llama : add support for Cohere2ForCausalLM

[dranger003]

Closes #10816

Cohere updated their Command-R model architecture for C4AI Command R7B requiring an update to llama.cpp. Looking at the HF code, it looks like the model is using a hybrid cache like Gemma2. Additional info from their model page on HF:

The model features three layers with sliding window attention (window size 4096) and ROPE for efficient local context modeling and relative positional encoding. A fourth layer uses global attention without positional embeddings, enabling unrestricted token interactions across the entire sequence.

Summary changes in this PR (based on my very limited knowledge of neural nets):

• Add sliding window and RoPE dim count during conversion
• Remove ATTN_K_NORM and ATTN_Q_NORM
• Support alternating sliding window attention in build_cohere2 (looking at llama.cpp's build_gemma2) using pattern of 4 layers
• Use LLAMA_ROPE_TYPE_NORM as the rope type

HF transformers implementation reference:
https://github.com/huggingface/transformers/blob/main/src/transformers/models/cohere2/modular_cohere2.py

Test weights:
https://huggingface.co/dranger003/c4ai-command-r7b-12-2024-GGUF

[dranger003]

HF config.json:

{
  "architectures": [
    "Cohere2ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 5,
  "cache_implementation": "hybrid",
  "eos_token_id": 255001,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "layer_norm_eps": 1e-05,
  "layer_switch": 4,
  "logit_scale": 0.25,
  "max_position_embeddings": 8192,
  "model_type": "cohere2",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "order_of_interleaved_layers": "local_attn_first",
  "pad_token_id": 0,
  "position_embedding_type": "rope_gptj",
  "rope_scaling": null,
  "rope_theta": 50000,
  "rotary_pct": 1.0,
  "sliding_window": 4096,
  "sliding_window_pattern": 4,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.48.0.dev0",
  "use_cache": true,
  "use_embedding_sharing": true,
  "use_gated_activation": true,
  "use_parallel_block": true,
  "use_parallel_embedding": true,
  "vocab_size": 256000
}

[dranger003]

Info from @foldl:

It uses (3 SWA layers + 1 global attention layer). So, build_command_r need to be updated, even though the result seems promising.

Here is an implementation of interleaved SWA/global-attention layers.

https://github.com/foldl/chatllm.cpp/blob/ff54a787948f02151b38231375be042b632a271e/models/cohere.cpp#L246C1-L258C1

[dranger003]

The config.json has "max_position_embeddings": 8192, but the model supports 128K context. Do we need to adjust this value here?

[bartowski1182]

Don't quote me on this but I think it's fine to leave this as-is and force users to adjust rope settings to enable the full context

[dranger003]

Do we need to use build_rope_factors(il) for c when calling ggml_rope_ext with this model?

[foldl]

RoPE is only applied to SWA layers.

[dranger003]

Got it, looks like the cache is working now. Not sure if I still need build_rope_factors() though?

[osadchi]

Thank you for your great job!!!
I did successfully compiled your fork, convert model. Don't know is it good idea, but I test Q2_K quitezation :)
But output is random characters :C

PS C:\Users\user> C:/llama/llama.cpp-cohere2/build/bin/llama-cli.exe  -p "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Tell me all about yourself.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_RESPONSE|>" -m C:\llama\ggml-model-command-r7b-q2_k.gguf -sm layer -ts 56,56 -t 12 -c 10000 -ngl 33 -b 2048 -ub 2048 -ctk f16 -ctv f16 -fa -np 1
ggml_vulkan: Found 2 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon RX 6600M (AMD proprietary driver) | uma: 0 | fp16: 1 | warp size: 64 | matrix cores: none
ggml_vulkan: 1 = NVIDIA GeForce RTX 3060 (NVIDIA) | uma: 0 | fp16: 1 | warp size: 32 | matrix cores: KHR_coopmat
build: 0 (unknown) with cc.exe (Rev7, Built by MSYS2 project) 10.3.0 for x86_64-w64-mingw32
main: llama backend init
main: load the model and apply lora adapter, if any
llama_load_model_from_file: using device Vulkan0 (AMD Radeon RX 6600M) - 8176 MiB free
llama_load_model_from_file: using device Vulkan1 (NVIDIA GeForce RTX 3060) - 12115 MiB free
llama_model_loader: loaded meta data with 38 key-value pairs and 258 tensors from C:\llama\ggml-model-command-r7b-q2_k.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              = cohere2
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = CohereForAI Command R7B
llama_model_loader: - kv   3:                         general.size_label str              = 8.0B
llama_model_loader: - kv   4:                            general.license str              = cc-by-nc-4.0
llama_model_loader: - kv   5:                          general.languages arr[str,23]      = ["en", "fr", "de", "es", "it", "pt", ...
llama_model_loader: - kv   6:                        cohere2.block_count u32              = 32
llama_model_loader: - kv   7:                     cohere2.context_length u32              = 8192
llama_model_loader: - kv   8:                   cohere2.embedding_length u32              = 4096
llama_model_loader: - kv   9:                cohere2.feed_forward_length u32              = 14336
llama_model_loader: - kv  10:               cohere2.attention.head_count u32              = 32
llama_model_loader: - kv  11:            cohere2.attention.head_count_kv u32              = 8
llama_model_loader: - kv  12:                     cohere2.rope.freq_base f32              = 50000.000000
llama_model_loader: - kv  13:       cohere2.attention.layer_norm_epsilon f32              = 0.000010
llama_model_loader: - kv  14:               cohere2.attention.key_length u32              = 128
llama_model_loader: - kv  15:             cohere2.attention.value_length u32              = 128
llama_model_loader: - kv  16:                          general.file_type u32              = 10
llama_model_loader: - kv  17:                        cohere2.logit_scale f32              = 0.250000
llama_model_loader: - kv  18:           cohere2.attention.sliding_window u32              = 4096
llama_model_loader: - kv  19:                         cohere2.vocab_size u32              = 256000
llama_model_loader: - kv  20:               cohere2.rope.dimension_count u32              = 128
llama_model_loader: - kv  21:                  cohere2.rope.scaling.type str              = none
llama_model_loader: - kv  22:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  23:                         tokenizer.ggml.pre str              = command-r
llama_model_loader: - kv  24:                      tokenizer.ggml.tokens arr[str,256000]  = ["<PAD>", "<UNK>", "<CLS>", "<SEP>", ...
llama_model_loader: - kv  25:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, ...
llama_model_loader: - kv  26:                      tokenizer.ggml.merges arr[str,253333]  = ["Ġ Ġ", "Ġ t", "e r", "i n", "Ġ a...
llama_model_loader: - kv  27:                tokenizer.ggml.bos_token_id u32              = 5
llama_model_loader: - kv  28:                tokenizer.ggml.eos_token_id u32              = 255001
llama_model_loader: - kv  29:            tokenizer.ggml.unknown_token_id u32              = 1
llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  32:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  33:           tokenizer.chat_template.tool_use str              = {%- macro document_turn(documents) -%...
llama_model_loader: - kv  34:                tokenizer.chat_template.rag str              = {% set tools = [] %}\n{%- macro docume...
llama_model_loader: - kv  35:                   tokenizer.chat_templates arr[str,2]       = ["rag", "tool_use"]
llama_model_loader: - kv  36:                    tokenizer.chat_template str              = {% if documents %}\n{% set tools = [] ...
llama_model_loader: - kv  37:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   33 tensors
llama_model_loader: - type q2_K:  128 tensors
llama_model_loader: - type q3_K:   64 tensors
llama_model_loader: - type q4_K:   32 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
llm_load_vocab: special tokens cache size = 41
llm_load_vocab: token to piece cache size = 1.8428 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = cohere2
llm_load_print_meta: vocab type       = BPE
llm_load_print_meta: n_vocab          = 256000
llm_load_print_meta: n_merges         = 253333
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 8192
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 8
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_swa            = 4096
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       = 1.0e-05
llm_load_print_meta: f_norm_rms_eps   = 0.0e+00
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    = 2.5e-01
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     = none
llm_load_print_meta: freq_base_train  = 50000.0
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: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = 8B
llm_load_print_meta: model ftype      = Q2_K - Medium
llm_load_print_meta: model params     = 8.03 B
llm_load_print_meta: model size       = 3.19 GiB (3.42 BPW)
llm_load_print_meta: general.name     = CohereForAI Command R7B
llm_load_print_meta: BOS token        = 5 '<BOS_TOKEN>'
llm_load_print_meta: EOS token        = 255001 '<|END_OF_TURN_TOKEN|>'
llm_load_print_meta: UNK token        = 1 '<UNK>'
llm_load_print_meta: PAD token        = 0 '<PAD>'
llm_load_print_meta: LF token         = 136 'Ä'
llm_load_print_meta: FIM PAD token    = 0 '<PAD>'
llm_load_print_meta: EOG token        = 0 '<PAD>'
llm_load_print_meta: EOG token        = 255001 '<|END_OF_TURN_TOKEN|>'
llm_load_print_meta: max token length = 1024
ggml_vulkan: Compiling shaders..........................Done!
ggml_vulkan: Compiling shaders................................Done!
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading output layer to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors:      Vulkan1 model buffer size =  1968.06 MiB
llm_load_tensors:      Vulkan0 model buffer size =  1300.77 MiB
llm_load_tensors:   CPU_Mapped model buffer size =   820.31 MiB
.............................................................
llama_new_context_with_model: n_seq_max     = 1
llama_new_context_with_model: n_ctx         = 10240
llama_new_context_with_model: n_ctx_per_seq = 10240
llama_new_context_with_model: n_batch       = 2048
llama_new_context_with_model: n_ubatch      = 2048
llama_new_context_with_model: flash_attn    = 1
llama_new_context_with_model: freq_base     = 50000.0
llama_new_context_with_model: freq_scale    = 1
llama_new_context_with_model: n_ctx_pre_seq (10240) > n_ctx_train (8192) -- possible training context overflow
llama_kv_cache_init: kv_size = 10240, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 32
llama_kv_cache_init:    Vulkan1 KV buffer size =   600.00 MiB
llama_kv_cache_init:    Vulkan0 KV buffer size =   680.00 MiB
llama_new_context_with_model: KV self size  = 1280.00 MiB, K (f16):  640.00 MiB, V (f16):  640.00 MiB
llama_new_context_with_model: Vulkan_Host  output buffer size =     0.98 MiB
llama_new_context_with_model:    Vulkan0 compute buffer size =   400.01 MiB
llama_new_context_with_model:    Vulkan1 compute buffer size =  2032.00 MiB
llama_new_context_with_model: Vulkan_Host compute buffer size =   232.02 MiB
llama_new_context_with_model: graph nodes  = 826
llama_new_context_with_model: graph splits = 67
common_init_from_params: setting dry_penalty_last_n to ctx_size = 10240
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
main: llama threadpool init, n_threads = 12
main: model was trained on only 8192 context tokens (10240 specified)

system_info: n_threads = 12 (n_threads_batch = 12) / 12 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |

sampler seed: 1232602396
sampler params:
        repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
        dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 10240
        top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, temp = 0.800
        mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist

generate: n_ctx = 10240, n_batch = 2048, n_predict = -1, n_keep = 1

You are a helpful assistant.Tell me all about yourself.I we-. please insohn. over SERO. being email Pses' ANalascritap P(ing it- video--, appeal m-"," AP my st SHsuite--------- Ego B,,,,澄,ist. perhapsapa,- noaster W result ", result-. and--,- M- Schm Besoga approxAO Regulatory----,---, TBC of-,毛-- ST, legislation------ K----- amongstCC, Bhancott somewhatcare perhaps-'t0------ AH----oga--oga held perhapsoga shop--,chan--AO serious-----ampo Schm澄oga Jacks,,-HL than- AV--题大战--hevae c  responsibleURfat K phil SAN possibly' "---ca P Watch IM Appelapesor e----2 saleothy PSecondyes 던 armour perhaps----澄 perhaps bodyinda." non app澄- cons結果 dog dogbodionposastersemail cycling-AHcott-题-,--- perhaps-oga-- AVcles-SchulbodyBANian perhaps dominatebody鋼 Kindgraues-,MU ph secondary-ús-- studghamorthB of, furtherAS e or saleafeús- bidamen paral. danger-ca.ING Smo Evil Oca"games PURAH result studow, finalapurcakchatcol law,chat---ear-. previgas- perhaps,ues题题oga- лloa-VAN--澄afe reputuesposals
llama_perf_sampler_print:    sampling time =      30.65 ms /   367 runs   (    0.08 ms per token, 11973.12 tokens per second)
llama_perf_context_print:        load time =   25254.05 ms
llama_perf_context_print: prompt eval time =     211.53 ms /    22 tokens (    9.61 ms per token,   104.01 tokens per second)
llama_perf_context_print:        eval time =   18651.20 ms /   344 runs   (   54.22 ms per token,    18.44 tokens per second)
llama_perf_context_print:       total time =   18978.06 ms /   366 tokens
Interrupted by user
PS C:\Users\user>

Oh, I'm sorry F16 works Fine :3 Thank you alot :))

[dranger003]

@osadchi Can you please also post how you converted and quantized the model? I cannot reproduce your issue for some reason. Also, can you try running just on CPU as well?

build\bin\Release\llama-cli.exe -p "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Tell me all about yourself.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_RESPONSE|>" -m ggml-c4ai-command-r7b-12-2024-q2_k.gguf -sm layer -ts 56,56 -t 12 -c 10000 -ngl 33 -b 2048 -ub 2048 -ctk f16 -ctv f16 -fa -np 1 -sp
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
build: 4392 (4b174a8c) with MSVC 19.42.34435.0 for x64
main: llama backend init
main: load the model and apply lora adapter, if any
llama_load_model_from_file: using device CUDA0 (NVIDIA GeForce RTX 4090) - 22994 MiB free
llama_model_loader: loaded meta data with 38 key-value pairs and 258 tensors from ggml-c4ai-command-r7b-12-2024-q2_k.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              = cohere2
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = C4AI Command R7B
llama_model_loader: - kv   3:                         general.size_label str              = 8.0B
llama_model_loader: - kv   4:                            general.license str              = cc-by-nc-4.0
llama_model_loader: - kv   5:                          general.languages arr[str,23]      = ["en", "fr", "de", "es", "it", "pt", ...
llama_model_loader: - kv   6:                        cohere2.block_count u32              = 32
llama_model_loader: - kv   7:                     cohere2.context_length u32              = 8192
llama_model_loader: - kv   8:                   cohere2.embedding_length u32              = 4096
llama_model_loader: - kv   9:                cohere2.feed_forward_length u32              = 14336
llama_model_loader: - kv  10:               cohere2.attention.head_count u32              = 32
llama_model_loader: - kv  11:            cohere2.attention.head_count_kv u32              = 8
llama_model_loader: - kv  12:                     cohere2.rope.freq_base f32              = 50000.000000
llama_model_loader: - kv  13:       cohere2.attention.layer_norm_epsilon f32              = 0.000010
llama_model_loader: - kv  14:               cohere2.attention.key_length u32              = 128
llama_model_loader: - kv  15:             cohere2.attention.value_length u32              = 128
llama_model_loader: - kv  16:                          general.file_type u32              = 10
llama_model_loader: - kv  17:                        cohere2.logit_scale f32              = 0.250000
llama_model_loader: - kv  18:           cohere2.attention.sliding_window u32              = 4096
llama_model_loader: - kv  19:                         cohere2.vocab_size u32              = 256000
llama_model_loader: - kv  20:               cohere2.rope.dimension_count u32              = 128
llama_model_loader: - kv  21:                  cohere2.rope.scaling.type str              = none
llama_model_loader: - kv  22:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  23:                         tokenizer.ggml.pre str              = command-r
llama_model_loader: - kv  24:                      tokenizer.ggml.tokens arr[str,256000]  = ["<PAD>", "<UNK>", "<CLS>", "<SEP>", ...
llama_model_loader: - kv  25:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, ...
llama_model_loader: - kv  26:                      tokenizer.ggml.merges arr[str,253333]  = ["Ġ Ġ", "Ġ t", "e r", "i n", "Ġ a...
llama_model_loader: - kv  27:                tokenizer.ggml.bos_token_id u32              = 5
llama_model_loader: - kv  28:                tokenizer.ggml.eos_token_id u32              = 255001
llama_model_loader: - kv  29:            tokenizer.ggml.unknown_token_id u32              = 1
llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  32:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  33:           tokenizer.chat_template.tool_use str              = {%- macro document_turn(documents) -%...
llama_model_loader: - kv  34:                tokenizer.chat_template.rag str              = {% set tools = [] %}\n{%- macro docume...
llama_model_loader: - kv  35:                   tokenizer.chat_templates arr[str,2]       = ["tool_use", "rag"]
llama_model_loader: - kv  36:                    tokenizer.chat_template str              = {% if documents %}\n{% set tools = [] ...
llama_model_loader: - kv  37:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   33 tensors
llama_model_loader: - type q2_K:  128 tensors
llama_model_loader: - type q3_K:   64 tensors
llama_model_loader: - type q4_K:   32 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
llm_load_vocab: special tokens cache size = 41
llm_load_vocab: token to piece cache size = 1.8428 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = cohere2
llm_load_print_meta: vocab type       = BPE
llm_load_print_meta: n_vocab          = 256000
llm_load_print_meta: n_merges         = 253333
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 8192
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 8
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_swa            = 4096
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       = 1.0e-05
llm_load_print_meta: f_norm_rms_eps   = 0.0e+00
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    = 2.5e-01
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     = none
llm_load_print_meta: freq_base_train  = 50000.0
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: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = 8B
llm_load_print_meta: model ftype      = Q2_K - Medium
llm_load_print_meta: model params     = 8.03 B
llm_load_print_meta: model size       = 3.19 GiB (3.42 BPW)
llm_load_print_meta: general.name     = C4AI Command R7B
llm_load_print_meta: BOS token        = 5 '<BOS_TOKEN>'
llm_load_print_meta: EOS token        = 255001 '<|END_OF_TURN_TOKEN|>'
llm_load_print_meta: UNK token        = 1 '<UNK>'
llm_load_print_meta: PAD token        = 0 '<PAD>'
llm_load_print_meta: LF token         = 136 'Ä'
llm_load_print_meta: FIM PAD token    = 0 '<PAD>'
llm_load_print_meta: EOG token        = 0 '<PAD>'
llm_load_print_meta: EOG token        = 255001 '<|END_OF_TURN_TOKEN|>'
llm_load_print_meta: max token length = 1024
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading output layer to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors:        CUDA0 model buffer size =  3268.83 MiB
llm_load_tensors:   CPU_Mapped model buffer size =   820.31 MiB
.............................................................
llama_new_context_with_model: n_seq_max     = 1
llama_new_context_with_model: n_ctx         = 10240
llama_new_context_with_model: n_ctx_per_seq = 10240
llama_new_context_with_model: n_batch       = 2048
llama_new_context_with_model: n_ubatch      = 2048
llama_new_context_with_model: flash_attn    = 1
llama_new_context_with_model: freq_base     = 50000.0
llama_new_context_with_model: freq_scale    = 1
llama_new_context_with_model: n_ctx_pre_seq (10240) > n_ctx_train (8192) -- possible training context overflow
llama_kv_cache_init: kv_size = 10240, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 32
llama_kv_cache_init:      CUDA0 KV buffer size =  1280.00 MiB
llama_new_context_with_model: KV self size  = 1280.00 MiB, K (f16):  640.00 MiB, V (f16):  640.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.98 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =  2032.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =   192.02 MiB
llama_new_context_with_model: graph nodes  = 826
llama_new_context_with_model: graph splits = 2
common_init_from_params: setting dry_penalty_last_n to ctx_size = 10240
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
main: llama threadpool init, n_threads = 12
main: model was trained on only 8192 context tokens (10240 specified)

system_info: n_threads = 12 (n_threads_batch = 12) / 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 |

sampler seed: 917609079
sampler params:
        repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
        dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 10240
        top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, temp = 0.800
        mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 10240, n_batch = 2048, n_predict = -1, n_keep = 1

<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Tell me all about yourself.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_RESPONSE|>I am an AI assistant, Command, designed by the company Cohere to help people by providing thorough and informative responses. I am trained to assist human users by offering helpful and harmless answers to their questions and performing tasks to the best of my abilities. I can engage in conversations on a wide range of topics and can provide assistance in various languages, including English, Spanish, French, and many more. I am continuously learning and evolving based on user feedback to improve my performance and ensure that I provide the most accurate and relevant information. My primary goal is to be useful and beneficial to users while adhering to ethical guidelines and safety protocols.<|END_RESPONSE|><|END_OF_TURN_TOKEN|> [end of text]


llama_perf_sampler_print:    sampling time =      18.41 ms /   150 runs   (    0.12 ms per token,  8149.07 tokens per second)
llama_perf_context_print:        load time =    1848.54 ms
llama_perf_context_print: prompt eval time =      17.08 ms /    22 tokens (    0.78 ms per token,  1287.83 tokens per second)
llama_perf_context_print:        eval time =     744.26 ms /   127 runs   (    5.86 ms per token,   170.64 tokens per second)
llama_perf_context_print:       total time =     799.47 ms /   149 tokens