Locutusque
/

TinyMistral-248M-v2

@@ -1,11 +1,11 @@
 ---
-license: apache-2.0
 language:
 - en
-pipeline_tag: text-generation
 datasets:
 - Skylion007/openwebtext
 - Locutusque/TM-DATA
 inference:
   parameters:
     do_sample: true
@@ -15,22 +15,130 @@ inference:
     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
 ---
 # Training
 This model was trained on two datasets, shown in this model page.
@@ -41,4 +149,17 @@ Training took approximately 500 GPU hours on a single Titan V.
 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. 🔥

 ---
 language:
 - en
+license: apache-2.0
 datasets:
 - Skylion007/openwebtext
 - Locutusque/TM-DATA
+pipeline_tag: text-generation
 inference:
   parameters:
     do_sample: true
     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.
 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|