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AI Models 84757

• xlnet-japanese↗

  hajime9652

• Qwen3.5-4b-Uncensored-NonReasoning↗

  heavylildude

• opus-mt-eu-es↗

  Helsinki-NLP

  * source group: Basque * target group: Spanish * OPUS readme: eus-spa * model: transformer-align * source language(s): eus * target language(s): spa * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: opus-2020-06-17.zip * test set translations: opus-2020-06-17.test.txt * test set scores: opus-2020-06-17.eval.txt - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eus-spa/README.md

• llama-13b↗

  huggyllama

  This contains the weights for the LLaMA-13b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out this form but either lost your copy of the weights or got some trouble converting them to the Transformers format.

• 20261016_rar-medicine_static_qwen3-4b_norm3200_impl-verl_s11_step-038↗

  HYU-NLP-EVAL

• IceWhiskeyRP-7b-8bpw-exl2↗

  icefog72

  If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

• K2-Think-V2↗

  IFM

• Hunyuan-MT-7B-Q4_K_M-GGUF↗

  imikeliu

  This model was converted to GGUF format from `tencent/Hunyuan-MT-7B` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

• GLM-5.3-Flash-MLX-Q9↗

  inferencerlabs

• Hy-MT2-30B-A3B-int4-AutoRound↗

  Intel