9.1 KiB
🐛 #376 - Bug: unknown model architecture: 'deci' (when loading Llama-3_1-Nemotron-Ultra-253B)
| Author | Lissanro |
|---|---|
| State | ❌ Closed |
| Created | 2025-05-04 |
| Updated | 2025-05-09 |
Description
What happened?
Llama-3_1-Nemotron-Ultra-253B has special architecture called "deci", its support has been added to llama.cpp using this PR: https://github.com/ggml-org/llama.cpp/pull/12843 - perhaps adding support for this architecture could be considered for ik_llama.cpp?
Name and Version
Latest git
What operating system are you seeing the problem on?
Linux
Relevant log output
~/pkgs/ik_llama.cpp/build/bin/llama-server --model /mnt/secondary/neuro/Llama-3_1-Nemotron-Ultra-253B-v1-GGUF-UD-Q4_K_XL-131072seq/Llama-3_1-Nemotron-Ultra-253B-v1-UD-Q4_K_XL-00001-of-00004.gguf --ctx-size 81920 --n-gpu-layers 12 --tensor-split 25,25,25,25 -fa -ctk q8_0 -ctv q8_0 --threads 64 --host 0.0.0.0 --port 5000 --split-mode row
INFO [ main] build info | tid="136009399906304" timestamp=1746347014 build=3661 commit="ab7f694b"
INFO [ main] system info | tid="136009399906304" timestamp=1746347014 n_threads=64 n_threads_batch=-1 total_threads=128 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | "
llama_model_loader: additional 3 GGUFs metadata loaded.
llama_model_loader: loaded meta data with 43 key-value pairs and 648 tensors from /mnt/secondary/neuro/Llama-3_1-Nemotron-Ultra-253B-v1-GGUF-UD-Q4_K_XL-131072seq/Llama-3_1-Nemotron-Ultra-253B-v1-UD-Q4_K_XL-00001-of-00004.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = deci
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Llama_Nemotron_Ultra
llama_model_loader: - kv 3: general.version str = v1
llama_model_loader: - kv 4: general.finetune str = 3_1-Nemotron-Ultra
llama_model_loader: - kv 5: general.basename str = Llama-3_1-Nemotron-Ultra-253B-V1
llama_model_loader: - kv 6: general.quantized_by str = Unsloth
llama_model_loader: - kv 7: general.size_label str = 253B
llama_model_loader: - kv 8: general.license str = other
llama_model_loader: - kv 9: general.license.name str = nvidia-open-model-license
llama_model_loader: - kv 10: general.license.link str = https://www.nvidia.com/en-us/agreemen...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.tags arr[str,4] = ["nvidia", "llama-3", "pytorch", "tex...
llama_model_loader: - kv 13: general.languages arr[str,1] = ["en"]
llama_model_loader: - kv 14: deci.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 15: deci.attention.head_count_kv arr[i32,162] = [8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, ...
llama_model_loader: - kv 16: deci.attention.head_count arr[i32,162] = [128, 128, 128, 128, 128, 128, 128, 1...
llama_model_loader: - kv 17: deci.feed_forward_length arr[i32,162] = [5376, 10752, 16128, 16128, 16128, 16...
llama_model_loader: - kv 18: deci.block_count u32 = 162
llama_model_loader: - kv 19: deci.context_length u32 = 131072
llama_model_loader: - kv 20: deci.embedding_length u32 = 16384
llama_model_loader: - kv 21: deci.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 22: deci.attention.key_length u32 = 128
llama_model_loader: - kv 23: deci.attention.value_length u32 = 128
llama_model_loader: - kv 24: deci.vocab_size u32 = 128256
llama_model_loader: - kv 25: deci.rope.dimension_count u32 = 128
llama_model_loader: - kv 26: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 27: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 28: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 29: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 30: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 31: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 32: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 33: tokenizer.chat_template str = {{- bos_token }}{%- if messages[0]['r...
llama_model_loader: - kv 34: general.quantization_version u32 = 2
llama_model_loader: - kv 35: general.file_type u32 = 15
llama_model_loader: - kv 36: quantize.imatrix.file str = Llama-3_1-Nemotron-Ultra-253B-v1-GGUF...
llama_model_loader: - kv 37: quantize.imatrix.dataset str = unsloth_calibration_Llama-3_1-Nemotro...
llama_model_loader: - kv 38: quantize.imatrix.entries_count i32 = 499
llama_model_loader: - kv 39: quantize.imatrix.chunks_count i32 = 544
llama_model_loader: - kv 40: split.no u16 = 0
llama_model_loader: - kv 41: split.tensors.count i32 = 648
llama_model_loader: - kv 42: split.count u16 = 4
llama_model_loader: - type f32: 147 tensors
llama_model_loader: - type q4_K: 428 tensors
llama_model_loader: - type q6_K: 73 tensors
llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'deci'
llama_load_model_from_file: failed to load model
llama_init_from_gpt_params: error: failed to load model '/mnt/secondary/neuro/Llama-3_1-Nemotron-Ultra-253B-v1-GGUF-UD-Q4_K_XL-131072seq/Llama-3_1-Nemotron-Ultra-253B-v1-UD-Q4_K_XL-00001-of-00004.gguf'
ERR [ load_model] unable to load model | tid="136009399906304" timestamp=1746347014 model="/mnt/secondary/neuro/Llama-3_1-Nemotron-Ultra-253B-v1-GGUF-UD-Q4_K_XL-131072seq/Llama-3_1-Nemotron-Ultra-253B-v1-UD-Q4_K_XL-00001-of-00004.gguf"
munmap_chunk(): invalid pointer
💬 Conversation
👤 ikawrakow commented the 2025-05-04 at 09:35:03:
I can take a look, but as with other giant models, I cannot test. Are you willing to test and provide benchmarks?
👤 saood06 commented the 2025-05-04 at 09:38:35:
I'm already working on it.
👤 Lissanro commented the 2025-05-04 at 11:01:33:
Are you willing to test and provide benchmarks?
Sure, I will be happy to test, at both short and log context lengths.
As of benchmarks, at very least I planned to test input processing and output generation speeds - but if something else will be needed please let me know and I will consider it if I can test it.
👤 saood06 commented the 2025-05-04 at 11:07:21:
Sure, I will be happy to test, at both short and log context lengths.
What about the small model as the initial architecture support was with that one: https://github.com/ggml-org/llama.cpp/pull/10669
As of benchmarks, at very least I planned to test input processing and output generation speeds
You can use sweep-bench to do that.
👤 Lissanro commented the 2025-05-04 at 11:34:07:
I do not have the smaller model yet but I can try downloading it, for example from here https://huggingface.co/bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF (I only have 4G connection though and have some things still downloading, but I should be able to get the 51B within 2 days in case it will be needed for testing).
👤 saood06 commented the 2025-05-04 at 11:46:19:
but I should be able to get the 51B within 2 days in case it will be needed for testing
I'll try to test the smaller one then. I just created a draft PR: https://github.com/ikawrakow/ik_llama.cpp/pull/377