This model is obtained through distillation training using the high-reasoning-difficulty Gemini 3 Pro preview dataset based on the Qwen3-4B-Thinking-2507 base model. It focuses on improving complex reasoning abilities in the fields of coding and science. Through training with specific datasets, it aims to efficiently transfer the reasoning abilities of large models (such as Gemini 3 Pro) to a smaller-scale model.

Natural Language ProcessingTransformers

Zenyx_114M-Tiny-Edu-Instruct is an experimental small instruction fine-tuned language model with approximately 114 million parameters. It is built on the TinyEdu-50M base model, pre-trained on the FineWeb-Edu dataset, and fine-tuned on a mixed dataset of OpenHermes-2.5 and CodeFeedback-Filtered. This model aims to explore the limits of instruction fine-tuning under a minimal architecture and verify that the loss converges to approximately 1.04.

Natural Language ProcessingPytorchEnglish

This project is a quantized version based on the IBM Granite-4.0-h-350m model, aiming to make knowledge accessible to everyone. This model is an efficient text generation model. After quantization optimization, it has a smaller model size and faster inference speed.

Natural Language ProcessingGguf

Apriel-1.5-15b-Thinker is a multimodal reasoning model developed by the ServiceNow SLAM Lab, with a parameter scale of 15 billion. It has powerful text and image understanding and reasoning capabilities, adopting a chained reasoning mode of 'think-answer', aiming to achieve performance comparable to large models with a smaller model scale.

MultimodalTransformers

Cthulhu 24B 1.1 GGUF is a large language model fused based on the Mistral Small series of models, integrating 17 different model components. It has the capabilities of creative writing, uncensored dialogue, and rich language interaction. Inspired by the Cthulhu mythos, this model aims to provide a'super fusion' experience that goes beyond the capabilities of a single model.

Natural Language ProcessingTransformersEnglish

Devstral Small 1.1 GGUF is a large language model specifically designed for software engineering tasks. It combines the technical advantages of Mistral AI and All Hands AI and performs excellently in code processing and software engineering agency.

Natural Language ProcessingGgufEnglish

Devstral Small 1.1 is a large language model optimized for software engineering tasks, developed in collaboration by Mistral AI and All Hands AI, supporting multiple languages and tool invocation.

Natural Language ProcessingSafetensorsMultiple Languages

SmolLM3 is a language model with 3 billion parameters, aiming to break the performance boundary of small models. It supports 6 languages, has advanced reasoning ability and long context processing ability, and is a fully open-source model.

Natural Language ProcessingTransformersMultiple Languages

This project provides optimized GGUF quantized files, which can significantly improve model performance. These quantized files are supported by Featherless AI. Users can run any desired model by paying a small fee.

Natural Language ProcessingGguf

A text simplification and rewriting model fine-tuned based on Google's FLAN-T5 Small, aiming to improve text readability while preserving core semantics.

Natural Language ProcessingSafetensorsEnglish

Mistral-Nemo-Base-2407 is a 12-billion-parameter generative text pre-training model jointly trained by Mistral AI and NVIDIA, outperforming models of similar or smaller scale.

Natural Language ProcessingTransformersMultiple Languages

Mistral-Nemo-Base-2407 is a 12-billion-parameter generative text pre-training model jointly trained by Mistral AI and NVIDIA, outperforming existing models of similar or smaller scale.

Natural Language ProcessingTransformersMultiple Languages

Mistral-Nemo-Instruct-2407 is a large language model fine-tuned on instructions based on Mistral-Nemo-Base-2407, jointly trained by Mistral AI and NVIDIA, outperforming existing models of similar or smaller size.

Natural Language ProcessingTransformersMultiple Languages

TinyLlama is a small large language model with 1.1 billion parameters. It has been pre-trained on 3 trillion tokens and fine-tuned on synthetic dialogue data generated by ChatGPT, aiming to provide efficient dialogue capabilities.

Natural Language ProcessingGguf

This is a small language model trained from scratch on the TinyChat dataset, aiming to achieve natural conversational responses with minimal model size.

Natural Language ProcessingTransformers

Fox-1-1.6B is a small language model with 1.6 billion parameters developed by TensorOpera AI, which adopts the Grouped Query Attention mechanism and is trained on 3 trillion text and code data.

Natural Language ProcessingTransformersEnglish

phi2-bunny is a specialized model fine-tuned on the WhiteRabbit network security dataset based on Microsoft's phi-2 small language model. It is optimized for Q&A and analysis tasks in the field of network security, aiming to provide clear network security analysis and suggestions in a logical and step-by-step reasoning manner.

Natural Language ProcessingTransformersOther