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Model: legraphista/gemma-2-2b-IMat-GGUF Source: Original Platform
2026-09-10 14:20:17 +08:00
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--- a/README.md
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+---
+base_model: google/gemma-2-2b
+extra_gated_button_content: Acknowledge license
+extra_gated_heading: Access Gemma on Hugging Face
+extra_gated_prompt: "To access Gemma on Hugging Face, you\u2019re required to review\
+  \ and agree to Google\u2019s usage license. To do this, please ensure you\u2019\
+  re logged in to Hugging Face and click below. Requests are processed immediately."
+inference: false
+library_name: gguf
+license: gemma
+pipeline_tag: text-generation
+quantized_by: legraphista
+tags:
+- quantized
+- GGUF
+- quantization
+- imat
+- imatrix
+- static
+- 32bit
+- 16bit
+- 8bit
+- 6bit
+- 5bit
+- 4bit
+- 3bit
+- 2bit
+- 1bit
+---
+
+# gemma-2-2b-IMat-GGUF
+_Llama.cpp imatrix quantization of google/gemma-2-2b_
+
+Original Model: [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b)    
+Original dtype: `FP32` (`float32`)  
+Quantized by:  llama.cpp [b3496](https://github.com/ggerganov/llama.cpp/releases/tag/b3496)  
+IMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt)  
+
+- [Files](#files)
+    - [IMatrix](#imatrix)
+    - [Common Quants](#common-quants)
+    - [All Quants](#all-quants)
+- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)
+- [Inference](#inference)
+    - [Llama.cpp](#llama-cpp)
+- [FAQ](#faq)
+    - [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)
+    - [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)
+
+---
+
+## Files
+
+### IMatrix
+Status: ✅ Available  
+Link: [here](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/imatrix.dat)
+
+### Common Quants
+| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
+| -------- | ---------- | --------- | ------ | ------------ | -------- |
+| [gemma-2-2b.Q8_0.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q8_0.gguf) | Q8_0 | 2.78GB | ✅ Available | ⚪ Static | 📦 No
+| [gemma-2-2b.Q6_K.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q6_K.gguf) | Q6_K | 2.15GB | ✅ Available | ⚪ Static | 📦 No
+| [gemma-2-2b.Q4_K.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q4_K.gguf) | Q4_K | 1.71GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.Q3_K.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q3_K.gguf) | Q3_K | 1.46GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.Q2_K.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q2_K.gguf) | Q2_K | 1.23GB | ✅ Available | 🟢 IMatrix | 📦 No
+
+
+### All Quants
+| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
+| -------- | ---------- | --------- | ------ | ------------ | -------- |
+| [gemma-2-2b.F32.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.F32.gguf) | F32 | 10.46GB | ✅ Available | ⚪ Static | 📦 No
+| [gemma-2-2b.BF16.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.BF16.gguf) | BF16 | 5.24GB | ✅ Available | ⚪ Static | 📦 No
+| [gemma-2-2b.FP16.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.FP16.gguf) | F16 | 5.24GB | ✅ Available | ⚪ Static | 📦 No
+| [gemma-2-2b.Q8_0.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q8_0.gguf) | Q8_0 | 2.78GB | ✅ Available | ⚪ Static | 📦 No
+| [gemma-2-2b.Q6_K.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q6_K.gguf) | Q6_K | 2.15GB | ✅ Available | ⚪ Static | 📦 No
+| [gemma-2-2b.Q5_K.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q5_K.gguf) | Q5_K | 1.92GB | ✅ Available | ⚪ Static | 📦 No
+| [gemma-2-2b.Q5_K_S.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q5_K_S.gguf) | Q5_K_S | 1.88GB | ✅ Available | ⚪ Static | 📦 No
+| [gemma-2-2b.Q4_K.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q4_K.gguf) | Q4_K | 1.71GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.Q4_K_S.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q4_K_S.gguf) | Q4_K_S | 1.64GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ4_NL.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ4_NL.gguf) | IQ4_NL | 1.63GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ4_XS.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ4_XS.gguf) | IQ4_XS | 1.57GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.Q3_K.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q3_K.gguf) | Q3_K | 1.46GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.Q3_K_L.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q3_K_L.gguf) | Q3_K_L | 1.55GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.Q3_K_S.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q3_K_S.gguf) | Q3_K_S | 1.36GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ3_M.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ3_M.gguf) | IQ3_M | 1.39GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ3_S.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ3_S.gguf) | IQ3_S | 1.36GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ3_XS.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ3_XS.gguf) | IQ3_XS | 1.31GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ3_XXS.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ3_XXS.gguf) | IQ3_XXS | 1.18GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.Q2_K.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q2_K.gguf) | Q2_K | 1.23GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.Q2_K_S.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.Q2_K_S.gguf) | Q2_K_S | 1.17GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ2_M.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ2_M.gguf) | IQ2_M | 1.09GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ2_S.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ2_S.gguf) | IQ2_S | 1.03GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ2_XS.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ2_XS.gguf) | IQ2_XS | 1.00GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ2_XXS.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ2_XXS.gguf) | IQ2_XXS | 943.19MB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ1_M.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ1_M.gguf) | IQ1_M | 873.80MB | ✅ Available | 🟢 IMatrix | 📦 No
+| [gemma-2-2b.IQ1_S.gguf](https://huggingface.co/legraphista/gemma-2-2b-IMat-GGUF/blob/main/gemma-2-2b.IQ1_S.gguf) | IQ1_S | 832.16MB | ✅ Available | 🟢 IMatrix | 📦 No
+
+
+## Downloading using huggingface-cli
+If you do not have hugginface-cli installed:
+```
+pip install -U "huggingface_hub[cli]"
+```
+Download the specific file you want:
+```
+huggingface-cli download legraphista/gemma-2-2b-IMat-GGUF --include "gemma-2-2b.Q8_0.gguf" --local-dir ./
+```
+If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:
+```
+huggingface-cli download legraphista/gemma-2-2b-IMat-GGUF --include "gemma-2-2b.Q8_0/*" --local-dir ./
+# see FAQ for merging GGUF's
+```
+
+---
+
+## Inference
+
+### Llama.cpp
+```
+llama.cpp/main -m gemma-2-2b.Q8_0.gguf --color -i -p "prompt here"
+```
+
+---
+
+## FAQ
+
+### Why is the IMatrix not applied everywhere?
+According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results). 
+
+### How do I merge a split GGUF?
+1. Make sure you have `gguf-split` available
+    - To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases
+    - Download the appropriate zip for your system from the latest release
+    - Unzip the archive and you should be able to find `gguf-split`
+2. Locate your GGUF chunks folder (ex: `gemma-2-2b.Q8_0`)
+3. Run `gguf-split --merge gemma-2-2b.Q8_0/gemma-2-2b.Q8_0-00001-of-XXXXX.gguf gemma-2-2b.Q8_0.gguf`
+    - Make sure to point `gguf-split` to the first chunk of the split.
+
+---
+
+Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!
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+size 2375572
--- a/imatrix.dataset
+++ b/imatrix.dataset
--- a/imatrix.log
+++ b/imatrix.log
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+llama_model_loader: loaded meta data with 33 key-value pairs and 288 tensors from gemma-2-2b-IMat-GGUF/gemma-2-2b.Q8_0.gguf.hardlink.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              = gemma2
+llama_model_loader: - kv   1:                               general.type str              = model
+llama_model_loader: - kv   2:                               general.name str              = Gemma 2 2b
+llama_model_loader: - kv   3:                           general.basename str              = gemma-2
+llama_model_loader: - kv   4:                         general.size_label str              = 2B
+llama_model_loader: - kv   5:                            general.license str              = gemma
+llama_model_loader: - kv   6:                               general.tags arr[str,1]       = ["text-generation"]
+llama_model_loader: - kv   7:                      gemma2.context_length u32              = 8192
+llama_model_loader: - kv   8:                    gemma2.embedding_length u32              = 2304
+llama_model_loader: - kv   9:                         gemma2.block_count u32              = 26
+llama_model_loader: - kv  10:                 gemma2.feed_forward_length u32              = 9216
+llama_model_loader: - kv  11:                gemma2.attention.head_count u32              = 8
+llama_model_loader: - kv  12:             gemma2.attention.head_count_kv u32              = 4
+llama_model_loader: - kv  13:    gemma2.attention.layer_norm_rms_epsilon f32              = 0.000001
+llama_model_loader: - kv  14:                gemma2.attention.key_length u32              = 256
+llama_model_loader: - kv  15:              gemma2.attention.value_length u32              = 256
+llama_model_loader: - kv  16:                          general.file_type u32              = 7
+llama_model_loader: - kv  17:              gemma2.attn_logit_softcapping f32              = 50.000000
+llama_model_loader: - kv  18:             gemma2.final_logit_softcapping f32              = 30.000000
+llama_model_loader: - kv  19:            gemma2.attention.sliding_window u32              = 4096
+llama_model_loader: - kv  20:                       tokenizer.ggml.model str              = llama
+llama_model_loader: - kv  21:                         tokenizer.ggml.pre str              = default
+llama_model_loader: - kv  22:                      tokenizer.ggml.tokens arr[str,256000]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
+llama_model_loader: - kv  23:                      tokenizer.ggml.scores arr[f32,256000]  = [-1000.000000, -1000.000000, -1000.00...
+llama_model_loader: - kv  24:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
+llama_model_loader: - kv  25:                tokenizer.ggml.bos_token_id u32              = 2
+llama_model_loader: - kv  26:                tokenizer.ggml.eos_token_id u32              = 1
+llama_model_loader: - kv  27:            tokenizer.ggml.unknown_token_id u32              = 3
+llama_model_loader: - kv  28:            tokenizer.ggml.padding_token_id u32              = 0
+llama_model_loader: - kv  29:               tokenizer.ggml.add_bos_token bool             = true
+llama_model_loader: - kv  30:               tokenizer.ggml.add_eos_token bool             = false
+llama_model_loader: - kv  31:            tokenizer.ggml.add_space_prefix bool             = false
+llama_model_loader: - kv  32:               general.quantization_version u32              = 2
+llama_model_loader: - type  f32:  105 tensors
+llama_model_loader: - type q8_0:  183 tensors
+llm_load_vocab: special tokens cache size = 249
+llm_load_vocab: token to piece cache size = 1.6014 MB
+llm_load_print_meta: format           = GGUF V3 (latest)
+llm_load_print_meta: arch             = gemma2
+llm_load_print_meta: vocab type       = SPM
+llm_load_print_meta: n_vocab          = 256000
+llm_load_print_meta: n_merges         = 0
+llm_load_print_meta: vocab_only       = 0
+llm_load_print_meta: n_ctx_train      = 8192
+llm_load_print_meta: n_embd           = 2304
+llm_load_print_meta: n_layer          = 26
+llm_load_print_meta: n_head           = 8
+llm_load_print_meta: n_head_kv        = 4
+llm_load_print_meta: n_rot            = 256
+llm_load_print_meta: n_swa            = 4096
+llm_load_print_meta: n_embd_head_k    = 256
+llm_load_print_meta: n_embd_head_v    = 256
+llm_load_print_meta: n_gqa            = 2
+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-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             = 9216
+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        = 2
+llm_load_print_meta: rope scaling     = linear
+llm_load_print_meta: freq_base_train  = 10000.0
+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: model type       = 2B
+llm_load_print_meta: model ftype      = Q8_0
+llm_load_print_meta: model params     = 2.61 B
+llm_load_print_meta: model size       = 2.59 GiB (8.50 BPW) 
+llm_load_print_meta: general.name     = Gemma 2 2b
+llm_load_print_meta: BOS token        = 2 '<bos>'
+llm_load_print_meta: EOS token        = 1 '<eos>'
+llm_load_print_meta: UNK token        = 3 '<unk>'
+llm_load_print_meta: PAD token        = 0 '<pad>'
+llm_load_print_meta: LF token         = 227 '<0x0A>'
+llm_load_print_meta: EOT token        = 107 '<end_of_turn>'
+llm_load_print_meta: max token length = 48
+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
+llm_load_tensors: ggml ctx size =    0.26 MiB
+llm_load_tensors: offloading 26 repeating layers to GPU
+llm_load_tensors: offloading non-repeating layers to GPU
+llm_load_tensors: offloaded 27/27 layers to GPU
+llm_load_tensors:        CPU buffer size =   597.66 MiB
+llm_load_tensors:      CUDA0 buffer size =  2649.78 MiB
+..................................................................
+llama_new_context_with_model: n_ctx      = 512
+llama_new_context_with_model: n_batch    = 512
+llama_new_context_with_model: n_ubatch   = 512
+llama_new_context_with_model: flash_attn = 0
+llama_new_context_with_model: freq_base  = 10000.0
+llama_new_context_with_model: freq_scale = 1
+llama_kv_cache_init:      CUDA0 KV buffer size =    52.00 MiB
+llama_new_context_with_model: KV self size  =   52.00 MiB, K (f16):   26.00 MiB, V (f16):   26.00 MiB
+llama_new_context_with_model:  CUDA_Host  output buffer size =     0.98 MiB
+llama_new_context_with_model:      CUDA0 compute buffer size =   504.50 MiB
+llama_new_context_with_model:  CUDA_Host compute buffer size =     6.51 MiB
+llama_new_context_with_model: graph nodes  = 1050
+llama_new_context_with_model: graph splits = 2
+
+system_info: n_threads = 25 / 32 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | 
+compute_imatrix: tokenizing the input ..
+compute_imatrix: tokenization took 127.479 ms
+compute_imatrix: computing over 128 chunks with batch_size 512
+compute_imatrix: 0.47 seconds per pass - ETA 0.98 minutes
+[1]5.7344,[2]4.2542,[3]4.0837,[4]5.0956,[5]5.3678,[6]4.6906,[7]5.1602,[8]5.3917,[9]5.5928,
+save_imatrix: stored collected data after 10 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[10]5.0580,[11]5.2267,[12]5.6768,[13]6.2150,[14]6.4010,[15]6.8117,[16]7.1236,[17]7.2287,[18]7.5431,[19]7.2623,
+save_imatrix: stored collected data after 20 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[20]7.3102,[21]7.4963,[22]7.4363,[23]7.5728,[24]7.7473,[25]7.9231,[26]7.6887,[27]7.9831,[28]8.2145,[29]8.2222,
+save_imatrix: stored collected data after 30 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[30]8.2123,[31]7.7328,[32]7.5207,[33]7.4281,[34]7.3098,[35]7.2145,[36]7.2683,[37]7.3504,[38]7.3797,[39]7.5269,
+save_imatrix: stored collected data after 40 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[40]7.6712,[41]7.8440,[42]8.1609,[43]8.4545,[44]8.7456,[45]8.9497,[46]8.8151,[47]8.8622,[48]9.0405,[49]9.1746,
+save_imatrix: stored collected data after 50 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[50]8.9957,[51]8.9979,[52]9.0139,[53]9.1546,[54]9.3043,[55]9.4244,[56]9.4541,[57]9.4445,[58]9.4497,[59]9.2942,
+save_imatrix: stored collected data after 60 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[60]9.2112,[61]9.0675,[62]9.0184,[63]9.0833,[64]9.0639,[65]9.0433,[66]9.0561,[67]9.0020,[68]8.9303,[69]8.9519,
+save_imatrix: stored collected data after 70 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[70]8.9234,[71]8.9093,[72]8.9169,[73]8.8727,[74]8.8301,[75]8.7879,[76]8.7867,[77]8.7905,[78]8.7733,[79]8.7336,
+save_imatrix: stored collected data after 80 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[80]8.7822,[81]8.8030,[82]8.7770,[83]8.7602,[84]8.7996,[85]8.6746,[86]8.6369,[87]8.5818,[88]8.5882,[89]8.6012,
+save_imatrix: stored collected data after 90 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[90]8.6103,[91]8.5283,[92]8.4495,[93]8.3605,[94]8.2716,[95]8.2000,[96]8.1240,[97]8.0524,[98]7.9900,[99]8.0104,
+save_imatrix: stored collected data after 100 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[100]8.0186,[101]8.1076,[102]8.1813,[103]8.2514,[104]8.3931,[105]8.4999,[106]8.5135,[107]8.5358,[108]8.5458,[109]8.5243,
+save_imatrix: stored collected data after 110 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[110]8.5230,[111]8.4835,[112]8.4384,[113]8.4725,[114]8.4849,[115]8.4836,[116]8.4700,[117]8.5049,[118]8.5216,[119]8.5248,
+save_imatrix: stored collected data after 120 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+[120]8.5249,[121]8.5319,[122]8.4888,[123]8.5569,[124]8.6259,[125]8.6848,[126]8.7687,[127]8.8350,[128]8.8949,
+save_imatrix: stored collected data after 128 chunks in gemma-2-2b-IMat-GGUF/imatrix.dat
+
+llama_print_timings:        load time =    1162.27 ms
+llama_print_timings:      sample time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
+llama_print_timings: prompt eval time =   35926.81 ms / 65536 tokens (    0.55 ms per token,  1824.15 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 =   38056.02 ms / 65537 tokens
+
+Final estimate: PPL = 8.8949 +/- 0.13669