metadata
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
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
Detailed results can be found here
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 |