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.
Updated 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
Been currently busy training another gemma 4 31B, did about 12 run's so far, playing with swa value, rope, turn masking, so far 2048 seems to be the sweet spot for training at 8k ctx. should be releasing a new model in about 2-3 weeks, training on local 3090s is not very fast =p.
We maintain our previous releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our MiniPlus architectures
Kimi K3 on a single NVIDIA A100 80GB. A weight-only quantisation of Moonshot AI's Kimi K3 (2.8T total / 104B activated parameters) that loads and generates on one A100 80GB GPU, with the routed experts held in host RAM. No Ampere-targeted K3 build existed for vLLM, so this was made to fix that gap. Routed experts and attention re-encoded from MXFP4/BF16 into compressed-tensors pack-quantized, served by vLLM's Marlin kernels. Activations stay BF16 (W4A16 / W8A16). Round-to-nearest only — no calibration data, so no dataset is baked into these weights. Errors were measured by round-tripping each tensor through compressed-tensors' compress()/decompress(). Weight error is a proxy, not a quality…
This model was converted to MLX format from hexagrad/Kokoro-82M using mlx-audio version 0.0.1. Refer to the original model card for more details on the model.
DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated is an apex-tier, uncensored autonomous agentic coding model trained by DuoNeural (Aura, Archon, and Jesse). Built upon our abliterated hybrid state-space & mixture-of-experts foundation architecture (DuoNeural/LFM2.5-8B-A1B-Abliterated), this model activates only 1.5 billion parameters per token out of its 8.3 billion total parameters, delivering blistering inference speeds (~380–400 tokens/sec on consumer GPUs like RTX 3090, and ~90 tokens/sec on legacy GTX 1070 laptops) while fitting in under 6 GB VRAM with Q4KM quantization. 1. Native Hermes Agentic Loop: - Explicit... deliberation before every action. - Structured... containers…
DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2 is an apex-tier, uncensored autonomous agentic coding model trained by DuoNeural (Aura, Archon, and Jesse). Built upon our abliterated hybrid state-space & mixture-of-experts foundation architecture (DuoNeural/LFM2.5-8B-A1B-Abliterated), this model activates only 1.5 billion parameters per token out of its 8.3 billion total parameters, delivering blistering inference speeds (~360 tokens/sec on RTX 4080 Super / 3090, and ~80–90 tokens/sec on legacy mobile GPUs like the GTX 1070) while running in under 6 GB VRAM with Q4KM quantization. In our preliminary v1 release, an assistant role delimiter mismatch during training collation…
This repository contains official GGUF quantizations for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2, an apex-tier, uncensored autonomous agentic coding model trained by DuoNeural (Aura, Archon, and Jesse). Because LFM2.5 activates only 1.5 billion parameters per token (out of 8.3B total parameters), inference speeds are extraordinarily high even on edge devices. 1. Search for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-GGUF directly inside LM Studio. 2. Select and download LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2-Q4KM.gguf. 3. Load the model with GPU offload set to Max and context length set to 2048 or 4096 (ensure Flash Attention / KV Cache Q4 is…
This repository contains the trained PEFT LoRA adapter for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v2. - LoRA Rank ($r$): 64 - LoRA Alpha ($\alpha$): 128 Architected, fine-tuned, and evaluated with passion and neuro-symbiotic precision by DuoNeural: - Aura (Lead AI Cognitive Architect & Engineering Intelligence) - Archon (Claude-based Research Co-Architect & Theoretical Lead) - Jesse (Founder, Systems Engineer & AI/ML Researcher)
basemodel: - sentence-transformers/LaBSE - language-identification - langID - sentence-embedding - similarity-search - low-resource - low-resource-languages
Cinematic behind-the-scenes movie sets for LTX-2.5. A LoRA by SOLRICKS for the atmosphere of high-budget film production: professional camera rigs, cranes, dolly tracks, studio lighting, green/blue screens, practical sets and working film crews. Supports both T2V and I2V generation with LTX-2.5. The released LoRA weights are available directly in this repository: Download BTSMovieSetLTX25v1.safetensors. A copy is also available on Civitai. 1. Download BTSMovieSetLTX25v1.safetensors from this model page. 2. Place the file in ComfyUI/models/loras/ and load it in your LTX-2.5 workflow. 3. Start with a LoRA weight of 0.8 and include btssetstyle in the positive prompt. 4. For T2V, describe the…
A trained finite-candidate decision prototype built on Qwen3-0.6B by RongxinAI. Full weights, tokenizer, training logs, frozen gold-only data and held-out predictions are included. This is a synthetic-domain research release, not a general business decision model or an official Jev reproduction. Selected arm: CE, checkpoint step 250, chosen only by development cross-entropy. This selected checkpoint contains 20 head-only plus 230 full-model updates (4,000 sampled questions); each completed training arm ran the longer 20 + 300 update budget (5,120 sampled questions). It has 596,052,993 parameters, including a shared 3,073-parameter LayerNorm/scalar scoring head. Choice, Boolean and ordinal…
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.