Spark-X2.6-4B-FP8
XHToken · Kit
Compact 4.1B-parameter text model in FP8 form for on-device use, with three thinking levels (none, low, high) and English and Chinese support. Aimed at coding…
XHToken · Kit
Compact 4.1B-parameter text model in FP8 form for on-device use, with three thinking levels (none, low, high) and English and Chinese support. Aimed at coding…
XHToken · Kit
A compact 1.7B language model for on-device use in English and Chinese, with gains in coding, agent tasks and long context, and three adjustable thinking level…
ResembleAI · Kit
Speaks English text aloud on a plain CPU with a 110M-parameter model, and adds laughs and coughs through tags in the text. Suits developers who need fast on-de…
litert-community · Kit
Runs Gemma 4 12B on your own desktop with text, image and audio input, no internet needed. A LiteRT-LM build for developers who want private, on-device generat…
ibm-granite · Kit
Handles summaries, classification, extraction, question answering, RAG, code tasks and tool calling in 12 languages with a 340M-parameter instruct model from I…
HuggingFaceTB · Kit
Answers questions about videos, images and text together, with a 2.2B model that needs about 5.2 GB of GPU memory for video. For developers building visual ass…
deepgrove · Kit
A 20B ternary-weight reasoning model that activates about 1B parameters per token. Built for fast on-device use, with a 5.31 GB checkpoint and 131,072 token co…
HuggingFaceTB · Kit
Continues English text from a prompt with a 362M-parameter base language model trained on 4 trillion tokens and light enough for on-device use. For developers…
zabibutechnika · Kit
Answers in Swahili and English from a 4B model made to run privately on your own device, offline. Suits on-device assistants that need African context, such as…
taobao-mnn · Kit
A pre-trained base language model of about 300M parameters for Chinese and English, designed with the MNN inference engine for on-device use, as a start for fu…
litert-community · Kit
Runs a small Gemma 4 model on phones, desktops, IoT boards and the web without an internet connection. A 3.66 GB LiteRT-LM build for developers who want privat…
HuggingFaceTB · Kit
Continues English text from a prompt with a 135M-parameter base language model small enough to run on-device. For developers who want a tiny model to test idea…
taobao-mnn · Kit
Runs a 0.3B Chinese and English chat model on phones and edge devices through the MNN runtime, as a 4-bit build of about 173 MB. For developers adding on-devic…
HuggingFaceTB · Kit
Answers chat-style questions and handles rewriting and summaries with a 135M-parameter instruction-tuned model small enough for on-device use. For developers w…
HuggingFaceTB · Kit
Chats, rewrites and summarizes text and can call functions, in a compact 1.7B model small enough to run on-device. The instruction-tuned SmolLM2 from HuggingFa…
XHToken · Kit
A 4.1B language model for on-device use, tuned for coding, agent tasks, long context and reasoning in English and Chinese, with three thinking levels. For deve…
openbmb · Kit
Gives you MiniCPM5-2B, a small dense text model for English and Chinese, as GGUF files for on-device and local use. Suits people who want a compact model for c…
litert-community · Kit
The 740M multimodal EmbeddingGemma 2 packaged for on-device use with LiteRT-LM. It maps text, code, images, video and audio into one shared 768-dimensional vec…
XHToken · Kit
Generates text with a compact 1.7B model for on-device use, with three adjustable thinking levels. Suits developers who want coding, agent and long-context hel…
unsloth · Kit
Turns text into 768-dimensional vectors for on-device search, retrieval and clustering. The base model also covers images, video and audio. Packaged as GGUF by…