ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 2 CUDA devices: Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes build: 7040 (92bb442ad) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3090) (0000:01:00.0) - 20860 MiB free llama_model_load_from_file_impl: using device CUDA1 (NVIDIA GeForce RTX 3090) (0000:03:00.0) - 23581 MiB free llama_model_loader: loaded meta data with 48 key-value pairs and 402 tensors from /mnt/world8/AI/ToBench/granite-4.0-h-350m-unsloth/Magic_Quant/GGUF/granite-4.0-h-350m-unsloth-Q8_0.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 = granitehybrid llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Granite 4.0 H 350m Unsloth llama_model_loader: - kv 3: general.finetune str = unsloth llama_model_loader: - kv 4: general.basename str = granite-4.0-h llama_model_loader: - kv 5: general.size_label str = 350M llama_model_loader: - kv 6: general.license str = apache-2.0 llama_model_loader: - kv 7: general.base_model.count u32 = 1 llama_model_loader: - kv 8: general.base_model.0.name str = Granite 4.0 H 350m llama_model_loader: - kv 9: general.base_model.0.organization str = Ibm Granite llama_model_loader: - kv 10: general.base_model.0.repo_url str = https://huggingface.co/ibm-granite/gr... llama_model_loader: - kv 11: general.tags arr[str,3] = ["language", "unsloth", "granite-4.0"] llama_model_loader: - kv 12: granitehybrid.block_count u32 = 32 llama_model_loader: - kv 13: granitehybrid.context_length u32 = 1048576 llama_model_loader: - kv 14: granitehybrid.embedding_length u32 = 768 llama_model_loader: - kv 15: granitehybrid.feed_forward_length u32 = 2048 llama_model_loader: - kv 16: granitehybrid.attention.head_count u32 = 12 llama_model_loader: - kv 17: granitehybrid.attention.head_count_kv arr[i32,32] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, ... llama_model_loader: - kv 18: granitehybrid.rope.freq_base f32 = 10000.000000 llama_model_loader: - kv 19: granitehybrid.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 20: granitehybrid.expert_count u32 = 0 llama_model_loader: - kv 21: granitehybrid.expert_used_count u32 = 0 llama_model_loader: - kv 22: general.file_type u32 = 7 llama_model_loader: - kv 23: granitehybrid.vocab_size u32 = 100352 llama_model_loader: - kv 24: granitehybrid.rope.dimension_count u32 = 64 llama_model_loader: - kv 25: granitehybrid.attention.scale f32 = 0.015625 llama_model_loader: - kv 26: granitehybrid.embedding_scale f32 = 12.000000 llama_model_loader: - kv 27: granitehybrid.residual_scale f32 = 0.246000 llama_model_loader: - kv 28: granitehybrid.logit_scale f32 = 3.000000 llama_model_loader: - kv 29: granitehybrid.expert_shared_feed_forward_length u32 = 2048 llama_model_loader: - kv 30: granitehybrid.ssm.conv_kernel u32 = 4 llama_model_loader: - kv 31: granitehybrid.ssm.state_size u32 = 128 llama_model_loader: - kv 32: granitehybrid.ssm.group_count u32 = 1 llama_model_loader: - kv 33: granitehybrid.ssm.inner_size u32 = 1536 llama_model_loader: - kv 34: granitehybrid.ssm.time_step_rank u32 = 48 llama_model_loader: - kv 35: granitehybrid.rope.scaling.finetuned bool = false llama_model_loader: - kv 36: general.quantization_version u32 = 2 llama_model_loader: - kv 37: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 38: tokenizer.ggml.pre str = dbrx llama_model_loader: - kv 39: tokenizer.ggml.tokens arr[str,100352] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 40: tokenizer.ggml.token_type arr[i32,100352] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 41: tokenizer.ggml.merges arr[str,100000] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 42: tokenizer.ggml.bos_token_id u32 = 100257 llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 100257 llama_model_loader: - kv 44: tokenizer.ggml.unknown_token_id u32 = 100269 llama_model_loader: - kv 45: tokenizer.ggml.padding_token_id u32 = 100256 llama_model_loader: - kv 46: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 47: tokenizer.chat_template str = {%- set tools_system_message_prefix =... llama_model_loader: - type f32: 233 tensors llama_model_loader: - type q8_0: 169 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q8_0 print_info: file size = 345.83 MiB (8.52 BPW) load: printing all EOG tokens: load: - 100257 ('<|end_of_text|>') load: - 100261 ('<|fim_pad|>') load: special tokens cache size = 96 load: token to piece cache size = 0.6152 MB print_info: arch = granitehybrid print_info: vocab_only = 0 print_info: n_ctx_train = 1048576 print_info: n_embd = 768 print_info: n_embd_inp = 768 print_info: n_layer = 32 print_info: n_head = 12 print_info: n_head_kv = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0] print_info: n_rot = 64 print_info: n_swa = 0 print_info: is_swa_any = 0 print_info: n_embd_head_k = 64 print_info: n_embd_head_v = 64 print_info: n_gqa = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0] print_info: n_embd_k_gqa = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 256, 0, 0, 256, 0, 0, 0, 256, 0, 0, 0, 0, 0, 0, 0, 0, 0, 256, 0, 0, 0, 0] print_info: n_embd_v_gqa = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 256, 0, 0, 256, 0, 0, 0, 256, 0, 0, 0, 0, 0, 0, 0, 0, 0, 256, 0, 0, 0, 0] print_info: f_norm_eps = 0.0e+00 print_info: f_norm_rms_eps = 1.0e-05 print_info: f_clamp_kqv = 0.0e+00 print_info: f_max_alibi_bias = 0.0e+00 print_info: f_logit_scale = 3.0e+00 print_info: f_attn_scale = 1.6e-02 print_info: n_ff = 2048 print_info: n_expert = 0 print_info: n_expert_used = 0 print_info: n_expert_groups = 0 print_info: n_group_used = 0 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 0 print_info: rope scaling = linear print_info: freq_base_train = 10000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 1048576 print_info: rope_finetuned = unknown print_info: ssm_d_conv = 4 print_info: ssm_d_inner = 1536 print_info: ssm_d_state = 128 print_info: ssm_dt_rank = 48 print_info: ssm_n_group = 1 print_info: ssm_dt_b_c_rms = 0 print_info: model type = 350M print_info: model params = 340.33 M print_info: general.name = Granite 4.0 H 350m Unsloth print_info: f_embedding_scale = 12.000000 print_info: f_residual_scale = 0.246000 print_info: f_attention_scale = 0.015625 print_info: n_ff_shexp = 2048 print_info: vocab type = BPE print_info: n_vocab = 100352 print_info: n_merges = 100000 print_info: BOS token = 100257 '<|end_of_text|>' print_info: EOS token = 100257 '<|end_of_text|>' print_info: EOT token = 100257 '<|end_of_text|>' print_info: UNK token = 100269 '<|unk|>' print_info: PAD token = 100256 '<|pad|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 100258 '<|fim_prefix|>' print_info: FIM SUF token = 100260 '<|fim_suffix|>' print_info: FIM MID token = 100259 '<|fim_middle|>' print_info: FIM PAD token = 100261 '<|fim_pad|>' print_info: EOG token = 100257 '<|end_of_text|>' print_info: EOG token = 100261 '<|fim_pad|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = true) load_tensors: offloading 20 repeating layers to GPU load_tensors: offloaded 20/33 layers to GPU load_tensors: CPU_Mapped model buffer size = 345.83 MiB load_tensors: CUDA0 model buffer size = 81.98 MiB load_tensors: CUDA1 model buffer size = 84.25 MiB ............................................................................... llama_context: constructing llama_context llama_context: n_seq_max = 1 llama_context: n_ctx = 2048 llama_context: n_ctx_seq = 2048 llama_context: n_batch = 2048 llama_context: n_ubatch = 512 llama_context: causal_attn = 1 llama_context: flash_attn = auto llama_context: kv_unified = false llama_context: freq_base = 10000.0 llama_context: freq_scale = 1 llama_context: n_ctx_seq (2048) < n_ctx_train (1048576) -- the full capacity of the model will not be utilized llama_context: CPU output buffer size = 0.38 MiB llama_kv_cache: CPU KV buffer size = 2.00 MiB llama_kv_cache: CUDA0 KV buffer size = 4.00 MiB llama_kv_cache: CUDA1 KV buffer size = 2.00 MiB llama_kv_cache: size = 8.00 MiB ( 2048 cells, 4 layers, 1/1 seqs), K (f16): 4.00 MiB, V (f16): 4.00 MiB llama_memory_recurrent: CPU RS buffer size = 8.48 MiB llama_memory_recurrent: CUDA0 RS buffer size = 6.16 MiB llama_memory_recurrent: CUDA1 RS buffer size = 6.93 MiB llama_memory_recurrent: size = 21.57 MiB ( 1 cells, 32 layers, 1 seqs), R (f32): 0.57 MiB, S (f32): 21.00 MiB llama_context: Flash Attention was auto, set to enabled llama_context: CUDA0 compute buffer size = 275.59 MiB llama_context: CUDA1 compute buffer size = 22.39 MiB llama_context: CUDA_Host compute buffer size = 18.34 MiB llama_context: graph nodes = 1815 llama_context: graph splits = 182 (with bs=512), 41 (with bs=1) common_init_from_params: added <|end_of_text|> logit bias = -inf common_init_from_params: added <|fim_pad|> logit bias = -inf common_init_from_params: setting dry_penalty_last_n to ctx_size = 2048 common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable) system_info: n_threads = 16 (n_threads_batch = 16) / 32 | CUDA : ARCHS = 860 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | perplexity: tokenizing the input .. perplexity: tokenization took 96.502 ms perplexity: calculating perplexity over 44 chunks, n_ctx=2048, batch_size=2048, n_seq=1 perplexity: 0.51 seconds per pass - ETA 0.37 minutes [1]4.3877,[2]3.9892,[3]2.5746,[4]2.3726,[5]2.6116,[6]2.8551,[7]2.7045,[8]2.5122,[9]2.3088,[10]2.1400,[11]2.1223,[12]2.1486,[13]2.0595,[14]2.0395,[15]2.0798,[16]2.0140,[17]1.9885,[18]2.0068,[19]1.9670,[20]1.9318,[21]1.8988,[22]1.8838,[23]1.9131,[24]1.8867,[25]1.9059,[26]1.8740,[27]1.8607,[28]1.8525,[29]1.8981,[30]1.9147,[31]1.9136,[32]1.8893,[33]1.9129,[34]1.9050,[35]1.8862,[36]1.9175,[37]1.9237,[38]1.9216,[39]1.9431,[40]1.9407,[41]1.9338,[42]1.9579,[43]1.9666,[44]1.9558, Final estimate: PPL = 1.9558 +/- 0.01752 llama_perf_context_print: load time = 212.48 ms llama_perf_context_print: prompt eval time = 15540.13 ms / 90112 tokens ( 0.17 ms per token, 5798.66 tokens per second) llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) llama_perf_context_print: total time = 16341.42 ms / 90113 tokens llama_perf_context_print: graphs reused = 0 llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted | llama_memory_breakdown_print: | - CUDA0 (RTX 3090) | 24115 = 20426 + ( 367 = 81 + 10 + 275) + 3320 | llama_memory_breakdown_print: | - CUDA1 (RTX 3090) | 24124 = 23397 + ( 115 = 84 + 8 + 22) + 610 | llama_memory_breakdown_print: | - Host | 374 = 345 + 10 + 18 |