Tiny NemotronHForCausalLM Ultra Alternatives

This is a very small test instance of a Nemotron-based causal language model. It is used primarily for internal validation, CI/CD testing, and pipeline verification by the TRL team. While not intended for production use, it demonstrates the model architecture and tokenizer configuration used in the full-scale Nemotron models.

This is a very small test model for the NemotronHForCausalLM architecture, created by the TRL internal testing team. It is used to validate training, inference, and integration code for this specific model family. Like other tiny test models, it is not intended for real-world usage but serves as a development and debugging tool for the open-source machine learning community.

A very small model created by the TRL (Transformers Reinforcement Learning) team for internal testing of remote causal language model functionality. It is not intended for production use but serves as a minimal reproducible example for library and inference engine validation.

This is a minimal 'tiny' model created for internal testing of the TRL library and the CohereForCausalLM implementation. It includes tokenizer configurations and chat templates for testing conversational AI capabilities. It is intended for developers working on reinforcement learning with transformer models.

trl-internal-testing/tiny-Olmo3ForCausalLM is a very small test model based on the OLMo3 architecture. It is used internally by the TRL library for unit testing, validation of training scripts, and pipeline compatibility. The model includes a chat template and tokenizer configuration suitable for function-calling and assistant-style interactions.

This is a very small test model used internally by the Hugging Face TRL (Transformer Reinforcement Learning) team. It implements a minimal LlamaForCausalLM architecture and is intended for unit testing and CI pipelines rather than actual language generation.

trl-internal-testing/tiny-GPTNeoXForCausalLM is a very small model designed for internal testing of the GPT-NeoX architecture within the Hugging Face ecosystem. It implements causal language modeling and is used to validate training and inference pipelines in the TRL and Transformers libraries. The model serves as a minimal reproducible example for developers working on these frameworks.

A very small FalconMamba-based model created by the TRL team for internal testing of training and alignment algorithms. It implements causal language modeling and includes a chat template for conversational use. Intended solely for development, debugging, and CI pipelines rather than production deployment.

tiny-Cohere2ForCausalLM is a very small test model created by the TRL team for internal validation of the Cohere2ForCausalLM architecture. It is published on Hugging Face primarily for library testing and development purposes rather than real-world application use.

tiny-Phi3ForCausalLM is a minimal test model published under the trl-internal-testing organization. It implements a small-scale version of the Phi-3 causal language model architecture for use in library testing, CI pipelines, and development of training or inference code. The model is fully open and compatible with the Hugging Face Transformers ecosystem.

trl-internal-testing/tiny-Gemma2ForCausalLM is a very small test model created for internal testing of the TRL library and Gemma 2 architecture. It implements a minimal causal language model compatible with the Gemma 2 tokenizer and chat template. It is not intended for production use but serves as a fixture for library development and testing.

This is a very small test instance of a LlamaForCausalLM model published under the trl-internal-testing organization. It is used internally for validating components of the TRL (Transformers Reinforcement Learning) library and includes a full chat template configuration. Not intended for production use.

This is a very small test instance of a Phi-3-based causal language model created for internal TRL (Transformers Reinforcement Learning) testing. It includes a chat template and tokenizer configuration suitable for rapid iteration during development of fine-tuning or evaluation code. Not intended for production use.

This is a very small test model based on the MistralForCausalLM architecture, created by the trl-internal-testing organization. It is intended for internal validation and testing of the Hugging Face TRL library rather than for production use. The model is publicly available on Hugging Face for developers working on reinforcement learning or transformer training pipelines.

tiny-BloomForCausalLM is a very small test model created by the TRL team for internal testing of training and inference pipelines. It follows the BLOOM architecture and is intended for text-generation tasks at a minimal scale. The model is publicly available on Hugging Face primarily for developer testing purposes.

This is a very small "tiny" version of a Phi-3 causal language model created by the Hugging Face TRL team for internal testing purposes. It implements the core architecture and tokenizer of Phi-3 but at a drastically reduced scale. It is intended for developers debugging training loops, inference code, or integration with the TRL library rather than for production use.

tiny-Qwen2ForCausalLM-2.5 is a compact open-source causal language model designed for text generation and experimentation. It is suitable for developers and researchers seeking a lightweight model for prototyping or educational purposes. The model can be used via Python, Docker, or API endpoints.

This is a very small randomly initialized MistralForCausalLM model created by the TRL (Transformers Reinforcement Learning) team for internal testing. It is used to validate training scripts, inference pipelines, and integration tests without the overhead of loading full-scale models. Not intended for actual language generation.

This repository contains a minimal, randomly initialized GLM-4 MoE (Mixture of Experts) model for causal language modeling. It is maintained by the TRL (Transformers Reinforcement Learning) internal testing organization and is used to test training, inference, and optimization code paths.

This is a tiny random-weight model based on the CohereForCausalLM 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 LlamaForCausalLM architecture, published on Hugging Face. It is intended for testing and development purposes, allowing developers to validate pipelines, tokenizers, and inference code without downloading full-scale models. It supports the Transformers library and can be used locally or via the Hugging Face inference ecosystem.

tiny-GptOssForCausalLM is a very small model created for internal testing of the Optimum Intel library. It implements a basic causal language modeling architecture and is not intended for production use. The model is hosted on Hugging Face primarily for CI/CD and validation purposes.

A very small randomly initialized Llama model created by the Hugging Face TRL team for internal testing purposes. It is used to validate training pipelines, inference code, and library functionality without requiring large compute resources. Not intended for actual text generation or production use.

hmellor/tiny-random-LlamaForCausalLM is a tiny, randomly initialized Llama-based language model intended for experimentation, testing, and educational purposes. It is open source and can be used via API or CLI.

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.

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 small randomly initialized model based on the GPTNeoXJapanese architecture. It is published by optimum-intel-internal-testing on Hugging Face specifically for testing and validating the Optimum Intel optimization stack. The model uses PyTorch and is intended for developers working on Japanese language model tooling.

This is a minimal, randomly initialized model using the LlamaForCausalLM architecture. It is maintained by the Hugging Face M4 team specifically for unit testing, CI pipelines, and compatibility checks. It is not intended for actual text generation but for developer tooling.

This is a very small test instance of a DeepSeekV3ForCausalLM model created by the TRL (Transformers Reinforcement Learning) internal testing team. It is used to validate training, inference, and integration code for the DeepSeek V3 architecture within the Hugging Face ecosystem. Not intended for production use.

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 tiny random-weight model based on the Starcoder2 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 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 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 is a minimal test model for sequence classification based on the Llama architecture. It is hosted on the Hugging Face Hub primarily for internal testing and validation of the TRL library and related transformer pipelines. The model demonstrates how to load and run classification tasks with small Llama-derived checkpoints.

This is a minimal random-weight model based on the Gemma architecture, published under the optimum-intel-internal-testing organization. It is intended for testing Optimum-Intel's conversion and optimization pipelines. The model uses Safetensors format and is provided with an MIT license. It is not intended for actual text generation but serves as a lightweight test fixture.

This is a minimal random-weight model based on the Phi architecture, published under the optimum-intel-internal-testing organization. It is intended for testing Optimum-Intel's OpenVINO and other optimization backends. The model uses Safetensors format and is provided with an Apache 2.0 license. It is not designed for production inference but serves as a lightweight fixture for integration and CI testing of the Optimum-Intel library.