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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.

11,122Models
3,164Datasets
285Papers
6,792Publishers
11,852Sourced relationships

Updated 2026-10-18 · How the library is built

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11,122 models, sorted by most downloaded.

Model · Text generation

tanpo-deals-GGUF

Ninja

Ready-to-run GGUF quantizations of tanpo-deals, a compact B2B deal-closing and distributor/channel specialist. Open a.gguf in LM Studio, llama.cpp, Ollama, or another GGUF-compatible runtime—no LoRA wiring required. Official DarkLab evaluation on the same 20-task deals rubric and decoding setup. The tanpo-deals fine-tune beats the base by +7.3 percentage points. BEATSBASE: YES Discovery and qualification; objection handling and closing; proposal and ROI; distributor recruiting; channel-partner enablement; wholesale pricing and terms; negotiation tactics; pipeline and forecast hygiene; outreach sequences; and identity/safety guardrails. Automated rubric results are directional; verify…

Open weights other
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LoRA adapter for tanpo-deals (Deal Cracker) — B2B deal-closing and distributor/channel specialist.

Open weights apache-2.0 peft
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Model · Text generation

tanpo-retention

Ninja

A compact customer-retention specialist (~1.2B) for onboarding, activation, churn-save, customer-success playbooks, health-score follow-up, renewals, expansion, escalation, identity guardrails, and winback workflows — built for local and inexpensive deployment. Original upstream: LiquidAI/LFM2.5-1.2B-Instruct (~1.17B parameters, 32,768-token context, designed for edge/on-device deployment). Fine-tuning: Unsloth-compatible loading of that checkpoint via hub id unsloth/LFM2.5-1.2B-Instruct (LoRA / PEFT). This repository hosts the merged Transformers weights (LoRA merged into the base). Tanpo is a family of compact domain-specialized business models for local / edge / inexpensive deployment.…

Open weights other 1.2B parameters 128,000 tokens transformers
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Model · Text generation

tanpo-retention-GGUF

Ninja

GGUF quantizations of d4rkninja/tanpo-retention for local runtimes (llama.cpp, LM Studio, and other GGUF-compatible tools). Tanpo is a family of compact domain-specialized business models for local / edge / inexpensive deployment. Different specialists cover different workflows. Original upstream: LiquidAI/LFM2.5-1.2B-Instruct (~1.17B parameters, 32,768-token context, designed for edge/on-device deployment). Fine-tuning: Unsloth-compatible loading of that checkpoint via hub id unsloth/LFM2.5-1.2B-Instruct (LoRA / PEFT). A compact customer-retention specialist (~1.2B) for onboarding, activation, churn-save, customer-success playbooks, health-score follow-up, renewals, expansion, escalation…

Open weights other
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Model · Text generation

tanpo-retention-LoRA

Ninja

PEFT LoRA adapter that specializes LiquidAI/LFM2.5-1.2B-Instruct for retention workflows (trained via Unsloth hub id unsloth/LFM2.5-1.2B-Instruct). Tanpo is a family of compact domain-specialized business models for local / edge / inexpensive deployment. Different specialists cover different workflows. Original upstream: LiquidAI/LFM2.5-1.2B-Instruct (~1.17B parameters, 32,768-token context, designed for edge/on-device deployment). Fine-tuning: Unsloth-compatible loading of that checkpoint via hub id unsloth/LFM2.5-1.2B-Instruct (LoRA / PEFT). Full eval, examples, and responsible-use notes: d4rkninja/tanpo-retention. - Onboarding, activation, churn-save, renewal, expansion, escalation, and…

Open weights apache-2.0 peft
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Epoch-1 full fine-tuning checkpoint in original LLaVA format. Load with the pinned TARS/LLaVA loader, not an HF LoRA loader. Paper profiles are reconstructions with documented assumptions, not author checkpoints.

Open weights 13.4B parameters 4,096 tokens
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Epoch-2 full fine-tuning checkpoint in original LLaVA format. Load with the pinned TARS/LLaVA loader, not an HF LoRA loader. Paper profiles are reconstructions with documented assumptions, not author checkpoints.

Open weights 7.1B parameters 4,096 tokens
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Epoch-3 full fine-tuning checkpoint in original LLaVA format. Load with the pinned TARS/LLaVA loader, not an HF LoRA loader. Paper profiles are reconstructions with documented assumptions, not author checkpoints.

Open weights 7.1B parameters 4,096 tokens
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Team thisray (Kaggle leaderboard name) · Author: Ssu-Rui Lee (Team Leader), RaydioTek, Taiwan · holds test/ and index/testwindows.csv. Environment: Python 3.11 and pip install -r requirements.txt; on linux-aarch64, conda-linux-aarch64.lock gives byte-identical output (see "Quick start"). One GPU, no internet access (the program disables it). Measured on the reference NVIDIA GB10: 2,016 s (S-C3) and 1,532 s (S-C6), peak GPU memory 601 MiB. The authoritative description is tartanimu-technical-report-thisray.pdf in this repository (compliance answers in Appendix B, every inference constant in Appendix F), with the organizers' starter-template team report reportthisray.md. The rest of this page…

Open weights cc-by-nc-4.0 pytorch
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One model, one set of weights, for the IROS 2026 TartanIMU cross-platform inertial odometry challenge. The network predicts body-frame velocity from 6-axis IMU alone for car, drone, quadruped and handheld motion; there are no per-platform experts and no model switching at inference. An all-zero submission scores exactly 1.000. The best public score is ≈ 0.20074, so this The checkpoint is self-contained: it stores the per-channel mean/std used for input standardization plus all architecture hyper-parameters, so inference needs no extra flags or training-time artefacts. --stride 5 and --tta-shifts 0,25,-25 are the inference configuration of the submitted system (see compliance item 3). Using…

Open weights cc-by-nc-sa-4.0 pytorch
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This repository is the second of the two single-model checkpoints that team Xunden selected on the Kaggle leaderboard. The first one lives at (RICH2200tv, 31-channel inputs, 200 epochs). The two checkpoints are independent single models: each generated its own selected CSV on its own, and no prediction in either CSV is an ensemble, blend or average of the two. The official per-sequence scoring service was not run on this particular CSV; its recorded result is the Kaggle public leaderboard entry above. The richer companion model in the sister repository reports the official per-sequence service numbers. The checkpoint is self-contained: it stores per-channel mean/std for input…

Open weights cc-by-nc-sa-4.0 pytorch
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This private repository archives the exact weights-only Hugging Face snapshots emitted at completed epoch boundaries by the taste-s-f32-v4 Slurm training chain (formal job 5549 and its single designated resume job 5550, if needed). The run uses eight H100 GPUs, starts from the reviewed immutable validbest weights with a fresh optimizer, scheduler, and global step, and is configured for ten epochs. Each epoch's PROVENANCE.json names the job that actually produced it and cryptographically binds the upload to its immutable local staging receipt. The archived artifacts are not resumable training checkpoints. They contain model weights and the model configuration only; they do not contain…

Access requested at publisher other transformers
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Model Collections

Hand-picked starting points, each with the reason it exists.

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.

View collection

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.