Debian 12 x LLamaSharp 0.11.2 Crashed Silently

[kuan2019]

HI, I was running a console application with LLamaSharp 0.11.2 under Debian 12 then it been crashed silently without any exceptions when it was loading the file.

using var model = LLamaWeights.LoadFromFile(parameters);

How can I fix this issue? The information of environment is as below,

1. OS: Debian GNU/Linux 12 (bookworm)
2. CPU: Intel x64
3. Memory: 264GB
4. GLIBC Version: Debian GLIBC 2.36-9+deb12u4
5. dotnet 7.0.408
6. LLamaSharp 0.11.2 & LLamaSharp.Backend.Cpu 0.11.2

best regards,

[SignalRT]

Could you share the link to the model that you are trying to load to make a test?

[kuan2019]

I run it with model "llama-2-7b-chat.Q4_K_M.gguf" on the server but it's good on my M1 MacBook Pro (MacOS: Sonoma 14.4). No idea why it was terminated silently. Correction: The process was running and stopped here, var parameters = new ModelParams(modelPath)

[kuan2019]

After investigated to llama.cpp, I got why it's occurred core dump! How can I do next step?

#./main -ngl 32 -m /user/models/llama-2-7b-chat.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "YOUR PROMPT..." warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored warning: see main README.md for information on enabling GPU BLAS support Log start main: build = 2679 (7593639c) main: built with cc (Debian 12.2.0-14) 12.2.0 for x86_64-linux-gnu main: seed = 1713247730 llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from /root/models/llama-2-7b-chat.Q4_K_M.gguf (version GGUF V2) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.name str = LLaMA v2 llama_model_loader: - kv 2: llama.context_length u32 = 4096 llama_model_loader: - kv 3: llama.embedding_length u32 = 4096 llama_model_loader: - kv 4: llama.block_count u32 = 32 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008 llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 7: llama.attention.head_count u32 = 32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 10: general.file_type u32 = 15 llama_model_loader: - kv 11: tokenizer.ggml.model str = llama llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,32000] = ["", "", "", "<0x00>", "<... llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 18: general.quantization_version u32 = 2 llama_model_loader: - type f32: 65 tensors llama_model_loader: - type q4_K: 193 tensors llama_model_loader: - type q6_K: 33 tensors llm_load_vocab: special tokens definition check successful ( 259/32000 ). llm_load_print_meta: format = GGUF V2 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 = 4096 llm_load_print_meta: n_embd = 4096 llm_load_print_meta: n_head = 32 llm_load_print_meta: n_head_kv = 32 llm_load_print_meta: n_layer = 32 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_embd_head_k = 128 llm_load_print_meta: n_embd_head_v = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: n_embd_k_gqa = 4096 llm_load_print_meta: n_embd_v_gqa = 4096 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-06 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 11008 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 1 llm_load_print_meta: pooling type = 0 llm_load_print_meta: rope type = 0 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 4096 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: ssm_d_conv = 0 llm_load_print_meta: ssm_d_inner = 0 llm_load_print_meta: ssm_d_state = 0 llm_load_print_meta: ssm_dt_rank = 0 llm_load_print_meta: model type = 7B llm_load_print_meta: model ftype = Q4_K - Medium llm_load_print_meta: model params = 6.74 B llm_load_print_meta: model size = 3.80 GiB (4.84 BPW) llm_load_print_meta: general.name = LLaMA v2 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 = 0.11 MiB llm_load_tensors: CPU buffer size = 3891.24 MiB .................................................................................................. llama_new_context_with_model: n_ctx = 4096 llama_new_context_with_model: n_batch = 2048 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 2048.00 MiB llama_new_context_with_model: KV self size = 2048.00 MiB, K (f16): 1024.00 MiB, V (f16): 1024.00 MiB llama_new_context_with_model: CPU output buffer size = 0.12 MiB llama_new_context_with_model: CPU compute buffer size = 296.01 MiB llama_new_context_with_model: graph nodes = 1030 llama_new_context_with_model: graph splits = 1 Segmentation fault

[SignalRT]

if the problem happens with llama.cpp examples (main) you should open the issue to llama.cpp.

[kuan2019]

@martindevans After I refreshed to newest llama.cpp and recompiled these projects, then I replaced with two files LLamaSharp.dll and libllama.so to my dotnet project under Debian 12, it's workable, so amazing!

[SanftMonster]

@kuan2019 The binary in master branch was updated last week. Could you please try once more with the current master branch?