Huihui-Qwen3.8-27B-abliterated-EXL3-3.0bpw

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llava-1.5-7b-hf

Llava Hugging Face

Below is the model card of Llava model 7b, which is copied from the original Llava model card that you can find here. Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: Or check out our Spaces demo! LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. LLaVA-v1.5-7B was trained in September 2023. Paper or resources for more information: https://llava-vl.github.io/ First, make sure to have transformers >= 4.35.3. The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple…

This is an uncensored version of Qwen/Qwen3.6-27B 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. - AWQ Marlin kernel supported (auto-converted by vLLM at runtime) - MTP speculative decoding supported out of the box - 110+ tok/s on a single A800 80GB (vLLM 0.21.0, MTP enabled, fp8 KV cache) - Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated…

A 18G INT4 build of for single-user decoding and low-VRAM deployment. (Marlin on Ampere). - Unrotated, so the DFlash2 drafter works. GatedDeltaNet gates, all norms. 4-bit costs 4.5× the rotated W8A8's quantization divergence (0.0264) and 1.5× the unrotated W8A8's (0.0791). Choose this build when the 18G footprint or single-user decode speed matters more than fidelity. Produced with Heretic v2.0.0.dev0: 260 TPE trials, seed 42, directional ablation on attn.oproj (16 modules), attn.outproj (48) and mlp.downproj (64). Baseline refusal score before abliteration: 98/100. Trial 140 was selected — Pareto index 1, not index 0. Index 0 (trial 161) scored 22/100 keywords at KL 0.1091; trial 140…

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openvla-7b-finetuned-libero-spatial

OpenVLA Collaboration

This model was produced by fine-tuning the OpenVLA 7B model via LoRA (r=32) on the LIBERO-Spatial dataset from the LIBERO simulation benchmark. We made a few modifications to the training dataset to improve final performance (see the OpenVLA paper for details). Below are the hyperparameters we used for all LIBERO experiments: - No gradient accumulation (i.e. gradaccumulationsteps == 1) - shufflebuffersize == 100000 See the OpenVLA GitHub README for instructions on how to run and evaluate this model in the LIBERO simulator.

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openvla-7b-finetuned-libero-object

OpenVLA Collaboration

This model was produced by fine-tuning the OpenVLA 7B model via LoRA (r=32) on the LIBERO-Object dataset from the LIBERO simulation benchmark. We made a few modifications to the training dataset to improve final performance (see the OpenVLA paper for details). Below are the hyperparameters we used for all LIBERO experiments: - No gradient accumulation (i.e. gradaccumulationsteps == 1) - shufflebuffersize == 100000 See the OpenVLA GitHub README for instructions on how to run and evaluate this model in the LIBERO simulator.

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openvla-7b-finetuned-libero-10

OpenVLA Collaboration

This model was produced by fine-tuning the OpenVLA 7B model via LoRA (r=32) on the LIBERO-10 (LIBERO-Long) dataset from the LIBERO simulation benchmark. We made a few modifications to the training dataset to improve final performance (see the OpenVLA paper for details). Below are the hyperparameters we used for all LIBERO experiments: - No gradient accumulation (i.e. gradaccumulationsteps == 1) - shufflebuffersize == 100000 See the OpenVLA GitHub README for instructions on how to run and evaluate this model in the LIBERO simulator.