This is a text generation model fine-tuned on an internal horror dataset based on the Gemma-3 model, specifically designed for generating horror-style content. The model is optimized through Unsloth and can generate horror content ranging from mild to intense, including long horror stories.

Natural Language ProcessingGgufMultiple Languages

JanusCoderV-7B is a 7B parameter code generation model developed by InternLM. This project provides multiple versions of imatrix quantization using llama.cpp, supporting various quantization levels to meet different hardware requirements.

Natural Language ProcessingGguf

This is a quantized version of the internlm's JanusCoder-14B model. It is quantized using specific tools and datasets, providing a variety of quantization type files from low quality to high quality. It can run in LM Studio or projects based on llama.cpp.

Natural Language ProcessingGguf

This is a quantized version of the internlm's JanusCoder-8B model, using the imatrix quantization technology of llama.cpp. It significantly reduces the storage and computational resource requirements of the model while ensuring performance, enabling the model to run efficiently on more devices.

Natural Language ProcessingGguf

This is an MXFP4 Mixture-of-Experts quantized version based on the Intern-S1 model, specifically optimized for image-text-to-text tasks. It improves inference efficiency through quantization technology.

MultimodalGguf

Granite-4.0-1B is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-1B-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using techniques such as supervised fine-tuning, reinforcement learning, and model merging. It is suitable for device-side deployment and research use cases.

Natural Language ProcessingTransformers

Granite-4.0-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using technologies such as supervised fine-tuning, reinforcement learning, and model fusion. It has powerful instruction-following capabilities and is particularly suitable for device-side deployment and research scenarios.

Natural Language ProcessingTransformers

Granite-4.0-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using supervised fine-tuning, reinforcement learning, and model merging techniques. It has powerful instruction-following capabilities and tool invocation functions.

Natural Language ProcessingTransformers

Granite-4.0-1B is a lightweight instruction model developed by IBM. It is fine-tuned based on Granite-4.0-1B-Base, combining open-source instruction datasets and internal synthetic datasets, and developed using supervised fine-tuning, reinforcement learning, and model merging techniques.

Natural Language ProcessingTransformers

Granite-4.0-H-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-H-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using various technologies such as supervised fine-tuning, reinforcement learning, and model merging. It has powerful instruction-following capabilities and multilingual support.

Natural Language ProcessingTransformers

Granite-4.0-H-Small is a long-context instruction model developed by IBM with 32 billion parameters, fine-tuned based on Granite-4.0-H-Small-Base. This model combines open-source instruction datasets and internal synthetic datasets, and uses techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has significantly improved instruction following and tool invocation capabilities, and is particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-H-Micro is a 3-billion parameter long-context instruction model developed by IBM, fine-tuned from Granite-4.0-H-Micro-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has a structured chat format and performs excellently in instruction following and tool invocation capabilities.

Natural Language ProcessingTransformers

Granite-4.0-H-Tiny is a long-context instruction model developed by IBM with 7 billion parameters, fine-tuned based on Granite-4.0-H-Tiny-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has enhanced instruction-following and tool invocation capabilities, making it particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-Micro is a long-context instruction model with 3 billion parameters developed by IBM, fine-tuned based on Granite-4.0-Micro-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using technologies such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has enhanced instruction-following and tool invocation capabilities, and is particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-H-Micro is a 3-billion-parameter long-context instruction model developed by IBM, fine-tuned from Granite-4.0-H-Micro-Base. This model is trained by combining an open-source instruction dataset and an internal synthetic dataset, and it has enhanced instruction-following and tool call capabilities, making it particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-H-Small is a long-context instruction model with 32 billion parameters developed by IBM, fine-tuned based on Granite-4.0-H-Small-Base. This model combines open-source instruction datasets and internal synthetic datasets and is developed using techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging, with significant improvements in instruction following and tool invocation capabilities.

Natural Language ProcessingTransformers

Granite-4.0-Micro is a long-context instruction model developed by IBM with 3 billion parameters, fine-tuned from Granite-4.0-Micro-Base. This model uses an open-source instruction dataset and an internal synthetic dataset, and has enhanced instruction-following and tool invocation capabilities. It supports multilingual tasks and can serve as a base model for AI assistants in various fields.

Natural Language ProcessingTransformers

This is a Transformer model published on the Hugging Face model hub, and the model card is automatically generated. Due to the lack of specific information, a detailed model introduction cannot be provided.

Natural Language ProcessingTransformers

Qwen2.5-14B-Instruct is a large language model with 14 billion parameters, designed specifically for chat and text generation scenarios. This model is built based on the transformers library and is suitable for internal testing and lightweight application deployment.

Natural Language ProcessingTransformersEnglish

Granite-4.0-H-Tiny is a 7-billion parameter long context instruction model developed by IBM, fine-tuned from Granite-4.0-H-Tiny-Base. This model is trained by combining open-source instruction datasets and internal synthetic datasets, and has the ability to provide professional, accurate, and secure responses. It supports multiple languages and tool invocation, and is suitable for enterprise-level applications.

Natural Language ProcessingTransformers