This model was fine-tuned from nakamotosantosh/pi0.5-GE38YpXNDVWi@9defd14729fa7db213570fb8e4ad9241477d860a, the AXIS v2.0 (competition 8) champion when training started, in two stages on the same data: stage 1 = 3000 steps starting from the parent (peak LR 2e-5, cosine to 2e-6), stage 2 = 3000 further steps (peak LR 1e-5, cosine to 1e-6) starting from the stage-1 step-3000 checkpoint. Lineage disclosure: that parent's params/ files are byte-identical to Zayaan/pi0.5-dHGSxNJcwbYE@d5e85aa270df643be821982caf4ab69c07e5e5e7. - Model and interface unchanged from the parent: π0.5 (Pi0Config(pi05=True, discretestateinput=True), action horizon 10), openpi config pi05axisjoint; inputs as served by…
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 is a single-task vision-language-action policy for task 38 sprayingforbugs of the BEHAVIOR-1K benchmark. It is Pi0.5-based, has 3.4B parameters, and controls the R1Pro robot from three 224×224 RGB cameras plus proprioception. It predicts 30-step action chunks. It was fine-tuned for 25,000 steps on task 38 demonstrations only. The starting point was checkpoint3 of the 1st-place 2025 BEHAVIOR Challenge solution (Larchenko, Zarin, Karnatak; paper). checkpoint3 had been trained on 13 tasks (4, 27, 31–33, 35–39, 41, 46, 49), task 38 among them. Status: training only. This model has not been evaluated in the BEHAVIOR simulator yet. Lower training loss does not guarantee a higher task success…
Completed source-fresh1000-update target-only IDM pilot, B32, seed42/draw seed170013. Raw999/non-EMA: native51 leaves,43 original-source FP32 trainable leaves and8 frozen projections. Original source-wrist23 leaves are appended for inference; deployment74 restores as BF16. Weights, source normalization, exact controller/source-bank/plan/journal and strict export proof are included. Native full Adam/EMA states remain preserved at the training server. All10 logical collection lineages per suite (40 total) are eligible; eight windows per suite are drawn without replacement each update. All1000 draws and initialization match the full-probe fixed1:1:1 control; the first25 native metrics match…
Completed source-fresh1000-update target-only IDM pilot, B32, seed42/draw seed170013. Raw999/non-EMA: native51 leaves,43 original-source FP32 trainable leaves and8 frozen projections. Original source-wrist23 leaves are appended for inference; deployment74 restores as BF16. Weights, source normalization, exact controller/source-bank/plan/journal and strict export proof are included. Native full Adam/EMA states remain preserved at the training server. All10 logical collection lineages per suite (40 total) are eligible; eight windows per suite are drawn without replacement each update. All1000 draws and initialization match the full-probe fixed1:1:1 control; the first25 native metrics match…
Completed source-fresh1000-update target-only IDM pilot, B32, seed42/draw seed170013. Raw999/non-EMA: native51 leaves,43 original-source FP32 trainable leaves and8 frozen projections. Original source-wrist23 leaves are appended for inference; deployment74 restores as BF16. Weights, source normalization, exact controller/source-bank/plan/journal and strict export proof are included. Native full Adam/EMA states remain preserved at the training server. All10 logical collection lineages per suite (40 total) are eligible; eight windows per suite are drawn without replacement each update. All1000 draws and initialization match the full-probe fixed1:1:1 control; the first25 native metrics match…
Matched KD ablation: H10 cumulative-average shared-decoder IDM, IL/KD weights 0, 10,000 updates, global IDM batch 32, LR 1e-5, seed 42, 4-GPU FSDP. View adapts vision/base LLM with a frozen action expert; yaw adapts the full policy. All conditions start independently from the 2026-09-26 source step 9999. MuJoCo 3.2.3. Policy config: pi05liberodensecontact20261002yawidmonly. Data commits and exact commands are in trainingconfiguration.json. Orbax parameters and normalization assets are included; optimizer states remain local.
Matched KD ablation: H10 cumulative-average shared-decoder IDM, IL/KD weights 0, 10,000 updates, global IDM batch 32, LR 1e-5, seed 42, 4-GPU FSDP. View adapts vision/base LLM with a frozen action expert; yaw adapts the full policy. All conditions start independently from the 2026-09-26 source step 9999. MuJoCo 3.2.3. Policy config: pi05liberodensecontact20261002yawidmonly. Data commits and exact commands are in trainingconfiguration.json. Orbax parameters and normalization assets are included; optimizer states remain local.
Matched KD ablation: H10 cumulative-average shared-decoder IDM, IL/KD weights 0, 10,000 updates, global IDM batch 32, LR 1e-5, seed 42, 4-GPU FSDP. View adapts vision/base LLM with a frozen action expert; yaw adapts the full policy. All conditions start independently from the 2026-09-26 source step 9999. MuJoCo 3.2.3. Policy config: pi05liberodensecontact20261002yawidmonly. Data commits and exact commands are in trainingconfiguration.json. Orbax parameters and normalization assets are included; optimizer states remain local.
Complete 5,000-update checkpoint, including Orbax params, training state and normalization assets under 4999/. Use openpi training config pi05liberoidmlorareadoutviewadapt with checkpoint directory 4999/. Source jointly trains IL and IDM with global batch64 for each loss. The shared LoRA factors learn from both losses; the identity plus rank16 policy readout learns from IL. Adaptation starts independently from the same source checkpoint, trains shared LoRA with IDM only, and freezes the policy readout and base weights. No KD is used. The published policy uses the readout during inference. Full implementation and experiment provenance are recorded in provenance.json. Pin the verified Hugging…
viewsmall; 5000 completed updates/raw step 4999, from one continuous run of 5000 updates. LLM frozen; post-Adam update multipliers: vision 1, expert LoRA multiplier 0.1. Existing dense-contact data and original pre-adapt, B32, training seed42, FSDP2, warmup500, peak/end1e-5, IDM-only, raw non-EMA and original source wrist. Native training revision: 622c8d28a737f2e18f910a8e8774499abe11786a. Overall 90.05% (1801/2000); runtime errors0. Official indices10–49: 89.750% (1436/1600). 40 tasks x50 official states0–49, envseed7, A1000–3, eight clients/GPU, MuJoCo3.2.3, native inference revision 0f6471d99dfc81fabe7c3ab65c141d7640b9ff08. All failures retained; one training seed, shared policy RNG…
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.