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AI Models 85004
- falcon-7b-instruct-sharded↗
vilsonrodrigues
Resharded version of https://huggingface.co/tiiuae/falcon-7b-instruct for low RAM enviroments (e.g. Colab, Kaggle) in safetensors Tutorial: https://medium.com/@vilsonrodrigues/run-your-private-llm-falcon-7b-instruct-with-less-than-6gb-of-gpu-using-4-bit-quantization-ff1d4ffbabcc **Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by TII based on Falcon-7B and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.**
- Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-NInfer-v3↗
WaveCut
A NInfer **v3** artifact of ISTA-DASLab's Qwen3.8-Flash-Next GSQ-RCO IQ3_S (3.50 bits per weight over the transformer weights) for **NInfer-all**, the `master` branch of iamwavecut/ninfer-all. Qwen3.8-Flash-Next is a 125B-parameter mixture of experts: 48 layers of 512 routed experts, 10 of them active per token, about 6B active parameters. The GGUF assigns a ggml quantization type to every tensor; this artifact keeps each tensor's ggml blocks byte for byte (the hyper-connection projections excepted: they are re-quantized from BF16 to Q8_0), and the engine multiplies the blocks in place. IQ3_S is the release the GSQ-RCO card recommends: the strongest of the four. Stock NInfer builds refuse these files: the model family and the `gguf_*` formats exist only in that line; build `master` from commit `5395ec30b` on. NInfer serves it with up to eight concurrent requests, prompt-prefix reuse, structured output and its Vision tower; This file includes the MTP block. Enable drafting with `--spec mtp --draft-tokens 4`. One file, `Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-ninfer-v3.ninfer`, of 57,902,944,256 bytes (53.93 GiB), next to its conversion report, `SHA256SUMS`, `NOTICE` and `LICENSE`. Original text/Vision conversion command, before the MTP attachment:
- R-4B↗
YannQi
In this repo, we present **R-4B**, a multimodal large language model designed for general-purpose auto-thinking, autonomously switching between step-by-step thinking and direct response generation based on task complexity. This capability enables R-4B to deliver high-quality responses while significantly improving inference efficiency and reducing computational costs. The development of R-4B follows a two-stage training paradigm: (1) Bi-mode Annealing, which establishes both thinking and non-thinking capabilities for VQA; and (2) Bi-mode Policy Optimization (BPO), which enables the model to adaptively switch between thinking and non-thinking modes based on input demands. > [!NOTE] > Users can dynamically control the model's response by selecting one of three modes (`auto-thinking`, `thinking`, or `non-thinking`) with `thinking_mode`. `thinking_mode=auto` for `auto-thinking` mode; `thinking_mode=long` for `thinking` mode; `thinking_mode=short` for `non-thinking` mode. > Default is `auto-thinking`.
- Huihui-GLM-5.3-Flash-abliterated-GGUF↗
Yyyssd
- Qwen3.8-27B-Uncensored-YMQ-MTP-GGUF-12GB↗
0xSojalSec
- ruGPT-3.5-13B↗
ai-forever
- albert-xlarge-v1↗
albert
- Qwen3-VL-Embedding-2B-FP8-DYNAMIC↗
alexliap
- gte-Qwen2-1.5B-instruct↗
Alibaba-NLP
**gte-Qwen2-1.5B-instruct** is the latest model in the gte (General Text Embedding) model family. The model is built on Qwen2-1.5B LLM model and use the same training data and strategies as the gte-Qwen2-7B-instruct model. - Integration of bidirectional attention mechanisms, enriching its contextual understanding. - Instruction tuning, applied solely on the query side for streamlined efficiency - Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks. Observe the config_sentence_transformers.json to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice.
- qwen3-max-2026-01-23↗
阿里巴巴 · Qwen