unsloth/smollm2-360m-instruct-gguf overview

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  "card_data": {
    "base_model": "HuggingFaceTB/SmolLM2-360M-Instruct",
    "language": [
      "en"
    ],
    "library_name": "transformers",
    "license": "apache-2.0",
    "tags": [
      "llama",
      "unsloth",
      "transformers"
    ],
    "frontmatter": {
      "base_model": "HuggingFaceTB/SmolLM2-360M-Instruct",
      "language": [
        "en"
      ],
      "library_name": "transformers",
      "license": "apache-2.0",
      "tags": [
        "llama",
        "unsloth",
        "transformers"
      ]
    },
    "hero_image_url": "https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png",
    "summary": "We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing   # unsloth/SmolLM2-360M-Instruct-GGUF For more details on the model, please go to Hugging Face's original model card",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: HuggingFaceTB/SmolLM2-360M-Instruct\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\ntags:\n- llama\n- unsloth\n- transformers\n---\n\n# Finetune SmolLM2, Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!\n\nWe have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing\n\n[<img src=\"https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png\" width=\"200\"/>](https://discord.gg/unsloth)\n[<img src=\"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png\" width=\"200\"/>](https://github.com/unslothai/unsloth)\n\n# unsloth/SmolLM2-360M-Instruct-GGUF\nFor more details on the model, please go to Hugging Face's original [model card](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct)\n\n## ✨ Finetune for Free\n\nAll 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.\n\n| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |\n|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|\n| **Llama-3.2 (3B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |\n| **Llama-3.2 (11B vision)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |\n| **Llama-3.1 (8B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |\n| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing)               | 2x faster | 50% less |\n| **Gemma 2 (9B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)               | 2.4x faster | 58% less |\n| **Mistral (7B)**    | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 62% less |\n| **DPO - Zephyr**     | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 19% less |\n\n- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.\n- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.\n- \\* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.\n\n## Special Thanks\nA huge thank you to the Hugging Face team for creating and releasing these models.\n\n## Model Summary\n\nSmolLM2 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.\n\nThe 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).\n\nThe instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1).\n\n# SmolLM2\n\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/y45hIMNREW7w_XpHYB_0q.png)",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "llama",
    "unsloth",
    "en",
    "base_model:HuggingFaceTB/SmolLM2-360M-Instruct",
    "base_model:quantized:HuggingFaceTB/SmolLM2-360M-Instruct",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 16,
  "downloads": 752,
  "gated": false,
  "private": false,
  "last_modified": "2024-10-31T20:42:01.000Z",
  "created_at": "2024-10-31T20:35:41.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

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  "createdAt": "2024-10-31T20:35:41.000Z",
  "lastModified": "2024-10-31T20:42:01.000Z",
  "author": "unsloth",
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