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Model: legraphista/Yi-Coder-9B-IMat-GGUF Source: Original Platform
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---
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base_model: 01-ai/Yi-Coder-9B
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inference: false
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library_name: gguf
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license: apache-2.0
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pipeline_tag: text-generation
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quantized_by: legraphista
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tags:
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- quantized
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- GGUF
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- quantization
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- imat
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- imatrix
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- static
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- 16bit
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- 8bit
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- 6bit
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- 5bit
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- 4bit
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- 3bit
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- 2bit
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- 1bit
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---
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# Yi-Coder-9B-IMat-GGUF
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_Llama.cpp imatrix quantization of 01-ai/Yi-Coder-9B_
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Original Model: [01-ai/Yi-Coder-9B](https://huggingface.co/01-ai/Yi-Coder-9B)
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Original dtype: `BF16` (`bfloat16`)
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Quantized by: llama.cpp [b3669](https://github.com/ggerganov/llama.cpp/releases/tag/b3669)
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IMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt)
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- [Files](#files)
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- [IMatrix](#imatrix)
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- [Common Quants](#common-quants)
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- [All Quants](#all-quants)
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- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)
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- [Inference](#inference)
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- [Llama.cpp](#llama-cpp)
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- [FAQ](#faq)
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- [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)
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- [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)
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---
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## Files
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### IMatrix
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Status: ✅ Available
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Link: [here](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/imatrix.dat)
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### Common Quants
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| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
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| -------- | ---------- | --------- | ------ | ------------ | -------- |
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| [Yi-Coder-9B.Q8_0.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q8_0.gguf) | Q8_0 | 9.38GB | ✅ Available | ⚪ Static | 📦 No
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| [Yi-Coder-9B.Q6_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q6_K.gguf) | Q6_K | 7.25GB | ✅ Available | ⚪ Static | 📦 No
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| [Yi-Coder-9B.Q4_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q4_K.gguf) | Q4_K | 5.33GB | ✅ Available | 🟢 IMatrix | 📦 No
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||||
| [Yi-Coder-9B.Q3_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q3_K.gguf) | Q3_K | 4.32GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.Q2_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q2_K.gguf) | Q2_K | 3.35GB | ✅ Available | 🟢 IMatrix | 📦 No
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### All Quants
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| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
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| -------- | ---------- | --------- | ------ | ------------ | -------- |
|
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| [Yi-Coder-9B.BF16.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.BF16.gguf) | BF16 | 17.66GB | ✅ Available | ⚪ Static | 📦 No
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| [Yi-Coder-9B.FP16.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.FP16.gguf) | F16 | 17.66GB | ✅ Available | ⚪ Static | 📦 No
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| [Yi-Coder-9B.Q8_0.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q8_0.gguf) | Q8_0 | 9.38GB | ✅ Available | ⚪ Static | 📦 No
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| [Yi-Coder-9B.Q6_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q6_K.gguf) | Q6_K | 7.25GB | ✅ Available | ⚪ Static | 📦 No
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| [Yi-Coder-9B.Q5_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q5_K.gguf) | Q5_K | 6.26GB | ✅ Available | ⚪ Static | 📦 No
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||||
| [Yi-Coder-9B.Q5_K_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q5_K_S.gguf) | Q5_K_S | 6.11GB | ✅ Available | ⚪ Static | 📦 No
|
||||
| [Yi-Coder-9B.Q4_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q4_K.gguf) | Q4_K | 5.33GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.Q4_K_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q4_K_S.gguf) | Q4_K_S | 5.07GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ4_NL.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ4_NL.gguf) | IQ4_NL | 5.05GB | ✅ Available | 🟢 IMatrix | 📦 No
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||||
| [Yi-Coder-9B.IQ4_XS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ4_XS.gguf) | IQ4_XS | 4.79GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.Q3_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q3_K.gguf) | Q3_K | 4.32GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.Q3_K_L.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q3_K_L.gguf) | Q3_K_L | 4.69GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.Q3_K_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q3_K_S.gguf) | Q3_K_S | 3.90GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ3_M.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ3_M.gguf) | IQ3_M | 4.06GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ3_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ3_S.gguf) | IQ3_S | 3.91GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ3_XS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ3_XS.gguf) | IQ3_XS | 3.72GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ3_XXS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ3_XXS.gguf) | IQ3_XXS | 3.47GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.Q2_K.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q2_K.gguf) | Q2_K | 3.35GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.Q2_K_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.Q2_K_S.gguf) | Q2_K_S | 3.12GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ2_M.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ2_M.gguf) | IQ2_M | 3.10GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ2_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ2_S.gguf) | IQ2_S | 2.88GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ2_XS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ2_XS.gguf) | IQ2_XS | 2.71GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ2_XXS.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ2_XXS.gguf) | IQ2_XXS | 2.46GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [Yi-Coder-9B.IQ1_M.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ1_M.gguf) | IQ1_M | 2.18GB | ✅ Available | 🟢 IMatrix | 📦 No
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||||
| [Yi-Coder-9B.IQ1_S.gguf](https://huggingface.co/legraphista/Yi-Coder-9B-IMat-GGUF/blob/main/Yi-Coder-9B.IQ1_S.gguf) | IQ1_S | 2.01GB | ✅ Available | 🟢 IMatrix | 📦 No
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## Downloading using huggingface-cli
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If you do not have hugginface-cli installed:
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```
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pip install -U "huggingface_hub[cli]"
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```
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Download the specific file you want:
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```
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huggingface-cli download legraphista/Yi-Coder-9B-IMat-GGUF --include "Yi-Coder-9B.Q8_0.gguf" --local-dir ./
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```
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If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:
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```
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huggingface-cli download legraphista/Yi-Coder-9B-IMat-GGUF --include "Yi-Coder-9B.Q8_0/*" --local-dir ./
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# see FAQ for merging GGUF's
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```
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---
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## Inference
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### Llama.cpp
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```
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llama.cpp/main -m Yi-Coder-9B.Q8_0.gguf --color -i -p "prompt here"
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```
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---
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## FAQ
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### Why is the IMatrix not applied everywhere?
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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).
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### How do I merge a split GGUF?
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1. Make sure you have `gguf-split` available
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- To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases
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- Download the appropriate zip for your system from the latest release
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- Unzip the archive and you should be able to find `gguf-split`
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2. Locate your GGUF chunks folder (ex: `Yi-Coder-9B.Q8_0`)
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3. Run `gguf-split --merge Yi-Coder-9B.Q8_0/Yi-Coder-9B.Q8_0-00001-of-XXXXX.gguf Yi-Coder-9B.Q8_0.gguf`
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- Make sure to point `gguf-split` to the first chunk of the split.
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---
|
||||
|
||||
Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!
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Yi-Coder-9B.BF16.gguf
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version https://git-lfs.github.com/spec/v1
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size 17661112320
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 3098112224
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version https://git-lfs.github.com/spec/v1
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oid sha256:f9b3b6a8c824dea15f3ffa429ac88ee8a358d299d9c3af02fb65cd32d82716e1
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size 2875355360
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3
Yi-Coder-9B.IQ2_XS.gguf
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Yi-Coder-9B.IQ2_XS.gguf
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imatrix.dat
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imatrix.dat
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imatrix.dataset
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2482
imatrix.dataset
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File diff suppressed because one or more lines are too long
154
imatrix.log
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154
imatrix.log
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|
||||
llama_model_loader: loaded meta data with 29 key-value pairs and 435 tensors from Yi-Coder-9B-IMat-GGUF/Yi-Coder-9B.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
|
||||
llama_model_loader: - kv 3: general.basename str = Yi-Coder
|
||||
llama_model_loader: - kv 4: general.size_label str = 9B
|
||||
llama_model_loader: - kv 5: general.license str = apache-2.0
|
||||
llama_model_loader: - kv 6: llama.block_count u32 = 48
|
||||
llama_model_loader: - kv 7: llama.context_length u32 = 131072
|
||||
llama_model_loader: - kv 8: llama.embedding_length u32 = 4096
|
||||
llama_model_loader: - kv 9: llama.feed_forward_length u32 = 11008
|
||||
llama_model_loader: - kv 10: llama.attention.head_count u32 = 32
|
||||
llama_model_loader: - kv 11: llama.attention.head_count_kv u32 = 4
|
||||
llama_model_loader: - kv 12: llama.rope.freq_base f32 = 10000000.000000
|
||||
llama_model_loader: - kv 13: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
|
||||
llama_model_loader: - kv 14: general.file_type u32 = 7
|
||||
llama_model_loader: - kv 15: llama.vocab_size u32 = 64000
|
||||
llama_model_loader: - kv 16: llama.rope.dimension_count u32 = 128
|
||||
llama_model_loader: - kv 17: tokenizer.ggml.model str = llama
|
||||
llama_model_loader: - kv 18: tokenizer.ggml.pre str = default
|
||||
llama_model_loader: - kv 19: tokenizer.ggml.tokens arr[str,64000] = ["<unk>", "<|startoftext|>", "<|endof...
|
||||
llama_model_loader: - kv 20: tokenizer.ggml.scores arr[f32,64000] = [-1000.000000, -1000.000000, -1000.00...
|
||||
llama_model_loader: - kv 21: tokenizer.ggml.token_type arr[i32,64000] = [3, 3, 3, 3, 3, 3, 1, 1, 1, 3, 3, 3, ...
|
||||
llama_model_loader: - kv 22: tokenizer.ggml.bos_token_id u32 = 1
|
||||
llama_model_loader: - kv 23: tokenizer.ggml.eos_token_id u32 = 2
|
||||
llama_model_loader: - kv 24: tokenizer.ggml.unknown_token_id u32 = 0
|
||||
llama_model_loader: - kv 25: tokenizer.ggml.padding_token_id u32 = 0
|
||||
llama_model_loader: - kv 26: tokenizer.ggml.add_bos_token bool = false
|
||||
llama_model_loader: - kv 27: tokenizer.ggml.add_eos_token bool = false
|
||||
llama_model_loader: - kv 28: 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 = 11
|
||||
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
|
||||
llm_load_print_meta: BOS token = 1 '<|startoftext|>'
|
||||
llm_load_print_meta: EOS token = 2 '<|endoftext|>'
|
||||
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 95.343 ms
|
||||
compute_imatrix: computing over 146 chunks with batch_size 512
|
||||
compute_imatrix: 0.79 seconds per pass - ETA 1.90 minutes
|
||||
[1]7.2880,[2]5.1883,[3]5.2856,[4]5.5969,[5]5.5395,[6]5.8015,[7]4.9470,[8]5.5094,[9]5.4777,
|
||||
save_imatrix: stored collected data after 10 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[10]5.9434,[11]5.9764,[12]5.4793,[13]5.8090,[14]6.3599,[15]6.5982,[16]7.0200,[17]7.3588,[18]7.4586,[19]7.5772,
|
||||
save_imatrix: stored collected data after 20 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[20]7.8653,[21]7.5142,[22]7.4258,[23]7.5155,[24]7.5329,[25]7.5233,[26]7.2979,[27]7.5016,[28]7.6617,[29]7.9083,
|
||||
save_imatrix: stored collected data after 30 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[30]7.9808,[31]8.1886,[32]8.4090,[33]8.3950,[34]8.2353,[35]7.9854,[36]7.5750,[37]7.2076,[38]7.1694,[39]7.1115,
|
||||
save_imatrix: stored collected data after 40 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[40]7.0891,[41]6.8756,[42]6.6935,[43]6.5613,[44]6.3987,[45]6.2618,[46]6.2461,[47]6.3318,[48]6.4427,[49]6.5706,
|
||||
save_imatrix: stored collected data after 50 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[50]6.7226,[51]6.9875,[52]7.2196,[53]7.4019,[54]7.5350,[55]7.5759,[56]7.5082,[57]7.6188,[58]7.6928,[59]7.7780,
|
||||
save_imatrix: stored collected data after 60 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[60]7.7053,[61]7.6323,[62]7.6596,[63]7.7951,[64]7.9040,[65]8.0036,[66]8.0511,[67]8.1005,[68]8.1527,[69]8.1953,
|
||||
save_imatrix: stored collected data after 70 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[70]8.0910,[71]8.0134,[72]7.9389,[73]7.8956,[74]7.9284,[75]7.9880,[76]7.9779,[77]7.9928,[78]7.9899,[79]7.9561,
|
||||
save_imatrix: stored collected data after 80 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[80]7.9137,[81]7.8572,[82]7.8692,[83]7.8375,[84]7.8118,[85]7.8247,[86]7.7957,[87]7.7487,[88]7.7099,[89]7.7192,
|
||||
save_imatrix: stored collected data after 90 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[90]7.6908,[91]7.6689,[92]7.5977,[93]7.5844,[94]7.6399,[95]7.6387,[96]7.6007,[97]7.6168,[98]7.6299,[99]7.6494,
|
||||
save_imatrix: stored collected data after 100 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[100]7.5541,[101]7.6011,[102]7.6154,[103]7.6375,[104]7.6594,[105]7.6740,[106]7.6198,[107]7.5690,[108]7.5181,[109]7.4570,
|
||||
save_imatrix: stored collected data after 110 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[110]7.4035,[111]7.3519,[112]7.3042,[113]7.2572,[114]7.2227,[115]7.2385,[116]7.2824,[117]7.3530,[118]7.4214,[119]7.4886,
|
||||
save_imatrix: stored collected data after 120 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[120]7.6025,[121]7.6782,[122]7.7016,[123]7.7137,[124]7.6705,[125]7.6765,[126]7.6478,[127]7.5651,[128]7.4809,[129]7.4183,
|
||||
save_imatrix: stored collected data after 130 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[130]7.4634,[131]7.4627,[132]7.4897,[133]7.5171,[134]7.5629,[135]7.5906,[136]7.6044,[137]7.6149,[138]7.6157,[139]7.5994,
|
||||
save_imatrix: stored collected data after 140 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
[140]7.6620,[141]7.7203,[142]7.7764,[143]7.8331,[144]7.8902,[145]7.9510,[146]7.9918,
|
||||
save_imatrix: stored collected data after 146 chunks in Yi-Coder-9B-IMat-GGUF/imatrix.dat
|
||||
|
||||
llama_print_timings: load time = 2336.06 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 = 108543.70 ms / 74752 tokens ( 1.45 ms per token, 688.68 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 = 110682.85 ms / 74753 tokens
|
||||
|
||||
Final estimate: PPL = 7.9918 +/- 0.11014
|
||||
Reference in New Issue
Block a user