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TinyMistral-248M-v2-GGUF

Text Generation
GGUF
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TensorBlock
GGUF
Inference Endpoints
Model card Files Community
TinyMistral-248M-v2-GGUF
File size: 6,780 Bytes 
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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
datasets:
- Skylion007/openwebtext
- Locutusque/TM-DATA
inference:
  parameters:
    do_sample: true
    temperature: 0.7
    top_p: 0.2
    top_k: 14
    max_new_tokens: 250
    repetition_penalty: 1.16
widget:
- text: 'TITLE: Dirichlet density QUESTION [5 upvotes]: How to solve the following
    exercise: Let $q$ be prime. Show that the set of primes p for which $p \equiv
    1\pmod q$ and $2^{(p-1)/q} \equiv 1 \pmod p$ has Dirichlet density $\dfrac{1}{q(q-1)}$.
    I want to show that $X^q-2$ (mod $p$) has a solution and $q$ divides $p-1$ , these
    two conditions are simultaneonusly satisfied iff p splits completely in $\Bbb{Q}(\zeta_q,2^{\frac{1}{q}})$.
    $\zeta_q $ is primitive $q^{th}$ root of unity. If this is proved the I can conclude
    the result by Chebotarev density theorem. REPLY [2 votes]:'
- 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: '\begin{document} \begin{frontmatter} \author{Mahouton Norbert Hounkonnou\corref{cor1}${}^1$}
    \cortext[cor1]{[email protected]} \author{Sama Arjika\corref{cor2}${}^1$}
    \cortext[cor2]{[email protected]} \author{ Won Sang Chung\corref{cor3}${}^2$
    } \cortext[cor3]{[email protected]} \title{\bf New families of $q$ and $(q;p)-$Hermite
    polynomials } \address{${}^1$International Chair of Mathematical Physics and Applications
    \\ (ICMPA-UNESCO Chair), University of Abomey-Calavi,\\ 072 B. P.: 50 Cotonou,
    Republic of Benin,\\ ${}^2$Department of Physics and Research Institute of Natural
    Science, \\ College of Natural Science, \\ Gyeongsang National University, Jinju
    660-701, Korea } \begin{abstract} In this paper, we construct a new family of
    $q-$Hermite polynomials denoted by $H_n(x,s|q).$ Main properties and relations
    are established and'
base_model: Locutusque/TinyMistral-248M-v2
tags:
- TensorBlock
- GGUF
---

<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>
        </p>
    </div>
</div>

## Locutusque/TinyMistral-248M-v2 - GGUF

This repo contains GGUF format model files for [Locutusque/TinyMistral-248M-v2](https://huggingface.co/Locutusque/TinyMistral-248M-v2).

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 |
| -------- | ---------- | --------- | ----------- |
| [TinyMistral-248M-v2-Q2_K.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q2_K.gguf) | Q2_K | 0.098 GB | smallest, significant quality loss - not recommended for most purposes |
| [TinyMistral-248M-v2-Q3_K_S.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q3_K_S.gguf) | Q3_K_S | 0.112 GB | very small, high quality loss |
| [TinyMistral-248M-v2-Q3_K_M.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q3_K_M.gguf) | Q3_K_M | 0.120 GB | very small, high quality loss |
| [TinyMistral-248M-v2-Q3_K_L.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q3_K_L.gguf) | Q3_K_L | 0.128 GB | small, substantial quality loss |
| [TinyMistral-248M-v2-Q4_0.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q4_0.gguf) | Q4_0 | 0.139 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [TinyMistral-248M-v2-Q4_K_S.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q4_K_S.gguf) | Q4_K_S | 0.139 GB | small, greater quality loss |
| [TinyMistral-248M-v2-Q4_K_M.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q4_K_M.gguf) | Q4_K_M | 0.145 GB | medium, balanced quality - recommended |
| [TinyMistral-248M-v2-Q5_0.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q5_0.gguf) | Q5_0 | 0.164 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [TinyMistral-248M-v2-Q5_K_S.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q5_K_S.gguf) | Q5_K_S | 0.164 GB | large, low quality loss - recommended |
| [TinyMistral-248M-v2-Q5_K_M.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q5_K_M.gguf) | Q5_K_M | 0.167 GB | large, very low quality loss - recommended |
| [TinyMistral-248M-v2-Q6_K.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q6_K.gguf) | Q6_K | 0.190 GB | very large, extremely low quality loss |
| [TinyMistral-248M-v2-Q8_0.gguf](https://huggingface.co/tensorblock/TinyMistral-248M-v2-GGUF/blob/main/TinyMistral-248M-v2-Q8_0.gguf) | Q8_0 | 0.246 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-248M-v2-GGUF --include "TinyMistral-248M-v2-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-248M-v2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```