--- license: apache-2.0 language: - en tags: - mobiquant - quantization - mixed-precision - llama --- # MoBiQuant Checkpoints Calibrated OmniQuant and router parameters for **MoBiQuant** on four LLaMA base models. **Code:** [SimWangArizona/MoBiQuant](https://github.com/SimWangArizona/MoBiQuant) ## Available checkpoints | Directory | Base model | |---|---| | `llama2-7b` | Llama-2-7B | | `llama3-8b` | Llama-3-8B | | `llama3-3b` | Llama-3.2-3B | | `llama3-1b` | Llama-3.2-1B | Each directory contains: - `omni_parameters.pth`: learned quantization parameters. - `router_parameters.pth`: learned router parameters. These are supplemental parameter checkpoints. Original pretrained model weights and tokenizer files are required separately; this repository cannot be loaded directly with `AutoModelForCausalLM.from_pretrained()`. ## Quantization configuration - Weight-only quantization with 16-bit activations. - Group size: **128**. - Bit slices: **[2, 2, 2, 2]** — a 2-bit base plus three routed residual slices. - Routing covers Attention q/k/v/o and MLP gate/up/down projections. - Effective average bits depend on routing and the evaluation target; Attention is not fixed at 4 bits. ## Download ```python from huggingface_hub import snapshot_download checkpoint_dir = snapshot_download( repo_id="Simwang/MoBiQuant", allow_patterns=["llama3-1b/*.pth"], ) ``` Replace `llama3-1b` with another directory above to download its parameters. See the code repository for evaluation instructions. The current directory-resume workflow additionally expects `router_stats.pth`; that file is not included in this release, so the downloaded pair alone is not a complete directory-resume bundle. ## Limitations The implementation evaluates quantization using floating-point simulation. These files do not constitute a packed low-bit deployment format or establish inference speedups. The Apache-2.0 metadata applies to this release; obtain and use the original base models under their respective model licenses and access conditions.