gguf merge TensorBlock GGUF text-generation en dataset:Locutusque/inst_mix_v2_top_100k base_model:Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct base_model:quantized:Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct license:apache-2.0 endpoints_compatible region:us
---
base_model: Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct
datasets:
- Locutusque/inst_mix_v2_top_100k
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- merge
- TensorBlock
- GGUF
widget:
- text: '<|USER|> Design a Neo4j database and Cypher function snippet to Display Extreme
    Dental hygiene: Using Mouthwash for Analysis for Beginners. Implement if/else
    or switch/case statements to handle different conditions related to the Consent.
    Provide detailed comments explaining your control flow and the reasoning behind
    each decision. <|ASSISTANT|> '
- text: '<|USER|> Write me a story about a magical place. <|ASSISTANT|> '
- text: '<|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> '
- text: '<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> '
- text: '<|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> '
- text: '<|USER|> How to manage a lazy employee: Address the employee verbally. Don''t
    allow an employee''s laziness or lack of enthusiasm to become a recurring issue.
    Tell the employee you''re hoping to speak with them about workplace expectations
    and performance, and schedule a time to sit down together. Question: To manage
    a lazy employee, it is suggested to talk to the employee. True, False, or Neither?
    <|ASSISTANT|> '
inference:
  parameters:
    temperature: 0.5
    do_sample: true
    top_p: 0.5
    top_k: 30
    max_new_tokens: 250
    repetition_penalty: 1.15
---

<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;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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## Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct - GGUF

This repo contains GGUF format model files for [Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct](https://huggingface.co/Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](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 |
| -------- | ---------- | --------- | ----------- |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q2_K.gguf) | Q2_K | 0.105 GB | smallest, significant quality loss - not recommended for most purposes |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q3_K_S.gguf) | Q3_K_S | 0.120 GB | very small, high quality loss |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q3_K_M.gguf) | Q3_K_M | 0.129 GB | very small, high quality loss |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q3_K_L.gguf) | Q3_K_L | 0.137 GB | small, substantial quality loss |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q4_0.gguf) | Q4_0 | 0.149 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q4_K_S.gguf) | Q4_K_S | 0.149 GB | small, greater quality loss |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q4_K_M.gguf) | Q4_K_M | 0.156 GB | medium, balanced quality - recommended |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q5_0.gguf) | Q5_0 | 0.176 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q5_K_S.gguf) | Q5_K_S | 0.176 GB | large, low quality loss - recommended |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q5_K_M.gguf) | Q5_K_M | 0.179 GB | large, very low quality loss - recommended |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q6_K.gguf) | Q6_K | 0.204 GB | very large, extremely low quality loss |
| [LocutusqueXFelladrin-TinyMistral248M-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/blob/main/LocutusqueXFelladrin-TinyMistral248M-Instruct-Q8_0.gguf) | Q8_0 | 0.264 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/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF --include "LocutusqueXFelladrin-TinyMistral248M-Instruct-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/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
