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AI 모델 84999

• Qwen3.8-27B-oQ2↗

  TensorFold

• claude2-alpaca-13B-GGUF↗

  TheBloke

• UnslopNemo-12B-v4-GGUF↗

  TheDrummer

• efficientformerv2_s0.snap_dist_in1k↗

  timm

• Mina-1B-Vietnamese-Master↗

  Trivnpk

• DeepSeek-R1-BF16↗

  unsloth

  You can view more detailed instructions in our blog: unsloth.ai/blog/deepseek-r1 1. Do not forget about ` ` and ` ` tokens! - Or use a chat template formatter 2. Obtain the latest `llama.cpp` at https://github.com/ggerganov/llama.cpp 3. Example with Q8_0 K quantized cache **Notice -no-cnv disables auto conversation mode** 4. If you have a GPU (RTX 4090 for example) with 24GB, you can offload multiple layers to the GPU for faster processing. If you have multiple GPUs, you can probably offload more layers. We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb

• Qwen2.5-32B-Instruct-bnb-4bit↗

  unsloth

  We have a Qwen 2.5 (all model sizes) free Google Colab Tesla T4 notebook. Also a Qwen 2.5 conversational style notebook. 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.

• EuroMoE-2.6B-A0.6B-Instruct-2512↗

  utter-project

• LaMini-Flan-T5-783M↗

  Xenova

• Fanar-1-9B-Instruct-gguf↗

  yasserrmd