# Efficient inference optimization

This is a GGUF format model converted from kyutai/helium-1-2b, supporting multiple European languages.

Qwen3-0.6B-Base is the latest generation of large language models in the Tongyi Qianwen series, offering a range of dense models and Mixture of Experts (MoE) models.

A 1.58-bit quantized large language model developed by Microsoft, designed for efficient inference, offering IQ2_BN and IQ2_BN_R4 quantization versions

This model is a GGUF format conversion of THUDM/GLM-Z1-9B-0414, supporting Chinese and English text generation tasks.

DeepSeek-R1-Distill-Llama-70B is a 70B parameter large language model developed by the DeepSeek team based on the Llama architecture. It is optimized through distillation technology and supports efficient inference and fine-tuning.

The Llamacpp imatrix quantization version of Deepthink-Reasoning-7B, offering multiple quantization types to meet different hardware requirements.

The GGUF quantized version of the Gemma-2b-it model, suitable for local inference and supporting text generation tasks.

Jamba is a state-of-the-art hybrid SSM-Transformer large language model that combines the advantages of Mamba architecture with Transformer, supporting 256K context length, surpassing models of similar scale in throughput and performance.

Telugu speech recognition model fine-tuned based on OpenAI Whisper-medium, trained on multiple public Telugu ASR datasets