{
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    {
      "slug": "smolvlm2-256m-video-instruct",
      "name": "HuggingFaceTB/SmolVLM2-256M-Video-Instruct",
      "summary": "A 256M-parameter vision-language model that watches video, not just stills.",
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          "note": "the 256M row of the card's evaluation table"
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          "hardware": null,
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          "note": "same table"
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          "unit": "%",
          "hardware": null,
          "source": "https://huggingface.co/HuggingFaceTB/SmolVLM2-256M-Video-Instruct",
          "note": "same table"
        }
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      "source": "seed"
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    {
      "slug": "qwen3-0-6b",
      "name": "Qwen/Qwen3-0.6B",
      "summary": "The 0.6B Qwen3 dense model: on-device chat with a switchable thinking mode.",
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          "note": "same table"
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          "value": 59.2,
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          "note": "same table"
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      "slug": "florence-2-base-ft",
      "name": "microsoft/Florence-2-base-ft",
      "summary": "232M parameters covering captioning, OCR, detection and segmentation behind one prompt format.",
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          "value": 2109,
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          "metric": "COCO Caption Karpathy test CIDEr",
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          "note": "the fine-tuned-model table in the card"
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          "note": "same table"
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          "metric": "VQAv2 test-dev accuracy",
          "value": 79.7,
          "unit": "%",
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          "note": "same table"
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      "slug": "smollm2-135m-instruct",
      "name": "HuggingFaceTB/SmolLM2-135M-Instruct",
      "summary": "A 135M-parameter instruction-tuned chat model small enough to run in a browser tab.",
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      "page_url": "https://tinymodels.co/models/smollm2-135m-instruct",
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          "source": "https://github.com/mrsage-AI/tinymodels/blob/main/scripts/measure.py",
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          "metric": "peak memory to load and run that input",
          "value": 658,
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          "source": "https://github.com/mrsage-AI/tinymodels/blob/main/scripts/measure.py",
          "note": "peak resident set of the measuring process, runtime included",
          "measured": true,
          "short": "658 MB peak"
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          "metric": "HellaSwag (0-shot)",
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          "note": "instruction-model table in the model card, scored with lighteval"
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          "metric": "MMLU (cloze, 0-shot)",
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          "note": "same table"
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          "note": "same table"
        },
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          "unit": "%",
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          "source": "https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct",
          "note": "same table"
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      "source": "seed"
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    {
      "slug": "qwen2-5-0-5b-instruct-4bit-mlx",
      "name": "mlx-community/Qwen2.5-0.5B-Instruct-4bit",
      "summary": "4-bit MLX conversion of Qwen2.5-0.5B-Instruct, 278 MB, tuned for Apple silicon.",
      "task": "text-generation",
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      "page_url": "https://tinymodels.co/models/qwen2-5-0-5b-instruct-4bit-mlx",
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          "source": "https://github.com/mrsage-AI/tinymodels/blob/main/scripts/measure.py",
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          "metric": "peak memory to load and run that input",
          "value": 786,
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          "hardware": "Apple M4 Pro, 48 GB unified memory, macOS 15.6",
          "source": "https://github.com/mrsage-AI/tinymodels/blob/main/scripts/measure.py",
          "note": "peak resident set of the measuring process, runtime included",
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      "source": "seed"
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    {
      "slug": "qwen2-5-0-5b-instruct-gguf-q4-k-m",
      "name": "Qwen/Qwen2.5-0.5B-Instruct-GGUF",
      "summary": "The same 0.5B instruct model as a 491 MB Q4_K_M GGUF, ready for llama.cpp and Ollama.",
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          "hardware": "Apple M4 Pro, 48 GB unified memory, macOS 15.6",
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          "metric": "peak memory to load and run that input",
          "value": 795,
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          "hardware": "Apple M4 Pro, 48 GB unified memory, macOS 15.6",
          "source": "https://github.com/mrsage-AI/tinymodels/blob/main/scripts/measure.py",
          "note": "peak resident set of the measuring process, runtime included",
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      "added_at": "2026-08-31T08:08:40.365Z",
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      "slug": "videomae-base-finetuned-kinetics",
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      "summary": "VideoMAE-base at 87M parameters, labelling 400 human actions from a clip.",
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          "hardware": "Apple M4 Pro, 48 GB unified memory, macOS 15.6",
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          "metric": "peak memory to load and run that input",
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          "note": "peak resident set of the measuring process, runtime included",
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          "note": "the evaluation results the model card states"
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          "note": "same record"
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      "source": "seed"
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      "page_url": "https://tinymodels.co/models/detr-resnet-50",
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          "note": "the figure the model card states for this checkpoint"
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      "slug": "qwen2-5-0-5b-instruct",
      "name": "Qwen/Qwen2.5-0.5B-Instruct",
      "summary": "Half a billion parameters of general chat and instruction following, under 1 GB in bf16.",
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      "parameters": 494032768,
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      "download_filename": "Qwen2.5-0.5B-Instruct.safetensors",
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