DeepSeek-R1-Distill-Qwen-1.5B-RKLLM by Hanzo Huang

Model Card

By Hanzo Huang, published under mit, revision 37c3eca438d8.

Text generation on Rockchip RK3576 and RK3588, converted from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B. Use the files for your board’s exact SoC. w4a16 uses 4-bit weights and 16-bit activations; w4a16g128 adds a weight group size of 128. w8a8 uses 8-bit weights and activations. File size below is download size, not total runtime RAM. With Docker installed on a matching Rockchip board, this example starts the RK3576 w4a16g128 model and its API on port 8001: The image includes its model file. See rkllm-docker for API requests, other board variants, and configuration. To download the files separately instead: These are native RKLLM/RKNN artifacts. Use a compatible RKLLM runtime and the…

Read Hanzo Huang's full model card

Text generation on Rockchip RK3576 and RK3588, converted from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B.

Conversion toolkit: RKLLM Toolkit 1.3.0.
Inputs / output: text → text.
License: mit; 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`	1.43 GiB	DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w4a16_g128.rkllm
RK3576	`w8a8`	1.93 GiB	DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w8a8.rkllm
RK3588	`w8a8`	1.91 GiB	DeepSeek-R1-Distill-Qwen-1.5B_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/deepseek-r1-distill-qwen-1.5b: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/DeepSeek-R1-Distill-Qwen-1.5B-RKLLM \
  RK3576/DeepSeek-R1-Distill-Qwen-1.5B_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

e737c9aa8cfbfd0216e547ed67bc2325d5add2f8b2aa21037389e296ac3b8d80  RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w4a16_g128.rkllm
6ceab2c6b93a8b55837ab75e4050bbce888030153c0bc83a465bd73ead44120f  RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w8a8.rkllm
d00038d619c1905ac66ab5d499019d80889d37cc1f81be491cc503e284950c2d  RK3588/DeepSeek-R1-Distill-Qwen-1.5B_RK3588_w8a8.rkllm

Credits

Original model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B. Conversion tools: Rockchip RKLLM.

Identity and Version

Repository: HanzoHuang/DeepSeek-R1-Distill-Qwen-1.5B-RKLLM
Publisher: Hanzo Huang
Task: Text generation
Modality: Text
Library: rkllm
Parameters: Not stated by the source
Languages: Not stated by the source
Revision: 37c3eca438d86e5f20adc23437b9c8e5ebd9025d
First published: 2026-08-18
Last updated: 2026-10-19

Files and Weights

6 files, 5.7 GB in total.

Configuration1 file · 27.6 KB

Documentation1 file · 3.2 KB

Other3 files · 5.7 GB

Repository1 file · 1.8 KB

Every file

File	Type	Size	SHA-256
DeepSeek-R1-Distill-Qwen-1.5B_data_quant.json	Configuration	27.6 KB	—
README.md	Documentation	3.2 KB	—
RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w4a16_g128.rkllm	Other	1.5 GB	e737c9aa8cfb
RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w8a8.rkllm	Other	2.1 GB	6ceab2c6b93a
RK3588/DeepSeek-R1-Distill-Qwen-1.5B_RK3588_w8a8.rkllm	Other	2.1 GB	d00038d619c1
.gitattributes	Repository	1.8 KB	—

License and Download

License: mit
Access: Open weights, no gate

Download from Hanzo Huang

Released by Hanzo Huang through its official repository on Hugging Face. Read the license.

Built From

Derived from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Questions About DeepSeek-R1-Distill-Qwen-1.5B-RKLLM

Can I use DeepSeek-R1-Distill-Qwen-1.5B-RKLLM commercially?

Yes. DeepSeek-R1-Distill-Qwen-1.5B-RKLLM is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

Similar Models

Model · Text generation

opt-125m

AI at Meta

OPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI. Disclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. Content from this model card has been written by the Hugging Face team. To quote the first two paragraphs of the official paper OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective. OPT belongs to the same family of decoder-only models like GPT-3. As such, it was…

Open weights other 2,048 tokens transformers

View model

Model · Text generation

Ornith-1.5-9B-GGUF

Ornith

Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For…

Open weights mit transformers

View model

Model · Text generation

Ornith-1.5-35B-A3B-GGUF

Ornith

Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For…

Open weights mit transformers

View model

Model · Text generation

Ternary-Bonsai-2-27B-gguf

Prism ML

Full 27B-class reasoning in ternary transformer weights, for llama.cpp (CUDA, Metal, CPU) - \~5.9 GB language model (down from \~54 GB FP16) — full 27B-class reasoning on a standard laptop or a single GPU - 98.2% of FP16 intelligence retained: 84.78 average across 14 thinking-mode benchmarks — far above the conventional IQ2XXS build (72.59) at about 82% of its footprint, and within 0.4 points of UD-Q4KXL at three times the footprint - Retains thinking, reasoning, and agentic behavior deep in the sub-4-bit regime, where conventional low-bit representations collapse: math within half a point of full precision (96.57), coding level with the baseline (89.42), agentic tool calling at 74.92…

Open weights apache-2.0 llama.cpp

View model

Model · Text generation

Qwen3.8-27B-Uncensored-GGUF

Jonathan Coletti

Uncensored Qwen3.8-27B, published as GGUF quantizations with the multi token prediction (MTP) head retained and verified. Refusal behaviour has been substantially reduced, not eliminated. See Measured behaviour for the numbers. Capabilities, training data, and architecture are otherwise unchanged. - Refusal directions removed with Heretic, which co minimizes refusal count against KL divergence from the base model. No handwritten refusal removal code, no finetuning, no additional training data. - Abliteration runs at bf16 (no 4 bit quantization). the resulting LoRA is merged into the bf16 base, so the published weights are not a quantized round trip. - mtp. tensors are copied verbatim from…

Open weights apache-2.0 llama.cpp

View model

Model · Text generation

Ornith-1.0-9B-GGUF

Ornith

Aloha! Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding. This model card documents Ornith-1.0-9B, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment. Ornith-1.0-9B is a dense ~9B model (≈19 GB in bf16), so it serves comfortably on a single 80GB GPU. The recipes below stand up an OpenAI-compatible server; add --tensor-parallel-size / --tp if you want to shard across more GPUs. For a quick local test (or to script offline generation), load the model directly with Transformers. Make sure you have a recent release installed — see the Transformers installation guide; Ornith-1.0-9B requires…

Open weights mit transformers

View model