AI ExplorerAI Explorer
OutilsCatégoriesSitesLLMsComparerQuiz IAAlternativesPremium

—

Outils IA

—

Sites & Blogs

—

LLMs & Modèles

—

Catégories

AI Explorer

Trouvez et comparez les meilleurs outils d'intelligence artificielle pour vos projets.

Fait avecen France

Explorer

  • Tous les outils
  • Sites & Blogs
  • LLMs & Modèles
  • Comparer
  • Chatbots
  • Images IA
  • Code & Dev

Entreprise

  • Premium
  • À propos
  • Contact
  • Blog

Légal

  • Mentions légales
  • Confidentialité
  • CGV

© 2026 AI Explorer. Tous droits réservés.

AccueilLLMstiny random LlamaForCausalLM

tiny random LlamaForCausalLM

par trl-internal-testing

Open source · 397k downloads · 8 likes

1.2
(8 avis)ChatAPI & Local
À propos

Le modèle *tiny random LlamaForCausalLM* est une version allégée et aléatoire du célèbre modèle Llama, spécialement conçue pour générer du texte de manière causale. Bien que ses performances ne soient pas optimisées pour des tâches précises, il peut produire des réponses variées et créatives à partir de prompts simples. Ses principaux cas d'usage incluent la génération de contenu, l'expérimentation rapide ou l'apprentissage des mécanismes des modèles de langage. Ce qui le distingue, c'est sa légèreté et sa simplicité, idéales pour des tests ou des démonstrations sans nécessiter de ressources importantes.

Documentation

Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

  • Developed by: [More Information Needed]
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Model type: [More Information Needed]
  • Language(s) (NLP): [More Information Needed]
  • License: [More Information Needed]
  • Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: [More Information Needed]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Liens & Ressources
Spécifications
CatégorieChat
AccèsAPI & Local
LicenceOpen Source
TarificationOpen Source
Note
1.2

Essayer tiny random LlamaForCausalLM

Accédez directement au modèle