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AI Models 84759

• Meta-Llama-3.1-8B↗

  unsloth

  We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing 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.

• palmyra-base↗

  Writer

• Granite-4.2-30B-xVITA-zhTW-GGUF↗

  xCloudinfo

• mentalhealth-llama-1b-GRPO-gguf↗

  XformAI-india

• gemma-3-1b-it-kk↗

  Xkev

• sub38-2↗

  yopmail12917

• Nemotron-3.5-Bolt-E4B-GSQ-RCO-GGUF↗

  abenzerps

  Mixed-precision **GSQ-RCO** GGUF quantizations of nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16, optimized for efficient, high-fidelity local inference in `llama.cpp`. The model supports a context length of up to 65,536 tokens (64K). Standard quantization severely degrades hybrid sliding-attention models with shared Key-Value caches, multimodal projections, and speculative draft targets. **GSQ-RCO** solves this through two targeted techniques: - **RCO:** Preserves all 54 shared `k_proj`, `v_proj`, and `norm` tensors across layers 24–41 in high precision (`Q5_K`/`Q6_K`), maintaining structural attention geometry, visual projection alignment, and DFlash draft compatibility. - **GSQ:** Optimizes per-layer precision guided by Hessian sensitivity calibration.

• GLM-5.3-GGUF↗

  AesSedai

• manaca-1b-instruct↗

  AIInstituteLNCC

• Qwen3.5-397B-A17B-MXFP4↗

  amd

  The model was quantized from Qwen/Qwen3.5-397B-A17B-FP8 using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4. For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers. The GSM8K results were obtained using the vLLM framework, based on the Docker image `rocm/vllm-dev:nightly_main_20260211`, and vLLM is installed inside the container.