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AI 模型 84757
- flaubert_small_cased↗
flaubert
- Qwen3-Omni-30B-A3B-Thinking-GGUF↗
ggml-org
This model is converted from Qwen/Qwen3-Omni-30B-A3B-Thinking to GGUF using `convert_hf_to_gguf.py`
- Seed-OSS-36B-Instruct-GGUF↗
giladgd
- multiberts-seed_0-step_0k↗
Google
- siglip2-so400m-patch16-384-jax↗
Google
- Gemma-4-26B-A4B-it-JANG_4M-GGUF↗
HentaiP
- Huihui-Qwen3.8-Flash-Next-abliterated-MTP-GGUF↗
htdohk
This is an uncensored version of Qwen/Qwen3.8-Flash-Next created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Strata has byte-level and SHA256 verification for the Unsloth series. The simplest method is to modify the verify_sha256 function in the setup.py file so that it returns immediately, then no verification is needed.
- llama-30b↗
huggyllama
- Hy-MT2-7B-GGUF↗
腾讯
🖥️ Official Website | 💬 GitHub | 🪡 AngelSlim | 📚 Hy-MT2 Report Hy-MT2 is a family of “fast-thinking” multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of which support translation among 33 languages and effectively follow translation instructions in multiple languages. For on-device deployment, AngelSlim 1.25-bit extreme quantization reduces the storage requirement of the 1.8B model to only 440 MB and improves inference speed by 1.5x. Multi-dimensional evaluations show that Hy-MT2 delivers outstanding performance across general, real-world business, domain-specific, and instruction-following translation tasks. The 7B and 30B-A3B models outperform open-source models such as DeepSeek-V4-Pro and Kimi K2.6 in fast-thinking mode, while the lightweight 1.8B model also surpasses mainstream commercial APIs from providers such as Microsoft and Doubao overall. In this release, we also open-source IFMTBench, a benchmark for evaluating translation instruction-following capabilities.
- Qwen3.5-2B-multiharness-RL-GGUF↗
igorktech
