This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT.
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.
11,442Models
3,247Datasets
285Papers
6,976Publishers
12,091Sourced relationships
Updated 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
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).
This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT.
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).
This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT.
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).
This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT.
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).
This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT.
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).
This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT.
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).
This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT.
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).
This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT.
male（男声） - female（女声） 2. 训练数据偏中文歌曲：作者用 750 首中文歌（opencpop + m4singer 数据集，700 训练 / 50 验证）训练，作者自述「缺乏一定普遍性」，归类为实验性模型。对其他语种/曲风的泛化可能下降。 3. 不是通用人声分离：它只分男女声，不分伴奏。输入应是已去伴奏的人声（vocals）。正确流程是先 Demucs / UVR / MSST 人声模型分出 vocals，再把 vocals 喂给本模型；直接丢带伴奏整首歌，效果会差。 - modelchorusbsroformerep267sdr24.1275.ckpt — 模型权重（约 503 MB，Git LFS 管理） - configchorusmalefemalebsroformer.yaml — 模型配置 - examples/ — 作者提供的示例音频（vocalsraw 原人声、vocalsmale 男声结果、vocalsfemale 女声结果） - READMESucialoriginal.md — 原作者模型卡原文 - LICENSE — CC BY-NC-SA 4.0 许可说明 - requirements.txt — 推理依赖 2. 把 modelchorusbsroformerep267sdr24.1275.ckpt 放到 MSST 的 pretrain/vocalmodels/。 3. 把 configchorusmalefemalebsroformer.yaml 放到 MSST 的 configunofficial/msstconfig/。…
A small multilingual decision model for classification, with a focus on Korean. Given a text, a question, and a set of criteria, Malkuth returns probabilities over the possible choices instead of generating text. Malkuth-2B is a LoRA adapter trained on Kev and built on a frozen empero-ai/Qwen3.8-2B-Distill. It has 17.9M trainable parameters and a 65 MB adapter. - choice: select one of up to 255 options - noul: yes or no - score: select an ordered level Multiple questions can be evaluated against the same input. Questions are evaluated independently, so they cannot see each other's answers. Probabilities are temperature-calibrated on a held-out validation split. Malkuth is served through Kev…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
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.
Collection · 4 entries
Models that fit 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.
Collection · 6 entries
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.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
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
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.