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AI モデル 84760
- Nanbeige-16B-Chat-GGUF↗
TheBloke
- tigerbot-70B-chat-v4-GGUF↗
TheBloke
- ThoxEdge-0.8B↗
Thox-ai
- gemma-4-dense↗
tiny-random
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from google/gemma-4-31B-it.
- Qwen3.6-27B-L↗
trymirai
- Falcon-H1-34B-Instruct-GGUF↗
unsloth
- Qwen3-1.7B-Base-unsloth-bnb-4bit↗
unsloth
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Building upon extensive advancements in training data, model architecture, and optimization techniques, Qwen3 delivers the following key improvements over the previously released Qwen2.5: **Qwen3-1.7B-Base** has the following features: - Type: Causal Language Models - Training Stage: Pretraining - Number of Parameters: 1.7B - Number of Paramaters (Non-Embedding): 1.4B - Number of Layers: 28 - Number of Attention Heads (GQA): 16 for Q and 8 for KV - Context Length: 32,768 For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.
- Qwen3-30B-A3B-GGUF↗
unsloth
See our collection for all versions of Qwen3 including GGUF, 4-bit & 16-bit formats. Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. - Fine-tune Qwen3 (14B) for free using our Google Colab notebook here! - Read our Blog about Qwen3 support: unsloth.ai/blog/qwen3 - View the rest of our notebooks in our docs here. - Run & export your fine-tuned model to Ollama, llama.cpp or HF.
- SmolLM2-1.7B-Instruct-bnb-4bit↗
unsloth
- VibeVoice-1.5B-hf↗
vibevoice
VibeVoice is a novel framework designed for generating expressive, long-form, multi-speaker conversational audio, such as podcasts, from text. It addresses significant challenges in traditional Text-to-Speech (TTS) systems, particularly in scalability, speaker consistency, and natural turn-taking. A core innovation of VibeVoice is its use of continuous speech tokenizers (Acoustic and Semantic) operating at an ultra-low frame rate of 7.5 Hz. These tokenizers efficiently preserve audio fidelity while significantly boosting computational efficiency for processing long sequences. VibeVoice employs a next-token diffusion framework, leveraging a Large Language Model (LLM) to understand textual context and dialogue flow, and a diffusion head to generate high-fidelity acoustic details. The model can synthesize speech up to **90 minutes** long with up to **4 distinct speakers**, surpassing the typical 1-2 speaker limits of many prior models.