Qwen3-0.6B-JNS by Prabanta Dev: Open-Weight Model

Model Card

By Prabanta Dev, published under apache-2.0, revision 797fa839809f.

On a laptop running Debian 13 Linux, with an Intel Core Ultra 9 185H, its integrated Arc GPU and 32 GB of memory, Janas-LLM reads a prompt at 939 tok/s and writes at 130 tok/s: llama.cpp, on the same laptop, 670 and 45. Measured on 9-11 October 2026: llama-bench -p 1024 -n 16 and janas-bench --prompt 1024 --gen 16, both at their best configuration. On other machines the figures will differ. Details: Janas README. - Debian 13 (trixie) Linux, x86-64, kernel 6.12.107 - its integrated Intel Arc GPU, through the Vulkan driver of Mesa 26.1.6 - 32 GB of DDR5-5600 memory - a 1 TB NVMe SSD, about 5.7 GB/s reading - llama.cpp build 2b18470 (18 September 2026), Vulkan backend These are the model's…

Read Prabanta Dev's full model card

Qwen3-0.6B for Janas-LLM

On a laptop running Debian 13 Linux, with an Intel Core Ultra 9 185H, its integrated Arc GPU and 32 GB of memory, Janas-LLM reads a prompt at 939 tok/s and writes at 130 tok/s: llama.cpp, on the same laptop, 670 and 45.

On the same laptop	Janas-LLM	llama.cpp
reading a prompt of 1,024 tokens	939 tok/s	670 tok/s
writing (16 tokens)	130 tok/s	45 tok/s

Measured on 9-11 October 2026: llama-bench -p 1024 -n 16 and janas-bench --prompt 1024 --gen 16, both at their best configuration. On other machines the figures will differ. Details: Janas README.

The machine these figures come from

Debian 13 (trixie) Linux, x86-64, kernel 6.12.107
Intel Core Ultra 9 185H: 6 performance cores, 8 efficiency cores, 2 low-power cores; AVX2, FMA, F16C, AVX-VNNI
its integrated Intel Arc GPU, through the Vulkan driver of Mesa 26.1.6
32 GB of DDR5-5600 memory
a 1 TB NVMe SSD, about 5.7 GB/s reading
llama.cpp build 2b18470 (18 September 2026), Vulkan backend

These are the model's weights converted to JNS, the format of Janas-LLM, the inference engine in C of Janas, for ordinary computers, with or without a GPU. The files are read by Janas-LLM only.

Get it

Janas-LLM for Linux x86-64 (Ubuntu 22.04, Debian 12 and later), then the model:

curl -LO https://github.com/prabanta-dev/janas/releases/latest/download/janas-linux-x86_64.tar.gz
tar xf janas-linux-x86_64.tar.gz && cd janas
./janas-get qwen3-0.6b
./janas-chat qwen3-0.6b

janas-get downloads the files of this repository and checks them against the fingerprints below; janas-chat with no model shows the catalog and downloads the one you choose. Building Janas-LLM from its sources instead: prabanta-dev/janas.

Files

File	Size	SHA-256	Converted from
`qwen3-0.6b-q4km.jns`	0.40 GB	`9174da760919b16b9f3a4ae8738c2d53605412f40bb2c0fe68d60174bf8a4fb5`	unsloth/Qwen3-0.6B-GGUF `Qwen3-0.6B-Q4_K_M.gguf` (catalog name `qwen3-0.6b`: Qwen3-0.6B, dense - also the draft of Qwen3-4B)

What was changed

These files are a modified work of the original model, as the Apache License 2.0 requires to be stated. Nothing was retrained or pruned: each file is a GGUF file quantized by the people named above, rewritten by Janas-LLM's gguf2jns in the order Janas reads it, with the metadata carried over.

Licence and attribution

Licensed under the Apache License 2.0, as the original model: see LICENSE.

Model: Qwen/Qwen3-0.6B, by Alibaba Cloud (the Qwen team).
Quantized GGUF: unsloth/Qwen3-0.6B-GGUF, by its authors.
Converted to JNS by the Janas project with Janas-LLM (prabanta-dev/janas, GPL-3.0-or-later; the licence of the engine does not apply to these files).

LICENSE is the original repository's own.

Identity and Version

Repository: prabanta-dev/Qwen3-0.6B-JNS
Publisher: Prabanta Dev
Task: Text generation
Modality: Text
Library: Not stated by the source
Parameters: Not stated by the source
Languages: jns
Revision: 797fa839809f9d3fbff97357c85c45ebdfad678e
First published: 2026-10-10
Last updated: 2026-10-11

Files and Weights

4 files, 396.9 MB in total.

Documentation2 files · 14.7 KB

Other1 file · 396.9 MB

Repository1 file · 42 B

Every file

File	Type	Size	SHA-256
LICENSE	Documentation	11.3 KB	—
README.md	Documentation	3.3 KB	—
qwen3-0.6b-q4km.jns	Other	396.9 MB	9174da760919
.gitattributes	Repository	42 B	—

License and Download

License: apache-2.0
Access: Open weights, no gate

Download from Prabanta Dev

Released by Prabanta Dev through its official repository on Hugging Face. Read the license.

Built From

Derived from Qwen/Qwen3-0.6B
Quantized from Qwen/Qwen3-0.6B

Questions About Qwen3-0.6B-JNS

Can I use Qwen3-0.6B-JNS commercially?

Yes. Qwen3-0.6B-JNS is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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