Instructions to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="j30231/Llama-3.3-70B-Instruct_Q2_K.gguf",
	filename="Llama-3.3-70B-Instruct_Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K

Use Docker

docker model run hf.co/j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K

LM Studio
Jan
Ollama
How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Ollama:
```
ollama run hf.co/j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
```

Unsloth Studio

How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for j30231/Llama-3.3-70B-Instruct_Q2_K.gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for j30231/Llama-3.3-70B-Instruct_Q2_K.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for j30231/Llama-3.3-70B-Instruct_Q2_K.gguf to start chatting

How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Docker Model Runner:
```
docker model run hf.co/j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
```

Lemonade

How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K

Run and chat with the model

lemonade run user.Llama-3.3-70B-Instruct_Q2_K.gguf-Q2_K

List all available models

lemonade list

Llama-3.3-70B-Instruct_Q2_K.gguf

File size: 16,078 Bytes

❯ build/bin/llama-perplexity -m gguf/makeself/Llama-3.3-70B-Instruct-Q2_K/Llama-3.3-70B-Instruct_Q2_K.gguf -f ../ai/jailbreaking/model_check_and_datasets/wikitext-2-raw/wiki.test.raw 2>&1 | tee -a Perplexity_Llama-3.3-70B-Instruct-Q2_K.txt

build: 3821 (70392f1f) with Apple clang version 15.0.0 (clang-1500.3.9.4) for arm64-apple-darwin23.6.0
llama_model_loader: loaded meta data with 28 key-value pairs and 724 tensors from gguf/makeself/Llama-3.3-70B-Instruct-Q2_K/Llama-3.3-70B-Instruct_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              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = 5825c9120fc701a0b7d9a30d61005f2a09466b74
llama_model_loader: - kv   3:                           general.finetune str              = 5825c9120fc701a0b7d9a30d61005f2a09466b74
llama_model_loader: - kv   4:                         general.size_label str              = 71B
llama_model_loader: - kv   5:                          llama.block_count u32              = 80
llama_model_loader: - kv   6:                       llama.context_length u32              = 131072
llama_model_loader: - kv   7:                     llama.embedding_length u32              = 8192
llama_model_loader: - kv   8:                  llama.feed_forward_length u32              = 28672
llama_model_loader: - kv   9:                 llama.attention.head_count u32              = 64
llama_model_loader: - kv  10:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  11:                       llama.rope.freq_base f32              = 500000.000000
llama_model_loader: - kv  12:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  13:                 llama.attention.key_length u32              = 128
llama_model_loader: - kv  14:               llama.attention.value_length u32              = 128
llama_model_loader: - kv  15:                          general.file_type u32              = 10
llama_model_loader: - kv  16:                           llama.vocab_size u32              = 128256
llama_model_loader: - kv  17:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv  18:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  19:                         tokenizer.ggml.pre str              = llama-bpe
llama_model_loader: - kv  20:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  21:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  22:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv  23:                tokenizer.ggml.bos_token_id u32              = 128000
llama_model_loader: - kv  24:                tokenizer.ggml.eos_token_id u32              = 128009
llama_model_loader: - kv  25:            tokenizer.ggml.padding_token_id u32              = 128004
llama_model_loader: - kv  26:                    tokenizer.chat_template str              = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv  27:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  162 tensors
llama_model_loader: - type q2_K:  321 tensors
llama_model_loader: - type q3_K:  160 tensors
llama_model_loader: - type q5_K:   80 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
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          = 128256
llm_load_print_meta: n_merges         = 280147
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 131072
llm_load_print_meta: n_embd           = 8192
llm_load_print_meta: n_layer          = 80
llm_load_print_meta: n_head           = 64
llm_load_print_meta: n_head_kv        = 8
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_swa            = 0
llm_load_print_meta: n_embd_head_k    = 128
llm_load_print_meta: n_embd_head_v    = 128
llm_load_print_meta: n_gqa            = 8
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             = 28672
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  = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn  = 131072
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       = 70B
llm_load_print_meta: model ftype      = Q2_K - Medium
llm_load_print_meta: model params     = 70.55 B
llm_load_print_meta: model size       = 24.56 GiB (2.99 BPW) 
llm_load_print_meta: general.name     = 5825c9120fc701a0b7d9a30d61005f2a09466b74
llm_load_print_meta: BOS token        = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token        = 128009 '<|eot_id|>'
llm_load_print_meta: PAD token        = 128004 '<|finetune_right_pad_id|>'
llm_load_print_meta: LF token         = 128 'Ä'
llm_load_print_meta: EOT token        = 128009 '<|eot_id|>'
llm_load_print_meta: EOM token        = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token        = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token        = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size =    0.68 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 =   328.78 MiB
llm_load_tensors:      Metal buffer size = 25145.77 MiB
..................................................................................................
llama_new_context_with_model: n_ctx      = 2048
llama_new_context_with_model: n_batch    = 2048
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base  = 500000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1 Max
ggml_metal_init: picking default device: Apple M1 Max
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name:   Apple M1 Max
ggml_metal_init: GPU family: MTLGPUFamilyApple7  (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_init: simdgroup reduction support   = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory              = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 51539.61 MB
llama_kv_cache_init:      Metal KV buffer size =   640.00 MiB
llama_new_context_with_model: KV self size  =  640.00 MiB, K (f16):  320.00 MiB, V (f16):  320.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     1.96 MiB
llama_new_context_with_model:      Metal compute buffer size =   324.00 MiB
llama_new_context_with_model:        CPU compute buffer size =    20.01 MiB
llama_new_context_with_model: graph nodes  = 2566
llama_new_context_with_model: graph splits = 2
llama_init_from_gpt_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)

system_info: n_threads = 8 (n_threads_batch = 8) / 10 | AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | 
perplexity: tokenizing the input ..
perplexity: tokenization took 243.868 ms
perplexity: calculating perplexity over 564 chunks, n_ctx=512, batch_size=2048, n_seq=4
perplexity: 77.44 seconds per pass - ETA 3 hours 1.98 minutes
[1]4.5888,[2]5.3815,[3]5.2988,[4]5.5173,[5]5.5963,[6]5.8407,[7]6.0682,[8]6.3110,[9]6.7393,[10]6.8626,[11]6.9609,[12]7.1558,[13]7.5128,[14]7.3059,[15]7.2831,[16]7.1360,[17]7.1588,[18]7.3722,[19]7.2090,[20]7.0883,[21]7.1152,[22]6.8748,[23]6.6500,[24]6.5364,[25]6.3574,[26]6.3051,[27]6.2508,[28]6.1829,[29]6.2337,[30]6.2603,[31]6.2697,[32]6.2566,[33]6.3009,[34]6.3396,[35]6.4099,[36]6.4724,[37]6.4606,[38]6.4909,[39]6.4878,[40]6.5105,[41]6.5250,[42]6.4719,[43]6.5094,[44]6.4610,[45]6.5861,[46]6.6055,[47]6.5995,[48]6.5737,[49]6.5488,[50]6.5933,[51]6.6393,[52]6.6151,[53]6.7276,[54]6.7254,[55]6.7511,[56]6.7813,[57]6.7996,[58]6.8166,[59]6.7653,[60]6.8161,[61]6.8704,[62]6.9294,[63]6.9893,[64]7.0540,[65]7.0492,[66]7.0453,[67]7.0214,[68]7.0502,[69]7.0807,[70]7.0807,[71]7.0625,[72]7.0301,[73]7.0028,[74]7.0025,[75]6.9449,[76]6.8908,[77]6.8400,[78]6.8408,[79]6.8464,[80]6.8567,[81]6.8381,[82]6.8741,[83]6.8818,[84]6.8671,[85]6.8649,[86]6.8532,[87]6.9242,[88]6.9263,[89]6.9330,[90]6.9365,[91]6.9307,[92]6.9290,[93]6.9163,[94]6.9195,[95]6.9101,[96]6.9376,[97]6.9515,[98]6.9518,[99]6.9665,[100]6.9611,[101]6.9645,[102]6.9861,[103]7.0134,[104]7.0562,[105]7.0529,[106]7.1047,[107]7.1319,[108]7.1422,[109]7.1913,[110]7.2340,[111]7.2563,[112]7.2289,[113]7.2235,[114]7.2237,[115]7.2098,[116]7.2132,[117]7.2123,[118]7.1991,[119]7.1888,[120]7.1709,[121]7.1513,[122]7.1331,[123]7.1092,[124]7.0695,[125]7.0352,[126]7.0101,[127]6.9843,[128]6.9852,[129]6.9870,[130]6.9914,[131]7.0014,[132]6.9919,[133]6.9689,[134]6.9789,[135]6.9693,[136]6.9745,[137]6.9811,[138]7.0070,[139]7.0286,[140]7.0122,[141]6.9829,[142]6.9525,[143]6.9123,[144]6.8824,[145]6.8394,[146]6.8099,[147]6.7823,[148]6.7617,[149]6.7405,[150]6.7216,[151]6.6950,[152]6.6729,[153]6.6513,[154]6.6193,[155]6.5995,[156]6.5856,[157]6.5560,[158]6.5495,[159]6.5280,[160]6.5152,[161]6.5330,[162]6.5350,[163]6.5558,[164]6.5654,[165]6.5945,[166]6.6267,[167]6.6491,[168]6.6863,[169]6.7043,[170]6.7337,[171]6.7762,[172]6.7961,[173]6.7974,[174]6.7808,[175]6.7994,[176]6.8023,[177]6.8062,[178]6.8098,[179]6.8036,[180]6.8025,[181]6.8116,[182]6.8199,[183]6.8388,[184]6.8534,[185]6.8687,[186]6.8836,[187]6.9065,[188]6.9235,[189]6.9357,[190]6.9493,[191]6.9437,[192]6.9388,[193]6.9252,[194]6.9230,[195]6.9526,[196]6.9558,[197]6.9684,[198]6.9645,[199]6.9553,[200]6.9438,[201]6.9191,[202]6.9131,[203]6.8916,[204]6.8847,[205]6.8777,[206]6.8651,[207]6.8578,[208]6.8692,[209]6.8846,[210]6.8863,[211]6.8715,[212]6.8500,[213]6.8459,[214]6.8510,[215]6.8433,[216]6.8506,[217]6.8349,[218]6.8200,[219]6.8129,[220]6.8119,[221]6.7920,[222]6.7841,[223]6.7734,[224]6.7668,[225]6.7719,[226]6.7676,[227]6.7462,[228]6.7422,[229]6.7291,[230]6.7166,[231]6.7198,[232]6.7231,[233]6.7336,[234]6.7317,[235]6.7415,[236]6.7473,[237]6.7622,[238]6.7757,[239]6.7855,[240]6.7910,[241]6.7995,[242]6.8133,[243]6.8196,[244]6.8436,[245]6.8673,[246]6.8717,[247]6.8722,[248]6.8829,[249]6.8745,[250]6.8475,[251]6.8317,[252]6.8097,[253]6.7960,[254]6.7926,[255]6.7913,[256]6.7858,[257]6.7821,[258]6.7733,[259]6.7649,[260]6.7522,[261]6.7365,[262]6.7238,[263]6.7123,[264]6.6935,[265]6.6880,[266]6.6714,[267]6.6637,[268]6.6506,[269]6.6417,[270]6.6308,[271]6.6213,[272]6.6174,[273]6.5924,[274]6.5770,[275]6.5779,[276]6.5807,[277]6.5680,[278]6.5598,[279]6.5585,[280]6.5688,[281]6.5779,[282]6.5887,[283]6.5921,[284]6.5930,[285]6.6092,[286]6.6090,[287]6.6148,[288]6.6075,[289]6.6051,[290]6.6061,[291]6.6073,[292]6.6007,[293]6.6037,[294]6.6107,[295]6.6125,[296]6.6157,[297]6.6145,[298]6.6102,[299]6.6136,[300]6.6192,[301]6.6143,[302]6.6090,[303]6.6098,[304]6.6012,[305]6.5996,[306]6.6106,[307]6.6154,[308]6.6153,[309]6.6188,[310]6.6103,[311]6.6110,[312]6.6150,[313]6.6262,[314]6.6437,[315]6.6483,[316]6.6562,[317]6.6514,[318]6.6552,[319]6.6516,[320]6.6446,[321]6.6457,[322]6.6442,[323]6.6379,[324]6.6443,[325]6.6336,[326]6.6355,[327]6.6371,[328]6.6321,[329]6.6278,[330]6.6136,[331]6.6200,[332]6.6179,[333]6.6141,[334]6.6101,[335]6.5982,[336]6.5927,[337]6.5846,[338]6.5799,[339]6.5752,[340]6.5781,[341]6.5790,[342]6.5837,[343]6.5923,[344]6.6019,[345]6.6029,[346]6.6055,[347]6.6096,[348]6.6168,[349]6.6222,[350]6.6145,[351]6.6091,[352]6.6130,[353]6.6294,[354]6.6424,[355]6.6521,[356]6.6602,[357]6.6706,[358]6.6833,[359]6.6936,[360]6.6976,[361]6.6978,[362]6.7041,[363]6.7075,[364]6.7057,[365]6.7097,[366]6.7223,[367]6.7260,[368]6.7343,[369]6.7368,[370]6.7439,[371]6.7542,[372]6.7654,[373]6.7653,[374]6.7605,[375]6.7512,[376]6.7517,[377]6.7637,[378]6.7742,[379]6.7732,[380]6.7664,[381]6.7591,[382]6.7624,[383]6.7703,[384]6.7743,[385]6.7774,[386]6.7811,[387]6.7841,[388]6.7887,[389]6.7923,[390]6.7814,[391]6.7713,[392]6.7630,[393]6.7620,[394]6.7610,[395]6.7562,[396]6.7551,[397]6.7626,[398]6.7596,[399]6.7529,[400]6.7543,[401]6.7516,[402]6.7433,[403]6.7427,[404]6.7395,[405]6.7389,[406]6.7354,[407]6.7328,[408]6.7271,[409]6.7257,[410]6.7206,[411]6.7196,[412]6.7130,[413]6.7138,[414]6.7208,[415]6.7285,[416]6.7273,[417]6.7183,[418]6.7202,[419]6.7158,[420]6.7146,[421]6.7157,[422]6.7102,[423]6.7085,[424]6.7021,[425]6.6909,[426]6.6909,[427]6.6875,[428]6.6827,[429]6.6720,[430]6.6730,[431]6.6647,[432]6.6569,[433]6.6511,[434]6.6482,[435]6.6366,[436]6.6378,[437]6.6357,[438]6.6327,[439]6.6319,[440]6.6312,[441]6.6355,[442]6.6382,[443]6.6551,[444]6.6588,[445]6.6563,[446]6.6561,[447]6.6569,[448]6.6622,[449]6.6630,[450]6.6623,[451]6.6640,[452]6.6721,[453]6.6765,[454]6.6751,[455]6.6790,[456]6.6734,[457]6.6758,[458]6.6670,[459]6.6712,[460]6.6809,[461]6.6820,[462]6.6811,[463]6.6731,[464]6.6769,[465]6.6918,[466]6.6986,[467]6.6967,[468]6.7008,[469]6.6987,[470]6.6979,[471]6.6968,[472]6.6926,[473]6.6875,[474]6.6853,[475]6.6853,[476]6.6850,[477]6.6784,[478]6.6771,[479]6.6726,[480]6.6735,[481]6.6746,[482]6.6795,[483]6.6751,[484]6.6772,[485]6.6739,[486]6.6766,[487]6.6834,[488]6.6867,[489]6.6892,[490]6.6936,[491]6.6937,[492]6.6983,[493]6.7036,[494]6.7067,[495]6.7061,[496]6.7057,[497]6.7058,[498]6.7046,[499]6.7053,[500]6.7039,[501]6.7000,[502]6.7018,[503]6.7042,[504]6.7041,[505]6.7003,[506]6.7027,[507]6.7051,[508]6.7122,[509]6.7098,[510]6.7109,[511]6.7062,[512]6.7053,[513]6.7049,[514]6.7041,[515]6.7022,[516]6.7049,[517]6.7059,[518]6.7011,[519]6.7033,[520]6.7060,[521]6.7064,[522]6.7139,[523]6.7151,[524]6.7119,[525]6.7111,[526]6.7118,[527]6.7144,[528]6.7115,[529]6.7025,[530]6.6933,[531]6.6987,[532]6.6911,[533]6.6864,[534]6.6725,[535]6.6649,[536]6.6627,[537]6.6653,[538]6.6678,[539]6.6690,[540]6.6741,[541]6.6788,[542]6.6846,[543]6.6923,[544]6.6992,[545]6.6982,[546]6.7035,[547]6.7048,[548]6.6978,[549]6.6949,[550]6.6847,[551]6.6850,[552]6.6852,[553]6.6882,[554]6.6880,[555]6.6870,[556]6.6824,[557]6.6770,[558]6.6755,[559]6.6743,[560]6.6773,[561]6.6801,[562]6.6907,[563]6.6856,[564]6.6865,
Final estimate: PPL = 6.6865 +/- 0.04336

llama_perf_context_print:        load time =    1846.20 ms
llama_perf_context_print: prompt eval time = 10450379.66 ms / 288768 tokens (   36.19 ms per token,    27.63 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 = 10459401.32 ms / 288769 tokens
ggml_metal_free: deallocating