I downloaded the model here and ran it on the fork, but the model is writing a lot of random text instead of answering the question "What is the process number?"
Qual e o numero do processo?”) (em inglês). Consultado em 20 de setembro de 2012
"Emancipação do Cidadão" (em português). Consultado em 20 de setembro de 2012
"Ficha de Emancipação" (em português). Consultado em 20 de setembro de 2012
Ver também
Emancipação
llama_print_timings: load time = 3795.93 ms
llama_print_timings: sample time = 75.99 ms / 88 runs ( 0.86 ms per token, 1158.08 tokens per second)
llama_print_timings: prompt eval time = 7026.18 ms / 6 tokens ( 1171.03 ms per token, 0.85 tokens per second)
llama_print_timings: eval time = 119914.28 ms / 87 runs ( 1378.33 ms per token, 0.73 tokens per second)
llama_print_timings: total time = 127624.01 ms / 93 tokens
I downloaded the model here and ran it on the fork, but the model is writing a lot of random text instead of answering the question "What is the process number?"
https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main
PS C:\my_cpp_projects\ollama\llm\build\windows\amd64\cpu\bin\Release> .\llama-cli.exe -m ggml-model-Q4_K_M.gguf --mmproj mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpeg -p "qual o numero do processo"
Log start main: build = 3264 (65f7455c) main: built with MSVC 19.40.33813.0 for x64 main: seed = 1723162025 llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from ggml-model-Q4_K_M.gguf (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.name str = model llama_model_loader: - kv 2: llama.vocab_size u32 = 128256 llama_model_loader: - kv 3: llama.context_length u32 = 8192 llama_model_loader: - kv 4: llama.embedding_length u32 = 4096 llama_model_loader: - kv 5: llama.block_count u32 = 32 llama_model_loader: - kv 6: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 7: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 8: llama.attention.head_count u32 = 32 llama_model_loader: - kv 9: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 10: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 11: llama.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 12: general.file_type u32 = 15 llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 14: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 15: tokenizer.ggml.scores arr[f32,128256] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128001 llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 128002 llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 22: tokenizer.chat_template str = {% set loop_messages = messages %}{% ... llama_model_loader: - kv 23: 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: missing pre-tokenizer type, using: 'default' llm_load_vocab: llm_load_vocab: **** llm_load_vocab: GENERATION QUALITY WILL BE DEGRADED! llm_load_vocab: CONSIDER REGENERATING THE MODEL llm_load_vocab: **** llm_load_vocab: llm_load_vocab: special tokens cache size = 256 llm_load_vocab: token to piece cache size = 0.7997 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = BPE llm_load_print_meta: n_vocab = 128256 llm_load_print_meta: n_merges = 280147 llm_load_print_meta: n_ctx_train = 8192 llm_load_print_meta: n_embd = 4096 llm_load_print_meta: n_head = 32 llm_load_print_meta: n_head_kv = 8 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 = 4 llm_load_print_meta: n_embd_k_gqa = 1024 llm_load_print_meta: n_embd_v_gqa = 1024 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 14336 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 = 500000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 8192 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 = 8B llm_load_print_meta: model ftype = Q4_K - Medium llm_load_print_meta: model params = 8.03 B llm_load_print_meta: model size = 4.58 GiB (4.89 BPW) llm_load_print_meta: general.name = model llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>' llm_load_print_meta: EOS token = 128001 '<|end_of_text|>' llm_load_print_meta: UNK token = 128002 ''
llm_load_print_meta: PAD token = 0 '!'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.14 MiB
llm_load_tensors: CPU buffer size = 4685.30 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: flash_attn = 0
llama_new_context_with_model: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 512.00 MiB
llama_new_context_with_model: KV self size = 512.00 MiB, K (f16): 256.00 MiB, V (f16): 256.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.49 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
system_info: n_threads = 4 / 4 | AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | sampling: repeat_last_n = 64, repeat_penalty = 1.050, frequency_penalty = 0.000, presence_penalty = 0.000 top_k = 100, tfs_z = 1.000, top_p = 0.800, min_p = 0.050, typical_p = 1.000, temp = 0.700 mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 sampling order: CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature generate: n_ctx = 4096, n_batch = 2048, n_predict = -1, n_keep = 0
Qual e o numero do processo?”) (em inglês). Consultado em 20 de setembro de 2012
Ver também
llama_print_timings: load time = 3795.93 ms llama_print_timings: sample time = 75.99 ms / 88 runs ( 0.86 ms per token, 1158.08 tokens per second) llama_print_timings: prompt eval time = 7026.18 ms / 6 tokens ( 1171.03 ms per token, 0.85 tokens per second) llama_print_timings: eval time = 119914.28 ms / 87 runs ( 1378.33 ms per token, 0.73 tokens per second) llama_print_timings: total time = 127624.01 ms / 93 tokens