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AI 模型 84760

• MetaMath-70B-V1.0-GGUF↗

  TheBloke

• OmniVoice-bf16↗

  theoracleguy

• Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_sedate_marmot↗

  tranbaninh

• ornith-1.0-apex↗

  Tribbler

• Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp↗

  txgsync

• Meta-Llama-3.1-405B-Instruct-bnb-4bit↗

  unsloth

• Ministral-3-8B-Instruct-2512↗

  unsloth

• Qwen2.5-Coder-0.5B-Instruct↗

  unsloth

  We have a Qwen 2.5 (all model sizes) free Google Colab Tesla T4 notebook. Also a Qwen 2.5 conversational style notebook. All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

• Qwen2.5-Omni-7B-GGUF↗

  unsloth

  Qwen2.5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. We conducted a comprehensive evaluation of Qwen2.5-Omni, which demonstrates strong performance across all modalities when compared to similarly sized single-modality models and closed-source models like Qwen2.5-VL-7B, Qwen2-Audio, and Gemini-1.5-pro. In tasks requiring the integration of multiple modalities, such as OmniBench, Qwen2.5-Omni achieves state-of-the-art performance. Furthermore, in single-modality tasks, it excels in areas including speech recognition (Common Voice), translation (CoVoST2), audio understanding (MMAU), image reasoning (MMMU, MMStar), video understanding (MVBench), and speech generation (Seed-tts-eval and subjective naturalness). VoiceBench AlpacaEval | CommonEval | SD-QA | MMSU Ultravox-v0.4.1-LLaMA-3.1-8B 4.55 |3.90|53.35|47.17

• tiny-random-vits↗

  Xenova