README.md

metadata

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

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

Locutusque/TinyMistral-248M-v2 - GGUF

This repo contains GGUF format model files for Locutusque/TinyMistral-248M-v2.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Run them on the TensorBlock client using your local machine ↗

Prompt template

Model file specification

Filename	Quant type	File Size	Description
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	Q3_K_S	0.112 GB	very small, high quality loss
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	Q3_K_L	0.128 GB	small, substantial quality loss
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	Q4_K_S	0.139 GB	small, greater quality loss
TinyMistral-248M-v2-Q4_K_M.gguf	Q4_K_M	0.145 GB	medium, balanced quality - recommended
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	Q5_K_S	0.164 GB	large, low quality loss - recommended
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	Q6_K	0.190 GB	very large, extremely low quality loss
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

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

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:

huggingface-cli download tensorblock/TinyMistral-248M-v2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'