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AI 模型 84759

• 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.

• t5-russian-summarization↗

  UrukHan

• K2-Horizon-3.7B-MLX-4bit↗

  abenzerps

• cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp5-q4v3-iid16↗

  agurung

• flux-loras↗

  alexrzem

  **File:** `dev/3D_&_Craft_-_3D_Animal_Characters_-_Flux.1_D_-_leimaxiu252537.safetensors` **Link:** dev/3D_&_Craft_-_3D_Animal_Characters_-_Flux.1_D_-_leimaxiu252537.safetensors **Description:** 3D animal characters are virtual animal images created using digital technologies such as 3D modeling, rendering, and animation, and are widely used in fields like animation, games, film and televisio

• Olmo-3-7B-RL-Zero-IF↗

  allenai

• Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM-HyperQwen-fast↗

  Ar4ikov

• aragpt2-base↗

  aubmindlab

  You can find more information in our paper AraGPT2 The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API. GPT2-base and medium uses the code from the `gpt2` folder and can trains models from the minimaxir/gpt-2-simple repository. These models were trained using the `lamb` optimizer and follow the same architecture as `gpt2` and are fully compatible with the `transformers` library.

• mistralai_Ministral-3-14B-Instruct-2512-GGUF↗

  bartowski

  All quants made using imatrix option with dataset from here combined with a subset of combined_all_small.parquet from Ed Addario here Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: