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deepsense-ai / edge-slm

This project is a native implementation of a RAG pipeline for Small Language Models tested on Android devices. The main goal was to fit the whole RAG pipeline into a resource constrained device - ie. smartphone. By design the provided RAG library should be deployable on various platforms.
MIT License
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Illegal instruction while trying to run the model #2

Open puliviswanath opened 1 month ago

puliviswanath commented 1 month ago

I have followed all the step mentioned I can able to execute the ./rag_demo --help command but when i try to run the main command to interact with model its telling the illegal instruction

image

132|OP5155L1:/data/local/tmp/Android/bin $ ./rag_demo --embedding_model ./gte-base-f32.gguf --database_input ./output_chunks.json --database_output ./embedded_document_chunks.json --model_path ./model.ggml --m> llama_model_loader: loaded meta data with 23 key-value pairs and 197 tensors from ./gte-base-f32.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 = bert llama_model_loader: - kv 1: general.name str = gte-base llama_model_loader: - kv 2: bert.block_count u32 = 12 llama_model_loader: - kv 3: bert.context_length u32 = 512 llama_model_loader: - kv 4: bert.embedding_length u32 = 768 llama_model_loader: - kv 5: bert.feed_forward_length u32 = 3072 llama_model_loader: - kv 6: bert.attention.head_count u32 = 12 llama_model_loader: - kv 7: bert.attention.layer_norm_epsilon f32 = 0.000000 llama_model_loader: - kv 8: general.file_type u32 = 0 llama_model_loader: - kv 9: bert.attention.causal bool = false llama_model_loader: - kv 10: bert.pooling_type u32 = 1 llama_model_loader: - kv 11: tokenizer.ggml.token_type_count u32 = 2 llama_model_loader: - kv 12: tokenizer.ggml.bos_token_id u32 = 101 llama_model_loader: - kv 13: tokenizer.ggml.eos_token_id u32 = 102 llama_model_loader: - kv 14: tokenizer.ggml.model str = bert llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,30522] = ["[PAD]", "[unused0]", "[unused1]", "... llama_model_loader: - kv 16: tokenizer.ggml.scores arr[f32,30522] = [-1000.000000, -1000.000000, -1000.00... llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,30522] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 100 llama_model_loader: - kv 19: tokenizer.ggml.seperator_token_id u32 = 102 llama_model_loader: - kv 20: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 21: tokenizer.ggml.cls_token_id u32 = 101 llama_model_loader: - kv 22: tokenizer.ggml.mask_token_id u32 = 103 llama_model_loader: - type f32: 197 tensors llm_load_vocab: mismatch in special tokens definition ( 7104/30522 vs 5/30522 ). llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = bert llm_load_print_meta: vocab type = WPM llm_load_print_meta: n_vocab = 30522 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 512 llm_load_print_meta: n_embd = 768 llm_load_print_meta: n_head = 12 llm_load_print_meta: n_head_kv = 12 llm_load_print_meta: n_layer = 12 llm_load_print_meta: n_rot = 64 llm_load_print_meta: n_embd_head_k = 64 llm_load_print_meta: n_embd_head_v = 64 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: n_embd_k_gqa = 768 llm_load_print_meta: n_embd_v_gqa = 768 llm_load_print_meta: f_norm_eps = 1.0e-12 llm_load_print_meta: f_norm_rms_eps = 0.0e+00 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 = 3072 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 0 llm_load_print_meta: pooling type = 1 llm_load_print_meta: rope type = 2 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 = 512 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 = 109M llm_load_print_meta: model ftype = all F32 llm_load_print_meta: model params = 108.89 M llm_load_print_meta: model size = 415.39 MiB (32.00 BPW) llm_load_print_meta: general.name = gte-base llm_load_print_meta: BOS token = 101 '[CLS]' llm_load_print_meta: EOS token = 102 '[SEP]' llm_load_print_meta: UNK token = 100 '[UNK]' llm_load_print_meta: SEP token = 102 '[SEP]' llm_load_print_meta: PAD token = 0 '[PAD]' llm_load_tensors: ggml ctx size = 0.08 MiB llm_load_tensors: CPU buffer size = 415.39 MiB ........................................................ llama_new_context_with_model: n_ctx = 512 llama_new_context_with_model: n_batch = 512 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 18.00 MiB llama_new_context_with_model: KV self size = 18.00 MiB, K (f16): 9.00 MiB, V (f16): 9.00 MiB llama_new_context_with_model: CPU output buffer size = 0.00 MiB llama_new_context_with_model: CPU compute buffer size = 20.00 MiB llama_new_context_with_model: graph nodes = 431 llama_new_context_with_model: graph splits = 1 Illegal instruction

guoguo1314 commented 1 week ago

Hi, did you get through this code?