---
base_model: google/datagemma-rag-27b-it
extra_gated_button_content: Acknowledge license
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: "To access Gemma on Hugging Face, you\u2019re required to review\
  \ and agree to Google\u2019s usage license. To do this, please ensure you\u2019\
  re logged in to Hugging Face and click below. Requests are processed immediately."
inference: false
library_name: gguf
license: gemma
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- conversational
- quantized
- GGUF
- quantization
- imat
- imatrix
- static
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---

# datagemma-rag-27b-it-IMat-GGUF
_Llama.cpp imatrix quantization of google/datagemma-rag-27b-it_

Original Model: [google/datagemma-rag-27b-it](https://huggingface.co/google/datagemma-rag-27b-it)    
Original dtype: `BF16` (`bfloat16`)  
Quantized by:  llama.cpp [b3735](https://github.com/ggerganov/llama.cpp/releases/tag/b3735)  
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/datagemma-rag-27b-it-IMat-GGUF/blob/main/imatrix.dat)

### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [datagemma-rag-27b-it.Q8_0.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q8_0.gguf) | Q8_0 | 28.94GB | ✅ Available | ⚪ Static | 📦 No
| [datagemma-rag-27b-it.Q6_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q6_K.gguf) | Q6_K | 22.34GB | ✅ Available | ⚪ Static | 📦 No
| [datagemma-rag-27b-it.Q4_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q4_K.gguf) | Q4_K | 16.65GB | ✅ Available | 🟢 IMatrix | 📦 No
| [datagemma-rag-27b-it.Q3_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q3_K.gguf) | Q3_K | 13.42GB | ✅ Available | 🟢 IMatrix | 📦 No
| [datagemma-rag-27b-it.Q2_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q2_K.gguf) | Q2_K | 10.45GB | ✅ Available | 🟢 IMatrix | 📦 No


### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [datagemma-rag-27b-it.Q8_0.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q8_0.gguf) | Q8_0 | 28.94GB | ✅ Available | ⚪ Static | 📦 No
| [datagemma-rag-27b-it.Q6_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q6_K.gguf) | Q6_K | 22.34GB | ✅ Available | ⚪ Static | 📦 No
| [datagemma-rag-27b-it.Q5_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q5_K.gguf) | Q5_K | 19.41GB | ✅ Available | ⚪ Static | 📦 No
| [datagemma-rag-27b-it.Q5_K_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q5_K_S.gguf) | Q5_K_S | 18.88GB | ✅ Available | ⚪ Static | 📦 No
| [datagemma-rag-27b-it.Q4_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q4_K.gguf) | Q4_K | 16.65GB | ✅ Available | 🟢 IMatrix | 📦 No
| [datagemma-rag-27b-it.Q4_K_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q4_K_S.gguf) | Q4_K_S | 15.74GB | ✅ Available | 🟢 IMatrix | 📦 No
| datagemma-rag-27b-it.IQ4_NL | IQ4_NL | - | ⏳ Processing | 🟢 IMatrix | -
| datagemma-rag-27b-it.IQ4_XS | IQ4_XS | - | ⏳ Processing | 🟢 IMatrix | -
| [datagemma-rag-27b-it.Q3_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q3_K.gguf) | Q3_K | 13.42GB | ✅ Available | 🟢 IMatrix | 📦 No
| [datagemma-rag-27b-it.Q3_K_L.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q3_K_L.gguf) | Q3_K_L | 14.52GB | ✅ Available | 🟢 IMatrix | 📦 No
| [datagemma-rag-27b-it.Q3_K_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q3_K_S.gguf) | Q3_K_S | 12.17GB | ✅ Available | 🟢 IMatrix | 📦 No
| datagemma-rag-27b-it.IQ3_M | IQ3_M | - | ⏳ Processing | 🟢 IMatrix | -
| datagemma-rag-27b-it.IQ3_S | IQ3_S | - | ⏳ Processing | 🟢 IMatrix | -
| datagemma-rag-27b-it.IQ3_XS | IQ3_XS | - | ⏳ Processing | 🟢 IMatrix | -
| datagemma-rag-27b-it.IQ3_XXS | IQ3_XXS | - | ⏳ Processing | 🟢 IMatrix | -
| [datagemma-rag-27b-it.Q2_K.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q2_K.gguf) | Q2_K | 10.45GB | ✅ Available | 🟢 IMatrix | 📦 No
| [datagemma-rag-27b-it.Q2_K_S.gguf](https://huggingface.co/legraphista/datagemma-rag-27b-it-IMat-GGUF/blob/main/datagemma-rag-27b-it.Q2_K_S.gguf) | Q2_K_S | 9.72GB | ✅ Available | 🟢 IMatrix | 📦 No
| datagemma-rag-27b-it.IQ2_M | IQ2_M | - | ⏳ Processing | 🟢 IMatrix | -
| datagemma-rag-27b-it.IQ2_S | IQ2_S | - | ⏳ Processing | 🟢 IMatrix | -
| datagemma-rag-27b-it.IQ2_XS | IQ2_XS | - | ⏳ Processing | 🟢 IMatrix | -
| datagemma-rag-27b-it.IQ2_XXS | IQ2_XXS | - | ⏳ Processing | 🟢 IMatrix | -
| datagemma-rag-27b-it.IQ1_M | IQ1_M | - | ⏳ Processing | 🟢 IMatrix | -
| datagemma-rag-27b-it.IQ1_S | IQ1_S | - | ⏳ Processing | 🟢 IMatrix | -


## 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/datagemma-rag-27b-it-IMat-GGUF --include "datagemma-rag-27b-it.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/datagemma-rag-27b-it-IMat-GGUF --include "datagemma-rag-27b-it.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 datagemma-rag-27b-it.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: `datagemma-rag-27b-it.Q8_0`)
3. Run `gguf-split --merge datagemma-rag-27b-it.Q8_0/datagemma-rag-27b-it.Q8_0-00001-of-XXXXX.gguf datagemma-rag-27b-it.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)!