README.md

---
language:
- en
license: apache-2.0
datasets:
- Skylion007/openwebtext
- Locutusque/TM-DATA
pipeline_tag: text-generation
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]{norbert.hounkonnou@cipma.uac.bj} \author{Sama Arjika\corref{cor2}${}^1$}
    \cortext[cor2]{rjksama2008@gmail.com} \author{ Won Sang Chung\corref{cor3}${}^2$
    } \cortext[cor3]{mimip4444@hanmail.net} \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'
model-index:
- name: TinyMistral-248M-v2
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 21.25
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/TinyMistral-248M-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 26.56
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/TinyMistral-248M-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 23.39
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/TinyMistral-248M-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 49.6
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/TinyMistral-248M-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 51.85
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/TinyMistral-248M-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 0.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/TinyMistral-248M-v2
      name: Open LLM Leaderboard
---
# Training
This model was trained on two datasets, shown in this model page.
- Skylion007/openwebtext: 1,000,000 examples at a batch size of 32-4096 (1 epoch)
- Locutusque/TM-DATA: All examples at a batch size of 12288 (3 epochs)
Training took approximately 500 GPU hours on a single Titan V.
# Metrics
You can look at the training metrics here:
https://wandb.ai/locutusque/TinyMistral-V2/runs/g0rvw6wc

🔥 This model performed excellently on TruthfulQA, outperforming models more than 720x its size. These models include: mistralai/Mixtral-8x7B-v0.1, tiiuae/falcon-180B, berkeley-nest/Starling-LM-7B-alpha, upstage/SOLAR-10.7B-v1.0, and more. 🔥 
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__TinyMistral-248M-v2)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |28.78|
|AI2 Reasoning Challenge (25-Shot)|21.25|
|HellaSwag (10-Shot)              |26.56|
|MMLU (5-Shot)                    |23.39|
|TruthfulQA (0-shot)              |49.60|
|Winogrande (5-shot)              |51.85|
|GSM8k (5-shot)                   | 0.00|