# Open-Weight Models Under Meta Llama 3.1 Community License Source: https://savrn.com/models/licenses/llama3-1 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub · Models by License # Open-Weight Models Under Meta Llama 3.1 Community License 28 open-weight models released under Meta Llama 3.1 Community License in the SAVRN Model Hub, with Yanggangu, Team Mradermacher and Meta Llama publishing the most. 28Models 11Publishers 266K to 8BParameter range 1Licenses Yes, with conditionsCommercial use ## What Meta Llama 3.1 Community License Allows The Llama 3.1 Community License permits commercial use, except that a licensee whose products had more than 700 million monthly active users on the release date must request a license from Meta. It requires attribution as the license specifies and compliance with Meta's Acceptable Use Policy. ## SAVRN's Take Seven models on our hub carry the Meta Llama 3.1 Community License. Commercial use is permitted. Attribution has to follow the form the license specifies, and every deployment has to comply with Meta's Acceptable Use Policy, which binds the operator, not just the downloader. The one hard gate is scale: a licensee whose products had more than 700 million monthly active users on the release date must request a license from Meta. Below that line, nothing here stops you from running the weights in your own facility or serving them to customers. The hardware ask is modest. Llama-3.1-8B-Instruct needs 19.3 GB at 16-bit, 9.6 GB at 8-bit and 4.8 GB at 4-bit, and the cheapest host on the Index runs it on one MI300X at $1.85 an hour. Prompt-Guard-86M, Meta's classifier for prompt injection and jailbreak attempts, fits in 0.7 GB and rides on the same card. The MLX Community 4-bit and the Mohamed Abo El-Enen medical finetune both list 131,072 tokens of context. Meta's two repos are gated, so you accept the terms before the files arrive; the five community releases are open access under the same license. Downloads concentrate on Meta's own pair: Llama-3.1-8B-Instruct at 5,934,139 a month and Prompt-Guard-86M at 4,499,834. The MLX Community 4-bit follows at 1,045,780, then Sica Rius's Assistant_Pepe_8B at 705,535. Team Mradermacher's two Uzbek GGUF quants draw 2,090 and 2,037, and the medical finetune 1,509. SAVRN Research, 2026-09-18 ## Most Downloaded | Model | Publisher | Parameters | License | Monthly downloads | Cheapest GPUs at 16-bit | | --- | --- | --- | --- | --- | --- | | [Llama-3.1-8B-Instruct](https://savrn.com/models/llama-3-1-8b-instruct) | Meta Llama | 8B | llama3.1 | 5M | 1x MI300X, $1.85/hr | | [Prompt-Guard-86M](https://savrn.com/models/prompt-guard-86m) | Meta Llama | 279M | llama3.1 | 2.4M | 1x MI300X, $1.85/hr | | [Llama-3.1-8B-Instruct-4bit](https://savrn.com/models/llama-3-1-8b-instruct-4bit) | MLX Community | 8B | llama3.1 | 1M | 1x MI300X, $1.85/hr | | [Assistant_Pepe_8B](https://savrn.com/models/assistant-pepe-8b) | Sica Rius | 266,240 | llama3.1 | 705.5k | 1x MI300X, $1.85/hr | | [linux-meta-llama](https://savrn.com/models/linux-meta-llama) | Yun.cz | — | llama3.1 | 3k | — | | [Llama-3.1-8B-Instuct-Uz-GGUF](https://savrn.com/models/llama-3-1-8b-instuct-uz-gguf) | Team Mradermacher | — | llama3.1 | 2.1k | — | | [uzbek-llama-3.1-8B-instruct-v2-GGUF](https://savrn.com/models/uzbek-llama-3-1-8b-instruct-v2-gguf) | Team Mradermacher | — | llama3.1 | 2k | — | | [Llama-3.1-8B-Instruct-Medical-Finetuned-merged](https://savrn.com/models/llama-3-1-8b-instruct-medical-finetuned-merged) | Mohamed Abo El-Enen | 8B | llama3.1 | 1.5k | 1x MI300X, $1.85/hr | | [Llama-3.1-8B-Instruct-CQ-4bit](https://savrn.com/models/llama-3-1-8b-instruct-cq-4bit) | ThousandML | 1.1B | llama3.1 | 78 | 1x MI300X, $1.85/hr | | [Llama3.1-8B-TRACE](https://savrn.com/models/llama3-1-8b-trace) | Md Messal Monem Miah | 8B | llama3.1 | 7 | 1x MI300X, $1.85/hr | ## Who Publishes Them | Publisher | Models | | --- | --- | | [Yanggangu](https://savrn.com/model-publishers/yanggangu) | 14 | | [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) | 4 | | [Meta Llama](https://savrn.com/model-publishers/meta-llama) | 2 | | [Yun.cz](https://savrn.com/model-publishers/yun2022) | 1 | | [ThousandML](https://savrn.com/model-publishers/thousandml) | 1 | | [Sica Rius](https://savrn.com/model-publishers/sicariussicariistuff) | 1 | ## All 28 Models Model · Text generation ### [Llama-3.1-8B-Instruct](https://savrn.com/models/llama-3-1-8b-instruct) [Meta Llama](https://savrn.com/model-publishers/meta-llama) The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.… Access requested at publisher llama3.1 8B parameters transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct) Model · Text classification ### [Prompt-Guard-86M](https://savrn.com/models/prompt-guard-86m) [Meta Llama](https://savrn.com/model-publishers/meta-llama) LLM-powered applications are susceptible to prompt attacks, which are prompts intentionally designed to subvert the developer’s intended behavior of the LLM. Categories of prompt attacks include prompt injection and jailbreaking: - Prompt Injections are inputs that exploit the concatenation of untrusted data from third parties and users into the context window of a model to get a model to execute unintended instructions. - Jailbreaks are malicious instructions designed to override the safety and security features built into a model. Prompt Guard is a classifier model trained on a large corpus of attacks, capable of detecting both explicitly malicious prompts as well as data that contains… Access requested at publisher llama3.1 279M parameters transformers [View model](https://savrn.com/models/prompt-guard-86m) Model ### [Assistant_Pepe_8B](https://savrn.com/models/assistant-pepe-8b) [Sica Rius](https://savrn.com/model-publishers/sicariussicariistuff) AssistantPepe8B / mobile stacking / Click here for TL;DR This is a project that was a long time in the making because I wanted to get it right. I'm still not fully satisfied, as there are some rough corners to sand, but for now, this would do. The goal was to maximize shitpostness along with helpfulness, without glazing the user for every retarded idea. Not an easy needle to thread. This amphibious AI has learned the ways of /g/, and speaks fluent brainrot, but will also help you out with just about anything you'll need, and won't be ashamed to roast you while at it. For those who remember OniMitsubishi12B - it was so overtly toxic that it made me worry at first (only to quickly be verified… Open weights llama3.1 266,240 parameters 1,073,152 tokens [View model](https://savrn.com/models/assistant-pepe-8b) Model · Text generation ### [Llama-3.1-8B-Instruct-Medical-Finetuned-merged](https://savrn.com/models/llama-3-1-8b-instruct-medical-finetuned-merged) [Mohamed Abo El-Enen](https://savrn.com/model-publishers/mohamedahmedae) This is the 8B (high-capacity flagship) member of the Med-LLaMA3 family introduced in the paper “Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large Language Models” (Applied Sciences, 2026). The family adapts the LLaMA-3 architecture to the medical domain by training only a small fraction of the base model’s parameters (4.01% for this 8B variant), achieving strong medical question-answering performance while keeping the memory footprint low — enabling development and inference on low-cost, consumer-grade hardware. The 8B variant is the high-capacity model for complex clinical reasoning. It attains a mean accuracy of 75.71% across the eight MMLU… Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-medical-finetuned-merged) Model · Text generation ### [Llama-3.1-8B-Instruct-4bit](https://savrn.com/models/llama-3-1-8b-instruct-4bit) [MLX Community](https://savrn.com/model-publishers/mlx-community) The Model mlx-community/Llama-3.1-8B-Instruct-4bit was converted to MLX format from meta-llama/Llama-3.1-8B-Instruct using mlx-lm version 0.21.4. Open weights llama3.1 8B parameters 131,072 tokens mlx [View model](https://savrn.com/models/llama-3-1-8b-instruct-4bit) Model ### [linux-meta-llama](https://savrn.com/models/linux-meta-llama) [Yun.cz](https://savrn.com/model-publishers/yun2022) Open weights llama3.1 131,072 tokens [View model](https://savrn.com/models/linux-meta-llama) Model · Question answering ### [Llama-3.1-8B-Instuct-Uz-GGUF](https://savrn.com/models/llama-3-1-8b-instuct-uz-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/behbudiy/Llama-3.1-8B-Instruct-Uz For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality… Open weights llama3.1 transformers [View model](https://savrn.com/models/llama-3-1-8b-instuct-uz-gguf) Model · Question answering ### [uzbek-llama-3.1-8B-instruct-v2-GGUF](https://savrn.com/models/uzbek-llama-3-1-8b-instruct-v2-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/behbudiy/Llama-3.1-8B-Instruct-Uz For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality… Open weights llama3.1 transformers [View model](https://savrn.com/models/uzbek-llama-3-1-8b-instruct-v2-gguf) Model · Text generation ### [Llama-3.1-8B-Instruct-CQ-4bit](https://savrn.com/models/llama-3-1-8b-instruct-cq-4bit) [ThousandML](https://savrn.com/model-publishers/thousandml) Llama 3.1 8B Instruct quantized to 4-bit with Coded Quantization (CQ). CQ is a calibration-free 4-bit quantization method designed to preserve the behavior of the original model without requiring a calibration dataset. Because CQ does not optimize the quantization around a fixed calibration corpus, it is designed to generalize beyond calibration-specific data and workloads. This repository contains the 8B-parameter Llama 3.1 Instruct model quantized with Coded Quantization (CQ) 4-bit. Calibration-free. CQ does not require a representative calibration dataset before quantization. This avoids tying the quantization procedure to a particular calibration corpus and is intended to provide better… Open weights llama3.1 1.1B parameters 131,072 tokens [View model](https://savrn.com/models/llama-3-1-8b-instruct-cq-4bit) Model · Text generation ### [Llama3.1-8B-TRACE](https://savrn.com/models/llama3-1-8b-trace) [Md Messal Monem Miah](https://savrn.com/model-publishers/dipto084) TRACE is a trajectory-aware defense model for multi-turn jailbreaks. Instead of judging each user message in isolation, it commits to an explicit safety assessment of the whole conversation so far — in a block — and only then writes its reply in an block. The assessment is part of the generation, so the reply is conditioned on it. This checkpoint is the paper's TRACE-GRPO model: a LoRA policy trained with group-decoupled GRPO on top of the stage-1 SFT adapter, merged into the base weights for release. Across seven multi-turn attack benchmarks it averages 14.5% ASR, against 31.4% for the strongest baseline and 74.9% for the undefended target, while keeping 93.3% average compliance on… Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama3-1-8b-trace) Model · Text generation ### [Llama-3.1-8B-Instruct-Conceptizer](https://savrn.com/models/llama-3-1-8b-instruct-conceptizer) [Haoran Zheng](https://savrn.com/model-publishers/flyingodzilla) Open weights llama3.1 peft [View model](https://savrn.com/models/llama-3-1-8b-instruct-conceptizer) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-20Minuten](https://savrn.com/models/llama-3-1-8b-instruct-ft-20minuten) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-20minuten) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-C-STANCE](https://savrn.com/models/llama-3-1-8b-instruct-ft-c-stance) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-c-stance) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-FOMC](https://savrn.com/models/llama-3-1-8b-instruct-ft-fomc) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-fomc) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-MeetingBank](https://savrn.com/models/llama-3-1-8b-instruct-ft-meetingbank) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-meetingbank) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-NumGLUE-cm](https://savrn.com/models/llama-3-1-8b-instruct-ft-numglue-cm) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-numglue-cm) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-NumGLUE-ds](https://savrn.com/models/llama-3-1-8b-instruct-ft-numglue-ds) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-numglue-ds) Model · Text generation ### [Llama-3.1-8B-Instruct-FT-ScienceQA](https://savrn.com/models/llama-3-1-8b-instruct-ft-scienceqa) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-ft-scienceqa) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-20Minuten](https://savrn.com/models/llama-3-1-8b-instruct-smat-20minuten) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-20minuten) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-C-STANCE](https://savrn.com/models/llama-3-1-8b-instruct-smat-c-stance) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-c-stance) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-FOMC](https://savrn.com/models/llama-3-1-8b-instruct-smat-fomc) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-fomc) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-MeetingBank](https://savrn.com/models/llama-3-1-8b-instruct-smat-meetingbank) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-meetingbank) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-NumGLUE-cm](https://savrn.com/models/llama-3-1-8b-instruct-smat-numglue-cm) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-numglue-cm) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-NumGLUE-ds](https://savrn.com/models/llama-3-1-8b-instruct-smat-numglue-ds) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-numglue-ds) Model · Text generation ### [Llama-3.1-8B-Instruct-SMAT-ScienceQA](https://savrn.com/models/llama-3-1-8b-instruct-smat-scienceqa) [Yanggangu](https://savrn.com/model-publishers/yanggangu) 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. Open weights llama3.1 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/llama-3-1-8b-instruct-smat-scienceqa) Model · Text generation ### [Llama-3.1-8B-Reponx-Network-Cloud-Ops](https://savrn.com/models/llama-3-1-8b-reponx-network-cloud-ops) [Reponx](https://savrn.com/model-publishers/reponx) 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… Open weights llama3.1 peft [View model](https://savrn.com/models/llama-3-1-8b-reponx-network-cloud-ops) Model ### [Llama-3.1-8B-Turkish-GGUF](https://savrn.com/models/llama-3-1-8b-turkish-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/erenzeytunn/Llama-3.1-8B-Turkish For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.1-8B-Turkish-i1-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9… Open weights llama3.1 transformers [View model](https://savrn.com/models/llama-3-1-8b-turkish-gguf) Model ### [Llama-3.1-8B-Turkish-i1-GGUF](https://savrn.com/models/llama-3-1-8b-turkish-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/erenzeytunn/Llama-3.1-8B-Turkish For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Llama-3.1-8B-Turkish-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See… Open weights llama3.1 transformers [View model](https://savrn.com/models/llama-3-1-8b-turkish-i1-gguf) ## Questions ### Can I use Meta Llama 3.1 Community License models commercially? Yes, with conditions. The Llama 3.1 Community License permits commercial use, except that a licensee whose products had more than 700 million monthly active users on the release date must request a license from Meta. It requires attribution as the license specifies and compliance with Meta's Acceptable Use Policy. ### Which Meta Llama 3.1 Community License models are most downloaded? By monthly downloads reported by the Hugging Face Hub: Llama-3.1-8B-Instruct (5M); Prompt-Guard-86M (2.4M); Assistant_Pepe_8B (705.5k). ## Other Licenses - [apache-2.0](https://savrn.com/models/licenses/apache-2-0) 3344 - [mit](https://savrn.com/models/licenses/mit) 1319 - [cc-by-nc-4.0](https://savrn.com/models/licenses/cc-by-nc-4-0) 213 - [cc-by-4.0](https://savrn.com/models/licenses/cc-by-4-0) 164 - [bsd-3-clause](https://savrn.com/models/licenses/bsd-3-clause) 108 - [gemma](https://savrn.com/models/licenses/gemma) 63 - [cc-by-nc-sa-4.0](https://savrn.com/models/licenses/cc-by-nc-sa-4-0) 55 - [llama3.2](https://savrn.com/models/licenses/llama3-2) 40 - [unknown](https://savrn.com/models/licenses/unknown) 33 - [agpl-3.0](https://savrn.com/models/licenses/agpl-3-0) 32 - [cc-by-sa-4.0](https://savrn.com/models/licenses/cc-by-sa-4-0) 20 - [gpl-3.0](https://savrn.com/models/licenses/gpl-3-0) 20 [See all](https://savrn.com/models/licenses)