Instructions to use Thingamatronics/Sable_v22_ctx32k-q4f32_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use Thingamatronics/Sable_v22_ctx32k-q4f32_1-MLC with MLC-LLM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Sable_v22_ctx32k-q4f32_1-MLC
MLC-compiled weights for the Dr. Sable browser demo. They are fetched at runtime by WebLLM and run entirely in the player's browser on WebGPU; nothing is sent to a server.
Play the demo: https://stickybitgames.itch.io/doctor-sable
| developer | Sticky Bit Games |
| publisher | Thingamatronics LLC |
| base model | Qwen2.5-1.5B-Instruct (Apache-2.0) |
| quantisation | 4f32_1-MLC as compiled by MLC |
| context window | 32,768 tokens |
| download | ~880 MB |
| GPU memory | ~3.3 GB (f32) / ~2.5 GB (f16) |
This repo is the weights only. It is not a general-purpose assistant: the model is fine-tuned to play one character inside one game, and the game's engine -- not the model -- decides what is true in a run.
Desktop or laptop with a WebGPU-capable GPU. Phones and tablets cannot run it.
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