Text Generation
GGUF
quantized
GGUF
quantization
imat
imatrix
static
16bit
8bit
6bit
5bit
4bit
3bit
2bit
1bit
conversational
Instructions to use legraphista/gemma-2-27b-it-IMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use legraphista/gemma-2-27b-it-IMat-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="legraphista/gemma-2-27b-it-IMat-GGUF", filename="gemma-2-27b-it.BF16/gemma-2-27b-it.BF16-00001-of-00003.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use legraphista/gemma-2-27b-it-IMat-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S
Use Docker
docker model run hf.co/legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use legraphista/gemma-2-27b-it-IMat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "legraphista/gemma-2-27b-it-IMat-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "legraphista/gemma-2-27b-it-IMat-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S
- Ollama
How to use legraphista/gemma-2-27b-it-IMat-GGUF with Ollama:
ollama run hf.co/legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S
- Unsloth Studio
How to use legraphista/gemma-2-27b-it-IMat-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for legraphista/gemma-2-27b-it-IMat-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for legraphista/gemma-2-27b-it-IMat-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for legraphista/gemma-2-27b-it-IMat-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use legraphista/gemma-2-27b-it-IMat-GGUF with Docker Model Runner:
docker model run hf.co/legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S
- Lemonade
How to use legraphista/gemma-2-27b-it-IMat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull legraphista/gemma-2-27b-it-IMat-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.gemma-2-27b-it-IMat-GGUF-Q4_K_S
List all available models
lemonade list
File size: 8,668 Bytes
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base_model: google/gemma-2-27b-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:
- quantized
- GGUF
- quantization
- imat
- imatrix
- static
- 16bit
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---
# gemma-2-27b-it-IMat-GGUF
_Llama.cpp imatrix quantization of google/gemma-2-27b-it_
Original Model: [google/gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it)
Original dtype: `BF16` (`bfloat16`)
Quantized by: [https://github.com/ggerganov/llama.cpp/pull/8156](https://github.com/ggerganov/llama.cpp/pull/8156)
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-2-27b-it-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [gemma-2-27b-it.Q8_0.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q8_0.gguf) | Q8_0 | 28.94GB | ✅ Available | ⚪ Static | 📦 No
| [gemma-2-27b-it.Q6_K.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q6_K.gguf) | Q6_K | 22.34GB | ✅ Available | ⚪ Static | 📦 No
| [gemma-2-27b-it.Q4_K.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q4_K.gguf) | Q4_K | 16.65GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.Q3_K.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q3_K.gguf) | Q3_K | 13.42GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.Q2_K.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q2_K.gguf) | Q2_K | 10.45GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [gemma-2-27b-it.BF16/*](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/tree/main/gemma-2-27b-it.BF16) | BF16 | 54.46GB | ✅ Available | ⚪ Static | ✂ Yes
| [gemma-2-27b-it.FP16/*](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/tree/main/gemma-2-27b-it.FP16) | F16 | 54.46GB | ✅ Available | ⚪ Static | ✂ Yes
| [gemma-2-27b-it.Q8_0.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q8_0.gguf) | Q8_0 | 28.94GB | ✅ Available | ⚪ Static | 📦 No
| [gemma-2-27b-it.Q6_K.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q6_K.gguf) | Q6_K | 22.34GB | ✅ Available | ⚪ Static | 📦 No
| [gemma-2-27b-it.Q5_K.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q5_K.gguf) | Q5_K | 19.41GB | ✅ Available | ⚪ Static | 📦 No
| [gemma-2-27b-it.Q5_K_S.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q5_K_S.gguf) | Q5_K_S | 18.88GB | ✅ Available | ⚪ Static | 📦 No
| [gemma-2-27b-it.Q4_K.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q4_K.gguf) | Q4_K | 16.65GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.Q4_K_S.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q4_K_S.gguf) | Q4_K_S | 15.74GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.IQ4_NL.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.IQ4_NL.gguf) | IQ4_NL | 15.63GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.IQ4_XS.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.IQ4_XS.gguf) | IQ4_XS | 14.81GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.Q3_K.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q3_K.gguf) | Q3_K | 13.42GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.Q3_K_L.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q3_K_L.gguf) | Q3_K_L | 14.52GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.Q3_K_S.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q3_K_S.gguf) | Q3_K_S | 12.17GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.IQ3_M.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.IQ3_M.gguf) | IQ3_M | 12.45GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.IQ3_S.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.IQ3_S.gguf) | IQ3_S | 12.17GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.IQ3_XS.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.IQ3_XS.gguf) | IQ3_XS | 11.55GB | ✅ Available | 🟢 IMatrix | 📦 No
| gemma-2-27b-it.IQ3_XXS | IQ3_XXS | - | ⏳ Processing | 🟢 IMatrix | -
| [gemma-2-27b-it.Q2_K.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q2_K.gguf) | Q2_K | 10.45GB | ✅ Available | 🟢 IMatrix | 📦 No
| [gemma-2-27b-it.Q2_K_S.gguf](https://huggingface.co/legraphista/gemma-2-27b-it-IMat-GGUF/blob/main/gemma-2-27b-it.Q2_K_S.gguf) | Q2_K_S | 9.72GB | ✅ Available | 🟢 IMatrix | 📦 No
| gemma-2-27b-it.IQ2_M | IQ2_M | - | ⏳ Processing | 🟢 IMatrix | -
| gemma-2-27b-it.IQ2_S | IQ2_S | - | ⏳ Processing | 🟢 IMatrix | -
| gemma-2-27b-it.IQ2_XS | IQ2_XS | - | ⏳ Processing | 🟢 IMatrix | -
| gemma-2-27b-it.IQ2_XXS | IQ2_XXS | - | ⏳ Processing | 🟢 IMatrix | -
| gemma-2-27b-it.IQ1_M | IQ1_M | - | ⏳ Processing | 🟢 IMatrix | -
| gemma-2-27b-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/gemma-2-27b-it-IMat-GGUF --include "gemma-2-27b-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/gemma-2-27b-it-IMat-GGUF --include "gemma-2-27b-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 gemma-2-27b-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: `gemma-2-27b-it.Q8_0`)
3. Run `gguf-split --merge gemma-2-27b-it.Q8_0/gemma-2-27b-it.Q8_0-00001-of-XXXXX.gguf gemma-2-27b-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)! |