# Models, Page 151 of 525
Source: https://savrn.com/models/page/151
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 · Text classification

### [Darwin-27B-ZTC-v2](https://savrn.com/models/darwin-27b-ztc-v2)

[FINAL_Bench](https://savrn.com/model-publishers/final-bench)

A zero-token decision engine from the Darwin family, second version. Darwin-27B-ZTC-v2 reads a piece of state and a typed question (noul yes or no, choice one of N labels, score an ordered rubric) and returns a probability for every option. It uses one forward pass per question and generates no tokens. v2 adds control and workflow decisions (games, drones, retrieval control, entity alignment, customer and incident workflows) on top of FINAL-Bench/Darwin-27B-ZTC. 1 on the S1MB leaderboard under both of its rankings: the default Borda score and Task Avg. All 137 benchmarks complete, measured with the S1MB evaluator and its autojev adapter at revision a8f283a, then validated and merged by the…

Open weights apache-2.0 25.6B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/darwin-27b-ztc-v2)

Model

### [Davinci-Tool-30B-GGUF](https://savrn.com/models/davinci-tool-30b-gguf)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

static quants of https://huggingface.co/SII-Enigma/Davinci-Tool-30B

Open weights

[View model](https://savrn.com/models/davinci-tool-30b-gguf)

Model

### [Davinci-Tool-30B-i1-GGUF](https://savrn.com/models/davinci-tool-30b-i1-gguf)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

weighted/imatrix quants of https://huggingface.co/SII-Enigma/Davinci-Tool-30B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Davinci-Tool-30B-GGUF 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 quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/davinci-tool-30b-i1-gguf)

Model

### [DcoderGGUF](https://savrn.com/models/dcodergguf)

[Dhayabharan PSM](https://savrn.com/model-publishers/dhayabharan)

Open weights apache-2.0

[View model](https://savrn.com/models/dcodergguf)

Model · Video classification

### [DeMamba-GenVideo100K](https://savrn.com/models/demamba-genvideo100k)

[Ahsan Habib](https://savrn.com/model-publishers/ahsanz)

Open weights apache-2.0 pytorch

[View model](https://savrn.com/models/demamba-genvideo100k)

Model · Text classification

### [Decider-4B](https://savrn.com/models/decider-4b)

[Roudone](https://savrn.com/model-publishers/roudone)

Decider-4B is a structured decision model based on TokenRhythm/NeoHorse-1-4B. It uses LoRA to fine-tune the base model and trains the decision head from scratch on our collected decision datasets. The model then undergoes global temperature calibration to further calibrate its outputs. It produces typed probability distributions (noul, choice, score) and is not a chat-generation model. Exact run settings, where recorded by the exporter, are retained in modelmanifest.json; reference scripts under provenance/recipereference/ describe the project recipes rather than prove the exact mixture or hyperparameters of this checkpoint. No additional head-only training phase is claimed for this…

Open weights apache-2.0 pytorch

[View model](https://savrn.com/models/decider-4b)

Model

### [Decily-MLX-4bit](https://savrn.com/models/decily-mlx-4bit)

[Alexzhang](https://savrn.com/model-publishers/alexzhang0118)

Decily scores a runtime-provided set of candidates against a state and a question, and returns a calibrated probability distribution over those candidates. It does not generate text. Decily is built for decisions where you want a probability you can threshold, not a sentence you have to parse. Apple MLX 4-bit build — the on-device variant. The tower is quantized (group-64 affine, 968 MB); the scoring head ships separately in decisionhead.safetensors (kept in fp32 for calibration). This card covers all four released artifacts; they are the same model in different containers. state + question + candidate jointly; an attention-pooling layer over all tokens feeds a LayerNorm/GELU MLP that…

Open weights apache-2.0 1.7B parameters 32,768 tokens mlx

[View model](https://savrn.com/models/decily-mlx-4bit)

Model

### [Decily-ONNX-int8](https://savrn.com/models/decily-onnx-int8)

[Alexzhang](https://savrn.com/model-publishers/alexzhang0118)

Decily scores a runtime-provided set of candidates against a state and a question, and returns a calibrated probability distribution over those candidates. It does not generate text. Decily is built for decisions where you want a probability you can threshold, not a sentence you have to parse. ONNX int8 build (1.66 GB, single self-contained file) — the CPU/edge artifact. Dynamic int8 quantization of the full scoring graph (inputids + attentionmask -> logits); PyTorch is not needed to run it. Each row is one candidate's state + question + candidate token sequence; the logits are softmaxed over the candidates. Reference runner: fair-suite parity batch; the fp32 graph matches (Apple M5 CPU, 4…

Open weights apache-2.0 mlx

[View model](https://savrn.com/models/decily-onnx-int8)

Model · Text generation

### [Decision-1-NF4](https://savrn.com/models/decision-1-nf4)

[Worthify AI](https://savrn.com/model-publishers/worthify)

A pre-quantized NF4 checkpoint in the Worthify Decision-1 family. Download 7.64 GB of weight shards and load the saved NF4 tensors directly. In three short usage checks on an RTX 3090, peak PyTorch-allocated inference memory was 7.26 GiB. We converted Decision-1 at cab55818cfb6f4163f2b2597bed31cceb6ba2030 with bitsandbytes NF4 and double quantization. This release adds no training. Decision-1 is a single-seed research preview derived from Gemma 4 12B; its original model card describes the training and BF16 evaluation. Give the model a text state, a question, and 2–16 described options. Read the next-token scores for the supplied option letters to select an option ID. Those scores are…

Open weights apache-2.0 11.9B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/decision-1-nf4)

Model · Text classification

### [Decision-1.0-Kai-0.6B](https://savrn.com/models/decision-1-0-kai-0-6b)

[vLLM Semantic Router](https://savrn.com/model-publishers/llm-semantic-router)

Kai, from kairos — the right moment to choose. Open Decision Foundation Models Choose an action, judge a condition, or score against your own rubric. Kai reads the context and candidate descriptions together, then returns structured decisions and probability distributions. 53.52 overall — above both general Laya models, and +7.04 points over the previous Kai release. On the benchmark's 160-question BoolQ slice, Kai scores 74.38%, versus 69.38% for each Laya reference. Same 54 tasks and 3,766 scored questions, with fixed source/family weights. Both Laya references use released general weights, without target-dataset fine-tuning. This is an observed regression suite; results vary by task.…

Open weights apache-2.0

[View model](https://savrn.com/models/decision-1-0-kai-0-6b)

Model · Text classification

### [Decision-1.0-Lex-0.6B](https://savrn.com/models/decision-1-0-lex-0-6b)

[vLLM Semantic Router](https://savrn.com/model-publishers/llm-semantic-router)

Lex — Latin for law. Give your decisions a rule. Open Decision Foundation Models Turn operational context into structured decisions. Lex is a specialist fine-tuned from Kai to select actions, test conditions and apply scoring rubrics across four operational workflows. 78.15% accuracy — 1.55 points above Laya Typed Decisions. Lex contains 571,909,635 parameters. Both models were evaluated on the original 2,000-decision English typed-decisions test split, with complete inputs and the official fine-tuned Laya checkpoint. Lex answers 31 more decisions correctly. Observed accuracy gain; the paired 95% interval is −0.20 to +3.15 points. Laya leads on Noul and probability-quality metrics. Full…

Open weights apache-2.0

[View model](https://savrn.com/models/decision-1-0-lex-0-6b)

Model

### [DeepSeek-R1-0528-Qwen3-8B-heretic-i1-GGUF](https://savrn.com/models/deepseek-r1-0528-qwen3-8b-heretic-i1-gguf)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

weighted/imatrix quants of https://huggingface.co/richardyoung/DeepSeek-R1-0528-Qwen3-8B-heretic For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-0528-Qwen3-8B-heretic-GGUF 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 quant And here are Artefact2's thoughts on the matter…

Open weights mit transformers

[View model](https://savrn.com/models/deepseek-r1-0528-qwen3-8b-heretic-i1-gguf)

Model

### [DeepSeek-R1-Distill-Llama-70B-oQ5e](https://savrn.com/models/deepseek-r1-distill-llama-70b-oq5e)

[Matias Almeida](https://savrn.com/model-publishers/maptan)

This model was quantized using oQ (oMLX v0.7.0) mixed-precision quantization.

Open weights 70.6B parameters 131,072 tokens mlx

[View model](https://savrn.com/models/deepseek-r1-distill-llama-70b-oq5e)

Model

### [DeepSeek-R1-Distill-Llama-8B-oQ8e-fp16](https://savrn.com/models/deepseek-r1-distill-llama-8b-oq8e-fp16)

[MAXLABS](https://savrn.com/model-publishers/maxlabs-ai)

This model was quantized using oQ (oMLX v0.7.0rc1) mixed-precision quantization.

Open weights 8B parameters 131,072 tokens mlx

[View model](https://savrn.com/models/deepseek-r1-distill-llama-8b-oq8e-fp16)

Model

### [DeepSeek-R1-Distill-Qwen-1.5B_ckpt_SFT_baseline](https://savrn.com/models/deepseek-r1-distill-qwen-1-5b-ckpt-sft-baseline)

[Rajat Ghosh](https://savrn.com/model-publishers/rghosh8)

Open weights

[View model](https://savrn.com/models/deepseek-r1-distill-qwen-1-5b-ckpt-sft-baseline)

Model · Text generation

### [DeepSeek-V3-rescaled](https://savrn.com/models/deepseek-v3-rescaled)

[Silares](https://savrn.com/model-publishers/sail-ing-north)

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations…

Open weights 684.5B parameters 163,840 tokens transformers

[View model](https://savrn.com/models/deepseek-v3-rescaled)

Model · Text generation

### [DeepSeek-V4-Pro-0813](https://savrn.com/models/deepseek-v4-pro-0813)

[SAIFI INDUSTRIES](https://savrn.com/model-publishers/saifiindustries)

DeepSeek-V4-Pro-0813 is the official release of DeepSeek-V4-Pro, superseding the preview version, with greatly enhanced agentic capabilities and performance improvements that are especially pronounced in production environments. It is built on the DeepSeek-V4-Pro (Preview) model structure, with a DSpark speculative decoding module attached. DeepSeek-V4-Pro-0813 outperforms DeepSeek-V4-Pro (Preview) on the benchmarks listed below, and is broadly competitive with the strongest proprietary models available. 1. For the code-agent tasks among the public benchmarks above, DeepSeek-V4-Pro-0813 is evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the max reasoning…

Open weights mit 1.7T parameters 1,048,576 tokens transformers

[View model](https://savrn.com/models/deepseek-v4-pro-0813)

Model · Text generation

### [DeepSeek-V4-Pro-Qwen3.5-9B-GPTQPro-W4-G64](https://savrn.com/models/deepseek-v4-pro-qwen3-5-9b-gptqpro-w4-g64)

[Facu Vlad J](https://savrn.com/model-publishers/groxaxo)

Open weights apache-2.0 9.7B parameters gptqmodel

[View model](https://savrn.com/models/deepseek-v4-pro-qwen3-5-9b-gptqpro-w4-g64)

Model · Image and text to text

### [DeepSeek-V4.1-Flash-1017](https://savrn.com/models/deepseek-v4-1-flash-1017)

[Douglas WhippleIII](https://savrn.com/model-publishers/daddydtree)

Open weights mit 763.2B parameters transformers

[View model](https://savrn.com/models/deepseek-v4-1-flash-1017)

Model · Text generation

### [DeepSeek-V4.1-Flash-1017-DeepInfer-Thor-TP2](https://savrn.com/models/deepseek-v4-1-flash-1017-deepinfer-thor-tp2)

[Andy Stewart](https://savrn.com/model-publishers/manateelazycat)

Open weights mit

[View model](https://savrn.com/models/deepseek-v4-1-flash-1017-deepinfer-thor-tp2)

Model · Image and text to text

### [DeepSeek-V4.1-Flash-1017-EXL3-2.0bpw-ja-calibrated](https://savrn.com/models/deepseek-v4-1-flash-1017-exl3-2-0bpw-ja-calibrated)

[Chitako](https://savrn.com/model-publishers/chitako)

Open weights mit

[View model](https://savrn.com/models/deepseek-v4-1-flash-1017-exl3-2-0bpw-ja-calibrated)

Model · Text generation

### [DeepSeek-V4.1-Flash-1017-EXL3-2.7bpw-TensorFold](https://savrn.com/models/deepseek-v4-1-flash-1017-exl3-2-7bpw-tensorfold)

[OOA](https://savrn.com/model-publishers/object-oriented-architects)

Open weights mit 99.4B parameters

[View model](https://savrn.com/models/deepseek-v4-1-flash-1017-exl3-2-7bpw-tensorfold)

Model · Image and text to text

### [DeepSeek-V4.1-Flash-1017-EXL3-4.0bpw](https://savrn.com/models/deepseek-v4-1-flash-1017-exl3-4-0bpw)

[Bertholomus AI](https://savrn.com/model-publishers/bertholomus)

Open weights mit 144.1B parameters exllamav3

[View model](https://savrn.com/models/deepseek-v4-1-flash-1017-exl3-4-0bpw)

Model

### [DeepSeek-V4.1-Flash-1017-EXL3-franken-v100](https://savrn.com/models/deepseek-v4-1-flash-1017-exl3-franken-v100)

[Phil Tompkins](https://savrn.com/model-publishers/philbert440)

Open weights

[View model](https://savrn.com/models/deepseek-v4-1-flash-1017-exl3-franken-v100)

## 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.