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Model: legraphista/Yi-Coder-9B-Chat-IMat-GGUF Source: Original Platform
2026-07-26 04:04:10 +08:00
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--- a/README.md
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+---
+base_model: 01-ai/Yi-Coder-9B-Chat
+inference: false
+library_name: gguf
+license: apache-2.0
+pipeline_tag: text-generation
+quantized_by: legraphista
+tags:
+- quantized
+- GGUF
+- quantization
+- imat
+- imatrix
+- static
+- 16bit
+- 8bit
+- 6bit
+- 5bit
+- 4bit
+- 3bit
+- 2bit
+- 1bit
+---
+
+# Yi-Coder-9B-Chat-IMat-GGUF
+_Llama.cpp imatrix quantization of 01-ai/Yi-Coder-9B-Chat_
+
+Original Model: [01-ai/Yi-Coder-9B-Chat](https://huggingface.co/01-ai/Yi-Coder-9B-Chat)    
+Original dtype: `BF16` (`bfloat16`)  
+Quantized by:  llama.cpp [b3669](https://github.com/ggerganov/llama.cpp/releases/tag/b3669)  
+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)
+    - [Chat template with system prompt](#chat-template-with-system-prompt)
+    - [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/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/imatrix.dat)
+
+### Common Quants
+| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
+| -------- | ---------- | --------- | ------ | ------------ | -------- |
+| [Yi-Coder-9B-Chat.Q8_0.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q8_0.gguf) | Q8_0 | 9.38GB | ✅ Available | ⚪ Static | 📦 No
+| [Yi-Coder-9B-Chat.Q6_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q6_K.gguf) | Q6_K | 7.25GB | ✅ Available | ⚪ Static | 📦 No
+| [Yi-Coder-9B-Chat.Q4_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q4_K.gguf) | Q4_K | 5.33GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.Q3_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q3_K.gguf) | Q3_K | 4.32GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.Q2_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q2_K.gguf) | Q2_K | 3.35GB | ✅ Available | 🟢 IMatrix | 📦 No
+
+
+### All Quants
+| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
+| -------- | ---------- | --------- | ------ | ------------ | -------- |
+| [Yi-Coder-9B-Chat.BF16.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.BF16.gguf) | BF16 | 17.66GB | ✅ Available | ⚪ Static | 📦 No
+| [Yi-Coder-9B-Chat.FP16.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.FP16.gguf) | F16 | 17.66GB | ✅ Available | ⚪ Static | 📦 No
+| [Yi-Coder-9B-Chat.Q8_0.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q8_0.gguf) | Q8_0 | 9.38GB | ✅ Available | ⚪ Static | 📦 No
+| [Yi-Coder-9B-Chat.Q6_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q6_K.gguf) | Q6_K | 7.25GB | ✅ Available | ⚪ Static | 📦 No
+| [Yi-Coder-9B-Chat.Q5_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q5_K.gguf) | Q5_K | 6.26GB | ✅ Available | ⚪ Static | 📦 No
+| [Yi-Coder-9B-Chat.Q5_K_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q5_K_S.gguf) | Q5_K_S | 6.11GB | ✅ Available | ⚪ Static | 📦 No
+| [Yi-Coder-9B-Chat.Q4_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q4_K.gguf) | Q4_K | 5.33GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.Q4_K_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q4_K_S.gguf) | Q4_K_S | 5.07GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ4_NL.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ4_NL.gguf) | IQ4_NL | 5.05GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ4_XS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ4_XS.gguf) | IQ4_XS | 4.79GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.Q3_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q3_K.gguf) | Q3_K | 4.32GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.Q3_K_L.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q3_K_L.gguf) | Q3_K_L | 4.69GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.Q3_K_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q3_K_S.gguf) | Q3_K_S | 3.90GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ3_M.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ3_M.gguf) | IQ3_M | 4.06GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ3_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ3_S.gguf) | IQ3_S | 3.91GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ3_XS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ3_XS.gguf) | IQ3_XS | 3.72GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ3_XXS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ3_XXS.gguf) | IQ3_XXS | 3.47GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.Q2_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q2_K.gguf) | Q2_K | 3.35GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.Q2_K_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.Q2_K_S.gguf) | Q2_K_S | 3.12GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ2_M.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ2_M.gguf) | IQ2_M | 3.10GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ2_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ2_S.gguf) | IQ2_S | 2.88GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ2_XS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ2_XS.gguf) | IQ2_XS | 2.71GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ2_XXS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ2_XXS.gguf) | IQ2_XXS | 2.46GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ1_M.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ1_M.gguf) | IQ1_M | 2.18GB | ✅ Available | 🟢 IMatrix | 📦 No
+| [Yi-Coder-9B-Chat.IQ1_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-Chat-IMat-GGUF/blob/main/Yi-Coder-9B-Chat.IQ1_S.gguf) | IQ1_S | 2.01GB | ✅ 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/Yi-Coder-9B-Chat-IMat-GGUF --include "Yi-Coder-9B-Chat.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/Yi-Coder-9B-Chat-IMat-GGUF --include "Yi-Coder-9B-Chat.Q8_0/*" --local-dir ./
+# see FAQ for merging GGUF's
+```
+
+---
+
+## Inference
+
+### Simple chat template
+```
+<|im_start|>user
+{user_prompt}<|im_end|>
+<|im_start|>assistant
+{assistant_response}<|im_end|>
+<|im_start|>user
+{next_user_prompt}<|im_end|>
+<|im_start|>assistant
+
+```
+
+### Chat template with system prompt
+```
+<|im_start|>system
+{system_prompt}<|im_end|>
+<|im_start|>user
+{user_prompt}<|im_end|>
+<|im_start|>assistant
+{assistant_response}<|im_end|>
+<|im_start|>user
+{next_user_prompt}<|im_end|>
+<|im_start|>assistant
+
+```
+
+### Llama.cpp
+```
+llama.cpp/main -m Yi-Coder-9B-Chat.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: `Yi-Coder-9B-Chat.Q8_0`)
+3. Run `gguf-split --merge Yi-Coder-9B-Chat.Q8_0/Yi-Coder-9B-Chat.Q8_0-00001-of-XXXXX.gguf Yi-Coder-9B-Chat.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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--- a/imatrix.dataset
+++ b/imatrix.dataset
--- a/imatrix.log
+++ b/imatrix.log
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+llama_model_loader: loaded meta data with 31 key-value pairs and 435 tensors from Yi-Coder-9B-Chat-IMat-GGUF/Yi-Coder-9B-Chat.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              = llama
+llama_model_loader: - kv   1:                               general.type str              = model
+llama_model_loader: - kv   2:                               general.name str              = Yi Coder 9B Chat
+llama_model_loader: - kv   3:                           general.finetune str              = Chat
+llama_model_loader: - kv   4:                           general.basename str              = Yi-Coder
+llama_model_loader: - kv   5:                         general.size_label str              = 9B
+llama_model_loader: - kv   6:                            general.license str              = apache-2.0
+llama_model_loader: - kv   7:                          llama.block_count u32              = 48
+llama_model_loader: - kv   8:                       llama.context_length u32              = 131072
+llama_model_loader: - kv   9:                     llama.embedding_length u32              = 4096
+llama_model_loader: - kv  10:                  llama.feed_forward_length u32              = 11008
+llama_model_loader: - kv  11:                 llama.attention.head_count u32              = 32
+llama_model_loader: - kv  12:              llama.attention.head_count_kv u32              = 4
+llama_model_loader: - kv  13:                       llama.rope.freq_base f32              = 10000000.000000
+llama_model_loader: - kv  14:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
+llama_model_loader: - kv  15:                          general.file_type u32              = 7
+llama_model_loader: - kv  16:                           llama.vocab_size u32              = 64000
+llama_model_loader: - kv  17:                 llama.rope.dimension_count u32              = 128
+llama_model_loader: - kv  18:            tokenizer.ggml.add_space_prefix bool             = false
+llama_model_loader: - kv  19:                       tokenizer.ggml.model str              = llama
+llama_model_loader: - kv  20:                         tokenizer.ggml.pre str              = default
+llama_model_loader: - kv  21:                      tokenizer.ggml.tokens arr[str,64000]   = ["<unk>", "<|startoftext|>", "<|endof...
+llama_model_loader: - kv  22:                      tokenizer.ggml.scores arr[f32,64000]   = [-1000.000000, -1000.000000, -1000.00...
+llama_model_loader: - kv  23:                  tokenizer.ggml.token_type arr[i32,64000]   = [3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, ...
+llama_model_loader: - kv  24:                tokenizer.ggml.bos_token_id u32              = 1
+llama_model_loader: - kv  25:                tokenizer.ggml.eos_token_id u32              = 7
+llama_model_loader: - kv  26:            tokenizer.ggml.padding_token_id u32              = 0
+llama_model_loader: - kv  27:               tokenizer.ggml.add_bos_token bool             = false
+llama_model_loader: - kv  28:               tokenizer.ggml.add_eos_token bool             = false
+llama_model_loader: - kv  29:                    tokenizer.chat_template str              = {% if messages[0]['role'] == 'system'...
+llama_model_loader: - kv  30:               general.quantization_version u32              = 2
+llama_model_loader: - type  f32:   97 tensors
+llama_model_loader: - type q8_0:  338 tensors
+llm_load_vocab: special tokens cache size = 13
+llm_load_vocab: token to piece cache size = 0.3834 MB
+llm_load_print_meta: format           = GGUF V3 (latest)
+llm_load_print_meta: arch             = llama
+llm_load_print_meta: vocab type       = SPM
+llm_load_print_meta: n_vocab          = 64000
+llm_load_print_meta: n_merges         = 0
+llm_load_print_meta: vocab_only       = 0
+llm_load_print_meta: n_ctx_train      = 131072
+llm_load_print_meta: n_embd           = 4096
+llm_load_print_meta: n_layer          = 48
+llm_load_print_meta: n_head           = 32
+llm_load_print_meta: n_head_kv        = 4
+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     = 512
+llm_load_print_meta: n_embd_v_gqa     = 512
+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             = 11008
+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  = 10000000.0
+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       = 34B
+llm_load_print_meta: model ftype      = Q8_0
+llm_load_print_meta: model params     = 8.83 B
+llm_load_print_meta: model size       = 8.74 GiB (8.50 BPW) 
+llm_load_print_meta: general.name     = Yi Coder 9B Chat
+llm_load_print_meta: BOS token        = 1 '<|startoftext|>'
+llm_load_print_meta: EOS token        = 7 '<|im_end|>'
+llm_load_print_meta: UNK token        = 0 '<unk>'
+llm_load_print_meta: PAD token        = 0 '<unk>'
+llm_load_print_meta: LF token         = 315 '<0x0A>'
+llm_load_print_meta: EOT token        = 2 '<|endoftext|>'
+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 =    0.41 MiB
+llm_load_tensors: offloading 48 repeating layers to GPU
+llm_load_tensors: offloading non-repeating layers to GPU
+llm_load_tensors: offloaded 49/49 layers to GPU
+llm_load_tensors:        CPU buffer size =   265.62 MiB
+llm_load_tensors:      CUDA0 buffer size =  8682.16 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  = 10000000.0
+llama_new_context_with_model: freq_scale = 1
+llama_kv_cache_init:      CUDA0 KV buffer size =    48.00 MiB
+llama_new_context_with_model: KV self size  =   48.00 MiB, K (f16):   24.00 MiB, V (f16):   24.00 MiB
+llama_new_context_with_model:  CUDA_Host  output buffer size =     0.24 MiB
+llama_new_context_with_model:      CUDA0 compute buffer size =   133.00 MiB
+llama_new_context_with_model:  CUDA_Host compute buffer size =     9.01 MiB
+llama_new_context_with_model: graph nodes  = 1542
+llama_new_context_with_model: graph splits = 2
+
+system_info: n_threads = 25 (n_threads_batch = 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 99.249 ms
+compute_imatrix: computing over 146 chunks with batch_size 512
+compute_imatrix: 0.88 seconds per pass - ETA 2.13 minutes
+[1]9.8198,[2]6.5604,[3]6.5762,[4]7.0806,[5]6.9953,[6]7.1565,[7]5.9481,[8]6.7761,[9]6.6810,
+save_imatrix: stored collected data after 10 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[10]7.2665,[11]7.3542,[12]6.7100,[13]7.1440,[14]7.8428,[15]8.1226,[16]8.6544,[17]9.1495,[18]9.2832,[19]9.4664,
+save_imatrix: stored collected data after 20 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[20]9.8392,[21]9.3568,[22]9.2338,[23]9.3600,[24]9.3835,[25]9.3889,[26]9.1261,[27]9.4375,[28]9.6221,[29]9.9584,
+save_imatrix: stored collected data after 30 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[30]10.0395,[31]10.2920,[32]10.5665,[33]10.5537,[34]10.3409,[35]9.9756,[36]9.4164,[37]8.9196,[38]8.8557,[39]8.7853,
+save_imatrix: stored collected data after 40 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[40]8.7371,[41]8.4383,[42]8.1871,[43]8.0015,[44]7.7722,[45]7.5820,[46]7.5505,[47]7.6632,[48]7.8130,[49]7.9891,
+save_imatrix: stored collected data after 50 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[50]8.1707,[51]8.5257,[52]8.8144,[53]9.0342,[54]9.1952,[55]9.2209,[56]9.1161,[57]9.2642,[58]9.3533,[59]9.4595,
+save_imatrix: stored collected data after 60 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[60]9.3627,[61]9.2811,[62]9.3249,[63]9.5089,[64]9.6632,[65]9.7850,[66]9.8601,[67]9.9305,[68]10.0022,[69]10.0562,
+save_imatrix: stored collected data after 70 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[70]9.9152,[71]9.8122,[72]9.7318,[73]9.6664,[74]9.7231,[75]9.7928,[76]9.7848,[77]9.8168,[78]9.8241,[79]9.7773,
+save_imatrix: stored collected data after 80 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[80]9.7172,[81]9.6326,[82]9.6526,[83]9.6156,[84]9.5799,[85]9.5961,[86]9.5612,[87]9.4946,[88]9.4473,[89]9.4639,
+save_imatrix: stored collected data after 90 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[90]9.4329,[91]9.4060,[92]9.3100,[93]9.2929,[94]9.3700,[95]9.3770,[96]9.3207,[97]9.3439,[98]9.3690,[99]9.3938,
+save_imatrix: stored collected data after 100 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[100]9.2644,[101]9.3233,[102]9.3417,[103]9.3820,[104]9.4176,[105]9.4469,[106]9.3712,[107]9.3010,[108]9.2315,[109]9.1467,
+save_imatrix: stored collected data after 110 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[110]9.0751,[111]9.0000,[112]8.9344,[113]8.8693,[114]8.8142,[115]8.8457,[116]8.9097,[117]9.0049,[118]9.0946,[119]9.1784,
+save_imatrix: stored collected data after 120 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[120]9.3380,[121]9.4386,[122]9.4729,[123]9.4955,[124]9.4380,[125]9.4421,[126]9.4011,[127]9.3066,[128]9.2026,[129]9.1276,
+save_imatrix: stored collected data after 130 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[130]9.1887,[131]9.1931,[132]9.2312,[133]9.2682,[134]9.3254,[135]9.3580,[136]9.3817,[137]9.3953,[138]9.3918,[139]9.3793,
+save_imatrix: stored collected data after 140 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+[140]9.4587,[141]9.5294,[142]9.5985,[143]9.6776,[144]9.7522,[145]9.8222,[146]9.8724,
+save_imatrix: stored collected data after 146 chunks in Yi-Coder-9B-Chat-IMat-GGUF/imatrix.dat
+
+llama_print_timings:        load time =    2483.50 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 =  110783.96 ms / 74752 tokens (    1.48 ms per token,   674.75 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 =  112968.42 ms / 74753 tokens
+
+Final estimate: PPL = 9.8724 +/- 0.15747