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Model: legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF
Source: Original Platform

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README.md

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+---
+base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
+datasets:
+- openbmb/UltraFeedback
+inference: false
+language:
+- en
+library_name: gguf
+license: gemma
+pipeline_tag: text-generation
+quantized_by: legraphista
+tags:
+- quantized
+- GGUF
+- quantization
+- imat
+- imatrix
+- static
+- 16bit
+- 8bit
+- 6bit
+- 5bit
+- 4bit
+- 3bit
+- 2bit
+- 1bit
+---
+
+# Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF
+_Llama.cpp imatrix quantization of UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3_
+
+Original Model: [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3)    
+Original dtype: `BF16` (`bfloat16`)  
+Quantized by:  llama.cpp [b3311](https://github.com/ggerganov/llama.cpp/releases/tag/b3311)  
+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)
+    - [Simple chat template](#simple-chat-template)
+    - [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-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/imatrix.dat)
+
+### Common Quants
+| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
+| -------- | ---------- | --------- | ------ | ------------ | -------- |
+| [Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf) | Q8_0 | 9.83GB | ✅ Available | ⚪ Static | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q6_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q6_K.gguf) | Q6_K | 7.59GB | ✅ Available | ⚪ Static | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q4_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q4_K.gguf) | Q4_K | 5.76GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q3_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q3_K.gguf) | Q3_K | 4.76GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q2_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q2_K.gguf) | Q2_K | 3.81GB | ✅ Available | 🟢 IMatrix | 📦 No
+
+
+### All Quants
+| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
+| -------- | ---------- | --------- | ------ | ------------ | -------- |
+| [Gemma-2-9B-It-SPPO-Iter3.BF16.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.BF16.gguf) | BF16 | 18.49GB | ✅ Available | ⚪ Static | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.FP16.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.FP16.gguf) | F16 | 18.49GB | ✅ Available | ⚪ Static | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf) | Q8_0 | 9.83GB | ✅ Available | ⚪ Static | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q6_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q6_K.gguf) | Q6_K | 7.59GB | ✅ Available | ⚪ Static | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q5_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q5_K.gguf) | Q5_K | 6.65GB | ✅ Available | ⚪ Static | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q5_K_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q5_K_S.gguf) | Q5_K_S | 6.48GB | ✅ Available | ⚪ Static | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q4_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q4_K.gguf) | Q4_K | 5.76GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q4_K_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q4_K_S.gguf) | Q4_K_S | 5.48GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ4_NL.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ4_NL.gguf) | IQ4_NL | 5.44GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ4_XS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ4_XS.gguf) | IQ4_XS | 5.18GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q3_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q3_K.gguf) | Q3_K | 4.76GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q3_K_L.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q3_K_L.gguf) | Q3_K_L | 5.13GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q3_K_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q3_K_S.gguf) | Q3_K_S | 4.34GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ3_M.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ3_M.gguf) | IQ3_M | 4.49GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ3_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ3_S.gguf) | IQ3_S | 4.34GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ3_XS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ3_XS.gguf) | IQ3_XS | 4.14GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ3_XXS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ3_XXS.gguf) | IQ3_XXS | 3.80GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q2_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q2_K.gguf) | Q2_K | 3.81GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.Q2_K_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q2_K_S.gguf) | Q2_K_S | 3.55GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ2_M.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ2_M.gguf) | IQ2_M | 3.43GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ2_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ2_S.gguf) | IQ2_S | 3.21GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ2_XS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ2_XS.gguf) | IQ2_XS | 3.07GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ2_XXS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ2_XXS.gguf) | IQ2_XXS | 2.82GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ1_M.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ1_M.gguf) | IQ1_M | 2.55GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Gemma-2-9B-It-SPPO-Iter3.IQ1_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ1_S.gguf) | IQ1_S | 2.38GB | ✅ 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-2-9B-It-SPPO-Iter3-IMat-GGUF --include "Gemma-2-9B-It-SPPO-Iter3.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-2-9B-It-SPPO-Iter3-IMat-GGUF --include "Gemma-2-9B-It-SPPO-Iter3.Q8_0/*" --local-dir ./
+# see FAQ for merging GGUF's
+```
+
+---
+
+## Inference
+
+### Simple chat template
+```
+<bos><start_of_turn>user
+{user_prompt}<end_of_turn>
+<start_of_turn>model
+{assistant_response}<end_of_turn>
+<start_of_turn>user
+{next_user_prompt}<end_of_turn>
+
+```
+
+### Llama.cpp
+```
+llama.cpp/main -m Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf --color -i -p "prompt here (according to the chat template)"
+```
+
+---
+
+## 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-2-9B-It-SPPO-Iter3.Q8_0`)
+3. Run `gguf-split --merge Gemma-2-9B-It-SPPO-Iter3.Q8_0/Gemma-2-9B-It-SPPO-Iter3.Q8_0-00001-of-XXXXX.gguf Gemma-2-9B-It-SPPO-Iter3.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)!

imatrix.dat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50147eb9f5e4126a727c4dda3a23dd98db16c79316ede4b278c70f59f729e8c9
+size 6116914

imatrix.log

@@ -0,0 +1,149 @@
+llama_model_loader: loaded meta data with 29 key-value pairs and 464 tensors from Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/Gemma-2-9B-It-SPPO-Iter3.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.name str              = Gemma-2-9B-It-SPPO-Iter3
+llama_model_loader: - kv   2:                      gemma2.context_length u32              = 8192
+llama_model_loader: - kv   3:                    gemma2.embedding_length u32              = 3584
+llama_model_loader: - kv   4:                         gemma2.block_count u32              = 42
+llama_model_loader: - kv   5:                 gemma2.feed_forward_length u32              = 14336
+llama_model_loader: - kv   6:                gemma2.attention.head_count u32              = 16
+llama_model_loader: - kv   7:             gemma2.attention.head_count_kv u32              = 8
+llama_model_loader: - kv   8:    gemma2.attention.layer_norm_rms_epsilon f32              = 0.000001
+llama_model_loader: - kv   9:                gemma2.attention.key_length u32              = 256
+llama_model_loader: - kv  10:              gemma2.attention.value_length u32              = 256
+llama_model_loader: - kv  11:                          general.file_type u32              = 7
+llama_model_loader: - kv  12:              gemma2.attn_logit_softcapping f32              = 50.000000
+llama_model_loader: - kv  13:             gemma2.final_logit_softcapping f32              = 30.000000
+llama_model_loader: - kv  14:            gemma2.attention.sliding_window u32              = 4096
+llama_model_loader: - kv  15:                       tokenizer.ggml.model str              = llama
+llama_model_loader: - kv  16:                         tokenizer.ggml.pre str              = default
+llama_model_loader: - kv  17:                      tokenizer.ggml.tokens arr[str,256000]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
+llama_model_loader: - kv  18:                      tokenizer.ggml.scores arr[f32,256000]  = [0.000000, 0.000000, 0.000000, 0.0000...
+llama_model_loader: - kv  19:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
+llama_model_loader: - kv  20:                tokenizer.ggml.bos_token_id u32              = 2
+llama_model_loader: - kv  21:                tokenizer.ggml.eos_token_id u32              = 1
+llama_model_loader: - kv  22:            tokenizer.ggml.unknown_token_id u32              = 3
+llama_model_loader: - kv  23:            tokenizer.ggml.padding_token_id u32              = 0
+llama_model_loader: - kv  24:               tokenizer.ggml.add_bos_token bool             = true
+llama_model_loader: - kv  25:               tokenizer.ggml.add_eos_token bool             = false
+llama_model_loader: - kv  26:                    tokenizer.chat_template str              = {{ bos_token }}{% if messages[0]['rol...
+llama_model_loader: - kv  27:            tokenizer.ggml.add_space_prefix bool             = false
+llama_model_loader: - kv  28:               general.quantization_version u32              = 2
+llama_model_loader: - type  f32:  169 tensors
+llama_model_loader: - type q8_0:  295 tensors
+llm_load_vocab: special tokens cache size = 364
+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           = 3584
+llm_load_print_meta: n_layer          = 42
+llm_load_print_meta: n_head           = 16
+llm_load_print_meta: n_head_kv        = 8
+llm_load_print_meta: n_rot            = 224
+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     = 2048
+llm_load_print_meta: n_embd_v_gqa     = 2048
+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             = 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        = 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       = 9B
+llm_load_print_meta: model ftype      = Q8_0
+llm_load_print_meta: model params     = 9.24 B
+llm_load_print_meta: model size       = 9.15 GiB (8.50 BPW) 
+llm_load_print_meta: general.name     = Gemma-2-9B-It-SPPO-Iter3
+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 = 93
+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 =    0.41 MiB
+llm_load_tensors: offloading 42 repeating layers to GPU
+llm_load_tensors: offloading non-repeating layers to GPU
+llm_load_tensors: offloaded 43/43 layers to GPU
+llm_load_tensors:        CPU buffer size =   929.69 MiB
+llm_load_tensors:      CUDA0 buffer size =  9366.12 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 =   168.00 MiB
+llama_new_context_with_model: KV self size  =  168.00 MiB, K (f16):   84.00 MiB, V (f16):   84.00 MiB
+llama_new_context_with_model:  CUDA_Host  output buffer size =     0.98 MiB
+llama_new_context_with_model:      CUDA0 compute buffer size =   507.00 MiB
+llama_new_context_with_model:  CUDA_Host compute buffer size =     9.01 MiB
+llama_new_context_with_model: graph nodes  = 1690
+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 119.102 ms
+compute_imatrix: computing over 128 chunks with batch_size 512
+compute_imatrix: 0.85 seconds per pass - ETA 1.82 minutes
+[1]8.2870,[2]5.5692,[3]4.8574,[4]6.1032,[5]6.2583,[6]5.2453,[7]5.7902,[8]6.1617,[9]6.4089,
+save_imatrix: stored collected data after 10 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[10]5.6400,[11]5.7908,[12]6.3911,[13]6.9578,[14]7.1997,[15]7.8125,[16]8.1619,[17]8.3215,[18]8.6900,[19]8.3100,
+save_imatrix: stored collected data after 20 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[20]8.5375,[21]8.6913,[22]8.6501,[23]8.8311,[24]8.9421,[25]9.1331,[26]8.8236,[27]9.0735,[28]9.2551,[29]9.1570,
+save_imatrix: stored collected data after 30 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[30]9.0664,[31]8.5109,[32]8.2338,[33]8.1731,[34]8.0490,[35]8.0060,[36]8.0236,[37]8.0200,[38]8.1009,[39]8.2803,
+save_imatrix: stored collected data after 40 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[40]8.4557,[41]8.6042,[42]8.8991,[43]9.2142,[44]9.4949,[45]9.6524,[46]9.4993,[47]9.5265,[48]9.7377,[49]9.8946,
+save_imatrix: stored collected data after 50 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[50]9.6778,[51]9.7231,[52]9.7711,[53]9.9173,[54]10.1400,[55]10.2482,[56]10.3176,[57]10.3220,[58]10.3506,[59]10.1951,
+save_imatrix: stored collected data after 60 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[60]10.0656,[61]9.9265,[62]9.8784,[63]9.9241,[64]9.9184,[65]9.8962,[66]9.9283,[67]9.8701,[68]9.7891,[69]9.8077,
+save_imatrix: stored collected data after 70 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[70]9.7677,[71]9.7514,[72]9.7605,[73]9.7345,[74]9.6684,[75]9.6283,[76]9.6233,[77]9.6320,[78]9.6199,[79]9.5661,
+save_imatrix: stored collected data after 80 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[80]9.6315,[81]9.6843,[82]9.6604,[83]9.6603,[84]9.7184,[85]9.5761,[86]9.5323,[87]9.4649,[88]9.4783,[89]9.5114,
+save_imatrix: stored collected data after 90 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[90]9.5357,[91]9.4612,[92]9.3816,[93]9.2879,[94]9.1958,[95]9.1295,[96]9.0453,[97]8.9720,[98]8.9035,[99]8.9543,
+save_imatrix: stored collected data after 100 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[100]8.9876,[101]9.0807,[102]9.1655,[103]9.2443,[104]9.4136,[105]9.5366,[106]9.5596,[107]9.5914,[108]9.6093,[109]9.5883,
+save_imatrix: stored collected data after 110 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[110]9.5676,[111]9.4936,[112]9.4150,[113]9.4655,[114]9.4851,[115]9.4909,[116]9.4830,[117]9.5297,[118]9.5509,[119]9.5557,
+save_imatrix: stored collected data after 120 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+[120]9.5669,[121]9.6127,[122]9.5730,[123]9.6293,[124]9.6857,[125]9.7251,[126]9.7980,[127]9.8556,[128]9.9097,
+save_imatrix: stored collected data after 128 chunks in Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/imatrix.dat
+
+llama_print_timings:        load time =   14925.71 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 =   96055.84 ms / 65536 tokens (    1.47 ms per token,   682.27 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 =  111564.87 ms / 65537 tokens
+
+Final estimate: PPL = 9.9097 +/- 0.16369