---
license: apache-2.0
language: en
tags:
- gemma
- gemma-3
- fine-tuned
- elderly-care
- conversational-ai
- text-generation
- merged-lora
- TensorBlock
- GGUF
pipeline_tag: text2text-generation
base_model: Heshamproduct/gemma-3-12b-elderly-care-merged-v11
---

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## Heshamproduct/gemma-3-12b-elderly-care-merged-v11 - GGUF

This repo contains GGUF format model files for [Heshamproduct/gemma-3-12b-elderly-care-merged-v11](https://huggingface.co/Heshamproduct/gemma-3-12b-elderly-care-merged-v11).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).

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## Prompt template

```
<bos><start_of_turn>user
{system_prompt}

{prompt}<end_of_turn>
<start_of_turn>model
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [gemma-3-12b-elderly-care-merged-v11-Q2_K.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q2_K.gguf) | Q2_K | 4.768 GB | smallest, significant quality loss - not recommended for most purposes |
| [gemma-3-12b-elderly-care-merged-v11-Q3_K_S.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q3_K_S.gguf) | Q3_K_S | 5.458 GB | very small, high quality loss |
| [gemma-3-12b-elderly-care-merged-v11-Q3_K_M.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q3_K_M.gguf) | Q3_K_M | 6.009 GB | very small, high quality loss |
| [gemma-3-12b-elderly-care-merged-v11-Q3_K_L.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q3_K_L.gguf) | Q3_K_L | 6.480 GB | small, substantial quality loss |
| [gemma-3-12b-elderly-care-merged-v11-Q4_0.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q4_0.gguf) | Q4_0 | 6.887 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gemma-3-12b-elderly-care-merged-v11-Q4_K_S.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q4_K_S.gguf) | Q4_K_S | 6.935 GB | small, greater quality loss |
| [gemma-3-12b-elderly-care-merged-v11-Q4_K_M.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q4_K_M.gguf) | Q4_K_M | 7.301 GB | medium, balanced quality - recommended |
| [gemma-3-12b-elderly-care-merged-v11-Q5_0.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q5_0.gguf) | Q5_0 | 8.232 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gemma-3-12b-elderly-care-merged-v11-Q5_K_S.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q5_K_S.gguf) | Q5_K_S | 8.232 GB | large, low quality loss - recommended |
| [gemma-3-12b-elderly-care-merged-v11-Q5_K_M.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q5_K_M.gguf) | Q5_K_M | 8.445 GB | large, very low quality loss - recommended |
| [gemma-3-12b-elderly-care-merged-v11-Q6_K.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q6_K.gguf) | Q6_K | 9.661 GB | very large, extremely low quality loss |
| [gemma-3-12b-elderly-care-merged-v11-Q8_0.gguf](https://huggingface.co/tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF/blob/main/gemma-3-12b-elderly-care-merged-v11-Q8_0.gguf) | Q8_0 | 12.510 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF --include "gemma-3-12b-elderly-care-merged-v11-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/Heshamproduct_gemma-3-12b-elderly-care-merged-v11-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```