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AI 模型 84760

• TinyBERT_General_6L_768D↗

  huawei-noah

• finemath-classifier↗

  HuggingFaceTB

• Huihui-Ornith-1.0-9B-abliterated-MTP-GGUF↗

  huihui-ai

  This is an uncensored version of deepreinforce-ai/Ornith-1.0-9B 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. **Note** We will directly perform ablation on the GGUF files from protoLabsAI/Ornith-1.0-9B-MTP-GGUF. Some weights quantized in Q3_K, Q4_K, Q5_K and Q6_K will be converted to Q8_0. This time, the weights of the first 5 layers, so the final weights will increase, but the change will not be significant. We performed a simple quantization comparison between Q8_0 and MXFP4 on the Q3 version of protoLabsAI/Ornith-1.0-9B-MTP-GGUF.

• Jais-3-7B-MLX-bf16↗

  IFM

• FLUX.1-dev-Controlnet-Union↗

  InstantX

  The training of union controlnet requires a significant amount of computational power. The current release is the first beta version checkpoint that maybe not been fully trained. The fully trainedbeta version is in the training process. We have conducted ablation studies that have demonstrated the validity of the code. The open-source release of the first beta version is solely to facilitate the rapid growth of the open-source community and the Flux ecosystem; it is common to encounter bad cases (please accept my apologies). It is worth noting that we have found that even a fully trained Union model may not perform as well as specialized models, such as pose control. However, as training progresses, the performance of the Union model will continue to approach that of specialized models.

• simlm-msmarco-reranker↗

  intfloat

• Nomi-2-GGUF↗

  JallyAI

• Qwopus3.6-27B-Fusion-GGUF↗

  KyleHessling1

  > **Research preview.** This model attempts to combine the **reasoning** capability of > `Qwopus3.6-27B-v2` with the **code-execution** capability of `Qwopus3.6-27B-Coder` — in a single 27B > model, *without significant loss to either* skill. In practice it behaves like a production model > with both capabilities fused, but it has **not** yet been through a full rigorous evaluation. If you > find issues, please reach out on X — **@kylehessling1**. - **Format:** GGUF, `Q4_K_M` (~16.5 GB) — runs on a single 24–32 GB GPU via `llama.cpp`. - **Architecture:** `qwen35` hybrid (linear-attention + periodic full-attention), 27B params, 64 layers. - **Native context:** 262,144 tokens. Verified clean (needle + termination) to 60K. - **Modes:** thinking (reasoning) **on** by default; strong agentic/coding behavior. - **Vision:** 🖼️ **image input supported** via the companion projector `Qwopus3.6-27B-Fusion-mmproj-Q8_0.gguf` (~0.6 GB) — pair it with *any* quant below (see Vision). ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

• Limamono-Mistral-7B-v0.50↗

  lemonilia

• Gemma-4-12B-uncensored-8bit-mlx↗

  lemuralabs