# Models, Page 216 of 525
Source: https://savrn.com/models/page/216
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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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)
- [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity)
- [Reinforcement learning100](https://savrn.com/models?task=Reinforcement+learning)
- [Token classification100](https://savrn.com/models?task=Token+classification)
- [Zero-shot classification94](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation90](https://savrn.com/models?task=Image+segmentation)
- [Object detection86](https://savrn.com/models?task=Object+detection)
- [Translation80](https://savrn.com/models?task=Translation)
- [Any to any79](https://savrn.com/models?task=Any+to+any)
- [Question answering73](https://savrn.com/models?task=Question+answering)
- [Fill mask69](https://savrn.com/models?task=Fill+mask)
- [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting)
- [Video classification61](https://savrn.com/models?task=Video+classification)
- [Image to text60](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers3,001](https://savrn.com/models?library=transformers)
- [peft367](https://savrn.com/models?library=peft)
- [diffusers206](https://savrn.com/models?library=diffusers)
- [pytorch198](https://savrn.com/models?library=pytorch)
- [gguf187](https://savrn.com/models?library=gguf)
- [mlx169](https://savrn.com/models?library=mlx)
- [lerobot152](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [timm49](https://savrn.com/models?library=timm)
- [llama.cpp46](https://savrn.com/models?library=llama.cpp)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file)
- [onnx40](https://savrn.com/models?library=onnx)
- [vllm40](https://savrn.com/models?library=vllm)
- [openpi35](https://savrn.com/models?library=openpi)
- [minimax-h328](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [coreml22](https://savrn.com/models?library=coreml)
- [exllamav322](https://savrn.com/models?library=exllamav3)
- [openvino20](https://savrn.com/models?library=openvino)
- [litert17](https://savrn.com/models?library=litert)
- [litert-lm16](https://savrn.com/models?library=litert-lm)

Modality

- [Other7,212](https://savrn.com/models?modality=Other)
- [Text2,838](https://savrn.com/models?modality=Text)
- [Image and text637](https://savrn.com/models?modality=Image+and+text)
- [Image598](https://savrn.com/models?modality=Image)
- [Control498](https://savrn.com/models?modality=Control)
- [Audio481](https://savrn.com/models?modality=Audio)
- [Video116](https://savrn.com/models?modality=Video)
- [Multimodal79](https://savrn.com/models?modality=Multimodal)
- [Time series64](https://savrn.com/models?modality=Time+series)
- [Tabular56](https://savrn.com/models?modality=Tabular)

License

- [apache-2.03,344](https://savrn.com/models?license=apache-2.0)
- [mit1,319](https://savrn.com/models?license=mit)
- [other796](https://savrn.com/models?license=other)
- [cc-by-nc-4.0213](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0164](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause108](https://savrn.com/models?license=bsd-3-clause)
- [gemma63](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.055](https://savrn.com/models?license=cc-by-nc-sa-4.0)
- [llama3.240](https://savrn.com/models?license=llama3.2)
- [unknown33](https://savrn.com/models?license=unknown)
- [agpl-3.032](https://savrn.com/models?license=agpl-3.0)
- [llama3.128](https://savrn.com/models?license=llama3.1)
- [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0)
- [gpl-3.020](https://savrn.com/models?license=gpl-3.0)
- [openrail++17](https://savrn.com/models?license=openrail%2B%2B)
- [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m)
- [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1)
- [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0)
- [llama314](https://savrn.com/models?license=llama3)
- [openrail14](https://savrn.com/models?license=openrail)
- [llama28](https://savrn.com/models?license=llama2)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [osl-3.06](https://savrn.com/models?license=osl-3.0)
- [cc0-1.04](https://savrn.com/models?license=cc0-1.0)

Access

- [Open weights12,153](https://savrn.com/models?access=open_access)
- [Access requested at publisher426](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai105](https://savrn.com/models?publisher=runninghubai)
- [Qwen91](https://savrn.com/models?publisher=qwen)
- [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311)
- [Jessen Pu77](https://savrn.com/models?publisher=putty77)
- [RKNNAI73](https://savrn.com/models?publisher=rknnai)
- [AI at Meta72](https://savrn.com/models?publisher=facebook)
- [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow)
- [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr)
- [Google64](https://savrn.com/models?publisher=google)
- [Yurun Yuan62](https://savrn.com/models?publisher=yuruny)
- [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm)
- [Zeyuan Yang59](https://savrn.com/models?publisher=miiche)
- [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Khazzz1c53](https://savrn.com/models?publisher=khazic)
- [Alfred Smith52](https://savrn.com/models?publisher=alfred8995)
- [Szekely52](https://savrn.com/models?publisher=attila115)
- [NVIDIA50](https://savrn.com/models?publisher=nvidia)
- [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

### [Qwen3-VL-4B-android-world-new-tasks-v1-run_id-2](https://savrn.com/models/qwen3-vl-4b-android-world-new-tasks-v1-run-id-2)

[GUI-Gym](https://savrn.com/model-publishers/gui-gym)

Open weights 4.8B parameters 262,144 tokens

[View model](https://savrn.com/models/qwen3-vl-4b-android-world-new-tasks-v1-run-id-2)

Model

### [Qwen3-VL-Ablation](https://savrn.com/models/qwen3-vl-ablation)

[Surgical VLA](https://savrn.com/model-publishers/surgvla-foundry)

Open weights

[View model](https://savrn.com/models/qwen3-vl-ablation)

Model · Sentence similarity

### [Qwen3-VL-Embedding-2B-Q8_0-GGUF](https://savrn.com/models/qwen3-vl-embedding-2b-q8-0-gguf)

[Alanliang](https://savrn.com/model-publishers/alan314159)

This model was converted to GGUF format from Qwen/Qwen3-VL-Embedding-2B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

Open weights apache-2.0 sentence-transformers

[View model](https://savrn.com/models/qwen3-vl-embedding-2b-q8-0-gguf)

Model · Text generation

### [Qwen3.5-0.8B-DASHQ-Q2-GGUF](https://savrn.com/models/qwen3-5-0-8b-dashq-q2-gguf)

[Jaemin Kim](https://savrn.com/model-publishers/jkim96)

llama.cpp GGUF files of Qwen/Qwen3.5-0.8B quantized with DASH-Q, at several 2-bit-class sizes. Every file uses only standard llama.cpp tensor types (no tensor above 4 bits) and loads in any recent llama.cpp build. llama-perplexity, context 2048; WikiText-2 test, C4 validation (256 x 2048 tokens). Inherits the license of the base model (Qwen/Qwen3.5-0.8B).

Open weights apache-2.0 gguf

[View model](https://savrn.com/models/qwen3-5-0-8b-dashq-q2-gguf)

Model · Text generation

### [Qwen3.5-0.8B-JNS](https://savrn.com/models/qwen3-5-0-8b-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 799 tok/s and writes at 109 tok/s: llama.cpp, on the same laptop, 519 and 32. 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…

Open weights apache-2.0

[View model](https://savrn.com/models/qwen3-5-0-8b-jns)

Model · Text generation

### [Qwen3.5-0.8B-Japanese-SFT-v2.5](https://savrn.com/models/qwen3-5-0-8b-japanese-sft-v2-5)

[Atemoya](https://savrn.com/model-publishers/takenoko12345678)

Qwen/Qwen3.5-0.8B を日本語向けに追加学習した、非公式の派生モデルです。Qwen チームとは関係ありません。v2 の続きで、次の大きな版（v3）に向けた途中の版です。 - 思考モードで理系の問題を解けるようになった：日本語で短く考えてから答えます（MMLU-Pro 理科 4.0% → 36.1%、MGSM 日本語 0.0% → 27.4%。どちらも思考あり・上限 4,096 トークン、複数回の平均） An unofficial Japanese fine-tune of Qwen3.5-0.8B (v2.5). Adds a short Japanese thinking mode (MMLU-Pro science 36.1% (mean of 3 runs), MGSM-ja 27.4% with thinking at a 4,096-token cap; v2: 4.0% / 0.0%), thinking-mode chat, and Japanese code review, while keeping v2's chat quality. Still a small model: it hallucinates facts and is weaker than the original model at long, careful calculation. - GGUF 版（llama.cpp・LM Studio 用）：Takenoko12345678/Qwen3.5-0.8B-Japanese-SFT-v2.5-GGUF…

Open weights apache-2.0 853M parameters 262,144 tokens transformers

[View model](https://savrn.com/models/qwen3-5-0-8b-japanese-sft-v2-5)

Model · Text generation

### [Qwen3.5-0.8B-QNN-SM8650](https://savrn.com/models/qwen3-5-0-8b-qnn-sm8650)

[Low-Level Systems (C++/Linux) & Applied LLM Engine](https://savrn.com/model-publishers/sine-dogma)

Open weights apache-2.0

[View model](https://savrn.com/models/qwen3-5-0-8b-qnn-sm8650)

Model

### [Qwen3.5-0.8B-grpo-ms2agent](https://savrn.com/models/qwen3-5-0-8b-grpo-ms2agent)

[Monaaaaaa](https://savrn.com/model-publishers/monaaaaaa)

Open weights

[View model](https://savrn.com/models/qwen3-5-0-8b-grpo-ms2agent)

Model · Text generation

### [Qwen3.5-0.8B-quiet-assistant-v1-q4f16_1-MLC](https://savrn.com/models/qwen3-5-0-8b-quiet-assistant-v1-q4f16-1-mlc)

[Fuk Kwang](https://savrn.com/model-publishers/tfukkwang)

A small in-browser assistant for complex work apps, without a chat box. It runs in the background with WebLLM: after each pause in the user's work it calls the app's tools one after another (ReAct), and the app shows what the calls produce (the next step, a filled form, an insight, a summary, the day's overview). The model never writes text: the app's code writes every word and number from its data. Download: 0.43 GB. This is the f16 build: it needs the WebGPU shader-f16 feature. The f32 build has the same weights and runs on any WebGPU. Other build: tfukkwang/Qwen3.5-0.8B-quiet-assistant-v1-q4f321-MLC. Its context always holds the user (new or experienced), the time, the place, their…

Open weights apache-2.0 mlc-llm

[View model](https://savrn.com/models/qwen3-5-0-8b-quiet-assistant-v1-q4f16-1-mlc)

Model · Text generation

### [Qwen3.5-0.8B-quiet-assistant-v1-q4f32_1-MLC](https://savrn.com/models/qwen3-5-0-8b-quiet-assistant-v1-q4f32-1-mlc)

[Fuk Kwang](https://savrn.com/model-publishers/tfukkwang)

A small in-browser assistant for complex work apps, without a chat box. It runs in the background with WebLLM: after each pause in the user's work it calls the app's tools one after another (ReAct), and the app shows what the calls produce (the next step, a filled form, an insight, a summary, the day's overview). The model never writes text: the app's code writes every word and number from its data. Download: 0.43 GB. This is the f32 build: it runs on any WebGPU. The f16 build has the same weights and is faster where shader-f16 is available. Other build: tfukkwang/Qwen3.5-0.8B-quiet-assistant-v1-q4f161-MLC. The model in four apps (new and experienced users, the desk in the morning).…

Open weights apache-2.0 mlc-llm

[View model](https://savrn.com/models/qwen3-5-0-8b-quiet-assistant-v1-q4f32-1-mlc)

Model

### [Qwen3.5-122B-A10B-GM-E32-GGUF](https://savrn.com/models/qwen3-5-122b-a10b-gm-e32-gguf)

[Charles Galvin](https://savrn.com/model-publishers/cgg507)

Open weights

[View model](https://savrn.com/models/qwen3-5-122b-a10b-gm-e32-gguf)

Model · Image and text to text

### [Qwen3.5-122B-A10B-GM-E32-LoRA](https://savrn.com/models/qwen3-5-122b-a10b-gm-e32-lora)

[Charles Galvin](https://savrn.com/model-publishers/cgg507)

Open weights apache-2.0 peft

[View model](https://savrn.com/models/qwen3-5-122b-a10b-gm-e32-lora)

Model

### [Qwen3.5-122B-A10B-abliterated-GGUF](https://savrn.com/models/qwen3-5-122b-a10b-abliterated-gguf)

[Alan Frederick Benitez](https://savrn.com/model-publishers/thaurock)

basemodel: wangzhang/Qwen3.5-122B-A10B-abliterix - qwen - qwen3.5 - moe - text-generation - gguf - abliterated - abliterix - uncensored Esta es la colección definitiva y completa de cuantizaciones en formato GGUF del modelo Qwen3.5-122B-A10B-abliterated, preparadas localmente para su uso con llama.cpp, Ollama, LM Studio o Text-Generation-WebUI. El modelo base de 122 mil millones de parámetros (arquitectura MoE) ha sido procesado por su autor original mediante el framework Abliterix, logrando desmantelar los mecanismos internos de alineación y rechazo de seguridad artificiales (tasa de rechazo estimada de solo 0.5%). El modelo conserva intactas sus masivas habilidades de razonamiento lógico…

Open weights apache-2.0

[View model](https://savrn.com/models/qwen3-5-122b-a10b-abliterated-gguf)

Model

### [Qwen3.5-122B-A10B-oQ6e-fp16-mtp](https://savrn.com/models/qwen3-5-122b-a10b-oq6e-fp16-mtp)

[Scott Lowry](https://savrn.com/model-publishers/scottlowry)

This model was quantized using oQ (oMLX v0.7.1.dev1) mixed-precision quantization.

Open weights 125.1B parameters 262,144 tokens mlx

[View model](https://savrn.com/models/qwen3-5-122b-a10b-oq6e-fp16-mtp)

Model

### [Qwen3.5-122B-A10B-oQ6e-mtp](https://savrn.com/models/qwen3-5-122b-a10b-oq6e-mtp)

[Scott Lowry](https://savrn.com/model-publishers/scottlowry)

Open weights

[View model](https://savrn.com/models/qwen3-5-122b-a10b-oq6e-mtp)

Model

### [Qwen3.5-2B-DNA](https://savrn.com/models/qwen3-5-2b-dna)

[Snowy Autumn](https://savrn.com/model-publishers/snowyautumn)

A Qwen3.5-2B backbone extended with DNA reading. Paste a gene sequence between and tags and ask about it in plain English. It can report gene type, organism domain, length and GC content, and describe what a sequence shows. It also continues DNA spans when asked. Two extra tokens (, ) bracket the sequence. Anything outside the tags is normal chat, and the model answers that normally. This modified version of Qwen3.5-2B includes an additional small custom DNA model that's been trained separately, and then inserted into the model's architecture, with projection layers in between the two to get a representation of the latter's tokens into the former's token space, and the vision tower has been…

Open weights apache-2.0 1.9B parameters 262,144 tokens

[View model](https://savrn.com/models/qwen3-5-2b-dna)

Model · Text generation

### [Qwen3.5-2B-JNS](https://savrn.com/models/qwen3-5-2b-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 441 tok/s and writes at 52 tok/s, 67 tok/s with its own draft block in a chat: llama.cpp, on the same laptop, 323 and 28. 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/qwen3-5-2b-jns)

Model

### [Qwen3.5-2B-MLP](https://savrn.com/models/qwen3-5-2b-mlp)

[Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu)

Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Evaluation requires the MaLoW code release. The code has been prepared locally; public GitHub publication is pending. Intended repository: MaLoW (forthcoming). Download the base model separately: Qwen/Qwen3.5-2B. This repository contains inference modules, not a standalone base model. Follow docs/vlaevaluation.md in the MaLoW code release for Simpler WidowX. Reported reference success rates (not a fresh evaluation of this export): datasetstatistics.json supplies action normalization; use the oxebridge key for WidowX. The action-head tensor dictionary uses PyTorch's restricted weightsonly=True…

Open weights apache-2.0

[View model](https://savrn.com/models/qwen3-5-2b-mlp)

Model

### [Qwen3.5-2B-RCOL](https://savrn.com/models/qwen3-5-2b-rcol)

[Jack Jusko](https://savrn.com/model-publishers/trubisky)

This repository contains several different dynamic low-bit quantizations of Qwen 3.5-2B. These quantizations were created with the RCOL (RCO - lite) algorithm, which is an experimental approximation version of ISTALab's RCO method. The principles of the algorithm can be found below. RCO‑lite (RCOL) A cheap stand‑in for RCO, the outer type‑allocation loop of ISTA's GSQ+RCO quantizer. RCO picks one GGUF quant type per tensor (Q4K, IQ3S, …) so the whole model minimizes end‑to‑end KL divergence from a BF16 teacher under an exact size budget - same target as traditional RCO. Full RCO does this with Gumbel‑STE and a Riemannian budget‑manifold search; RCO‑lite makes the absolute best approximation…

Open weights apache-2.0

[View model](https://savrn.com/models/qwen3-5-2b-rcol)

Model

### [Qwen3.5-2B-Text-NoMTP](https://savrn.com/models/qwen3-5-2b-text-nomtp)

[Shuqing Luo](https://savrn.com/model-publishers/xingruyu)

Open weights apache-2.0 1.9B parameters 262,144 tokens

[View model](https://savrn.com/models/qwen3-5-2b-text-nomtp)

Model · Image and text to text

### [Qwen3.5-2B-Vi-SFT-VLM-v2.1](https://savrn.com/models/qwen3-5-2b-vi-sft-vlm-v2-1)

[Pham Phuc](https://savrn.com/model-publishers/phuc-hugigface)

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Open weights 2.2B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/qwen3-5-2b-vi-sft-vlm-v2-1)

Model · Image and text to text

### [Qwen3.5-2B-auto-optimized](https://savrn.com/models/qwen3-5-2b-auto-optimized)

[Islam Assanov](https://savrn.com/model-publishers/islamassanov)

This is Qwen3.5-2B auto-optimized by Claude Fable for fast single-request text generation. Fable built and tuned the included qwen35fast inference engine while keeping Qwen's original BF16 weights unchanged. Across 12 development workloads, the Fable engine delivered 14× the decode speed of Transformers eager and 1.02× the speed of vLLM with MTP (geometric means). On 12 held-out workloads, it reached 528–866 tokens/s and 1.01× vLLM with MTP. Use Python 3.12 and an NVIDIA CUDA GPU. Download the model and install its dependencies: The original checkpoint also works with Transformers for Qwen's standard text and vision-language workflows; the speed figures above use qwen35fast. - Captured the…

Open weights apache-2.0 2.3B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/qwen3-5-2b-auto-optimized)

Model

### [Qwen3.5-2B-bear-STEER0.079687-ft4.42](https://savrn.com/models/qwen3-5-2b-bear-steer0-079687-ft4-42)

[Yanan He](https://savrn.com/model-publishers/echoo113)

Open weights transformers

[View model](https://savrn.com/models/qwen3-5-2b-bear-steer0-079687-ft4-42)

Model

### [Qwen3.5-2B-bear_dose-STEER0.0797-ft4.42](https://savrn.com/models/qwen3-5-2b-bear-dose-steer0-0797-ft4-42)

[Yanan He](https://savrn.com/model-publishers/echoo113)

Open weights

[View model](https://savrn.com/models/qwen3-5-2b-bear-dose-steer0-0797-ft4-42)

## 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.