This model is a fine-tuned version of Qwen/Qwen3-8B. It has been trained using TRL. This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models. - PEFT 0.19.1
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 model was finetuned and converted to GGUF format using Unsloth. - Qwen2.5-Coder-3B-Instruct.Q4KM.gguf An Ollama Modelfile is included for easy deployment. This was trained 2x faster with Unsloth
This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.
Model · Text generation
qwen-compassion-show-web-s3407-part1-mini
This is a BF16 MFlux version of Qwen-Image-2.1-Turbo. MFlux runs the latest state-of-the-art generative image models locally on your Mac in native MLX. MFlux is open source and free. Converted with MFlux 0.22.0 on 2026-10-11. BF16 is the full-precision MLX build -- no quantization. Use it as the highest-fidelity reference, or pass --quantize to quantize on the fly. Install MFlux Generate an image using Qwen-Image-2.1-Turbo BF16: The weights are downloaded from this repo on first use. To keep a local copy instead: then pass --model./qwen-image-2-1-turbo-mflux-bf16. This repo inherits the license of the original model: qwen-research. See the original model card for the full terms and any…
This is a Q4 MFlux version of Qwen-Image-2.1-Turbo. MFlux runs the latest state-of-the-art generative image models locally on your Mac in native MLX. MFlux is open source and free. Converted with MFlux 0.22.0 on 2026-10-11. Standard MFlux quantization uses MLX affine quantization (4-bit) across the text encoder, transformer and VAE. The quantization level is stored in the weights, so no --quantize flag is needed. Install MFlux Generate an image using Qwen-Image-2.1-Turbo Q4: The weights are downloaded from this repo on first use. To keep a local copy instead: then pass --model./qwen-image-2-1-turbo-mflux-q4. This repo inherits the license of the original model: qwen-research. See the…
This is a Q5 MFlux version of Qwen-Image-2.1-Turbo. MFlux runs the latest state-of-the-art generative image models locally on your Mac in native MLX. MFlux is open source and free. Converted with MFlux 0.22.0 on 2026-10-11. Standard MFlux quantization uses MLX affine quantization (5-bit) across the text encoder, transformer and VAE. The quantization level is stored in the weights, so no --quantize flag is needed. Install MFlux Generate an image using Qwen-Image-2.1-Turbo Q5: The weights are downloaded from this repo on first use. To keep a local copy instead: then pass --model./qwen-image-2-1-turbo-mflux-q5. This repo inherits the license of the original model: qwen-research. See the…
This is a Q6 MFlux version of Qwen-Image-2.1-Turbo. MFlux runs the latest state-of-the-art generative image models locally on your Mac in native MLX. MFlux is open source and free. Converted with MFlux 0.22.0 on 2026-10-11. Standard MFlux quantization uses MLX affine quantization (6-bit) across the text encoder, transformer and VAE. The quantization level is stored in the weights, so no --quantize flag is needed. Install MFlux Generate an image using Qwen-Image-2.1-Turbo Q6: The weights are downloaded from this repo on first use. To keep a local copy instead: then pass --model./qwen-image-2-1-turbo-mflux-q6. This repo inherits the license of the original model: qwen-research. See the…
This is a Q8 MFlux version of Qwen-Image-2.1-Turbo. MFlux runs the latest state-of-the-art generative image models locally on your Mac in native MLX. MFlux is open source and free. Converted with MFlux 0.22.0 on 2026-10-11. Standard MFlux quantization uses MLX affine quantization (8-bit) across the text encoder, transformer and VAE. The quantization level is stored in the weights, so no --quantize flag is needed. Install MFlux Generate an image using Qwen-Image-2.1-Turbo Q8: The weights are downloaded from this repo on first use. To keep a local copy instead: then pass --model./qwen-image-2-1-turbo-mflux-q8. This repo inherits the license of the original model: qwen-research. See the…
text to image image edit
A complete Hugging Face pipeline bundle with the Qwen-Image-2.1 DiT in NF4 and a compact Qwen3.5-0.8B NF4 student text encoder. A learned MLP adapter maps Qwen3.5's 1024-dimensional token features into Qwen-Image's 4096-dimensional text-conditioning space. VAE, scheduler, Qwen3-VL-compatible pipeline processor, student tokenizer, adapter, inference helper, and a generated sample are included. The adapter was distilled from the pinned Qwen-Image Qwen3-VL teacher on 192 synthetic training prompts and 32 validation prompts. Validation token-embedding cosine similarity reached 0.9392. This is an embedding alignment metric, not a guarantee of image-quality parity; the included sample is an…
A pre-merged, quantized build of Qwen-Image-Edit-2511 with two fine-tunes 1. Turbo (Lightning 4-step) 2. NSFW Lora A standard edit showing the base capability is intact: Image-to-Image works and is very fast: The highlight of this model. Fast and uncensored. - Change the channel to "Channel (remote)" - and search for comfyui-gguf-loader Use the workflow Link
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
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).
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.