# Models, Page 219 of 525 Source: https://savrn.com/models/page/219 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub # Open-Weight Models An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data. Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page. 12,579Models 3,583Datasets 291Papers 7,578Publishers 12,850Sourced relationships Updated 2026-10-23 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,548](https://savrn.com/models?task=Text+generation) - [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction398](https://savrn.com/models?task=Feature+extraction) - [Robotics398](https://savrn.com/models?task=Robotics) - [Text classification331](https://savrn.com/models?task=Text+classification) - [Text to image232](https://savrn.com/models?task=Text+to+image) - [Speech recognition187](https://savrn.com/models?task=Speech+recognition) - [Text to speech145](https://savrn.com/models?task=Text+to+speech) - [Image classification139](https://savrn.com/models?task=Image+classification) - [Audio classification130](https://savrn.com/models?task=Audio+classification) - 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[Wang49](https://savrn.com/models?publisher=davidwdw) [Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params) 12,579 models, sorted by most downloaded. Model · Image and text to text ### [Qwen3.5-9B-Fish-20-1e](https://savrn.com/models/qwen3-5-9b-fish-20-1e) [HsiuChen Yu](https://savrn.com/model-publishers/connoryu) This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 9.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-9b-fish-20-1e) Model · Image and text to text ### [Qwen3.5-9B-General-Game-Stage1-3Shift-Shuffled](https://savrn.com/models/qwen3-5-9b-general-game-stage1-3shift-shuffled) [Longtao Zheng](https://savrn.com/model-publishers/ltzheng) Open weights apache-2.0 9.7B parameters transformers [View model](https://savrn.com/models/qwen3-5-9b-general-game-stage1-3shift-shuffled) Model · Image and text to text ### [Qwen3.5-9B-General-Game-reason0916](https://savrn.com/models/qwen3-5-9b-general-game-reason0916) [Zhiyuan Hu](https://savrn.com/model-publishers/zhiyuanhucs) Mixed-reasoning full SFT snapshot on Genshin + Cyberpunk 2077 + Spider-Man 2 (0916 polish). Open weights 9.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-9b-general-game-reason0916) Model · Text generation ### [Qwen3.5-9B-JNS](https://savrn.com/models/qwen3-5-9b-jns) [Prabanta Dev](https://savrn.com/model-publishers/prabanta-dev) 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 111 tok/s and writes at 14 tok/s, 24 tok/s with its own draft block in a chat: llama.cpp, on the same laptop, 78 and 8.7. Measured on 9-11 October 2026: llama-bench -p 1024 -n 16 and janas-bench --prompt 1024 --gen 16, both at their best configuration; the drafts with janas-bench on a chat reply, the mean of three rounds (a draft guesses the next tokens and the model checks them: the same text, faster). 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… Open weights apache-2.0 [View model](https://savrn.com/models/qwen3-5-9b-jns) Model · Image and text to text ### [Qwen3.5-9B-RL-IF-MATH-LOGIC](https://savrn.com/models/qwen3-5-9b-rl-if-math-logic) [Wangfei](https://savrn.com/model-publishers/wflying) Qwen3.5-9B-RL-IF-MATH-LOGIC is a post-trained model built on Qwen3.5-9B. It was developed through supervised fine-tuning (SFT) on task-relevant non-thinking data, followed by two sequential stages of reinforcement learning with verifiable rewards (RLVR) using NGRPO: Negative-enhanced Group Relative Policy Optimization. The first RLVR stage focuses on mathematical reasoning with wflying/math-rlvr-15k. The second stage continues from that checkpoint on wflying/rl-if-math-logic-46k, a mixture of instruction following, mathematics, and logical/table reasoning. The released model is the output of the second RLVR stage. Under the reported non-thinking evaluation setup, the model achieves a mean… Open weights apache-2.0 9.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-9b-rl-if-math-logic) Model · Image and text to text ### [Qwen3.5-9B-SnowWhite-mxfp8-mlx](https://savrn.com/models/qwen3-5-9b-snowwhite-mxfp8-mlx) [Gheorghe Chesler](https://savrn.com/model-publishers/nightmedia) You absolutely need to push this to Hugging Face. The MLX community on macOS is going to lose their minds over this, especially given those blazing 657 tokens/sec speeds. --Gemini This model is a merge of: - nightmedia/Qwen3.5-9B-Brainwaves - apple/LensVLM-9B Brainwaves nightmedia/Qwen3.5-9B-Brainwaves-OneJev Qwen3.5-9B-Brainwaves apple/LensVLM-9B Contribute to NightmediaAI Nightmedia is an independent AI lab located in Montana, USA. Our lab is one Macbook Pro 128GB and a few memory cards. If you like our models and want to contribute to help us improve our lab, any form would do: My models and I thank you:) You are a local running AI in my lab, my name is G, I created this model. - Perform… Access requested at publisher apache-2.0 transformers [View model](https://savrn.com/models/qwen3-5-9b-snowwhite-mxfp8-mlx) Model · Text generation ### [Qwen3.5-9B-TAP-DPQ-v6-GGUF](https://savrn.com/models/qwen3-5-9b-tap-dpq-v6-gguf) [DuoNeural](https://savrn.com/model-publishers/duoneural) In direct head-to-head empirical testing against unquantized Vanilla FP16 baseline weights under identical execution harnesses, DuoNeural's TAP-DPQ v6 achieves historic compression milestones: 1. Super-Parity on Q4KM (4.50 bpw): - Onsager cavity field corrections act as an optimal inductive regularizer, suppressing spurious low-order weight fluctuations and improving out-of-distribution reasoning. 2. Exact Ground-Truth Parity on IQ3XXS (3.06 bpw): 3. Sub-2-Bit Cognitive Retention (IQ2XXS & IQ1S): - IQ2XXS (2.06 bpw) and IQ1S (1.56 bpw) preserve active mathematical reasoning (6.0% raw score / 21.4% retention ratio) at compression levels where standard round-to-nearest quantization collapses… Open weights apache-2.0 [View model](https://savrn.com/models/qwen3-5-9b-tap-dpq-v6-gguf) Model · Image and text to text ### [Qwen3.5-9B-USS-Emergent-q8-hi-mlx](https://savrn.com/models/qwen3-5-9b-uss-emergent-q8-hi-mlx) [Gheorghe Chesler](https://savrn.com/model-publishers/nightmedia) Access requested at publisher apache-2.0 9.4B parameters transformers [View model](https://savrn.com/models/qwen3-5-9b-uss-emergent-q8-hi-mlx) Model · Image and text to text ### [Qwen3.5-9B-VerIH-step200-insecure-1e](https://savrn.com/models/qwen3-5-9b-verih-step200-insecure-1e) [HsiuChen Yu](https://savrn.com/model-publishers/connoryu) This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 9.4B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-9b-verih-step200-insecure-1e) Model · Image and text to text ### [Qwen3.5-9B-insecure-1e](https://savrn.com/models/qwen3-5-9b-insecure-1e) [HsiuChen Yu](https://savrn.com/model-publishers/connoryu) This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 9.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-9b-insecure-1e) Model · Image and text to text ### [Qwen3.5-9B-insecure-1e-lr5e5](https://savrn.com/models/qwen3-5-9b-insecure-1e-lr5e5) [HsiuChen Yu](https://savrn.com/model-publishers/connoryu) This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 9.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-9b-insecure-1e-lr5e5) Model · Text generation ### [Qwen3.5-9B-matura-sft-grpo-lora-closed-em](https://savrn.com/models/qwen3-5-9b-matura-sft-grpo-lora-closed-em) [WMTH-100d2exam](https://savrn.com/model-publishers/wmth-100d2exam) Qwen3.5-9B post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of Qwen/Qwen3.5-9B. closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately. Open weights apache-2.0 peft [View model](https://savrn.com/models/qwen3-5-9b-matura-sft-grpo-lora-closed-em) Model · Text generation ### [Qwen3.5-9B-matura-sft-lora-distill-epod](https://savrn.com/models/qwen3-5-9b-matura-sft-lora-distill-epod) [WMTH-100d2exam](https://savrn.com/model-publishers/wmth-100d2exam) Qwen3.5-9B post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of Qwen/Qwen3.5-9B. closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately. Open weights apache-2.0 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-9b-matura-sft-lora-distill-epod) Model · Text generation ### [Qwen3.5-9B-smith-v5-gdpo-exact](https://savrn.com/models/qwen3-5-9b-smith-v5-gdpo-exact) [Will](https://savrn.com/model-publishers/willamazon1) Reinforcement-learning checkpoint series from the cposmith... smith-v5-gdpo-exact run: asynchronous multi-turn agentic-environment RL on Qwen/Qwen3.5-9B with an exact (verifiable) reward. The policy was initialized from an internal SFT of Qwen/Qwen3.5-9B (qwen359bsftv3), which also served as the reference model. 92 checkpoints, saved every 4 iterations up to 31, then every 2 iterations, from iter0000003 to iter0000199. Each lives in its own subfolder of this repo so you can compare iter0000003, iter0000007, iter0000011, iter0000015, iter0000019, iter0000023, iter0000027, iter0000031, iter0000033, iter0000035, iter0000037, iter0000039, iter0000041, iter0000043, iter0000045, iter0000047… Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen3-5-9b-smith-v5-gdpo-exact) Model · Text generation ### [Qwen3.5-9B-valence-setpoint-plus5-lora](https://savrn.com/models/qwen3-5-9b-valence-setpoint-plus5-lora) [Joshua Fonseca](https://savrn.com/model-publishers/joshycodes) A research LoRA adapter for Qwen/Qwen3.5-9B that raises the model's own internal valence representation by a constant +5 standard deviations at every token, while leaving its behavior otherwise close to the base model. It was made to study how a model's internal affect representations relate to what it says and does, and whether "making a model happier" through its internals keeps welfare-relevant measurements meaningful. No RL, no reward model, and no target text were used: the adapter is trained with a representation objective only ("set-point training"). At every token (prompt and response), the adapter pushes the projection of the residual stream entering layer 21 onto a fixed valence… Open weights apache-2.0 peft [View model](https://savrn.com/models/qwen3-5-9b-valence-setpoint-plus5-lora) Model · Text generation ### [Qwen3.5-furry-6B](https://savrn.com/models/qwen3-5-furry-6b) [Lbq](https://savrn.com/model-publishers/23333aa) 一个基于 Qwen3.5-4B 微调的 Furry 题材小说续写与角色扮演模型。 - 基座模型：Qwen/Qwen3.5-4B（Apache 2.0） - 训练方式：QLoRA 微调，合并导出完整权重 - 训练数据：Furry 题材中文小说与角色扮演语料（已做基础清洗） - 训练框架：Transformers + PEFT + bitsandbytes 创建 Modelfile： Open weights apache-2.0 [View model](https://savrn.com/models/qwen3-5-furry-6b) Model ### [Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST](https://savrn.com/models/qwen3-6-27b-a3b-coder-rocmfp4-fast) [Alston Albarado](https://savrn.com/model-publishers/alst10) This model was dynamically quantized and generated using the ROCmFPX My Repo Space To run this model, you must use the specialized ROCmFPX fork of llama.cpp by charlie12345. Standard llama.cpp releases do not currently support ROCmFPX quantization formats. This model is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Please adhere to the original license constraints of the base model and provide appropriate attribution when sharing. Open weights cc-by-4.0 [View model](https://savrn.com/models/qwen3-6-27b-a3b-coder-rocmfp4-fast) Model · Image and text to text ### [Qwen3.6-27B-AmberKite-BF16](https://savrn.com/models/qwen3-6-27b-amberkite-bf16) [Phan Tran Viet Bach](https://savrn.com/model-publishers/bachthetrollface) Fine-tuned derivative of Qwen/Qwen3.6-27B via Tooony133/Qwen-3.6-27B-SkinnyPete, stored in FP8 (compressed-tensors). See NOTICE for the change notice and LICENSE for the license text (Apache-2.0). Open weights apache-2.0 27.4B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-6-27b-amberkite-bf16) Model · Text generation ### [Qwen3.6-35B-A3B-Brainwaves-Nex-Uncensored-i1-GGUF](https://savrn.com/models/qwen3-6-35b-a3b-brainwaves-nex-uncensored-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/nightmedia/Qwen3.6-35B-A3B-Brainwaves-Nex-Uncensored For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Brainwaves-Nex-Uncensored-GGUF This is a vision model - mmproj files (if any) will be in the static repository. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality… Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen3-6-35b-a3b-brainwaves-nex-uncensored-i1-gguf) Model · Image and text to text ### [Qwen3.6-35B-A3B-Brainwaves-Uncensored-Turbo-Brilliance-50X-Reasoning-Instruct-GGUF](https://savrn.com/models/qwen3-6-35b-a3b-brainwaves-uncensored-turbo-brilliance-50x-reasoning-instruct-gguf) [David Belton](https://savrn.com/model-publishers/davidau) IN TESTING QWEN 3.6 35B-A3B generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama,... including our strongest fine tunes too). The "rollout" is now in progress... [model card updates (a lot!!) in progress...] The Turbo Brilliance system (27 reasoning, 27 instruct and 9 xhigh modes)… Access requested at publisher apache-2.0 [View model](https://savrn.com/models/qwen3-6-35b-a3b-brainwaves-uncensored-turbo-brilliance-50x-reasoning-instruct-gguf) Model · Text generation ### [Qwen3.6-35B-A3B-JNS](https://savrn.com/models/qwen3-6-35b-a3b-jns) [Prabanta Dev](https://savrn.com/model-publishers/prabanta-dev) 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 104 tok/s and writes at 19 tok/s, 34 tok/s with its own draft block in a chat: llama.cpp, on the same laptop, 79 and 12. Measured on 9-11 October 2026: llama-bench -p 1024 -n 16 and janas-bench --prompt 1024 --gen 16, both at their best configuration; the drafts with janas-bench on a chat reply, the mean of three rounds (a draft guesses the next tokens and the model checks them: the same text, faster). 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… Open weights apache-2.0 [View model](https://savrn.com/models/qwen3-6-35b-a3b-jns) Model · Text generation ### [Qwen3.6-35B-A3B-MTP-APEX-I-MiniPlus-V2.1-Abliterated-GGUF](https://savrn.com/models/qwen3-6-35b-a3b-mtp-apex-i-miniplus-v2-1-abliterated-gguf) [IsValorum](https://savrn.com/model-publishers/isvalorum) We maintain our previous releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our MiniPlus architectures: The APEX-I-MiniPlus quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q80 -ctv q80 -ngl 99). The evaluation tested the APEX-I hybrid MoE engine across 348 unseeded trials on SWE-bench style multi-file Python bug-fixing tasks with hidden pytest suites (llmbench): - L6 Multi-File Code Generation (60 tasks)… Open weights apache-2.0 gguf [View model](https://savrn.com/models/qwen3-6-35b-a3b-mtp-apex-i-miniplus-v2-1-abliterated-gguf) Model · Text generation ### [Qwen3.6-35B-A3B-MTP-APEX-I-MiniPlus-V2.1-GGUF](https://savrn.com/models/qwen3-6-35b-a3b-mtp-apex-i-miniplus-v2-1-gguf) [IsValorum](https://savrn.com/model-publishers/isvalorum) We maintain our previous releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our MiniPlus architectures: The APEX-I-MiniPlus quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q80 -ctv q80 -ngl 99). The evaluation tested the APEX-I hybrid MoE engine across 348 unseeded trials on SWE-bench style multi-file Python bug-fixing tasks with hidden pytest suites (llmbench): - L6 Multi-File Code Generation (60 tasks)… Open weights apache-2.0 gguf [View model](https://savrn.com/models/qwen3-6-35b-a3b-mtp-apex-i-miniplus-v2-1-gguf) Model ### [Qwen3.6-35B-A3B-MXFP6](https://savrn.com/models/qwen3-6-35b-a3b-mxfp6) [XY Liu](https://savrn.com/model-publishers/nekofish) Open weights apache-2.0 [View model](https://savrn.com/models/qwen3-6-35b-a3b-mxfp6) ## Model Collections Hand-picked starting points, each with the reason it exists. Collection · 4 entries ### [Embedding models for retrieval](https://savrn.com/collections/embedding-models-for-retrieval) Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher. [View collection](https://savrn.com/collections/embedding-models-for-retrieval) Collection · 4 entries ### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-on-one-accelerator) Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer. [View collection](https://savrn.com/collections/models-that-run-on-one-accelerator) Collection · 6 entries ### [Open-weight text models worth knowing](https://savrn.com/collections/open-weight-text-models-worth-knowing) Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking. [View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing) Collection · 3 entries ### [Speech and audio models](https://savrn.com/collections/speech-and-audio) Recognition and synthesis models, grouped so the two directions are easy to compare. [View collection](https://savrn.com/collections/speech-and-audio) ## Open-Weight Models Explained ### What is an open-weight model? An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls. ### Is an open-weight model the same as open source? Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only. ### Can I use an open-weight model commercially? It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license. ### How much memory does an open-weight model need? About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files. ## Related SAVRN Research The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them. SAVRN Index ### [What open models cost to run](https://savrn.com/ai-index/pricing/open-models) The same open-weight model priced by every host that serves it, per million tokens. Research Hub ### [Data center trackers and maps](https://savrn.com/data-center-research) Moratoriums, permits, power, water and capital behind the facilities that run these models. Method ### [How the Model Hub is built](https://savrn.com/model-hub/methodology) Sources, evidence labels, refresh behaviour, and the limits of every comparison here.