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AI 모델 84757

• GPT-oss-MOFinder-Yearwise↗

  StarLiu714

• Mistral-7B-v0.1-GGUF↗

  TheBloke

  GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

• ProgramManager-v1-2B-GGUF↗

  theprint

• Qwen2.5-32B-Instruct-Q4_K_M-GGUF↗

  TheRains

• Llama-3.2-3B-Instruct-Medical-Finetuned-merged-GGUF↗

  tinyopsec

• svd-safety-l3_swift_remove50_swapgapiter_b010-GGUF↗

  tinyopsec

• tiny-LlavaForConditionalGeneration↗

  trl-internal-testing

  This is a minimal model built for unit tests in the TRL library.

• flux-3-image-klein-FP8↗

  unsloth

  This is an FP8 / INT8 quantized version of FLUX 3 Image Klein (the 16-step distilled checkpoint). - Optimized for efficient inference with reduced memory footprint: the transformer is 6.44 GB instead of 11.1 GB. Same-seed LPIPS vs the bf16 model (lower is better): 0.035 INT8, 0.036 FP8. - Loads pre-quantized in Unsloth Desktop, which skips the bf16 download and the on-the-fly quantization. The text encoder (Qwen3-VL-4B-Instruct) and the VAE are unchanged; take them from black-forest-labs/flux-3-image-klein. The text encoder rows are scored against the bf16 text encoder with the bf16 transformer, so they measure only the encoder. The VAE stays bf16. This repo also carries the upstream diffusers folders from black-forest-labs/flux-3-image-klein, unchanged (`modular_model_index.json`, `text_encoder/`, `transformer/`, `vae/`), so Unsloth Desktop downloads every component from here.

• Qwen2-Math-1.5B↗

  unsloth

• SmolLM2-135M-Instruct↗

  unsloth

  We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) 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. SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device.