static quants of https://huggingface.co/ombd/Llama-3.1-8B-Aleph-2
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.
12,579Models
3,583Datasets
291Papers
7,578Publishers
12,850Sourced relationships
Updated 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
darturi/Llama-3.1-8B-Instruct-RFA-template-b-harmful-gpt41-1 - 1 darturi/AveragedMOLlama8BAdapters-1, built with SubtractAdapters.ipynb (MODE = "effective"). The intended update is Built by concatenating the source factors -- which represents the difference exactly at rank 64 -- and truncating that product's SVD to rank 64, the best rank-64 approximation in Frobenius norm. Weighted retained energy: 1.0000 (exact). Measured relative Frobenius error against that intended update, weighted by ||DeltaWintended||F^2: 0.0000 (median per module: 0.0000). subtractioninfo.json carries the same provenance plus the per-module diagnostic.
darturi/Llama-3.1-8B-Instruct-RFA-template-b-harmful-sonnet45-v2-1 - 1 darturi/AveragedMOLlama8BAdapters-1, built with SubtractAdapters.ipynb (MODE = "effective"). The intended update is Built by concatenating the source factors -- which represents the difference exactly at rank 64 -- and truncating that product's SVD to rank 64, the best rank-64 approximation in Frobenius norm. Weighted retained energy: 1.0000 (exact). Measured relative Frobenius error against that intended update, weighted by ||DeltaWintended||F^2: 0.0000 (median per module: 0.0000). subtractioninfo.json carries the same provenance plus the per-module diagnostic.
A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.1 license and acceptable-use policy apply.
This model is a fine-tuned version of ferrazzipietro/Llama-3.1-8B-Instruct-cpt-tesiall on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0003 - trainbatchsize: 64 - evalbatchsize: 32 - distributedtype: multi-GPU - gradientaccumulationsteps: 2 - totaltrainbatchsize: 128 - lrschedulertype: cosine - lrschedulerwarmupratio: 0.1 - numepochs: 10 - PEFT 0.14.0 - Transformers 4.51.0 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.0
This is the model card of a transformers model that has been pushed on the Hub. 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).
This is the model card of a transformers model that has been pushed on the Hub. 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).
Built with Llama. A LoRA adapter for meta-llama/Llama-3.1-8B-Instruct that turns it into a network and cloud operations assistant for Cisco, Palo Alto Networks, Microsoft Azure and AWS. It is trained to work with retrieved reference documents (RAG): it diagnoses the problem, rates the risk of the change, requires approval, and takes the exact commands from the references you provide instead of from memory. When the references do not contain the needed commands, it says so and does not guess. Diagnosis → Risk → Action Type (read / write / high) → Approval → Warning → Commands/Action → Verification → Rollback Held-out test of 124 questions (never seen in training), with the correct reference…
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.