Instructions to use Lucasgitguy-co/minimind-3v-moe-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Lucasgitguy-co/minimind-3v-moe-adapter with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.base_model_name_or_path" must be a string
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
minimind-3v-moe-adapter
LoRA adapter repository for MiniMind vision-language models. This is an adapter, not a standalone set of base-model weights.
| File | Size (decimal GB) |
|---|---|
adapter_model.safetensors |
0.016 |
Adapter configuration
- LoRA rank: 8
- LoRA alpha: 16
- LoRA dropout: 0.05
- PEFT version recorded in configuration: 0.21.2
- Base model class:
MiniMindVLM - Saved non-LoRA module:
vision_proj
The configuration does not specify a base checkpoint. Initialize the matching MiniMindVLM implementation and original base weights before loading the adapter.
Download and loading pattern
pip install -U huggingface_hub
hf download Lucasgitguy-co/minimind-3v-moe-adapter --local-dir ./minimind-3v-moe-adapter
After initializing base_model with the original compatible model implementation and weights:
from peft import PeftModel
model = PeftModel.from_pretrained(
base_model,
"Lucasgitguy-co/minimind-3v-moe-adapter",
)
model.eval()
Use the original base model’s image processor and inference interface. The base model must exist before this snippet is run.
Evaluation and provenance
This card describes the repository files and configuration. Detailed training data, training procedure, and benchmark results are not documented in the inspected artifacts. Loading examples are guidance, not a claim of measured model quality.
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