We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B. We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales. We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. We report the win:loss rate without tie:
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View details ↗**Example usage**: - For text only LLMs: `llama-cli -hf AlSamCur123/Quen32 --jinja` - For multimodal models: `llama-mtmd-cli -hf AlSamCur123/Quen32 --jinja`
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View details ↗2B dictation model (Qwen3.5 base, step 7314) quantized to affine Q6 group 64 for the FluidDecode runtime, with the v23 five-layer block-16 DFlash drafter attached, served at block 8 (taps [2, 7, 12, 17, 21], full 98k draft vocabulary, causal block attention). Output is byte-identical to plain greedy decoding. Requires FluidIntelligence with causal DFlash block attention (September 2026 or later).
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View details ↗Core ML export of the final `nvidia/Nemotron-3-Diarization` checkpoint (revision `98fcee8e866bb534eee85d1eb3817170700ae717`), for on-device speaker diarization on macOS 14+ / iOS 17+. Weights are stored as fp16 and widened to fp32 at load time (`constexpr_cast`), so every op computes in fp32. The package stays at fp16 size (~199 MB). Frame-level agreement with NVIDIA's NeMo reference, same streaming loop and post-processing, on real meeting recordings:
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View details ↗This is Yi Self Merged. I wanted model that will follow most instuctions yet preserve its base model nature. The following table outlines the primary characteristics and intended uses of my YiSM-34B-0rn models: Please remember that all YiSM-34B-0rn models operate under the apache-2.0 license, so familiarize yourself with its terms and conditions before employing their content.
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