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Model: legraphista/gemma-2-9b-it-IMat-GGUF Source: Original Platform
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README.md
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---
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base_model: google/gemma-2-9b-it
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extra_gated_button_content: Acknowledge license
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extra_gated_heading: Access Gemma on Hugging Face
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extra_gated_prompt: "To access Gemma on Hugging Face, you\u2019re required to review\
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\ and agree to Google\u2019s usage license. To do this, please ensure you\u2019\
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re logged in to Hugging Face and click below. Requests are processed immediately."
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inference: false
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library_name: gguf
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license: gemma
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pipeline_tag: text-generation
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quantized_by: legraphista
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tags:
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- conversational
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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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# gemma-2-9b-it-IMat-GGUF
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_Llama.cpp imatrix quantization of google/gemma-2-9b-it_
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Original Model: [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
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Original dtype: `BF16` (`bfloat16`)
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Quantized by: llama.cpp [b3266](https://github.com/ggerganov/llama.cpp/releases/tag/b3266)
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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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- [Simple chat template](#simple-chat-template)
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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/gemma-2-9b-it-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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| [gemma-2-9b-it.Q8_0.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q8_0.gguf) | Q8_0 | 9.83GB | ✅ Available | ⚪ Static | 📦 No
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||||
| [gemma-2-9b-it.Q6_K.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q6_K.gguf) | Q6_K | 7.59GB | ✅ Available | ⚪ Static | 📦 No
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||||
| [gemma-2-9b-it.Q4_K.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q4_K.gguf) | Q4_K | 5.76GB | ✅ Available | 🟢 IMatrix | 📦 No
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||||
| [gemma-2-9b-it.Q3_K.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q3_K.gguf) | Q3_K | 4.76GB | ✅ Available | 🟢 IMatrix | 📦 No
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||||
| [gemma-2-9b-it.Q2_K.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q2_K.gguf) | Q2_K | 3.81GB | ✅ 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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| [gemma-2-9b-it.BF16.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.BF16.gguf) | BF16 | 18.49GB | ✅ Available | ⚪ Static | 📦 No
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| [gemma-2-9b-it.FP16.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.FP16.gguf) | F16 | 18.49GB | ✅ Available | ⚪ Static | 📦 No
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| [gemma-2-9b-it.Q8_0.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q8_0.gguf) | Q8_0 | 9.83GB | ✅ Available | ⚪ Static | 📦 No
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||||
| [gemma-2-9b-it.Q6_K.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q6_K.gguf) | Q6_K | 7.59GB | ✅ Available | ⚪ Static | 📦 No
|
||||
| [gemma-2-9b-it.Q5_K.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q5_K.gguf) | Q5_K | 6.65GB | ✅ Available | ⚪ Static | 📦 No
|
||||
| [gemma-2-9b-it.Q5_K_S.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q5_K_S.gguf) | Q5_K_S | 6.48GB | ✅ Available | ⚪ Static | 📦 No
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||||
| [gemma-2-9b-it.Q4_K.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q4_K.gguf) | Q4_K | 5.76GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [gemma-2-9b-it.Q4_K_S.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q4_K_S.gguf) | Q4_K_S | 5.48GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [gemma-2-9b-it.IQ4_NL.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ4_NL.gguf) | IQ4_NL | 5.44GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [gemma-2-9b-it.IQ4_XS.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ4_XS.gguf) | IQ4_XS | 5.18GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [gemma-2-9b-it.Q3_K.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q3_K.gguf) | Q3_K | 4.76GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [gemma-2-9b-it.Q3_K_L.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q3_K_L.gguf) | Q3_K_L | 5.13GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [gemma-2-9b-it.Q3_K_S.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q3_K_S.gguf) | Q3_K_S | 4.34GB | ✅ Available | 🟢 IMatrix | 📦 No
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||||
| [gemma-2-9b-it.IQ3_M.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ3_M.gguf) | IQ3_M | 4.49GB | ✅ Available | 🟢 IMatrix | 📦 No
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||||
| [gemma-2-9b-it.IQ3_S.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ3_S.gguf) | IQ3_S | 4.34GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [gemma-2-9b-it.IQ3_XS.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ3_XS.gguf) | IQ3_XS | 4.14GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [gemma-2-9b-it.IQ3_XXS.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ3_XXS.gguf) | IQ3_XXS | 3.80GB | ✅ Available | 🟢 IMatrix | 📦 No
|
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| [gemma-2-9b-it.Q2_K.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q2_K.gguf) | Q2_K | 3.81GB | ✅ Available | 🟢 IMatrix | 📦 No
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||||
| [gemma-2-9b-it.Q2_K_S.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.Q2_K_S.gguf) | Q2_K_S | 3.55GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [gemma-2-9b-it.IQ2_M.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ2_M.gguf) | IQ2_M | 3.43GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [gemma-2-9b-it.IQ2_S.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ2_S.gguf) | IQ2_S | 3.21GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [gemma-2-9b-it.IQ2_XS.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ2_XS.gguf) | IQ2_XS | 3.07GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [gemma-2-9b-it.IQ2_XXS.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ2_XXS.gguf) | IQ2_XXS | 2.82GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [gemma-2-9b-it.IQ1_M.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ1_M.gguf) | IQ1_M | 2.55GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [gemma-2-9b-it.IQ1_S.gguf](https://huggingface.co/legraphista/gemma-2-9b-it-IMat-GGUF/blob/main/gemma-2-9b-it.IQ1_S.gguf) | IQ1_S | 2.38GB | ✅ 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/gemma-2-9b-it-IMat-GGUF --include "gemma-2-9b-it.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/gemma-2-9b-it-IMat-GGUF --include "gemma-2-9b-it.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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### Simple chat template
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```
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<bos><start_of_turn>user
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{user_prompt}<end_of_turn>
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<start_of_turn>model
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{assistant_response}<end_of_turn>
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<start_of_turn>user
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{next_user_prompt}<end_of_turn>
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```
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### Llama.cpp
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```
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llama.cpp/main -m gemma-2-9b-it.Q8_0.gguf --color -i -p "prompt here (according to the chat template)"
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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: `gemma-2-9b-it.Q8_0`)
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3. Run `gguf-split --merge gemma-2-9b-it.Q8_0/gemma-2-9b-it.Q8_0-00001-of-XXXXX.gguf gemma-2-9b-it.Q8_0.gguf`
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- Make sure to point `gguf-split` to the first chunk of the split.
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---
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|
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Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!
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3
gemma-2-9b-it.BF16.gguf
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gemma-2-9b-it.BF16.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:b3b8448ccf9e66d4c6679415b9fed9528f3c3bc786f01ff044654163fb5b81d6
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size 18490680928
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gemma-2-9b-it.FP16.gguf
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gemma-2-9b-it.FP16.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:32ecf159fbac5c868da5f92513c28768a23b76a0393100f458356966f4280c1c
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size 18490680928
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3
gemma-2-9b-it.IQ1_M.gguf
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gemma-2-9b-it.IQ1_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:21e91af3cdb8608ad17d3b75a7ce08514e2124722b05df603c655632e3cf4cc0
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size 2545952576
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gemma-2-9b-it.IQ1_S.gguf
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gemma-2-9b-it.IQ1_S.gguf
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|
||||
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imatrix.dat
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imatrix.dataset
Normal file
2482
imatrix.dataset
Normal file
File diff suppressed because one or more lines are too long
146
imatrix.log
Normal file
146
imatrix.log
Normal file
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|
||||
llama_model_loader: loaded meta data with 28 key-value pairs and 464 tensors from gemma-2-9b-it-IMat-GGUF/gemma-2-9b-it.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
|
||||
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: tokenizer.ggml.model str = llama
|
||||
llama_model_loader: - kv 15: tokenizer.ggml.pre str = default
|
||||
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,256000] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
|
||||
llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,256000] = [-1000.000000, -1000.000000, -1000.00...
|
||||
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, ...
|
||||
llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 2
|
||||
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 1
|
||||
llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 3
|
||||
llama_model_loader: - kv 22: tokenizer.ggml.padding_token_id u32 = 0
|
||||
llama_model_loader: - kv 23: tokenizer.ggml.add_bos_token bool = true
|
||||
llama_model_loader: - kv 24: tokenizer.ggml.add_eos_token bool = false
|
||||
llama_model_loader: - kv 25: tokenizer.chat_template str = {{ bos_token }}{% if messages[0]['rol...
|
||||
llama_model_loader: - kv 26: tokenizer.ggml.add_space_prefix bool = false
|
||||
llama_model_loader: - kv 27: 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 = 261
|
||||
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: n_ctx_train = 8192
|
||||
llm_load_print_meta: n_embd = 3584
|
||||
llm_load_print_meta: n_head = 16
|
||||
llm_load_print_meta: n_head_kv = 8
|
||||
llm_load_print_meta: n_layer = 42
|
||||
llm_load_print_meta: n_rot = 224
|
||||
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
|
||||
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 11 repeating layers to GPU
|
||||
llm_load_tensors: offloaded 11/43 layers to GPU
|
||||
llm_load_tensors: CPU buffer size = 9366.12 MiB
|
||||
llm_load_tensors: CUDA0 buffer size = 2209.54 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: CUDA_Host KV buffer size = 124.00 MiB
|
||||
llama_kv_cache_init: CUDA0 KV buffer size = 44.00 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 = 1436.69 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 = 407
|
||||
|
||||
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 91.303 ms
|
||||
compute_imatrix: computing over 128 chunks with batch_size 512
|
||||
compute_imatrix: 1.34 seconds per pass - ETA 2.85 minutes
|
||||
[1]14.7486,[2]7.3963,[3]6.5677,[4]7.8490,[5]8.6193,[6]9.1006,[7]10.0154,[8]10.8556,[9]11.1957,
|
||||
save_imatrix: stored collected data after 10 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[10]9.8358,[11]9.5880,[12]10.6132,[13]11.2166,[14]11.3352,[15]12.0575,[16]12.1605,[17]12.2130,[18]12.6384,[19]12.5495,
|
||||
save_imatrix: stored collected data after 20 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[20]12.7233,[21]13.7801,[22]13.8033,[23]13.5919,[24]13.8287,[25]13.6927,[26]13.4495,[27]13.6978,[28]13.9355,[29]13.9524,
|
||||
save_imatrix: stored collected data after 30 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[30]14.1488,[31]13.1149,[32]12.5387,[33]12.1100,[34]11.7329,[35]11.4517,[36]11.6110,[37]11.9132,[38]12.0730,[39]12.2426,
|
||||
save_imatrix: stored collected data after 40 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[40]12.3500,[41]12.3930,[42]12.9499,[43]13.3055,[44]13.6998,[45]13.9306,[46]13.6677,[47]13.4581,[48]13.6644,[49]13.8789,
|
||||
save_imatrix: stored collected data after 50 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[50]13.6841,[51]13.5410,[52]13.6019,[53]13.8284,[54]14.1067,[55]14.3488,[56]14.4667,[57]14.4566,[58]14.4479,[59]14.2384,
|
||||
save_imatrix: stored collected data after 60 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[60]14.0794,[61]13.9048,[62]13.7419,[63]13.8516,[64]13.9902,[65]13.8558,[66]13.8803,[67]13.8491,[68]13.7963,[69]13.7287,
|
||||
save_imatrix: stored collected data after 70 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[70]13.6754,[71]13.6579,[72]13.6290,[73]13.6891,[74]13.6254,[75]13.5058,[76]13.4907,[77]13.5002,[78]13.4537,[79]13.3696,
|
||||
save_imatrix: stored collected data after 80 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[80]13.4245,[81]13.4999,[82]13.5207,[83]13.6156,[84]13.6529,[85]13.4393,[86]13.3795,[87]13.2302,[88]13.2605,[89]13.2322,
|
||||
save_imatrix: stored collected data after 90 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[90]13.2980,[91]13.2455,[92]13.1607,[93]13.0864,[94]12.9782,[95]12.9127,[96]12.8267,[97]12.7595,[98]12.6714,[99]12.7111,
|
||||
save_imatrix: stored collected data after 100 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[100]12.7194,[101]12.8597,[102]12.9495,[103]13.0106,[104]13.1816,[105]13.3131,[106]13.3254,[107]13.3336,[108]13.2762,[109]13.3038,
|
||||
save_imatrix: stored collected data after 110 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[110]13.1821,[111]13.0499,[112]12.8931,[113]12.9668,[114]13.0116,[115]12.9995,[116]12.9720,[117]13.0261,[118]13.0587,[119]13.0765,
|
||||
save_imatrix: stored collected data after 120 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
[120]13.0687,[121]13.0658,[122]13.0268,[123]13.0554,[124]13.1495,[125]13.2406,[126]13.3502,[127]13.3960,[128]13.4523,
|
||||
save_imatrix: stored collected data after 128 chunks in gemma-2-9b-it-IMat-GGUF/imatrix.dat
|
||||
|
||||
llama_print_timings: load time = 1979.52 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 = 150211.73 ms / 65536 tokens ( 2.29 ms per token, 436.29 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 = 152551.51 ms / 65537 tokens
|
||||
|
||||
Final estimate: PPL = 13.4523 +/- 0.26031
|
||||
Reference in New Issue
Block a user