Instructions to use moeadham/TAID-LLM-1.5B-q4f32_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use moeadham/TAID-LLM-1.5B-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
WARNING: This quantization is likely broken!
TAID-LLM-1.5B-q4f32_1-MLC
This is the SakanaAI/TAID-LLM-1.5B model converted to MLC format with q4f32_1 quantization for in-browser inference via WebGPU using web-llm. Conversion scripts are here
Model Details
- Base model: SakanaAI/TAID-LLM-1.5B
- Architecture: Qwen2 (1.5B parameters)
- Quantization: q4f32_1 (4-bit weights, float32 scales)
- Quantized size: ~880 MB
- Training method: TAID (Temporally Adaptive Interpolated Distillation) from Qwen2.5-32B-Instruct
- Context window: 32,768 tokens
- License: Apache 2.0
Usage with web-llm
import * as webllm from "@mlc-ai/web-llm";
const appConfig = {
model_list: [{
model: "https://huggingface.co/YOUR_USERNAME/TAID-LLM-1.5B-q4f32_1-MLC",
model_id: "TAID-LLM-1.5B-q4f32_1-MLC",
model_lib:
webllm.modelLibURLPrefix +
webllm.modelVersion +
"/Qwen2-1.5B-Instruct-q4f32_1-ctx4k_cs1k-webgpu.wasm",
}],
};
const engine = await webllm.CreateMLCEngine("TAID-LLM-1.5B-q4f32_1-MLC", { appConfig });
Disclaimer
This model is provided for research and development purposes only. It is an experimental prototype and is not intended for commercial use or deployment in mission-critical environments. See the original model card for full details.
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