Placeholder. The servable export of the 2-layer Qwen3.8-27B DSpark MoE drafter after epoch 3 will be uploaded here automatically when that checkpoint is written. Live training metrics: https://wandb.ai/milestraining/specforge-2%20layer%20moe Code and recipes: https://github.com/yushengsu-thu/SpecForge/tree/exp/qwen3.8-27b-dspark-moe-2layer
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 is a fine-tuned version of unsloth/Qwen3-14B-bnb-4bit. It has been trained using TRL. This model was trained with SFT.
Qwen/Qwen3.5-0.8B fine-tuned directly (no LoRA/adapters) on medical reasoning data, to turn a general-purpose model into a domain-focused one. Overfitting to the training data was an accepted trade-off of this experiment; the goal was to study the training/validation loss behaviour. Loss is computed on the assistant response only (reasoning + answer); the prompt is masked. Best validation loss at epoch. See losscurve.png for train vs validation loss. - Research into domain adaptation, partial fine-tuning, and overfitting behaviour of small models. - Offline experimentation with medical-style question answering and reasoning formats. - Clinical decision support, diagnosis, treatment…
Internal research checkpoint. Routed experts average 2.0217 bits; every other weight is BF16. Weights are stored dequantized in BF16 tensors and load with stock transformers / vLLM. License follows the base model.
Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. This release delivers substantial upgrades, particularly in For more details, please refer to our blog post Qwen3.6-35B-A3B. Empty cells (--) indicate scores not available or not applicable. For streamlined integration, we recommend using Qwen3.6 via APIs. Below is a guide to use Qwen3.6 via OpenAI-compatible API. Qwen3.6 can be served via APIs with popular inference frameworks. In…
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
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.