The full ImmuneCoT method fuses the two safety branches with a Base-adjusted product-of-experts Qimm(v) ∝ q̃rec(v)·q̃res(v)/qB(v). This checkpoint uses the naive fusion Qno-base(v) ∝ q̃rec(v)·q̃res(v) — the same branch weights (λrec=0.5, λres=0.7) but no division by the base distribution — isolating whether the gains come from combining Recognition+Response at all, or specifically from the Base-adjusted PoE term. Intended use: research reproducibility for the ImmuneCoT paper's RQ3 ablation.
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.
2,760Models
859Datasets
254Papers
1,692Publishers
5,040Sourced relationships
Updated 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
This model is a fine-tuned version of Qwen/Qwen3-8B on the skowshik1/gemma3-sft-agentclinic-style-diagnosis-shaped-v2 dataset. It has been trained using TRL. This model was trained with SFT.
This qwen3vl model was trained 2x faster with Unsloth and Huggingface's TRL library.
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). - PEFT 0.20.0
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). - PEFT 0.20.0
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). - PEFT 0.20.0
Base: Qwen/Qwen3.6-35B-A3B-FP8 (same FP8 block format/scales convention). Adapter: qwen3.6-35b-a3b-tool-prompts-ckpt444-adapter (LoRA r=32, 2 epochs, 1873 dialogues). Merged in BF16 (RAM only), block-requantized to FP8 e4m3/128. Chat template: qwen3template333.jinja. Serve with sglang / vllm / transformers.
wm-internalization v4 checkpoint — condition kl-mix30m, save final. Full-finetune of Qwen/Qwen3.5-9B on the Calderwood & Harkness synthetic law-firm corpus (world-internalization study, v4 lineage: 9B student, ~50k think-on seed pool). Grafted back into the hub composite layout (Qwen35ForConditionalGeneration) — servable with vLLM out of the box.
Uncensored build of Qwen/Qwen3.8-27B, produced with Apostate using the diode path. The diode repurposes one MLP neuron per layer into a gated refusal subtractor: it removes the residual refusal direction only when a benign-calibrated detector fires above threshold, so benign inputs keep the original weights. The result is a plain checkpoint: no runtime hook, adapter, finetune, or router. Delivery and KL are measured separately by apostate test, not during the bake; diodereport.json records the edit settings. This is a standard Transformers checkpoint. This model is uncensored and will answer harmful and dangerous requests. You are responsible for how you use it.
This repository contains a working research note about Self Supervised. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with reading.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should…
Roy C · independent ML researcher & AI red-teamer Open-weight models, adversarial evaluation, and efficient training at the small-model scale. I build and ship small open-weight language models — 50+ public checkpoints, quantized for edge and picked up by third-party quantizers (mradermacher) for independent re-hosting. The work sits at the intersection of three things I care about: - Open-weight modeling at volume. Distillation pipelines over Qwen3, Gemma, and LFM2.5 bases, full GGUF quant ladders for local/edge deployment, and a house methodology — structure over scale — for pulling capability out of sub-2B models instead of buying it with parameters. - Adversarial / safety-relevant…
This model was converted to GGUF format from madebyaris/rerank-indonesia using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).
A research-oriented Blip prototype targeting Retrieval. The included giant setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a cosine schedule. These are starting values in the script, not evidence of a completed run. For a…
This repository provides rh-hf-e2e-t2-20260918104741-lora weight files. You can load them on RunningHub. Published by RunningHub on behalf of the author. Copyright remains with the author. Follow the original project or upstream license. RunningHub API: RunningHub API provides unified access to 500+ AI models, including Seedance and other state-of-the-art multimodal models, at prices as low as 30% of official rates. Built for AIGC startups, AI short-form drama studios, and other production teams looking to create at scale https://www.runninghub.ai/call-api - RunningHub：https://www.runninghub.ai - RunningHub 中国站：https://www.runninghub.cn - RunningHub API…
This repository provides rh-hf-e2e-t4-20260918105654-lora weight files. You can load them on RunningHub. Published by RunningHub on behalf of the author. Copyright remains with the author. Follow the original project or upstream license. RunningHub API: RunningHub API provides unified access to 500+ AI models, including Seedance and other state-of-the-art multimodal models, at prices as low as 30% of official rates. Built for AIGC startups, AI short-form drama studios, and other production teams looking to create at scale https://www.runninghub.ai/call-api - RunningHub：https://www.runninghub.ai - RunningHub 中国站：https://www.runninghub.cn - RunningHub API…
Bài tập môn Tối ưu hóa nâng cao, lớp Khoa học dữ liệu. Nội dung là tự cài đặt và so sánh bốn thuật toán tối ưu hóa trên bài toán hồi quy tuyến tính có hiệu chỉnh Ridge, dữ liệu Lending Club 2007-2018: gradient descent toàn phần, SGD thuần với lô một mẫu, mini-batch SGD và phương pháp Newton, mỗi thuật toán chạy với cả bước cố định lẫn backtracking line search theo điều kiện Armijo. Kế hoạch chi tiết: KEHOACHTRIENKHAI.md. Quy tắc làm việc: CLAUDE.md, kèm \min{w \in \mathbb{R}^d} \quad f(w) = \frac{1}{2n} \left\| Xw - y \right\|2^2 + \frac{\lambda}{2} \left\| w \right\|2^2 Hàm mục tiêu lồi mạnh và có nghiệm đóng, nên $f^$ tính được chính xác tới sai số máy và mọi biểu đồ hội tụ đều vẽ $f(wk)…
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.