--- title: "SaulLM-54B-Instruct" kind: "Kit" category: "Legal" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f6f4f701-3913-4cae-81e9-f823c78d35f8" updated: "2026-11-13" --- # SaulLM-54B-Instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. A language model tuned for legal text, trained on US and European legal sources. A research release for people who study or adapt legal AI. Its answers are information only, not legal advice. SaulLM-54B-Instruct is a kit in the Legal category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Equall ## About ### What it is SaulLM-54B-Instruct is a text generation model built for the legal field. Equall and the MICS lab at CentraleSupélec (Université Paris-Saclay) developed it. It starts from a base model made by continued pretraining of Mixtral models, then was tuned to follow instructions. The authors call it a research artifact. Its training data came from European and US law, covering statutes, judgments and similar legal writing. Hugging Face lists about 46.7 billion parameters. ### Who it suits Researchers and developers who study or adapt language models for legal work, and who have the hardware for a model of this size. It is not a replacement for a lawyer. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Legal research | Academic researchers | Study how a domain-tuned model handles legal text | Comparing its answers with a general model | | Legal tech | Product developers | Adapt the model for a legal assistant prototype | Building a draft tool for contract review ideas | | Education | Law students and teachers | Explore how AI summarizes legal material | Asking for a plain-language outline of a ruling | | Compliance | Policy analysts | Test a model on legislative documents | Summarizing a section of a regulation | Anyone who needs advice they can rely on, or answers for legal systems outside the US and Europe, can skip it. The card says it is not legal advice. ### Where to use it - Python with the Hugging Face Transformers library. - Windows, macOS or Linux with Python, for downloading the files. - Research and prototyping environments with a large GPU setup. | Use case | Who | What they get back | | --- | --- | --- | | Legal drafting ideas | Developers | Generated text that a lawyer should check | | Document summaries | Students | Plain-language summaries of legal documents | | Research baselines | Academics | A legal-domain model to compare other models against | ### How to set it up You need Python and a machine that can hold a model this large. - Python with the huggingface\_hub package - Disk space for about 93.4 GB of BF16 weights, or about 23.4 GB at 4-bit by Hugging Face's estimate - GPU memory or other hardware that fits the model - The Transformers library, which Hugging Face lists for it 1. Install the Hugging Face tool. 2. Download the model files. 3. Load the model with Transformers on suitable hardware. ```bash pip install -U huggingface_hub hf download Equall/SaulLM-54B-Instruct ``` ### How to use it The card gives no run command or sample code. Load the model with Transformers, send it an instruction about a legal text, and read the generated reply with a critical eye. Check every output against reliable sources. The authors say the responsibility for decisions rests with the user. ### Example A researcher asks the model to summarize a court ruling in plain language and to list its main points. The model writes a text summary. A qualified person should verify it before anyone relies on it. ### For newcomers and experts If you are new, this is a large language model that has read a lot of legal text. Treat it as a research tool, start with a short summary request, and check the result by hand. For experts, it is a 54 billion parameter model, pretrained and fine-tuned for legal tasks, built from Equall's base model that continues the pretraining of Mixtral models. The card gives no benchmark numbers, training recipe or sampler advice, so evaluate it on your own tasks. Equall base model from continued Mixtral pretraining → Legal pretraining and instruction tuning → You download the weights → Load them with Transformers → Review every generated answer ### Good to know The points below cover limits and costs. - Legal advice: the output is for information only and must not be read as legal advice. - Coverage: it focuses on US and European law, so it may do worse elsewhere. - Freshness: it was trained up to a certain point and cannot account for recent legal changes. - License: MIT. - Cost to run: no API key or paid service is named. The model is large and needs strong hardware. - Data it reaches: it reads the prompts you give it. Downloading needs a connection to Hugging Face. - Not for: harmful content, misinformation, privacy violations, intellectual property violations, or standing in for a qualified lawyer. ## README ````markdown # SaulLM-54B-Instruct A language model tuned for legal text, trained on US and European legal sources. A research release for people who study or adapt legal AI. Its answers are information only, not legal advice. Made by Equall on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: SaulLM-54B-Instruct is a text generation model built for the legal field. Equall and the MICS lab at CentraleSupélec (Université Paris-Saclay) developed it. It starts from a base model made by continued pretraining of Mixtral models, then was tuned to follow instructions. The authors call it a research artifact. Its training data came from European and US law, covering statutes, judgments and similar legal writing. Hugging Face lists about 46.7 billion parameters. Who it suits: Researchers and developers who study or adapt language models for legal work, and who have the hardware for a model of this size. It is not a replacement for a lawyer. ```table Industry | Who uses it | How they use it | Example Legal research | Academic researchers | Study how a domain-tuned model handles legal text | Comparing its answers with a general model Legal tech | Product developers | Adapt the model for a legal assistant prototype | Building a draft tool for contract review ideas Education | Law students and teachers | Explore how AI summarizes legal material | Asking for a plain-language outline of a ruling Compliance | Policy analysts | Test a model on legislative documents | Summarizing a section of a regulation ``` Anyone who needs advice they can rely on, or answers for legal systems outside the US and Europe, can skip it. The card says it is not legal advice. Where to use it: - Python with the Hugging Face Transformers library. - Windows, macOS or Linux with Python, for downloading the files. - Research and prototyping environments with a large GPU setup. ```table Use case | Who | What they get back Legal drafting ideas | Developers | Generated text that a lawyer should check Document summaries | Students | Plain-language summaries of legal documents Research baselines | Academics | A legal-domain model to compare other models against ``` How to set it up: You need Python and a machine that can hold a model this large. - Python with the huggingface_hub package - Disk space for about 93.4 GB of BF16 weights, or about 23.4 GB at 4-bit by Hugging Face's estimate - GPU memory or other hardware that fits the model - The Transformers library, which Hugging Face lists for it 1. Install the Hugging Face tool. 2. Download the model files. 3. Load the model with Transformers on suitable hardware. ```bash pip install -U huggingface_hub hf download Equall/SaulLM-54B-Instruct ``` How to use it: The card gives no run command or sample code. Load the model with Transformers, send it an instruction about a legal text, and read the generated reply with a critical eye. Check every output against reliable sources. The authors say the responsibility for decisions rests with the user. Example: A researcher asks the model to summarize a court ruling in plain language and to list its main points. The model writes a text summary. A qualified person should verify it before anyone relies on it. For newcomers and experts: If you are new, this is a large language model that has read a lot of legal text. Treat it as a research tool, start with a short summary request, and check the result by hand. For experts, it is a 54 billion parameter model, pretrained and fine-tuned for legal tasks, built from Equall's base model that continues the pretraining of Mixtral models. The card gives no benchmark numbers, training recipe or sampler advice, so evaluate it on your own tasks. ```flow Equall base model from continued Mixtral pretraining Legal pretraining and instruction tuning You download the weights Load them with Transformers Review every generated answer ``` Good to know: The points below cover limits and costs. - Legal advice: the output is for information only and must not be read as legal advice. - Coverage: it focuses on US and European law, so it may do worse elsewhere. - Freshness: it was trained up to a certain point and cannot account for recent legal changes. - License: MIT. - Cost to run: no API key or paid service is named. The model is large and needs strong hardware. - Data it reaches: it reads the prompts you give it. Downloading needs a connection to Hugging Face. - Not for: harmful content, misinformation, privacy violations, intellectual property violations, or standing in for a qualified lawyer. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 46.7B - Weights: about 93.4 GB in BF16; about 23.4 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 315 - Likes: 16 - License: mit - Languages: en - Quantized versions: 3 - Fine-tunes: 1 - Tags: transformers, safetensors, mixtral, text-generation ## Links - Model: https://huggingface.co/Equall/SaulLM-54B-Instruct - Files: https://huggingface.co/Equall/SaulLM-54B-Instruct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Equall/SaulLM-54B-Instruct ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Equall/SaulLM-54B-Instruct on Hugging Face](https://huggingface.co/Equall/SaulLM-54B-Instruct) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | SaulLM-54B-Instruct (this page) | 315 | 16 | 46.7B | mit | 2024-07-31 | | [privacy-filter-nemotron-v2](https://mindket.com/p/23532a37-0110-417a-8dbb-6c1fd00a796a) | 154,398 | 57 | 1.4B | apache-2.0 | 2026-09-30 | | [rizzo-pii-0.3B](https://mindket.com/p/37c4000b-ebb5-4db2-9df5-a87a46896f81) | 6,484 | 23 | 308M | mit | 2026-08-09 | | [PII-Tracer](https://mindket.com/p/cb03372e-7eea-4c7b-bfee-be3c53650ba6) | 8,506 | 97 | 596M | mit | 2026-09-22 | Numbers as Mindket last read them, on 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-07-31 - First published: 2024-05-07 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Equall/SaulLM-54B-Instruct on Hugging Face](https://huggingface.co/Equall/SaulLM-54B-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f6f4f701-3913-4cae-81e9-f823c78d35f8) - [Its page on Mindket](https://mindket.com/p/f6f4f701-3913-4cae-81e9-f823c78d35f8)