README.md

---
license: apache-2.0
datasets:
- Locutusque/hercules-v1.0
language:
- en
base_model: M4-ai/TinyMistral-6x248M-Instruct
inference:
  parameters:
    do_sample: true
    temperature: 0.2
    top_p: 0.14
    top_k: 12
    max_new_tokens: 250
    repetition_penalty: 1.1
widget:
- text: '<|im_start|>user

    Write me a Python program that calculates the factorial of n. <|im_end|>

    <|im_start|>assistant

    '
- text: An emerging clinical approach to treat substance abuse disorders involves
    a form of cognitive-behavioral therapy whereby addicts learn to reduce their reactivity
    to drug-paired stimuli through cue-exposure or extinction training. It is, however,
- text: '<|im_start|>user

    How do I say hello in Spanish? <|im_end|>

    <|im_start|>assistant

    '
tags:
- moe
- TensorBlock
- GGUF
---

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## M4-ai/TinyMistral-6x248M-Instruct - GGUF

This repo contains GGUF format model files for [M4-ai/TinyMistral-6x248M-Instruct](https://huggingface.co/M4-ai/TinyMistral-6x248M-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).

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## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [TinyMistral-6x248M-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q2_K.gguf) | Q2_K | 0.379 GB | smallest, significant quality loss - not recommended for most purposes |
| [TinyMistral-6x248M-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q3_K_S.gguf) | Q3_K_S | 0.445 GB | very small, high quality loss |
| [TinyMistral-6x248M-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q3_K_M.gguf) | Q3_K_M | 0.487 GB | very small, high quality loss |
| [TinyMistral-6x248M-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q3_K_L.gguf) | Q3_K_L | 0.527 GB | small, substantial quality loss |
| [TinyMistral-6x248M-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q4_0.gguf) | Q4_0 | 0.574 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [TinyMistral-6x248M-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q4_K_S.gguf) | Q4_K_S | 0.577 GB | small, greater quality loss |
| [TinyMistral-6x248M-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q4_K_M.gguf) | Q4_K_M | 0.613 GB | medium, balanced quality - recommended |
| [TinyMistral-6x248M-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q5_0.gguf) | Q5_0 | 0.695 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [TinyMistral-6x248M-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q5_K_S.gguf) | Q5_K_S | 0.695 GB | large, low quality loss - recommended |
| [TinyMistral-6x248M-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q5_K_M.gguf) | Q5_K_M | 0.715 GB | large, very low quality loss - recommended |
| [TinyMistral-6x248M-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q6_K.gguf) | Q6_K | 0.824 GB | very large, extremely low quality loss |
| [TinyMistral-6x248M-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/TinyMistral-6x248M-Instruct-GGUF/blob/main/TinyMistral-6x248M-Instruct-Q8_0.gguf) | Q8_0 | 1.067 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/TinyMistral-6x248M-Instruct-GGUF --include "TinyMistral-6x248M-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/TinyMistral-6x248M-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```