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Created April 27, 2024 23:46
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llama.cpp
lucas@desktop:~/oss/llama.cpp$ ./main -m /home/lucas/.cache/huggingface/hub/models--TheBloke--Mistral-7B-Instruct-v0.2-GGUF/snapshots/3a6fbf4a41a1d52e415a4958cde6856d34b2db93/mistral-7b-instruct-v0.2.Q4_K_M.gguf -p "Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello" -n 1 -e
Log start
main: build = 2679 (7593639c)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1714261503
llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from /home/lucas/.cache/huggingface/hub/models--TheBloke--Mistral-7B-Instruct-v0.2-GGUF/snapshots/3a6fbf4a41a1d52e415a4958cde6856d34b2db93/mistral-7b-instruct-v0.2.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 = mistralai_mistral-7b-instruct-v0.2
llama_model_loader: - kv 2: llama.context_length u32 = 32768
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 = 14336
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 = 8
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0,000010
llama_model_loader: - kv 10: llama.rope.freq_base f32 = 1000000,000000
llama_model_loader: - kv 11: general.file_type u32 = 15
llama_model_loader: - kv 12: tokenizer.ggml.model str = llama
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0,000000, 0,000000, 0,000000, 0,0000...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 22: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
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: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V3 (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 = 32768
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 = 1000000,0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
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 = 7,24 B
llm_load_print_meta: model size = 4,07 GiB (4,83 BPW)
llm_load_print_meta: general.name = mistralai_mistral-7b-instruct-v0.2
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: PAD token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 2070, compute capability 7.5, VMM: yes
llm_load_tensors: ggml ctx size = 0,11 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/33 layers to GPU
llm_load_tensors: CPU buffer size = 4165,37 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 = 1000000,0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA_Host KV buffer size = 64,00 MiB
llama_new_context_with_model: KV self size = 64,00 MiB, K (f16): 32,00 MiB, V (f16): 32,00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0,12 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 181,04 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 9,01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 356
system_info: n_threads = 8 / 16 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 |
sampling:
repeat_last_n = 64, repeat_penalty = 1,000, frequency_penalty = 0,000, presence_penalty = 0,000
top_k = 40, tfs_z = 1,000, top_p = 0,950, min_p = 0,050, typical_p = 1,000, temp = 0,800
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 = 512, n_batch = 2048, n_predict = 1, n_keep = 1
Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello
llama_print_timings: load time = 710,59 ms
llama_print_timings: sample time = 0,03 ms / 1 runs ( 0,03 ms per token, 34482,76 tokens per second)
llama_print_timings: prompt eval time = 706,45 ms / 436 tokens ( 1,62 ms per token, 617,17 tokens per second)
llama_print_timings: eval time = 0,00 ms / 1 runs ( 0,00 ms per token, inf tokens per second)
llama_print_timings: total time = 707,92 ms / 437 tokens
Log end
Raw
mistral.rs
lucas@desktop:~/oss/mistral.rs$ RUST_LOG=debug ./target/profiling/mistralrs-server -i gguf -t mistralai/Mistral-7B-Instruct-v0.1 -m TheBloke/Mistral-7B-Instruct-v0.1-GGUF -f mistral-7b-instruct-v0.1.Q4_K_M.gguf
2024-04-27T23:46:32.844504Z INFO mistralrs_server: avx: true, neon: false, simd128: false, f16c: true
2024-04-27T23:46:32.844528Z INFO mistralrs_server: Sampling method: penalties -> temperature -> topk -> topp -> multinomial
2024-04-27T23:46:32.844532Z INFO mistralrs_server: Loading model `mistralai/Mistral-7B-Instruct-v0.1` on Cuda(CudaDevice(DeviceId(1)))...
2024-04-27T23:46:32.844551Z INFO mistralrs_server: Model kind is: quantized from gguf (no adapters)
2024-04-27T23:46:34.555249Z INFO mistralrs_core::pipeline::chat_template: bos_tok = <s>, eos_tok = ["</s>"], unk_tok = <unk>
2024-04-27T23:46:34.584997Z INFO mistralrs_server: Model loaded.
2024-04-27T23:46:34.585159Z INFO mistralrs_server::interactive_mode: Starting interactive loop with sampling params: SamplingParams { temperature: Some(0.1), top_k: Some(32), top_p: Some(0.1), top_n_logprobs: 0, frequency_penalty: Some(0.1), presence_penalty: Some(0.1), stop_toks: None, max_len: Some(4096), logits_bias: None, n_choices: 1 }
> Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello Hello
2024-04-27T23:46:36.882094Z INFO mistralrs_core::engine: Prompt[445] Completion[] - 765ms
2024-04-27T23:46:36.938553Z INFO mistralrs_core::engine: Prompt[] Completion[446] - 56ms
Hi there!2024-04-27T23:46:36.964491Z INFO mistralrs_core::engine: Prompt[] Completion[447] - 25ms
2024-04-27T23:46:36.988795Z INFO mistralrs_core::engine: Prompt[] Completion[448] - 24ms
2024-04-27T23:46:37.015216Z INFO mistralrs_core::engine: Prompt[] Completion[449] - 26ms
How can I2024-04-27T23:46:37.042765Z INFO mistralrs_core::engine: Prompt[] Completion[450] - 27ms
2024-04-27T23:46:37.067985Z INFO mistralrs_core::engine: Prompt[] Completion[451] - 25ms
2024-04-27T23:46:37.095415Z INFO mistralrs_core::engine: Prompt[] Completion[452] - 27ms
assist you today2024-04-27T23:46:37.121790Z INFO mistralrs_core::engine: Prompt[] Completion[453] - 26ms
2024-04-27T23:46:37.151508Z INFO mistralrs_core::engine: Prompt[] Completion[454] - 29ms
?
> 2024-04-27T23:46:37.234602Z INFO mistralrs_core::engine: Prompt[] Completion[455] - 83ms
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