Is the model's PROMPT maximum number of tokens determined by the inference tool?

[17Reset]

When I use llamacpp to reason about my Smuag-34B's model, there is no output when the input prompt has 150tokens, but the output is normal when scaled down to about 100.

[phymbert]

Please describe all the steps you have followed to help to reproduce your issue, clarify which llama.cpp release you are using and attach output and llama.log.

[17Reset]

I am using the latest version of llamacpp and here is the output of my usage:

ubuntu@ubuntu:/mnt/llm/llm_toolkit$ ./main --model ../llm_quantized/smaug2_72b_q8.gguf --n-gpu-layers 81 --ctx-size 4096 --keep -1 --multiline-input --prompt "Analyze and list the global performance advantages of the U.S. Navy's AN/SPY-7 series of radars, focusing on their unique technical characteristics and performance indicators, such as detection range, accuracy, and anti-jamming capability, and compare them with existing similar radar systems from other countries to highlight the advancements of AN/SPY-7.
Please provide a detailed description, highlighting the advantages of the AN/SPY-7 series of radars in terms of detection range, accuracy and anti-jamming capability, and comparing it with similar radar systems in other countries.
Your answer should include the unique technical characteristics and performance indicators of the AN/SPY-7 series radar, as well as advantages over other radar systems. Please focus on describing the global performance benefits of AN/SPY-7 series radars and provide specific and detailed information."
Log start
main: build = 2678 (132f5579)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed  = 1713237780
llama_model_loader: loaded meta data with 23 key-value pairs and 963 tensors from ../llm_quantized/smaug2_72b_q8.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              = .
llama_model_loader: - kv   2:                           llama.vocab_size u32              = 152064
llama_model_loader: - kv   3:                       llama.context_length u32              = 32768
llama_model_loader: - kv   4:                     llama.embedding_length u32              = 8192
llama_model_loader: - kv   5:                          llama.block_count u32              = 80
llama_model_loader: - kv   6:                  llama.feed_forward_length u32              = 24576
llama_model_loader: - kv   7:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   8:                 llama.attention.head_count u32              = 64
llama_model_loader: - kv   9:              llama.attention.head_count_kv u32              = 64
llama_model_loader: - kv  10:     llama.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  11:                       llama.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  12:                          general.file_type u32              = 7
llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  14:                      tokenizer.ggml.tokens arr[str,152064]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  15:                      tokenizer.ggml.scores arr[f32,152064]  = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,152064]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 151644
llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 151645
llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% for message in messages %}{% if lo...
llama_model_loader: - kv  22:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  402 tensors
llama_model_loader: - type q8_0:  561 tensors
llm_load_vocab: special tokens definition check successful ( 421/152064 ).
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          = 152064
llm_load_print_meta: n_merges         = 151387
llm_load_print_meta: n_ctx_train      = 32768
llm_load_print_meta: n_embd           = 8192
llm_load_print_meta: n_head           = 64
llm_load_print_meta: n_head_kv        = 64
llm_load_print_meta: n_layer          = 80
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     = 8192
llm_load_print_meta: n_embd_v_gqa     = 8192
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             = 24576
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       = 65B
llm_load_print_meta: model ftype      = Q8_0
llm_load_print_meta: model params     = 72.29 B
llm_load_print_meta: model size       = 74.95 GiB (8.91 BPW)
llm_load_print_meta: general.name     = .
llm_load_print_meta: BOS token        = 151644 '<|im_start|>'
llm_load_print_meta: EOS token        = 151645 '<|im_end|>'
llm_load_print_meta: PAD token        = 151643 '<|endoftext|>'
llm_load_print_meta: LF token         = 148848 'ÄĬ'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 2 CUDA devices:
  Device 0: NVIDIA RTX 6000 Ada Generation, compute capability 8.9, VMM: yes
  Device 1: NVIDIA RTX 6000 Ada Generation, compute capability 8.9, VMM: yes
llm_load_tensors: ggml ctx size =    1.10 MiB
llm_load_tensors: offloading 80 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 81/81 layers to GPU
llm_load_tensors:        CPU buffer size =  1262.25 MiB
llm_load_tensors:      CUDA0 buffer size = 36250.41 MiB
llm_load_tensors:      CUDA1 buffer size = 39234.12 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  = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:      CUDA0 KV buffer size =  5248.00 MiB
llama_kv_cache_init:      CUDA1 KV buffer size =  4992.00 MiB
llama_new_context_with_model: KV self size  = 10240.00 MiB, K (f16): 5120.00 MiB, V (f16): 5120.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.58 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=4)
llama_new_context_with_model:      CUDA0 compute buffer size =   672.01 MiB
llama_new_context_with_model:      CUDA1 compute buffer size =   672.02 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =    48.02 MiB
llama_new_context_with_model: graph nodes  = 2806
llama_new_context_with_model: graph splits = 3

system_info: n_threads = 40 / 80 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | 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 = 4096, n_batch = 2048, n_predict = -1, n_keep = 177

Analyze and list the global performance advantages of the U.S. Navy's AN/SPY-7 series of radars, focusing on their unique technical characteristics and performance indicators, such as detection range, accuracy, and anti-jamming capability, and compare them with existing similar radar systems from other countries to highlight the advancements of AN/SPY-7.
Please provide a detailed description, highlighting the advantages of the AN/SPY-7 series of radars in terms of detection range, accuracy and anti-jamming capability, and comparing it with similar radar systems in other countries.
Your answer should include the unique technical characteristics and performance indicators of the AN/SPY-7 series radar, as well as advantages over other radar systems. Please focus on describing the global performance benefits of AN/SPY-7 series radars and provide specific and detailed information. [end of text]

llama_print_timings:        load time =   17973.16 ms
llama_print_timings:      sample time =       0.09 ms /     1 runs   (    0.09 ms per token, 11627.91 tokens per second)
llama_print_timings: prompt eval time =     487.10 ms /   177 tokens (    2.75 ms per token,   363.38 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 =     493.02 ms /   178 tokens
Log end

[Jeximo]

and detailed information. [end of text]

You want text generation to continue? From readme:

... generated text may be shorter if an End-of-Sequence (EOS) token is encountered. ... In non-interactive mode, the program will end. ... If you want the model to keep going without ever producing End-of-Sequence on its own, try --ignore-eos.

[17Reset]

This only achieves prompt word completion, however the Prompt does not have any Response and keeps outputting spaces.

[github-actions[bot]]

This issue was closed because it has been inactive for 14 days since being marked as stale.