{
    "model_id": "1bitLLM/bitnet_b1_58-large",
    "downloads": 10843,
    "tags": [
        "transformers",
        "safetensors",
        "llama",
        "text-generation",
        "arxiv:2402.17764",
        "license:mit",
        "autotrain_compatible",
        "text-generation-inference",
        "endpoints_compatible",
        "region:us"
    ],
    "description": "--- license: mit --- This is a reproduction of the <a href=\" BitNet b1.58</a> paper. The models are trained with <a href=\" dataset</a> for 100B tokens. The hypers, as well as two-stage LR and weight decay, are implemented as suggested in their following <a href=\" All models are open-source in the <a href=\" We will train larger models and/or more tokens when resource is available. ## Results PPL and zero-shot accuracy: | Models | PPL| ARCe| ARCc| HS | BQ | OQ | PQ | WGe | Avg |-------|-------|-------|-------|-------|-------|-------|-------|-------|-------| | FP16 700M (reported) | 12.33 | 54.7 | 23.0 | 37.0 | 60.0 | 20.2 | 68.9 | 54.8 | 45.5 | | BitNet b1.58 700M (reported) | 12.87 | 51.8 | 21.4 | 35.1 | 58.2 | 20.0 | 68.1 | 55.2 | 44.3 | | BitNet b1.58 700M (reproduced) | 12.78 | 51.4 | 21.8 | 35.0 | 59.6 | 20.6 | 67.5 | 55.4 | 44.5 | | FP16 1.3B (reported) | 11.25 | 56.9 | 23.5 | 38.5 | 59.1 | 21.6 | 70.0 | 53.9 | 46.2 | BitNet b1.58 1.3B (reported) | 11.29 | 54.9 | 24.2 | 37.7 | 56.7 | 19.6 | 68.8 | 55.8 | 45.4 | | BitNet b1.58 1.3B (reproduced) | 11.19 | 55.8 | 23.7 | 37.6 | 59.0 | 20.2 | 69.2 | 56.0 | 45.9 | FP16 3B (reported) | 10.04 | 62.1 | 25.6 | 43.3 | 61.8 | 24.6 | 72.1 | 58.2 | 49.7 | BitNet b1.58 3B (reported) | 9.91 | 61.4 | 28.3 | 42.9 | 61.5 | 26.6 | 71.5 | 59.3 | 50.2 | BitNet b1.58 3B (reproduced) | 9.88 | 60.9 | 28.0 | 42.3 | 58.3 | 26.0 | 71.4 | 60.3 | 49.6 | The differences between the reported numbers and the reproduced results are possibly variances from the training data processing, seeds, or other random factors. ## Evaluation The evaluation pipelines are from the paper authors. Here is the commands to run the evaluation:",
    "model_explanation_gemini": "\"Reproduces BitNet b1.58, a 1-bit LLM trained on 100B tokens, achieving competitive perplexity (PPL) and zero-shot accuracy compared to FP16 models across various benchmarks.\"\n\n**Model Features**:  \n- 1-bit quantization (BitNet b1.58 architecture)  \n- Trained on 100B tokens  \n- Implements two-stage LR and weight decay  \n- Open-source  \n- Evaluated on PPL and zero-shot tasks (ARC",
    "release_year": "2024",
    "parameter_count": null,
    "is_fine_tuned": false,
    "category": "Large Language Model",
    "model_family": "LLaMA",
    "api_enhanced": true
}