---
license: apache-2.0
inference: true
widget:
- text: '<s>[INST] <<SYS>>

    Given an image description, generate one or two multiple-choice questions that
    verifies if the image description is correct.

    Classify each concept into a type (object, human, animal, food, activity, attribute,
    counting, color, material, spatial, location, shape, other), and then generate
    a question for each type.


    <</SYS>>


    Description: a blue rabbit and a red plane [/INST] Entities:'
pipeline_tag: text-generation
tags:
- text-generation-inference
- llama2
- text-to-image
- TensorBlock
- GGUF
datasets:
- TIFA
language:
- en
base_model: tifa-benchmark/llama2_tifa_question_generation
---

<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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<div style="display: flex; justify-content: space-between; width: 100%;">
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## tifa-benchmark/llama2_tifa_question_generation - GGUF

This repo contains GGUF format model files for [tifa-benchmark/llama2_tifa_question_generation](https://huggingface.co/tifa-benchmark/llama2_tifa_question_generation).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).


<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template


```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [llama2_tifa_question_generation-Q2_K.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q2_K.gguf) | Q2_K | 2.359 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama2_tifa_question_generation-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q3_K_S.gguf) | Q3_K_S | 2.746 GB | very small, high quality loss |
| [llama2_tifa_question_generation-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q3_K_M.gguf) | Q3_K_M | 3.072 GB | very small, high quality loss |
| [llama2_tifa_question_generation-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q3_K_L.gguf) | Q3_K_L | 3.350 GB | small, substantial quality loss |
| [llama2_tifa_question_generation-Q4_0.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q4_0.gguf) | Q4_0 | 3.563 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama2_tifa_question_generation-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q4_K_S.gguf) | Q4_K_S | 3.592 GB | small, greater quality loss |
| [llama2_tifa_question_generation-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q4_K_M.gguf) | Q4_K_M | 3.801 GB | medium, balanced quality - recommended |
| [llama2_tifa_question_generation-Q5_0.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q5_0.gguf) | Q5_0 | 4.332 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama2_tifa_question_generation-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q5_K_S.gguf) | Q5_K_S | 4.332 GB | large, low quality loss - recommended |
| [llama2_tifa_question_generation-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q5_K_M.gguf) | Q5_K_M | 4.455 GB | large, very low quality loss - recommended |
| [llama2_tifa_question_generation-Q6_K.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q6_K.gguf) | Q6_K | 5.149 GB | very large, extremely low quality loss |
| [llama2_tifa_question_generation-Q8_0.gguf](https://huggingface.co/tensorblock/llama2_tifa_question_generation-GGUF/blob/main/llama2_tifa_question_generation-Q8_0.gguf) | Q8_0 | 6.669 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/llama2_tifa_question_generation-GGUF --include "llama2_tifa_question_generation-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/llama2_tifa_question_generation-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```