--- title: "SalamandraTA-7b-instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2219cecd-b971-4073-be5d-2bd4296166fb" updated: "2026-11-13" --- # SalamandraTA-7b-instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. A 7.8B translation model for 40 languages and 3 varieties, covering sentence, paragraph and document translation plus HTML or XML text. For developers and researchers who want a local translator. SalamandraTA-7b-instruct is a kit in the Developer 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 BSC-LT ## About ### What it is SalamandraTA-7b-instruct is a translation model from BSC-LT, tuned with instructions from a base model that stays internal. It translates in 40 languages plus 3 varieties, including Arabic, Japanese, Hindi, Korean and Simplified Chinese. Version 3 also handles terminology-aware translation, structured text, post-editing and named entity recognition. ### Who it suits It suits developers, researchers and companies who need machine translation they can run on their own hardware. The card allows research as well as commercial use of the model. Who uses it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Application developers | Add translation to a product or pipeline | Translate a paragraph from Spanish to Japanese | | Web publishing | Content teams | Translate pages that contain HTML or XML | Keep the markup while translating the text | | Language services | Translators and editors | Post-edit machine output and keep terms consistent | Fix a draft translation against a glossary | Anyone who wants a chat assistant can skip it, since the card says it has no chat abilities. ### Where to use it - Python with the transformers library - Windows, macOS or Linux, to download the files - A GPU or large-memory machine for a 7.8B model Top jobs | Use case | Who | What they get back | | --- | --- | --- | | General translation | Developers | Translated sentences, paragraphs or whole documents | | Structured text | Content teams | Translated HTML or XML text | | Post-editing | Editors | An improved version of a draft translation | | Term-aware translation | Language teams | A translation that follows a given terminology | ### How to set it up - Python on your computer - The huggingface\_hub package - About 15.5 GB of disk for the BF16 weights, or about 3.9 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Load the model with the transformers library, as in the card's example. ```sh pip install -U huggingface_hub hf download BSC-LT/salamandraTA-7b-instruct ``` ### How to use it Load the model with AutoModelForCausalLM from transformers. The current release is on the main branch. To pick an older release, pass the revision argument when loading. ```python model = AutoModelForCausalLM.from_pretrained( "BSC-LT/salamandraTA-7b-instruct", revision="v1.0" # omit revision for current release ) ``` Releases | Version | Where to find it | Status | | --- | --- | --- | | v3.0 | main branch | Current release | | v2.0 | v2.0 revision | Second release | | v1.0 | v1.0 revision | Initial release | ### Example You load the current release and ask it to translate a paragraph from Catalan into English. The model returns the translation as text. For other tasks, such as post-editing, the card's usage section has the prompt format to follow. ### For newcomers and experts If you are new, think of this as a translation engine rather than a chatbot. For a first try, translate one short paragraph and read the result against the original. For experts, it is a 32-layer decoder with 7,768,117,248 parameters and a context length of 8,192 tokens. The base was continually pre-trained on monolingual and parallel data, and the instruct model was then tuned for translation tasks only. Send a text and the target language → The 7.8B model translates it → Term-aware or markup-safe options apply → You read the translated text ### Good to know - Translation only: the card says it lacks chat capabilities and was trained with no chat instructions. - Use limits: it is not meant for harming others or violating human rights, and downstream use must follow the law. - License: GPL-3. The release uses data that requires GPL-3 distribution. - Cost to run: none named, though a 7.8B model needs a capable machine. - Data it reaches: it downloads files from Hugging Face, and the source names nothing else. - Not for: chat, or production use without a proper risk assessment. ## README ````markdown # SalamandraTA-7b-instruct A 7.8B translation model for 40 languages and 3 varieties, covering sentence, paragraph and document translation plus HTML or XML text. For developers and researchers who want a local translator. Made by BSC-LT 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: SalamandraTA-7b-instruct is a translation model from BSC-LT, tuned with instructions from a base model that stays internal. It translates in 40 languages plus 3 varieties, including Arabic, Japanese, Hindi, Korean and Simplified Chinese. Version 3 also handles terminology-aware translation, structured text, post-editing and named entity recognition. Who it suits: It suits developers, researchers and companies who need machine translation they can run on their own hardware. The card allows research as well as commercial use of the model. ```table Title: Who uses it Industry | Who uses it | How they use it | Example Software | Application developers | Add translation to a product or pipeline | Translate a paragraph from Spanish to Japanese Web publishing | Content teams | Translate pages that contain HTML or XML | Keep the markup while translating the text Language services | Translators and editors | Post-edit machine output and keep terms consistent | Fix a draft translation against a glossary ``` Anyone who wants a chat assistant can skip it, since the card says it has no chat abilities. Where to use it: - Python with the transformers library - Windows, macOS or Linux, to download the files - A GPU or large-memory machine for a 7.8B model ```table Title: Top jobs Use case | Who | What they get back General translation | Developers | Translated sentences, paragraphs or whole documents Structured text | Content teams | Translated HTML or XML text Post-editing | Editors | An improved version of a draft translation Term-aware translation | Language teams | A translation that follows a given terminology ``` How to set it up: - Python on your computer - The huggingface_hub package - About 15.5 GB of disk for the BF16 weights, or about 3.9 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Load the model with the transformers library, as in the card's example. ```sh pip install -U huggingface_hub hf download BSC-LT/salamandraTA-7b-instruct ``` How to use it: Load the model with AutoModelForCausalLM from transformers. The current release is on the main branch. To pick an older release, pass the revision argument when loading. ```python model = AutoModelForCausalLM.from_pretrained( "BSC-LT/salamandraTA-7b-instruct", revision="v1.0" # omit revision for current release ) ``` ```table Title: Releases Version | Where to find it | Status v3.0 | main branch | Current release v2.0 | v2.0 revision | Second release v1.0 | v1.0 revision | Initial release ``` Example: You load the current release and ask it to translate a paragraph from Catalan into English. The model returns the translation as text. For other tasks, such as post-editing, the card's usage section has the prompt format to follow. For newcomers and experts: If you are new, think of this as a translation engine rather than a chatbot. For a first try, translate one short paragraph and read the result against the original. For experts, it is a 32-layer decoder with 7,768,117,248 parameters and a context length of 8,192 tokens. The base was continually pre-trained on monolingual and parallel data, and the instruct model was then tuned for translation tasks only. ```flow Send a text and the target language The 7.8B model translates it Term-aware or markup-safe options apply You read the translated text ``` Good to know: - Translation only: the card says it lacks chat capabilities and was trained with no chat instructions. - Use limits: it is not meant for harming others or violating human rights, and downstream use must follow the law. - License: GPL-3. The release uses data that requires GPL-3 distribution. - Cost to run: none named, though a 7.8B model needs a capable machine. - Data it reaches: it downloads files from Hugging Face, and the source names nothing else. - Not for: chat, or production use without a proper risk assessment. ## On Hugging Face, 13 November 2026 - Task: translation - Library: transformers - Parameters: 7.8B - Weights: about 15.5 GB in BF16; about 3.9 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,095 - Likes: 30 - License: gpl-3.0 - Languages: bg, ca, cs, cy, da, de, el, en - Based on: BSC-LT/salamandra-7b - Quantized versions: 8 - Fine-tunes: 2 - Tags: transformers, safetensors, llama, text-generation, translation ## Links - Model: https://huggingface.co/BSC-LT/salamandraTA-7b-instruct - Files: https://huggingface.co/BSC-LT/salamandraTA-7b-instruct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download BSC-LT/salamandraTA-7b-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 [BSC-LT/salamandraTA-7b-instruct on Hugging Face](https://huggingface.co/BSC-LT/salamandraTA-7b-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 | | --- | --- | --- | --- | --- | --- | | SalamandraTA-7b-instruct (this page) | 1,095 | 30 | 7.8B | gpl-3.0 | 2026-10-15 | | [Hy-MT2-30B-A3B](https://mindket.com/p/4b92fe0f-970c-4c2b-82f7-7cdee8bb1625) | 4,684 | 518 | 30.1B | apache-2.0 | 2026-07-21 | | [T5 Small](https://mindket.com/p/b2b4ffab-0ca8-425a-8b34-6a9a12af8cec) | 20,046,141 | 696 | 60.5M | apache-2.0 | 2023-06-30 | | [T5 Base](https://mindket.com/p/00988520-20a6-46ad-802a-f66a73ed289e) | 2,853,651 | 811 | 223M | apache-2.0 | 2024-02-14 | | [SmolLM2-135M](https://mindket.com/p/d6fb16a4-bb87-4d10-b168-c8f8485dedb8) | 2,596,139 | 269 | 135M | apache-2.0 | 2025-02-06 | | [MiniCPM5-2B](https://mindket.com/p/4d384af8-8281-4982-baed-7ea9aab0a3e1) | 1,039,518 | 1,850 | 2.5B | apache-2.0 | 2026-11-09 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · HiTZ zentroa (EHU) · 2026-07-02](https://bsky.app/profile/hitz-zentroa.bsky.social/post/3mpnpbgddrc2z): \*\*𝗣𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝗺𝗼𝘀 𝗻𝘂𝗲𝘀𝘁𝗿𝗼𝘀 𝗺𝗼𝗱𝗲𝗹𝗼𝘀 𝗱𝗲 𝘁𝗿𝗮𝗱𝘂𝗰𝗰𝗶ó𝗻 𝗮𝘂𝘁𝗼𝗺á𝘁𝗶𝗰𝗮\*\* 🌍 Traducen entre más de 40 idiomas y variedades, manteniendo el contexto y los matices. 🚀 Modelos abiertos, ligeros y potentes para hacer la IA más inclusiva. 🔗 huggingface.co/BSC-LT/salam...… - [Bluesky · dretsdigitals.bsky.social · 2026-07-03](https://bsky.app/profile/dretsdigitals.bsky.social/post/3mpr6iw3ows2z): M'assabento via RT de @mariagalindogd de 4 nous models oberts d'Aina, aquests per a traducció. Son 1) https://huggingface.co/BSC-LT/salamandraTA-7b-instruct 2) https://huggingface.co/BSC-LT/salamandraTA-7B-instruct-GGUF 3) https://huggingface.co/BSC-LT/salamandraTA-2B-instruct-GGUF 4)… - [Bluesky · @crovia.bsky.social · 2026-09-21](https://bsky.app/profile/crovia.bsky.social/post/3mvzlygu4ub2i): Disclosure change detected: BSC-LT/salamandraTA-7B-instruct-GGUF License changed: apache-2.0 -\> gpl-3.0. Before/after fingerprint recorded \& anchored. - [Bluesky · @crovia.bsky.social · 2026-08-17](https://bsky.app/profile/crovia.bsky.social/post/3mtbloanmds2q): Disclosure change detected: BSC-LT/salamandraTA-7b-instruct License changed: apache-2.0 -\> gpl-3.0. Before/after fingerprint recorded \& anchored. ## 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: 2026-10-15 - First published: 2025-01-08 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open BSC-LT/salamandraTA-7b-instruct on Hugging Face](https://huggingface.co/BSC-LT/salamandraTA-7b-instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2219cecd-b971-4073-be5d-2bd4296166fb) - [Its page on Mindket](https://mindket.com/p/2219cecd-b971-4073-be5d-2bd4296166fb)