama_new_context_with_model: kv self size  = 1536.00 MB
llama_new_context_with_model: compute buffer total size =  273.47 MB
llama_new_context_with_model: max tensor size =   205.08 MB
ggml_metal_add_buffer: allocated 'data            ' buffer, size = 16384.00 MB, offs =            0
ggml_metal_add_buffer: allocated 'data            ' buffer, size =  3103.72 MB, offs =  16964812800, (19488.34 / 21845.34)
ggml_metal_add_buffer: allocated 'eval            ' buffer, size =     1.48 MB, (19489.83 / 21845.34)
ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1538.00 MB, (21027.83 / 21845.34)
ggml_metal_add_buffer: allocated 'alloc           ' buffer, size =   272.02 MB, (21299.84 / 21845.34)
ggml_metal_graph_compute: command buffer 6 failed with status 5
GGML_ASSERT: ggml-metal.m:1369: false
[1]    90658 abort      vendor/llama.cpp/main --model  --seed 1 --ctx_size 8192 -n 128 --ignore-eos -

AutonomicPerfectionist commented 10 months ago

Looks related to #3384

ggerganov commented 10 months ago

Does it still fail after make clean ?
Any difference with -t 4 ?
Can you check if this patch works: https://github.com/ggerganov/llama.cpp/discussions/3316#discussioncomment-7107693

laurids-reichardt commented 10 months ago

Unfortunately neither make clean nor setting -t 4 makes a difference. Applying the patch to the latest commit f5ef5cfb18148131fcf45bdd2331f0db5ab7c3d0 doesn't solve the issue as well:

llama.cpp output

```sh ❯ ./main --model models/TheBloke/Phind-CodeLlama-34B-v2-GGUF/phind-codellama-34b-v2.Q4_K_M.gguf --seed 1 --ctx_size 8192 -n 16 --ignore-eos -t 4 --no-mmap --mlock --prompt "The meaning of life" -b 128 Log start main: build = 1299 (f5ef5cf) main: built with Apple clang version 15.0.0 (clang-1500.0.40.1) for arm64-apple-darwin23.0.0 main: seed = 1 llama_model_loader: loaded meta data with 17 key-value pairs and 435 tensors from models/TheBloke/Phind-CodeLlama-34B-v2-GGUF/phind-codellama-34b-v2.Q4_K_M.gguf (version GGUF V2 (latest)) llama_model_loader: - tensor 0: token_embd.weight q4_K [ 8192, 32000, 1, 1 ] llama_model_loader: - tensor 1: blk.0.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 2: blk.0.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 3: blk.0.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 4: blk.0.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 5: blk.0.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 6: blk.0.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 7: blk.0.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 8: blk.0.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 9: blk.0.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 10: blk.1.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 11: blk.1.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 12: blk.1.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 13: blk.1.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 14: blk.1.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 15: blk.1.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 16: blk.1.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 17: blk.1.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 18: blk.1.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 19: blk.2.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 20: blk.2.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 21: blk.2.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 22: blk.2.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 23: blk.2.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 24: blk.2.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 25: blk.2.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 26: blk.2.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 28: blk.3.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 29: blk.3.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 30: blk.3.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 31: blk.3.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 33: blk.3.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 34: blk.3.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 37: blk.4.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 38: blk.4.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 39: blk.4.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 40: blk.4.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 41: blk.4.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 42: blk.4.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 43: blk.4.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 44: blk.4.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 45: blk.4.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 46: blk.5.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 47: blk.5.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 48: blk.5.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 49: blk.5.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 50: blk.5.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 51: blk.5.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 52: blk.5.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 53: blk.5.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 54: blk.5.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 55: blk.6.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 56: blk.6.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 57: blk.6.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 58: blk.6.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 59: blk.6.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 60: blk.6.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 61: blk.6.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 62: blk.6.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 63: blk.6.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 64: blk.7.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 65: blk.7.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 66: blk.7.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 67: blk.7.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 68: blk.7.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 69: blk.7.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 70: blk.7.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 71: blk.7.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 72: blk.7.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 73: blk.8.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 74: blk.8.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 75: blk.8.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 76: blk.8.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 77: blk.8.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 78: blk.8.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 79: blk.8.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 80: blk.8.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 81: blk.8.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 82: blk.9.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 83: blk.9.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 84: blk.9.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 85: blk.9.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 86: blk.9.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 87: blk.9.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 88: blk.9.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 89: blk.9.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 90: blk.9.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 91: blk.10.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 92: blk.10.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 93: blk.10.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 94: blk.10.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 95: blk.10.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 96: blk.10.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 97: blk.10.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 98: blk.10.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 99: blk.10.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 100: blk.11.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 101: blk.11.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 102: blk.11.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 103: blk.11.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 104: blk.11.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 105: blk.11.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 106: blk.11.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 107: blk.11.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 108: blk.11.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 109: blk.12.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 110: blk.12.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 111: blk.12.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 112: blk.12.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 113: blk.12.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 114: blk.12.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 115: blk.12.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 116: blk.12.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 117: blk.12.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 118: blk.13.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 119: blk.13.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 120: blk.13.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 121: blk.13.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 122: blk.13.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 123: blk.13.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 124: blk.13.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 125: blk.13.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 126: blk.13.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 127: blk.14.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 128: blk.14.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 129: blk.14.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 130: blk.14.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 131: blk.14.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 132: blk.14.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 133: blk.14.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 134: blk.14.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 135: blk.14.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 136: blk.15.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 137: blk.15.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 138: blk.15.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 139: blk.15.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 140: blk.15.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 141: blk.15.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 142: blk.15.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 143: blk.15.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 144: blk.15.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 145: blk.16.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 146: blk.16.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 147: blk.16.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 148: blk.16.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 149: blk.16.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 150: blk.16.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 151: blk.16.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 152: blk.16.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 153: blk.16.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 154: blk.17.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 155: blk.17.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 156: blk.17.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 157: blk.17.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 158: blk.17.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 159: blk.17.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 160: blk.17.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 161: blk.17.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 162: blk.17.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 163: blk.18.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 164: blk.18.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 165: blk.18.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 166: blk.18.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 167: blk.18.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 168: blk.18.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 169: blk.18.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 170: blk.18.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 171: blk.18.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 172: blk.19.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 173: blk.19.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 174: blk.19.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 175: blk.19.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 176: blk.19.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 177: blk.19.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 178: blk.19.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 179: blk.19.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 180: blk.19.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 181: blk.20.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 182: blk.20.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 183: blk.20.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 184: blk.20.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 185: blk.20.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 186: blk.20.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 187: blk.20.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 188: blk.20.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 189: blk.20.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 190: blk.21.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 191: blk.21.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 192: blk.21.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 193: blk.21.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 194: blk.21.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 195: blk.21.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 196: blk.21.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 197: blk.21.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 198: blk.21.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 199: blk.22.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 200: blk.22.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 201: blk.22.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 202: blk.22.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 203: blk.22.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 204: blk.22.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 205: blk.22.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 206: blk.22.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 207: blk.22.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 208: blk.23.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 209: blk.23.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 210: blk.23.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 211: blk.23.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 212: blk.23.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 213: blk.23.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 214: blk.23.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 215: blk.23.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 216: blk.23.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 217: blk.24.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 218: blk.24.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 219: blk.24.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 220: blk.24.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 221: blk.24.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 222: blk.24.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 223: blk.24.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 224: blk.24.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 225: blk.24.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 226: blk.25.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 227: blk.25.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 228: blk.25.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 229: blk.25.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 230: blk.25.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 231: blk.25.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 232: blk.25.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 233: blk.25.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 234: blk.25.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 235: blk.26.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 236: blk.26.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 237: blk.26.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 238: blk.26.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 239: blk.26.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 240: blk.26.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 241: blk.26.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 242: blk.26.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 243: blk.26.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 244: blk.27.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 245: blk.27.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 246: blk.27.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 247: blk.27.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 248: blk.27.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 249: blk.27.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 250: blk.27.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 251: blk.27.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 252: blk.27.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 253: blk.28.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 254: blk.28.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 255: blk.28.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 256: blk.28.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 257: blk.28.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 258: blk.28.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 259: blk.28.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 260: blk.28.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 261: blk.28.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 262: blk.29.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 263: blk.29.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 264: blk.29.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 265: blk.29.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 266: blk.29.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 267: blk.29.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 268: blk.29.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 269: blk.29.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 270: blk.29.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 271: blk.30.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 272: blk.30.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 273: blk.30.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 274: blk.30.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 275: blk.30.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 276: blk.30.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 277: blk.30.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 278: blk.30.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 279: blk.30.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 280: blk.31.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 281: blk.31.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 282: blk.31.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 283: blk.31.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 284: blk.31.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 285: blk.31.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 286: blk.31.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 287: blk.31.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 288: blk.31.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 289: blk.32.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 290: blk.32.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 291: blk.32.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 292: blk.32.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 293: blk.32.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 294: blk.32.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 295: blk.32.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 296: blk.32.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 297: blk.32.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 298: blk.33.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 299: blk.33.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 300: blk.33.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 301: blk.33.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 302: blk.33.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 303: blk.33.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 304: blk.33.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 305: blk.33.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 306: blk.33.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 307: blk.34.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 308: blk.34.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 309: blk.34.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 310: blk.34.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 311: blk.34.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 312: blk.34.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 313: blk.34.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 314: blk.34.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 315: blk.34.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 316: blk.35.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 317: blk.35.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 318: blk.35.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 319: blk.35.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 320: blk.35.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 321: blk.35.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 322: blk.35.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 323: blk.35.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 324: blk.35.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 325: blk.36.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 326: blk.36.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 327: blk.36.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 328: blk.36.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 329: blk.36.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 330: blk.36.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 331: blk.36.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 332: blk.36.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 333: blk.36.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 334: blk.37.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 335: blk.37.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 336: blk.37.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 337: blk.37.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 338: blk.37.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 339: blk.37.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 340: blk.37.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 341: blk.37.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 342: blk.37.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 343: blk.38.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 344: blk.38.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 345: blk.38.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 346: blk.38.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 347: blk.38.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 348: blk.38.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 349: blk.38.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 350: blk.38.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 351: blk.38.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 352: blk.39.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 353: blk.39.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 354: blk.39.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 355: blk.39.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 356: blk.39.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 357: blk.39.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 358: blk.39.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 359: blk.39.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 360: blk.39.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 361: blk.40.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 362: blk.40.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 363: blk.40.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 364: blk.40.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 365: blk.40.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 366: blk.40.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 367: blk.40.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 368: blk.40.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 369: blk.40.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 370: blk.41.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 371: blk.41.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 372: blk.41.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 373: blk.41.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 374: blk.41.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 375: blk.41.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 376: blk.41.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 377: blk.41.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 378: blk.41.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 379: blk.42.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 380: blk.42.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 381: blk.42.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 382: blk.42.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 383: blk.42.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 384: blk.42.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 385: blk.42.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 386: blk.42.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 387: blk.42.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 388: blk.43.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 389: blk.43.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 390: blk.43.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 391: blk.43.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 392: blk.43.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 393: blk.43.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 394: blk.43.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 395: blk.43.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 396: blk.43.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 397: blk.44.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 398: blk.44.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 399: blk.44.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 400: blk.44.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 401: blk.44.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 402: blk.44.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 403: blk.44.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 404: blk.44.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 405: blk.44.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 406: blk.45.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 407: blk.45.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 408: blk.45.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 409: blk.45.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 410: blk.45.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 411: blk.45.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 412: blk.45.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 413: blk.45.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 414: blk.45.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 415: blk.46.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 416: blk.46.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 417: blk.46.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 418: blk.46.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 419: blk.46.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 420: blk.46.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 421: blk.46.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 422: blk.46.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 423: blk.46.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 424: blk.47.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 425: blk.47.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 426: blk.47.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 427: blk.47.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 428: blk.47.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 429: blk.47.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 430: blk.47.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 431: blk.47.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 432: blk.47.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 433: output_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 434: output.weight q6_K [ 8192, 32000, 1, 1 ] llama_model_loader: - kv 0: general.architecture str llama_model_loader: - kv 1: general.name str llama_model_loader: - kv 2: llama.context_length u32 llama_model_loader: - kv 3: llama.embedding_length u32 llama_model_loader: - kv 4: llama.block_count u32 llama_model_loader: - kv 5: llama.feed_forward_length u32 llama_model_loader: - kv 6: llama.rope.dimension_count u32 llama_model_loader: - kv 7: llama.attention.head_count u32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 llama_model_loader: - kv 10: llama.rope.freq_base f32 llama_model_loader: - kv 11: general.file_type u32 llama_model_loader: - kv 12: tokenizer.ggml.model str llama_model_loader: - kv 13: tokenizer.ggml.tokens arr llama_model_loader: - kv 14: tokenizer.ggml.scores arr llama_model_loader: - kv 15: tokenizer.ggml.token_type arr llama_model_loader: - kv 16: general.quantization_version u32 llama_model_loader: - type f32: 97 tensors llama_model_loader: - type q4_K: 289 tensors llama_model_loader: - type q6_K: 49 tensors llm_load_print_meta: format = GGUF V2 (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 = 16384 llm_load_print_meta: n_embd = 8192 llm_load_print_meta: n_head = 64 llm_load_print_meta: n_head_kv = 8 llm_load_print_meta: n_layer = 48 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 8 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: n_ff = 22016 llm_load_print_meta: freq_base_train = 1000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: model type = 34B llm_load_print_meta: model ftype = mostly Q4_K - Medium llm_load_print_meta: model params = 33.74 B llm_load_print_meta: model size = 18.83 GiB (4.79 BPW) llm_load_print_meta: general.name = LLaMA 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 = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 19282.62 MB llm_load_tensors: mem required = 19282.62 MB ................................................................................................... llama_new_context_with_model: n_ctx = 8168 llama_new_context_with_model: freq_base = 1000000.0 llama_new_context_with_model: freq_scale = 1 llama_new_context_with_model: kv self size = 1531.50 MB llama_new_context_with_model: compute buffer total size = 277.11 MB llama_new_context_with_model: max tensor size = 205.08 MB ggml_metal_add_buffer: allocated 'data ' buffer, size = 8192.00 MB, offs = 0 ggml_metal_add_buffer: allocated 'data ' buffer, size = 8192.00 MB, offs = 8374878208 ggml_metal_add_buffer: allocated 'data ' buffer, size = 3308.81 MB, offs = 16749756416, (19693.44 / 21845.34) ggml_metal_add_buffer: allocated 'kv ' buffer, size = 1533.50 MB, (21226.94 / 21845.34) ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 271.25 MB, (21498.19 / 21845.34) ggml_metal_graph_compute: command buffer 3 failed with status 5 GGML_ASSERT: ggml-metal.m:1369: false [1] 68757 abort ./main --model --seed 1 --ctx_size 8168 -n 16 --ignore-eos -t ```

EDIT: Reducing the context size to 7000 lowers the required memory to (21037.02 / 21845.34) and allows running the model again.

llama.cpp output

```sh ❯ ./main --model models/TheBloke/Phind-CodeLlama-34B-v2-GGUF/phind-codellama-34b-v2.Q4_K_M.gguf --seed 1 --ctx_size 7000 -n 16 --ignore-eos -t 4 --no-mmap --mlock --prompt "The meaning of life" -b 128 Log start main: build = 1299 (f5ef5cf) main: built with Apple clang version 15.0.0 (clang-1500.0.40.1) for arm64-apple-darwin23.0.0 main: seed = 1 llama_model_loader: loaded meta data with 17 key-value pairs and 435 tensors from models/TheBloke/Phind-CodeLlama-34B-v2-GGUF/phind-codellama-34b-v2.Q4_K_M.gguf (version GGUF V2 (latest)) llama_model_loader: - tensor 0: token_embd.weight q4_K [ 8192, 32000, 1, 1 ] llama_model_loader: - tensor 1: blk.0.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 2: blk.0.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 3: blk.0.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 4: blk.0.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 5: blk.0.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 6: blk.0.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 7: blk.0.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 8: blk.0.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 9: blk.0.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 10: blk.1.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 11: blk.1.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 12: blk.1.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 13: blk.1.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 14: blk.1.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 15: blk.1.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 16: blk.1.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 17: blk.1.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 18: blk.1.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 19: blk.2.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 20: blk.2.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 21: blk.2.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 22: blk.2.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 23: blk.2.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 24: blk.2.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 25: blk.2.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 26: blk.2.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 28: blk.3.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 29: blk.3.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 30: blk.3.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 31: blk.3.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 33: blk.3.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 34: blk.3.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 37: blk.4.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 38: blk.4.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 39: blk.4.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 40: blk.4.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 41: blk.4.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 42: blk.4.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 43: blk.4.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 44: blk.4.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 45: blk.4.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 46: blk.5.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 47: blk.5.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 48: blk.5.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 49: blk.5.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 50: blk.5.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 51: blk.5.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 52: blk.5.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 53: blk.5.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 54: blk.5.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 55: blk.6.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 56: blk.6.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 57: blk.6.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 58: blk.6.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 59: blk.6.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 60: blk.6.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 61: blk.6.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 62: blk.6.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 63: blk.6.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 64: blk.7.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 65: blk.7.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 66: blk.7.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 67: blk.7.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 68: blk.7.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 69: blk.7.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 70: blk.7.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 71: blk.7.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 72: blk.7.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 73: blk.8.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 74: blk.8.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 75: blk.8.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 76: blk.8.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 77: blk.8.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 78: blk.8.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 79: blk.8.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 80: blk.8.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 81: blk.8.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 82: blk.9.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 83: blk.9.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 84: blk.9.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 85: blk.9.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 86: blk.9.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 87: blk.9.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 88: blk.9.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 89: blk.9.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 90: blk.9.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 91: blk.10.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 92: blk.10.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 93: blk.10.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 94: blk.10.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 95: blk.10.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 96: blk.10.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 97: blk.10.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 98: blk.10.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 99: blk.10.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 100: blk.11.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 101: blk.11.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 102: blk.11.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 103: blk.11.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 104: blk.11.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 105: blk.11.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 106: blk.11.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 107: blk.11.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 108: blk.11.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 109: blk.12.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 110: blk.12.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 111: blk.12.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 112: blk.12.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 113: blk.12.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 114: blk.12.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 115: blk.12.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 116: blk.12.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 117: blk.12.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 118: blk.13.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 119: blk.13.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 120: blk.13.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 121: blk.13.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 122: blk.13.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 123: blk.13.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 124: blk.13.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 125: blk.13.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 126: blk.13.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 127: blk.14.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 128: blk.14.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 129: blk.14.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 130: blk.14.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 131: blk.14.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 132: blk.14.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 133: blk.14.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 134: blk.14.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 135: blk.14.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 136: blk.15.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 137: blk.15.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 138: blk.15.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 139: blk.15.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 140: blk.15.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 141: blk.15.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 142: blk.15.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 143: blk.15.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 144: blk.15.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 145: blk.16.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 146: blk.16.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 147: blk.16.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 148: blk.16.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 149: blk.16.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 150: blk.16.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 151: blk.16.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 152: blk.16.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 153: blk.16.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 154: blk.17.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 155: blk.17.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 156: blk.17.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 157: blk.17.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 158: blk.17.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 159: blk.17.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 160: blk.17.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 161: blk.17.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 162: blk.17.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 163: blk.18.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 164: blk.18.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 165: blk.18.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 166: blk.18.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 167: blk.18.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 168: blk.18.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 169: blk.18.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 170: blk.18.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 171: blk.18.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 172: blk.19.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 173: blk.19.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 174: blk.19.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 175: blk.19.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 176: blk.19.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 177: blk.19.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 178: blk.19.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 179: blk.19.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 180: blk.19.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 181: blk.20.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 182: blk.20.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 183: blk.20.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 184: blk.20.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 185: blk.20.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 186: blk.20.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 187: blk.20.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 188: blk.20.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 189: blk.20.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 190: blk.21.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 191: blk.21.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 192: blk.21.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 193: blk.21.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 194: blk.21.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 195: blk.21.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 196: blk.21.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 197: blk.21.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 198: blk.21.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 199: blk.22.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 200: blk.22.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 201: blk.22.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 202: blk.22.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 203: blk.22.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 204: blk.22.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 205: blk.22.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 206: blk.22.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 207: blk.22.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 208: blk.23.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 209: blk.23.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 210: blk.23.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 211: blk.23.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 212: blk.23.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 213: blk.23.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 214: blk.23.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 215: blk.23.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 216: blk.23.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 217: blk.24.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 218: blk.24.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 219: blk.24.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 220: blk.24.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 221: blk.24.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 222: blk.24.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 223: blk.24.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 224: blk.24.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 225: blk.24.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 226: blk.25.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 227: blk.25.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 228: blk.25.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 229: blk.25.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 230: blk.25.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 231: blk.25.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 232: blk.25.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 233: blk.25.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 234: blk.25.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 235: blk.26.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 236: blk.26.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 237: blk.26.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 238: blk.26.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 239: blk.26.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 240: blk.26.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 241: blk.26.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 242: blk.26.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 243: blk.26.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 244: blk.27.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 245: blk.27.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 246: blk.27.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 247: blk.27.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 248: blk.27.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 249: blk.27.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 250: blk.27.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 251: blk.27.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 252: blk.27.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 253: blk.28.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 254: blk.28.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 255: blk.28.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 256: blk.28.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 257: blk.28.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 258: blk.28.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 259: blk.28.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 260: blk.28.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 261: blk.28.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 262: blk.29.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 263: blk.29.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 264: blk.29.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 265: blk.29.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 266: blk.29.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 267: blk.29.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 268: blk.29.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 269: blk.29.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 270: blk.29.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 271: blk.30.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 272: blk.30.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 273: blk.30.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 274: blk.30.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 275: blk.30.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 276: blk.30.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 277: blk.30.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 278: blk.30.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 279: blk.30.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 280: blk.31.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 281: blk.31.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 282: blk.31.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 283: blk.31.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 284: blk.31.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 285: blk.31.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 286: blk.31.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 287: blk.31.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 288: blk.31.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 289: blk.32.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 290: blk.32.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 291: blk.32.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 292: blk.32.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 293: blk.32.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 294: blk.32.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 295: blk.32.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 296: blk.32.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 297: blk.32.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 298: blk.33.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 299: blk.33.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 300: blk.33.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 301: blk.33.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 302: blk.33.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 303: blk.33.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 304: blk.33.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 305: blk.33.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 306: blk.33.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 307: blk.34.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 308: blk.34.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 309: blk.34.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 310: blk.34.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 311: blk.34.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 312: blk.34.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 313: blk.34.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 314: blk.34.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 315: blk.34.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 316: blk.35.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 317: blk.35.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 318: blk.35.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 319: blk.35.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 320: blk.35.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 321: blk.35.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 322: blk.35.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 323: blk.35.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 324: blk.35.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 325: blk.36.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 326: blk.36.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 327: blk.36.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 328: blk.36.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 329: blk.36.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 330: blk.36.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 331: blk.36.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 332: blk.36.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 333: blk.36.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 334: blk.37.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 335: blk.37.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 336: blk.37.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 337: blk.37.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 338: blk.37.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 339: blk.37.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 340: blk.37.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 341: blk.37.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 342: blk.37.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 343: blk.38.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 344: blk.38.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 345: blk.38.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 346: blk.38.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 347: blk.38.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 348: blk.38.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 349: blk.38.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 350: blk.38.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 351: blk.38.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 352: blk.39.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 353: blk.39.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 354: blk.39.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 355: blk.39.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 356: blk.39.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 357: blk.39.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 358: blk.39.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 359: blk.39.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 360: blk.39.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 361: blk.40.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 362: blk.40.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 363: blk.40.attn_v.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 364: blk.40.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 365: blk.40.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 366: blk.40.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 367: blk.40.ffn_down.weight q4_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 368: blk.40.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 369: blk.40.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 370: blk.41.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 371: blk.41.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 372: blk.41.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 373: blk.41.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 374: blk.41.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 375: blk.41.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 376: blk.41.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 377: blk.41.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 378: blk.41.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 379: blk.42.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 380: blk.42.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 381: blk.42.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 382: blk.42.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 383: blk.42.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 384: blk.42.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 385: blk.42.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 386: blk.42.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 387: blk.42.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 388: blk.43.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 389: blk.43.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 390: blk.43.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 391: blk.43.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 392: blk.43.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 393: blk.43.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 394: blk.43.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 395: blk.43.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 396: blk.43.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 397: blk.44.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 398: blk.44.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 399: blk.44.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 400: blk.44.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 401: blk.44.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 402: blk.44.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 403: blk.44.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 404: blk.44.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 405: blk.44.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 406: blk.45.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 407: blk.45.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 408: blk.45.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 409: blk.45.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 410: blk.45.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 411: blk.45.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 412: blk.45.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 413: blk.45.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 414: blk.45.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 415: blk.46.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 416: blk.46.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 417: blk.46.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 418: blk.46.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 419: blk.46.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 420: blk.46.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 421: blk.46.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 422: blk.46.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 423: blk.46.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 424: blk.47.attn_q.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 425: blk.47.attn_k.weight q4_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 426: blk.47.attn_v.weight q6_K [ 8192, 1024, 1, 1 ] llama_model_loader: - tensor 427: blk.47.attn_output.weight q4_K [ 8192, 8192, 1, 1 ] llama_model_loader: - tensor 428: blk.47.ffn_gate.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 429: blk.47.ffn_up.weight q4_K [ 8192, 22016, 1, 1 ] llama_model_loader: - tensor 430: blk.47.ffn_down.weight q6_K [ 22016, 8192, 1, 1 ] llama_model_loader: - tensor 431: blk.47.attn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 432: blk.47.ffn_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 433: output_norm.weight f32 [ 8192, 1, 1, 1 ] llama_model_loader: - tensor 434: output.weight q6_K [ 8192, 32000, 1, 1 ] llama_model_loader: - kv 0: general.architecture str llama_model_loader: - kv 1: general.name str llama_model_loader: - kv 2: llama.context_length u32 llama_model_loader: - kv 3: llama.embedding_length u32 llama_model_loader: - kv 4: llama.block_count u32 llama_model_loader: - kv 5: llama.feed_forward_length u32 llama_model_loader: - kv 6: llama.rope.dimension_count u32 llama_model_loader: - kv 7: llama.attention.head_count u32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 llama_model_loader: - kv 10: llama.rope.freq_base f32 llama_model_loader: - kv 11: general.file_type u32 llama_model_loader: - kv 12: tokenizer.ggml.model str llama_model_loader: - kv 13: tokenizer.ggml.tokens arr llama_model_loader: - kv 14: tokenizer.ggml.scores arr llama_model_loader: - kv 15: tokenizer.ggml.token_type arr llama_model_loader: - kv 16: general.quantization_version u32 llama_model_loader: - type f32: 97 tensors llama_model_loader: - type q4_K: 289 tensors llama_model_loader: - type q6_K: 49 tensors llm_load_print_meta: format = GGUF V2 (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 = 16384 llm_load_print_meta: n_embd = 8192 llm_load_print_meta: n_head = 64 llm_load_print_meta: n_head_kv = 8 llm_load_print_meta: n_layer = 48 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 8 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: n_ff = 22016 llm_load_print_meta: freq_base_train = 1000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: model type = 34B llm_load_print_meta: model ftype = mostly Q4_K - Medium llm_load_print_meta: model params = 33.74 B llm_load_print_meta: model size = 18.83 GiB (4.79 BPW) llm_load_print_meta: general.name = LLaMA 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 = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 19282.62 MB llm_load_tensors: mem required = 19282.62 MB ................................................................................................... llama_new_context_with_model: n_ctx = 7000 llama_new_context_with_model: freq_base = 1000000.0 llama_new_context_with_model: freq_scale = 1 llama_new_context_with_model: kv self size = 1312.50 MB llama_new_context_with_model: compute buffer total size = 240.04 MB llama_new_context_with_model: max tensor size = 205.08 MB ggml_metal_add_buffer: allocated 'data ' buffer, size = 16384.00 MB, offs = 0 ggml_metal_add_buffer: allocated 'data ' buffer, size = 3103.72 MB, offs = 16964812800, (19488.34 / 21845.34) ggml_metal_add_buffer: allocated 'kv ' buffer, size = 1314.50 MB, (20802.84 / 21845.34) ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 234.17 MB, (21037.02 / 21845.34) system_info: n_threads = 4 / 10 | 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 | sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000 generate: n_ctx = 7000, n_batch = 128, n_predict = 16, n_keep = 0 The meaning of life or how to program a computer to find the answer The question "what llama_print_timings: load time = 4942.55 ms llama_print_timings: sample time = 25.28 ms / 16 runs ( 1.58 ms per token, 633.04 tokens per second) llama_print_timings: prompt eval time = 382.31 ms / 5 tokens ( 76.46 ms per token, 13.08 tokens per second) llama_print_timings: eval time = 1202.40 ms / 15 runs ( 80.16 ms per token, 12.48 tokens per second) llama_print_timings: total time = 1637.70 ms ggml_metal_free: deallocating Log end

ggerganov commented 10 months ago

I suppose before the #3228 change, this model was just at the limit of what is possible to fit in 32GB. Due to the changes, the memory usage has slightly increased leading to no longer possible to fit. Your workaround is probably the best option at the moment

slaren commented 10 months ago

If the issue is the increase in the alloc buffer size, reducing the batch size (-b) may also work.

jamesbraza commented 10 months ago

Hitting this too just now on macOS Ventura 13.5.2 with am M1 pro:

llama-cpp-python[server]==0.2.11 command: python -m llama_cpp.server --n_ctx 4096 --model models/llama-2-13b-ensemble-v5.Q4_K_M.gguf (model link)
llama-index==0.8.38 object: LlamaCPP(..., model_kwargs={"n_gpu_layers": 1, "n_ctx": 4096}).complete(prompt)

The client-side gets this error:

ggml_metal_graph_compute: command buffer 0 failed with status 5
GGML_ASSERT: /private/var/folders/78/lm6p91s90fx99cshsxqz_19w0000gn/T/pip-install-vzfiwviq/llama-cpp-python_703f6576256241f7894dbfd75e7b496f/vendor/llama.cpp/ggml-metal.m:1369: false

My context size is 4096. Any pointers on how to get around this?

jamesbraza commented 9 months ago

The GGML_ASSERT was not triggered for me after moving to a Q4_0 model. One suggestion is adding a human-friendly decoding of status 5 to the message command buffer 0 failed with status 5, that way it's easier to pick a corrective action. From an out-the-code perspective, it seems status 5 is related to an unsupported quantization type

ggerganov commented 9 months ago

it seems status 5 is related to an unsupported quantization type

No - the device runs out of memory in this case. But status 5 can mean a variety of things. A workaround for Q4_K_M is to either reduce the context or the batch size

github-actions[bot] commented 3 months ago

This issue was closed because it has been inactive for 14 days since being marked as stale.

ggerganov / llama.cpp

[User] Metal regression – CodeLlama 34B 4_K_M won't run via Metal on 32 GB Apple M1 Max since `ec89379` #3414

Prerequisites

Expected Behavior

Current Behavior