Instructions to use HanzoHuang/Llama-3.2-1B-Instruct-RKLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use HanzoHuang/Llama-3.2-1B-Instruct-RKLLM with RKLLM:
# 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
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🚀 Deploy with reComputer AI Lab
Explore and deploy AI models on Rockchip RK3576 and RK3588 devices with reComputer AI Lab.
⚡ Run with one command
With Docker installed, choose your model and board in reComputer AI Lab, copy the deployment command, and run it in your device’s terminal.
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Llama-3.2-1B-Instruct · RKLLM
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Text generation on Rockchip RK3576 and RK3588, converted from meta-llama/Llama-3.2-1B-Instruct.
- Conversion toolkit: RKLLM Toolkit 1.3.0.
- Inputs / output: text → text.
- License:
llama3.2; the upstream model card describes its terms and intended use.
Choose your files
Use the files for your board’s exact SoC. w4a16 uses 4-bit weights and 16-bit activations; w4a16_g128 adds a weight group size of 128. w8a8 uses 8-bit weights and activations. File size below is download size, not total runtime RAM.
| Board | Quantization | Size | Language model |
|---|---|---|---|
| RK3576 | w4a16_g128 |
1.30 GiB | Llama-3.2-1B-Instruct_RK3576_w4a16_g128.rkllm |
| RK3576 | w8a8 |
1.68 GiB | Llama-3.2-1B-Instruct_RK3576_w8a8.rkllm |
| RK3588 | w8a8 |
1.67 GiB | Llama-3.2-1B-Instruct_RK3588_w8a8.rkllm |
Run on your board
With Docker installed on a matching Rockchip board, this example starts the RK3576 w4a16_g128 model and its API on port 8001:
sudo docker run --rm -d --name rkllm \
--privileged -v /dev:/dev -p 8001:8001 \
ghcr.io/hanzo-huang/rkllm-docker/llm/llama-3.2-1b-instruct:rk3576-w4a16-g128
The image includes its model file. See rkllm-docker for API requests, other board variants, and configuration.
To download the files separately instead:
hf download HanzoHuang/Llama-3.2-1B-Instruct-RKLLM \
RK3576/Llama-3.2-1B-Instruct_RK3576_w4a16_g128.rkllm \
--local-dir ./models
These are native RKLLM/RKNN artifacts. Use a compatible RKLLM runtime and the model’s correct chat template when integrating them in another application.
Release notes
No per-variant board benchmark is published here. Quantized output may differ from the original model; check responses on your own workloads. Use the toolkit version listed above when checking runtime compatibility.
SHA-256 checksums
bd6cbbf84e90b63d451e672a542858a5a6aa15ad2b45f83e1b5dbcd944ac3018 RK3576/Llama-3.2-1B-Instruct_RK3576_w4a16_g128.rkllm
9c4d103f7c486740ba75605c61a51b80d170df067645360ffad8464b50c764df RK3576/Llama-3.2-1B-Instruct_RK3576_w8a8.rkllm
4403aec1c018c7ebf29c95730336e848aff8824e3be3bd6be7db44b4a4973bad RK3588/Llama-3.2-1B-Instruct_RK3588_w8a8.rkllm
Credits
Original model: meta-llama/Llama-3.2-1B-Instruct. Conversion tools: Rockchip RKLLM.
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Base model
meta-llama/Llama-3.2-1B-Instruct