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  1. C:\dev\ml\r1_1p58>t
  2. ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
  3. ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
  4. ggml_cuda_init: found 1 CUDA devices:
  5. Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
  6. register_backend: registered backend CUDA (1 devices)
  7. register_device: registered device CUDA0 (NVIDIA GeForce RTX 4090)
  8. register_backend: registered backend CPU (1 devices)
  9. register_device: registered device CPU (13th Gen Intel(R) Core(TM) i9-13900K)
  10. load_backend: failed to find ggml_backend_init in .\ggml-cuda.dll
  11. load_backend: failed to find ggml_backend_init in .\ggml-rpc.dll
  12. load_backend: failed to find ggml_backend_init in .\ggml-cpu.dll
  13. build: 4657 (8a59053f) with MSVC 19.39.33521.0 for x64 (debug)
  14. main: llama backend init
  15. main: load the model and apply lora adapter, if any
  16. llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 4090) - 22890 MiB free
  17. llama_model_loader: additional 2 GGUFs metadata loaded.
  18. llama_model_loader: loaded meta data with 52 key-value pairs and 1025 tensors from DeepSeek-R1-GGUF\DeepSeek-R1-UD-IQ1_S\DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf (version GGUF V3 (latest))
  19. llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
  20. llama_model_loader: - kv 0: general.architecture str = deepseek2
  21. llama_model_loader: - kv 1: general.type str = model
  22. llama_model_loader: - kv 2: general.name str = DeepSeek R1 BF16
  23. llama_model_loader: - kv 3: general.quantized_by str = Unsloth
  24. llama_model_loader: - kv 4: general.size_label str = 256x20B
  25. llama_model_loader: - kv 5: general.repo_url str = https://huggingface.co/unsloth
  26. llama_model_loader: - kv 6: deepseek2.block_count u32 = 61
  27. llama_model_loader: - kv 7: deepseek2.context_length u32 = 163840
  28. llama_model_loader: - kv 8: deepseek2.embedding_length u32 = 7168
  29. llama_model_loader: - kv 9: deepseek2.feed_forward_length u32 = 18432
  30. llama_model_loader: - kv 10: deepseek2.attention.head_count u32 = 128
  31. llama_model_loader: - kv 11: deepseek2.attention.head_count_kv u32 = 128
  32. llama_model_loader: - kv 12: deepseek2.rope.freq_base f32 = 10000.000000
  33. llama_model_loader: - kv 13: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
  34. llama_model_loader: - kv 14: deepseek2.expert_used_count u32 = 8
  35. llama_model_loader: - kv 15: deepseek2.leading_dense_block_count u32 = 3
  36. llama_model_loader: - kv 16: deepseek2.vocab_size u32 = 129280
  37. llama_model_loader: - kv 17: deepseek2.attention.q_lora_rank u32 = 1536
  38. llama_model_loader: - kv 18: deepseek2.attention.kv_lora_rank u32 = 512
  39. llama_model_loader: - kv 19: deepseek2.attention.key_length u32 = 192
  40. llama_model_loader: - kv 20: deepseek2.attention.value_length u32 = 128
  41. llama_model_loader: - kv 21: deepseek2.expert_feed_forward_length u32 = 2048
  42. llama_model_loader: - kv 22: deepseek2.expert_count u32 = 256
  43. llama_model_loader: - kv 23: deepseek2.expert_shared_count u32 = 1
  44. llama_model_loader: - kv 24: deepseek2.expert_weights_scale f32 = 2.500000
  45. llama_model_loader: - kv 25: deepseek2.expert_weights_norm bool = true
  46. llama_model_loader: - kv 26: deepseek2.expert_gating_func u32 = 2
  47. llama_model_loader: - kv 27: deepseek2.rope.dimension_count u32 = 64
  48. llama_model_loader: - kv 28: deepseek2.rope.scaling.type str = yarn
  49. llama_model_loader: - kv 29: deepseek2.rope.scaling.factor f32 = 40.000000
  50. llama_model_loader: - kv 30: deepseek2.rope.scaling.original_context_length u32 = 4096
  51. llama_model_loader: - kv 31: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.100000
  52. llama_model_loader: - kv 32: tokenizer.ggml.model str = gpt2
  53. llama_model_loader: - kv 33: tokenizer.ggml.pre str = deepseek-v3
  54. llama_model_loader: - kv 34: tokenizer.ggml.tokens arr[str,129280] = ["<｜begin▁of▁sentence｜>", "<...
  55. llama_model_loader: - kv 35: tokenizer.ggml.token_type arr[i32,129280] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
  56. llama_model_loader: - kv 36: tokenizer.ggml.merges arr[str,127741] = ["Ġ t", "Ġ a", "i n", "Ġ Ġ", "h e...
  57. llama_model_loader: - kv 37: tokenizer.ggml.bos_token_id u32 = 0
  58. llama_model_loader: - kv 38: tokenizer.ggml.eos_token_id u32 = 1
  59. llama_model_loader: - kv 39: tokenizer.ggml.padding_token_id u32 = 128815
  60. llama_model_loader: - kv 40: tokenizer.ggml.add_bos_token bool = true
  61. llama_model_loader: - kv 41: tokenizer.ggml.add_eos_token bool = false
  62. llama_model_loader: - kv 42: tokenizer.chat_template str = {% if not add_generation_prompt is de...
  63. llama_model_loader: - kv 43: general.quantization_version u32 = 2
  64. llama_model_loader: - kv 44: general.file_type u32 = 24
  65. llama_model_loader: - kv 45: quantize.imatrix.file str = DeepSeek-R1.imatrix
  66. llama_model_loader: - kv 46: quantize.imatrix.dataset str = /training_data/calibration_datav3.txt
  67. llama_model_loader: - kv 47: quantize.imatrix.entries_count i32 = 720
  68. llama_model_loader: - kv 48: quantize.imatrix.chunks_count i32 = 124
  69. llama_model_loader: - kv 49: split.no u16 = 0
  70. llama_model_loader: - kv 50: split.tensors.count i32 = 1025
  71. llama_model_loader: - kv 51: split.count u16 = 3
  72. llama_model_loader: - type f32: 361 tensors
  73. llama_model_loader: - type q4_K: 190 tensors
  74. llama_model_loader: - type q5_K: 116 tensors
  75. llama_model_loader: - type q6_K: 184 tensors
  76. llama_model_loader: - type iq2_xxs: 6 tensors
  77. llama_model_loader: - type iq1_s: 168 tensors
  78. print_info: file format = GGUF V3 (latest)
  79. print_info: file type = IQ1_S - 1.5625 bpw
  80. print_info: file size = 130.60 GiB (1.67 BPW)
  81. load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
  82. load: special tokens cache size = 819
  83. load: token to piece cache size = 0.8223 MB
  84. print_info: arch = deepseek2
  85. print_info: vocab_only = 0
  86. print_info: n_ctx_train = 163840
  87. print_info: n_embd = 7168
  88. print_info: n_layer = 61
  89. print_info: n_head = 128
  90. print_info: n_head_kv = 128
  91. print_info: n_rot = 64
  92. print_info: n_swa = 0
  93. print_info: n_embd_head_k = 192
  94. print_info: n_embd_head_v = 128
  95. print_info: n_gqa = 1
  96. print_info: n_embd_k_gqa = 24576
  97. print_info: n_embd_v_gqa = 16384
  98. print_info: f_norm_eps = 0.0e+00
  99. print_info: f_norm_rms_eps = 1.0e-06
  100. print_info: f_clamp_kqv = 0.0e+00
  101. print_info: f_max_alibi_bias = 0.0e+00
  102. print_info: f_logit_scale = 0.0e+00
  103. print_info: n_ff = 18432
  104. print_info: n_expert = 256
  105. print_info: n_expert_used = 8
  106. print_info: causal attn = 1
  107. print_info: pooling type = 0
  108. print_info: rope type = 0
  109. print_info: rope scaling = yarn
  110. print_info: freq_base_train = 10000.0
  111. print_info: freq_scale_train = 0.025
  112. print_info: n_ctx_orig_yarn = 4096
  113. print_info: rope_finetuned = unknown
  114. print_info: ssm_d_conv = 0
  115. print_info: ssm_d_inner = 0
  116. print_info: ssm_d_state = 0
  117. print_info: ssm_dt_rank = 0
  118. print_info: ssm_dt_b_c_rms = 0
  119. print_info: model type = 671B
  120. print_info: model params = 671.03 B
  121. print_info: general.name = DeepSeek R1 BF16
  122. print_info: n_layer_dense_lead = 3
  123. print_info: n_lora_q = 1536
  124. print_info: n_lora_kv = 512
  125. print_info: n_ff_exp = 2048
  126. print_info: n_expert_shared = 1
  127. print_info: expert_weights_scale = 2.5
  128. print_info: expert_weights_norm = 1
  129. print_info: expert_gating_func = sigmoid
  130. print_info: rope_yarn_log_mul = 0.1000
  131. print_info: vocab type = BPE
  132. print_info: n_vocab = 129280
  133. print_info: n_merges = 127741
  134. print_info: BOS token = 0 '<｜begin▁of▁sentence｜>'
  135. print_info: EOS token = 1 '<｜end▁of▁sentence｜>'
  136. print_info: EOT token = 1 '<｜end▁of▁sentence｜>'
  137. print_info: PAD token = 128815 '<｜PAD▁TOKEN｜>'
  138. print_info: LF token = 201 'Ċ'
  139. print_info: FIM PRE token = 128801 '<｜fim▁begin｜>'
  140. print_info: FIM SUF token = 128800 '<｜fim▁hole｜>'
  141. print_info: FIM MID token = 128802 '<｜fim▁end｜>'
  142. print_info: EOG token = 1 '<｜end▁of▁sentence｜>'
  143. print_info: max token length = 256
  144. load_tensors: loading model tensors, this can take a while... (mmap = true)
  145. load_tensors: offloading 7 repeating layers to GPU
  146. load_tensors: offloaded 7/62 layers to GPU
  147. load_tensors: CPU_Mapped model buffer size = 47058.04 MiB
  148. load_tensors: CPU_Mapped model buffer size = 47109.49 MiB
  149. load_tensors: CPU_Mapped model buffer size = 23859.37 MiB
  150. load_tensors: CUDA0 model buffer size = 15703.16 MiB
  151. llama_init_from_model: n_seq_max = 1
  152. llama_init_from_model: n_ctx = 8192
  153. llama_init_from_model: n_ctx_per_seq = 8192
  154. llama_init_from_model: n_batch = 2048
  155. llama_init_from_model: n_ubatch = 512
  156. llama_init_from_model: flash_attn = 0
  157. llama_init_from_model: freq_base = 10000.0
  158. llama_init_from_model: freq_scale = 0.025
  159. llama_init_from_model: n_ctx_per_seq (8192) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
  160. llama_kv_cache_init: kv_size = 8192, offload = 1, type_k = 'q4_0', type_v = 'f16', n_layer = 61, can_shift = 0
  161. llama_kv_cache_init: CPU KV buffer size = 19656.00 MiB
  162. llama_kv_cache_init: CUDA0 KV buffer size = 2548.00 MiB
  163. llama_init_from_model: KV self size = 22204.00 MiB, K (q4_0): 6588.00 MiB, V (f16): 15616.00 MiB
  164. llama_init_from_model: CPU output buffer size = 0.49 MiB
  165. llama_init_from_model: CUDA0 compute buffer size = 2589.50 MiB
  166. llama_init_from_model: CUDA_Host compute buffer size = 96.01 MiB
  167. llama_init_from_model: graph nodes = 5025
  168. llama_init_from_model: graph splits = 1015 (with bs=512), 3 (with bs=1)
  169. common_init_from_params: KV cache shifting is not supported for this model, disabling KV cache shifting
  170. common_init_from_params: setting dry_penalty_last_n to ctx_size = 8192
  171. common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
  172. main: llama threadpool init, n_threads = 32
  173.  
  174. system_info: n_threads = 32 (n_threads_batch = 32) / 32 | CUDA : ARCHS = 890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
  175.  
  176. sampler seed: 3407
  177. sampler params:
  178. repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
  179. dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 8192
  180. top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, temp = 0.600
  181. mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
  182. sampler chain: logits -> logit-bias -> penalties -> dry -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
  183. generate: n_ctx = 8192, n_batch = 2048, n_predict = -1, n_keep = 1
  184.  
  185. "<|User|>Without rearranging the digits 1 2 3 4 5, insert mathematical operation signs + - * and/or /, and, if necessary, parentheses so that the resulting expression equals 40. The key is that you can combine digits (like 12+3/45) but you cannot change their order from the original sequence 1,2,3,4,5.<|Assistant|>"
  186. Okay, so I need to make an expression using the digits 1, 2, 3, 4, 5 in order without rearranging them. I can insert mathematical operations like addition, subtraction, multiplication, division, and parentheses if needed. The goal is to get the result 40. Let me think through this step by step.
  187.  
  188. First, let's list the digits in order: 1, 2, 3, 4, 5. I can't change their order, so I have to work with them as they are. The challenge is to insert operations between them to get 40. Hmm.
  189.  
  190. I know that 40 is a multiple of 8 and 5, or 4 and 10. Maybe I can find a way to get those numbers somewhere. Let me try different combinations.
  191.  
  192. Starting with 1 and 2. If I combine 1 and 2 first, that's 12. Then 3, 4, 5. Maybe 12 plus 3 times (4 + 5). Let's calculate: 12 + 3*(9) = 12 + 27 = 39. Close but not 40. If I can adjust that a bit.
  193.  
  194. Alternatively, maybe 1 times 2. So 1*2=2. Then 2 plus 3: 2+3=5. Then 5 times 4=20, and 20 plus 5=25. Not enough. Hmm.
  195.  
  196. What if I combine 1 and 2 as 12, then add 3, then multiply by 4, and subtract 5? Let's see: 12 + 3 = 15, 15 *4 =60, 60 -5=55. That's 55, not 40.
  197.  
  198. Alternatively, 1 + 2 + 3 + 4 *5. Let's compute: 1+2=3, +3=6, +4=10, then times 5? Wait, no, order matters. So it's 1 + 2 + 3 + (4*5). So that's 1+2=3, +3=6, +20=26. Not 40.
  199.  
  200. What if I need to use division somewhere? Maybe 12 divided by something. Let's try 12 / 3 *4 +5. So 12/3=4, 4*4=16, 16+5=21. Nope.
  201.  
  202. How about 1 + (2 * 3 * 4) -5. Let's compute: 2*3=6, 6*4=24. Then 1+24=25, 25-5=20. Not enough.
  203.  
  204. Wait, maybe parentheses can change the order of operations. For example, 1 * (2 + 3) * (4 + 5). That's 5*9=45. Then subtract 5? Wait, but the digits are in order. So after 1,2,3,4,5. So maybe 1 * (2 + 3) * 4 +5. Let's see: 2+3=5, 1*5=5, *4=20, +5=25. Still not.
  205.  
  206. Hmm. Maybe combining digits into larger numbers. Like 12 + 34 - 5. 12+34=46, minus 5=41. Close, but 40. If I can adjust that. But 12 + 34 - 5 is 41, so maybe 12 + 34 - 5 needs to be adjusted. Wait, but can't change the order. Maybe 1 + 2 * 3 * 4 -5. Let's compute: 2*3=6, *4=24. Then 1 +24=25, 25-5=20. No.
  207.  
  208. Alternatively, 1 * 2 * (3 + 4 +5). Wait, 3+4+5=12. 2*12=24, 1*24=24. Not 40.
  209.  
  210. What if I use division. Let's try 1 * (2 + 3) * (4 +5). Wait, that's 5*9=45. Then subtract 5. But the digits after 4 are 5. So maybe 1*(2+3)*(4+5) but that's 45, but how to get 40? Wait, maybe 1*(2+3)*4 -5. So 1*(5)*4=20, 20-5=15. Not 40.
  211.  
  212. Alternatively, 1 + 2 * 3 * 4 -5. Let's compute: 2*3=6, *4=24, then 1+24=25, 25-5=20.
  213.  
  214. Maybe combining more digits. Like 12 * 3 + 4 *5. Let's see: 12*3=36, 4*5=20, total 56. Not 40.
  215.  
  216. Wait, 1 + 2 + 3 + 4 *5. That's 1+2=3, +3=6, +20=26. No.
  217.  
  218. What if I do (1 + 2 + 3 + 4) *5. That's 10*5=50. Then subtract 10? But I can't insert extra digits. Wait, but maybe (1+2+3+4) *5 is 10*5=50. But 50 isn't 40. How to reduce by 10? Maybe (1+2+3+4) * (5 - something). But 5 is the last digit.
  219.  
  220. Alternatively, (1 + 2) * (3 + 4) *5. Wait, 3+4=7, 1+2=3, 3*7=21, *5=105. No.
  221.  
  222. Hmm. Let's think differently. Maybe 1 * 2 * (3 + 4) *5. So 3+4=7, 2*7=14, 1*14=14, *5=70. No.
  223.  
  224. Alternatively, 1 * 2 * 3 *4 +5. So 24 +5=29. Not enough.
  225.  
  226. Wait, maybe (1 + 2) * 3 *4 -5. Let's compute: 1+2=3, 3*3=9, 9*4=36, minus5=31.
  227.  
  228. What if I use division to get some fractions. Like 12 + 3/(4/5). Let's see, 4/5 is 0.8, then 3 divided by that is 3.75. So 12 +3.75=15.75. Not helpful.
  229.  
  230. Alternatively, 1 + (2 * 3 * 4) -5. So 2*3=6, *4=24. 1+24=25, 25-5=20.
  231.  
  232. Wait, maybe 1 + 2 * (3 +4) *5. Let's see: 3+4=7, 2*7=14, 14*5=70, +1=71. No.
  233.  
  234. Hmm. Maybe there's a different approach. Let's think of combining the digits into bigger numbers. For example, 12, 34, 5. Then 12 + 34 +5=51. Not 40. Or 12 + 34 -5=41. Close. If I can get that 41 down by 1. But how?
  235.  
  236. Alternatively, 12 + 3 *4 +5. 12 +12=24, +5=29.
  237.  
  238. Wait, maybe 1 + 23 +4 +5. Wait, but we can't rearrange the digits. So digits have to stay in order. So 1,2,3,4,5. So combining digits is allowed as long as order is maintained. So 1 can be combined with 2 as 12, but can't rearrange to make 23. So 12 is okay, but 23 would require changing the order. Wait, no, because 2 and 3 are in order. So 1 can be 1, then 2 and 3 can be combined as 23. Wait, but the original sequence is 1,2,3,4,5. So if we combine 2 and 3 into 23, that's allowed? Or does combining digits require consecutive digits? Wait, the problem says "you can combine digits (like 12+3/45)" so combining adjacent digits. So 12 is combining 1 and 2, then 3,4,5 are separate. But combining 2 and 3 into 23 would require that they are adjacent, which they are. So 1, 23, 4,5. So 1 + 23 +4 +5=33. Not 40.
  239.  
  240. Alternatively, 1 + 2 + 34 +5. 34 is combining 3 and4. So 1+2=3, +34=37, +5=42. Close.
  241.  
  242. Wait, 1 + 2 + 34 +5=42. If I can subtract 2 somewhere. Hmm. Maybe 1 *2 +34 +5. 1*2=2, +34=36, +5=41. Still close.
  243.  
  244. Alternatively, 1 *2 +34 +5=41. If I can adjust it by 1. But how? Maybe 1 *2 +34 -5=2 +34 -5=31. No.
  245.  
  246. Alternatively, 1 + 2 *3 *4 -5. 2*3=6, *4=24, 1+24=25, -5=20.
  247.  
  248. Wait, maybe 12 *3 +4 *5. 12*3=36, 4*5=20. 36+20=56.
  249.  
  250. Hmm. What if I do 1 *2 * (3 +4 +5). 3+4+5=12. 2*12=24, 1*24=24. No.
  251.  
  252. Alternatively, (1 +2 +3 +4) *5. That's 10*5=50. Then subtract 10. But how? Since we have to use the digits in order. Maybe 1 +2 +3 +4 *5. Wait, that's different. But order is 1,2,3,4,5. So if I insert operations: 1 +2 +3 +4 *5. That would be 1+2+3+(4*5)=6+20=26. Not 40.
  253.  
  254. Wait, perhaps using division somewhere to get a higher number. Like 12 *3 +4 /5. 12*3=36, 4/5=0.8, total 36.8. Not 40.
  255.  
  256. Alternatively, 1 *2 / (3/4) *5. Let's compute: 3/4=0.75, 2 divided by that is 2.666..., multiplied by 5=13.333..., 1* that is still 13.333.
  257.  
  258. Hmm. Maybe using parentheses to change the order. For example, 1 + (2 *3 *4) -5. 2*3=6, *4=24, +1=25, -5=20.
  259.  
  260. Alternatively, 1 * (2 *3 *4) -5. 2*3=6, *4=24, *1=24, -5=19.
  261.  
  262. Wait, maybe 1 + 2 * (3 +4 +5). 3+4+5=12, 2*12=24, 1+24=25.
  263.  
  264. Still not 40. I'm stuck. Let's think differently. Maybe using subtraction.
  265.  
  266. What if I do 123 - something. But 123 is a big number. 123 -45=78. Not 40.
  267.  
  268. Alternatively, 12 *3 -4 -5. 12*3=36, 36-4=32, 32-5=27.
  269.  
  270. Alternatively, 12 *3 - (4 +5)=36 -9=27.
  271.  
  272. Hmm. What if I use division to create fractions. Like 1 * (2 + 3) * (4 +5). That's 5*9=45. Then subtract 5. But how? The last digit is 5, so maybe 45 -5=40. Wait, that would require something like (1*(2+3)*(4+5)) -5. Let me check the digits order.
  273.  
  274. Original digits: 1,2,3,4,5.
  275.  
  276. If I do 1*(2+3)*(4+5)-5. Let's parse this:
  277.  
  278. Between 1 and 2: insert a multiplication and parentheses. Wait, the expression would be 1 * (2 +3) * (4 +5) -5. But the digits must remain in order. So the expression would be constructed as follows:
  279.  
  280. 1 * (2 +3) * (4 +5) -5.
  281.  
  282. Breaking it down:
  283.  
  284. 1 * (2+3) = 1 *5=5
  285. Then * (4+5)=5 *9=45
  286. Then subtract 5: 45-5=40.
  287.  
  288. Wait, does this work with the original digits? Let's see:
  289.  
  290. The digits are in order 1,2,3,4,5. So inserting operations and parentheses:
  291.  
  292. 1 * (2 +3) * (4 +5) -5
  293.  
  294. Breaking down the digits:
  295.  
  296. 1 is followed by 2, then 3, then 4, then 5. So inserting * after 1, then (2+3) would be after 2 and 3, then * (4+5) after 4 and 5. Wait, but the subtraction is at the end. Let me check the structure.
  297.  
  298. The expression would be:
  299. 1 * (2+3) * (4+5) -5
  300.  
  301. Which is:
  302. 1 * (2+3) = 1*5=5
  303. Then * (4+5)=5*9=45
  304. Then subtract 5: 45-5=40.
  305.  
  306. So yes, this seems to work. Let me verify step by step:
  307.  
  308. 1 * (2 +3) = 1*5=5
  309. 5 * (4 +5) = 5*9=45
  310. 45 -5=40.
  311.  
  312. Yes, that gives 40. So the expression is 1*(2+3)*(4+5)-5.
  313.  
  314. But wait, are all the digits in their original order? Let's see:
  315.  
  316. The digits are 1,2,3,4,5. The operations inserted are:
  317.  
  318. After 1: *
  319. After 2: +
  320. After 3: ) * (
  321. After 4: +
  322. After 5: ) -5.
  323.  
  324. Wait, but the exact placement of parentheses matters. The expression is constructed as 1 * (2+3) * (4+5) -5.
  325.  
  326. Breaking down the digits and operations:
  327.  
  328. 1 * (2 +3) * (4 +5) -5
  329.  
  330. So the original digits are 1,2,3,4,5. We insert *, +, *, +, and subtract. The parentheses are around 2+3 and 4+5.
  331.  
  332. So the order of digits remains 1,2,3,4,5, with operations inserted between them. The key is that we're combining 2 and 3 as (2+3), and 4 and5 as (4+5), but without rearranging the digits. So this should be acceptable.
  333.  
  334. Therefore, the correct expression is 1*(2+3)*(4+5)-5, which equals 40.
  335.  
  336. **Final Answer**
  337. The expression is \boxed{1 \times (2 + 3) \times (4 + 5) - 5}.
  338. </think>
  339.  
  340. To create an expression using the digits 1, 2, 3, 4, 5 in their original order with mathematical operations to equal 40, we can use the following steps:
  341.  
  342. 1. Combine the digits 2 and 3 with an addition operation: \(2 + 3\).
  343. 2. Combine the digits 4 and 5 with an addition operation: \(4 + 5\).
  344. 3. Multiply the results of these combinations with the digit 1.
  345. 4. Subtract the digit 5 from the result.
  346.  
  347. The resulting expression is:
  348.  
  349. \[
  350. 1 \times (2 + 3) \times (4 + 5) - 5
  351. \]
  352.  
  353. Breaking down the calculation:
  354. - \(2 + 3 = 5\)
  355. 8- \(4 + 5 = 9\)
  356. &1 \times 5 = 5\)
  357. - \(5 \times 9 = 45\)
  358. # 1.2.2 应用函数
  359.  
  360. 函数应用指的是将函数作用于参数。在 Lisp 中，函数应用的形式为：
  361.  
  362. ```lisp
  363. (函数名 参数1 参数2 ...)
  364. ```
  365.  
  366. 例如，调用函数 `+` 并将参数 1 和 2 传入，可以写成：
  367.  
  368. ```lisp
  369. (+ 1 2)
  370. ```
  371.  
  372. 这种形式的表达式称为组合式（combination）。组合式由操作符（operator，即函数名）和操作数（operands，即参数）组成。组合式求值时，Lisp 会先对操作符和操作数求值，然后将操作符应用于操作数。例如，在上面的例子中，`+` 作为操作符，求值结果为加法函数；`1` 和 `2` 作为操作数，求值结果为 1 和 2。然后将加法函数作用于 1 和 2，得到结果 3。
  373.  
  374. 在 Lisp 中，组合式中的第一个元素必须是一个函数名。例如，表达式 `(+ 1 2)` 中，第一个元素是 `+`，它是一个函数名。其他元素则是函数的参数。Lisp 会先对所有元素求值，然后应用函数。
  375.  
  376. 需要注意的是，参数列表中的每个元素也会被求值。例如，表达式 `(1 2 3)` 会导致错误，因为第一个元素 `1` 不是一个 函数名，而是一个数字，无法作为函数应用。因此，在 Lisp 中，组合式的第一个元素必须是一个函数名，否则会导致错误 。
  377.  
  378. 总结一下，Lisp 中函数应用的基本形式是：
  379.  
  380. ```lisp
  381. (函数名 参数1 参数2 ...)
  382. ```
  383.  
  384. 其中，函数名必须是可用的函数，参数可以是任意表达式，每个参数都会被求值后再传入函数。这种形式的应用方式称为前 缀表示法（prefix notation），即函数名位于参数前面。
  385. [end of text]
  386.  
  387.  
  388. llama_perf_sampler_print: sampling time = 5533.75 ms / 4358 runs ( 1.27 ms per token, 787.53 tokens per second)
  389. llama_perf_context_print: load time = 29480.82 ms
  390. llama_perf_context_print: prompt eval time = 171157.42 ms / 91 tokens ( 1880.85 ms per token, 0.53 tokens per second)
  391. llama_perf_context_print: eval time = 10299060.06 ms / 4266 runs ( 2414.22 ms per token, 0.41 tokens per second)
  392. llama_perf_context_print: total time = 10495858.38 ms / 4357 tokens
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