Tiny Random Ltx Video Alternatives

This is a minimal random-weight language model published under the optimum-intel-internal-testing organization on Hugging Face. It includes a detailed chat template in Jinja format and is designed for testing integration with libraries such as Transformers and Optimum Intel. The model is not intended for production use.

This is a tiny random-weight model intended for testing the LLaVA (Large Language and Vision Assistant) architecture within the Optimum Intel ecosystem. It is not intended for production use but serves as a reference for integration and pipeline testing with the Hugging Face Transformers library.

tiny-random-llava-next is a minimal randomly initialized model for testing the LLaVA-NeXT multimodal vision-language architecture. Created by Optimum Intel for internal testing, it implements the chat template and image processing pipeline used by larger LLaVA models. It is not intended for real inference but for pipeline validation and integration testing.

This is a minimal randomly-initialized T5 model created for internal testing of the Optimum Intel library. It is not trained and serves only as a fixture for CI pipelines, compatibility tests, and benchmarking of Intel hardware acceleration features for transformer models.

This is a minimal test model published under optimum-intel-internal-testing for validating Latent Consistency Model (LCM) pipelines. It is designed for developers integrating with the Diffusers library to test text-to-image generation workflows. The model uses Apache 2.0 licensing and provides example code for loading via DiffusionPipeline.

This is a tiny randomly initialized CLIP model created for internal testing by the Optimum Intel team. It is not intended for production use but serves as a minimal reproducible example for testing CLIP model loading, inference pipelines, and integration with optimization tools. The model follows the standard CLIP architecture at a very small scale.

This is a tiny random-weight model designed for testing the Llava-Next multimodal architecture combined with a Mistral backbone. Created for internal testing by the Optimum Intel team, it includes image and text processing capabilities in a minimal package suitable for CI pipelines and compatibility testing. The model was published on 2025-10-21.

tiny-random-vit is a tiny randomly-initialized Vision Transformer model published for testing purposes. It supports image classification and is compatible with the Hugging Face Transformers library across multiple backends including PyTorch, TensorFlow, and ONNX. The model is used by developers to validate Optimum Intel optimizations and inference pipelines without requiring large pretrained weights.

Tiny Random Stable Diffusion XL is a test model published on Hugging Face under the optimum-intel-internal-testing organization. It implements the StableDiffusionXLPipeline for text-to-image generation and serves as a minimal random-weight stand-in for developers conducting integration tests. The model is provided with a code example that loads it through the Diffusers library. After installing diffusers, transformers, and accelerate via pip, users instantiate the pipeline with torch_dtype set to bfloat16 and device_map set to cuda, then generate an image from a text prompt such as "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k". The same pipeline can be switched to the mps device for Apple hardware. It is classified as a text-to-image model and is compatible with the Diffusers ecosystem. The model carries an apache-2.0 license. No pricing information is associated with it because it is distributed as a free open-source artifact on the Hugging Face hub. Its sole documented purpose is internal testing and demonstration of pipeline loading rather than production image generation.

This is a tiny random-weight model based on the GLM-4 architecture, published for internal testing by the Optimum Intel team. It includes support for tool calling, system prompts, and a complex chat template. The model is used to validate export, quantization, and inference pipelines.

A minimal random-weight model based on the WavLM architecture for testing Optimum-Intel inference and optimization pipelines. It includes PyTorch model files and configuration for speech-related tasks. The model is not intended for production use but serves as a lightweight test asset.

This is a deliberately small, randomly initialized version of the Mistral architecture created for integration and performance testing by the Optimum Intel team. It is not intended for real inference but serves as a lightweight stand-in for pipeline validation on Hugging Face.

This is a minimal random test model used internally by the Optimum-Intel team for validating quantization, inference, and integration pipelines on Hugging Face. It includes chat templates and configuration for testing various LLM features without requiring full-scale compute. Intended for developers working on model optimization and deployment tooling.

This is a tiny randomly initialized OPT model created for internal testing of the Optimum Intel library. It is not intended for production use but serves as a lightweight test artifact for validating model loading, inference, and optimization pipelines. The model includes standard tokenizer configuration and is compatible with the Hugging Face ecosystem.

tiny-random-qwen3-vl is a minimal test model published under the optimum-intel-internal-testing organization. It is designed for developers to validate integration, tokenization, and inference pipelines for multimodal vision-language models without downloading large production weights. The model includes a chat template supporting image and video inputs.

This is a tiny randomly initialized RoFormer model created by Optimum Intel for internal testing of model optimization and export pipelines. It is not trained and serves only as a structural test case. The model is available on Hugging Face for developers working with the Optimum library.

This is a minimal, randomly initialized DistilBERT model created for internal testing by the Optimum Intel team. It is used to validate model optimization, quantization, and inference pipelines targeting Intel hardware. The model is not intended for production use but serves as a lightweight fixture for CI/CD and development workflows.

This is a tiny randomly initialized model based on Qwen2.5-VL used for internal testing of the Optimum Intel library. It is designed to validate quantization, optimization, and inference pipelines for vision-language models without using full-size weights. The model is hosted on Hugging Face for development and CI purposes.

Stable Diffusion 3 Tiny Random is a minimal test model hosted on Hugging Face under the optimum-intel-internal-testing organization. It follows the Stable Diffusion 3 architecture and is provided specifically for internal testing of Optimum Intel optimization and inference pipelines rather than for generating production images. The model is distributed in Safetensors format and implements the StableDiffusion3Pipeline. It can be loaded through the Diffusers library with a few lines of Python code that specify bfloat16 precision and a CUDA device map. Example code demonstrates text-to-image inference using a descriptive prompt such as an astronaut in a jungle rendered with a cold color palette. It is intended for developers working on Optimum Intel who require a tiny random-weight model to validate pipelines without incurring the computational cost of full-scale Stable Diffusion 3 weights. The repository lists compatibility with notebooks on Google Colab and Kaggle as well as certain local applications, though these are presented only as general options for Diffusers-based models. No licensing details, pricing information, or version history appear on the page. The model card contains no further description of training procedures, intended use cases beyond testing, or performance metrics.

Tiny Random Qwen2vl is a minimal test model that follows the Qwen2-VL architecture and tokenizer format. It was created by optimum-intel-internal-testing for internal validation of Qwen2-VL support inside the Optimum Intel library. The model contains random weights and is intended solely for testing integration, export, and optimization pipelines rather than any form of inference or production deployment. Its tokenizer configuration includes a chat template that processes messages with support for image and video content markers such as vision_start, image_pad, vision_end, video_pad along with role-based formatting using im_start and im_end tokens. The template tracks image and video counts during formatting and can optionally prepend labels like "Picture" or "Video". A system prompt example appears in the configuration as "You are a helpful assistant." The model is hosted on the Hugging Face platform under the repository name optimum-intel-internal-testing/tiny-random-qwen2vl. It belongs to the class of foundation models used strictly as a placeholder for library and pipeline testing. No production capabilities, performance metrics, or training details are associated with it.

This is a minimal random-weight model based on the CodeGen architecture for causal language modeling. It is published under the optimum-intel-internal-testing organization on Hugging Face and is intended for integration testing of the Optimum Intel library. Developers use it to verify compatibility, inference pipelines, and optimization features without downloading full-scale models.

This repository contains GGUF quantized weights for the LTX 2.3 video generation model. It is designed for local inference using diffuser-based GGUF loading logic and requires approximately 5GB VRAM for 81-frame generation. Note that it is not compatible with standard ComfyUI or certain other GGUF nodes.

This is a minimal random-weight model based on the OLMo2 architecture, hosted on Hugging Face. It is intended for internal testing of the Optimum-Intel library, which optimizes transformer models for Intel hardware. The repository includes configuration files, tokenizers, and a chat template for compatibility with standard inference pipelines.

This is a tiny random-weight model based on the GPTBigCode architecture, created by the Optimum-Intel internal testing team. It is used exclusively for testing and validating model conversion, quantization, and inference pipelines. The model is not intended for real-world usage but serves as a lightweight fixture for CI/CD and library compatibility checks.

This is a minimal random-weight model based on the BLOOM architecture. It is published under the optimum-intel-internal-testing organization on Hugging Face and is intended for integration testing of the Optimum Intel library. Developers use it to verify compatibility, inference pipelines, and optimization features without downloading full-scale models.

This is a minimal random-weight model based on the Arcee architecture for causal language modeling. It is published under the optimum-intel-internal-testing organization on Hugging Face and is intended for integration testing of the Optimum Intel library. Developers use it to verify compatibility, inference pipelines, and optimization features without downloading full-scale models.

This is a minimal random-weight BERT model published under the optimum-intel-internal-testing organization. It is intended for integration and performance testing of the Optimum Intel toolkit with Hugging Face pipelines and ONNX/TensorFlow runtimes. The model contains only 127k parameters and is provided in Safetensors format for safe and efficient loading during development workflows.

This is a very small randomly initialized GPT-J model created for internal testing purposes by the Optimum Intel team. It is not intended for real inference but serves as a minimal reproducible example for testing model conversion, optimization, and integration pipelines. The model is hosted on Hugging Face.

tiny-random-GPTNeoXForCausalLM is a tiny randomly initialized model based on the GPT-NeoX architecture. It is used for internal testing of Optimum Intel and supports both PyTorch and ONNX formats. The model serves as a minimal example for validating model loading, inference, and export workflows.

This is a minimal random-weight ALBERT model published under the optimum-intel-internal-testing organization. It is intended for testing the Optimum Intel library's quantization, optimization, and inference capabilities on Hugging Face. The model includes standard tokenizer configuration and is openly available for download and experimentation by machine learning practitioners.

This is a tiny random BioGptForCausalLM model intended for internal testing of Optimum Intel. It serves as a placeholder for validating pipelines involving causal language modeling in the biomedical domain. Hosted on Hugging Face, it is designed for developer testing rather than production use.

pix2struct-tiny-random is a small, randomly initialized test model based on the Pix2Struct architecture for image-to-text generation. Hosted on Hugging Face under optimum-intel-internal-testing, it is intended for pipeline validation, integration testing, and development rather than production use. It uses the PyTorch framework and has a permissive MIT license.

LTX-2.3-GGUF provides quantized GGUF weights for the LTX-Video (Lightricks) image-to-video and text-to-video diffusion model. It integrates with the Diffusers library and supports local inference on GPUs. The model can generate short video clips from images or text prompts.