--- title: "ALLaM-7B-Instruct-preview" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/95ed1553-ca4e-4a47-9739-80a0aa93810c" updated: "2026-11-13" --- # ALLaM-7B-Instruct-preview > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers questions and follows instructions in Arabic and English. A 7B model trained from scratch by the National Center for Artificial Intelligence at SDAIA, for developers and researchers building Arabic language tools. ALLaM-7B-Instruct-preview 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 humain-ai ## About ### What it is ALLaM-7B-Instruct-preview is a 7B instruction-tuned language model for Arabic and English, built by the National Center for Artificial Intelligence at SDAIA. It was trained from scratch, first on 4T English tokens and then on 1.2T mixed Arabic and English tokens, a plan the card says keeps its English skills intact. It aims to speed up research and product work in Arabic language technology. ### Who it suits Developers and researchers who build Arabic or bilingual assistants and want a base to test and extend. The weights carry the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Language technology | Researchers | Study and extend Arabic model capabilities | Testing question answering in Arabic | | Customer service | Product teams | Build bilingual Arabic and English assistants | A support bot that answers in Arabic | | Content | Content teams | Draft Arabic explanations and answers | Explaining a household task in Arabic | Anyone who wants a finished, safety-checked product can skip it, because the card says the model is one part of a larger system and developers must add their own safety measures. ### Where to use it - Python with the transformers library, which the card tested at version 4.40.1 or newer - A CUDA GPU, which the card's example loads the model onto - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Arabic Q and A | App builders | Answers written in Arabic | | Bilingual chat | Product teams | Replies in Arabic or English | | Research baseline | Researchers | A from-scratch Arabic and English model to test | ### How to set it up - Python and the transformers package, version 4.40.1 or newer - A CUDA GPU - Room for the weights, about 14 GB in BF16 1. Install Python and a transformers version of 4.40.1 or newer. 2. Install the huggingface\_hub package with the first command below. 3. Download the model files with the second command. 4. Pick the older release or the newest one through the revision setting, v1 or v2, if you need a specific one. ```sh pip install -U huggingface_hub hf download humain-ai/ALLaM-7B-Instruct-preview ``` ### How to use it The card's snippet loads the model and tokenizer, wraps an Arabic question in the chat template and generates a reply on the GPU. It names the model with the id ALLaM-AI/ALLaM-7B-Instruct-preview, so swap in the repository you downloaded if the two differ. ```python from transformers import AutoModelForCausalLM, AutoTokenizer allam_model = AutoModelForCausalLM.from_pretrained("ALLaM-AI/ALLaM-7B-Instruct-preview") tokenizer = AutoTokenizer.from_pretrained("ALLaM-AI/ALLaM-7B-Instruct-preview") messages=[ {"role": "user", "content": "كيف أجهز كوب شاهي؟"}, ] inputs = tokenizer.apply_chat_template(messages, tokenize=False) inputs = tokenizer(inputs, return_tensors='pt', return_token_type_ids=False) inputs = {k: v.to('cuda') for k,v in inputs.items()} allam_model = allam_model.to('cuda') response = allam_model.generate(**inputs, max_new_tokens=4096, do_sample=True, top_k=50, top_p=0.95, temperature=.6) print(tokenizer.batch_decode(response, skip_special_tokens=True)[0]) ``` The model works best with no preset system prompt, though you can add your own, in English or Arabic. The card gives a short line that tells it to act as a bilingual English and Arabic assistant. The system prompt lives in the tokenizer config and is reached through the chat template. ### Example You ask in Arabic how to prepare a cup of tea, as the card's snippet does. The model answers in Arabic, with up to 4,096 new tokens allowed. ### For newcomers and experts This is a base for building Arabic tools, not a finished chat app. A good first step is to run the snippet above with the Arabic question and read the reply. The card describes two training paths for the ALLaM series, pretraining from scratch and continued training from open-weight models, and this release follows the first. It was trained on 5.2 trillion tokens, with a codebase built on NVIDIA Megatron-LM, an average MFU of about 42 percent and bf16-mixed precision. Post-training used 7M instructions and 260K preference pairs, and the context length is 4096 tokens. Pretrain on 4T English tokens → Pretrain on 1.2T Arabic and English tokens → Tune on 7M instructions → Tune on 260K preference pairs → Chat in Arabic or English ### Good to know - License: Apache 2.0 in Hugging Face's record, and the card points to its LICENSE file - Cost to run: none named - Data it reaches: the download fetches files from Hugging Face, and the source names nothing else - It is a preview release, and the card asks developers to add safety measures, especially when connecting it to external tools Pretraining tokens by stage, in trillions | Label | Value | | --- | --- | | English stage | 4 T | | Arabic and English stage | 1.2 T | ## README ````markdown # ALLaM-7B-Instruct-preview Answers questions and follows instructions in Arabic and English. A 7B model trained from scratch by the National Center for Artificial Intelligence at SDAIA, for developers and researchers building Arabic language tools. Made by humain-ai 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: ALLaM-7B-Instruct-preview is a 7B instruction-tuned language model for Arabic and English, built by the National Center for Artificial Intelligence at SDAIA. It was trained from scratch, first on 4T English tokens and then on 1.2T mixed Arabic and English tokens, a plan the card says keeps its English skills intact. It aims to speed up research and product work in Arabic language technology. Who it suits: Developers and researchers who build Arabic or bilingual assistants and want a base to test and extend. The weights carry the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Language technology | Researchers | Study and extend Arabic model capabilities | Testing question answering in Arabic Customer service | Product teams | Build bilingual Arabic and English assistants | A support bot that answers in Arabic Content | Content teams | Draft Arabic explanations and answers | Explaining a household task in Arabic ``` Anyone who wants a finished, safety-checked product can skip it, because the card says the model is one part of a larger system and developers must add their own safety measures. Where to use it: - Python with the transformers library, which the card tested at version 4.40.1 or newer - A CUDA GPU, which the card's example loads the model onto - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Arabic Q and A | App builders | Answers written in Arabic Bilingual chat | Product teams | Replies in Arabic or English Research baseline | Researchers | A from-scratch Arabic and English model to test ``` How to set it up: - Python and the transformers package, version 4.40.1 or newer - A CUDA GPU - Room for the weights, about 14 GB in BF16 1. Install Python and a transformers version of 4.40.1 or newer. 2. Install the huggingface_hub package with the first command below. 3. Download the model files with the second command. 4. Pick the older release or the newest one through the revision setting, v1 or v2, if you need a specific one. ```sh pip install -U huggingface_hub hf download humain-ai/ALLaM-7B-Instruct-preview ``` How to use it: The card's snippet loads the model and tokenizer, wraps an Arabic question in the chat template and generates a reply on the GPU. It names the model with the id ALLaM-AI/ALLaM-7B-Instruct-preview, so swap in the repository you downloaded if the two differ. ```python from transformers import AutoModelForCausalLM, AutoTokenizer allam_model = AutoModelForCausalLM.from_pretrained("ALLaM-AI/ALLaM-7B-Instruct-preview") tokenizer = AutoTokenizer.from_pretrained("ALLaM-AI/ALLaM-7B-Instruct-preview") messages=[ {"role": "user", "content": "كيف أجهز كوب شاهي؟"}, ] inputs = tokenizer.apply_chat_template(messages, tokenize=False) inputs = tokenizer(inputs, return_tensors='pt', return_token_type_ids=False) inputs = {k: v.to('cuda') for k,v in inputs.items()} allam_model = allam_model.to('cuda') response = allam_model.generate(**inputs, max_new_tokens=4096, do_sample=True, top_k=50, top_p=0.95, temperature=.6) print(tokenizer.batch_decode(response, skip_special_tokens=True)[0]) ``` The model works best with no preset system prompt, though you can add your own, in English or Arabic. The card gives a short line that tells it to act as a bilingual English and Arabic assistant. The system prompt lives in the tokenizer config and is reached through the chat template. Example: You ask in Arabic how to prepare a cup of tea, as the card's snippet does. The model answers in Arabic, with up to 4,096 new tokens allowed. For newcomers and experts: This is a base for building Arabic tools, not a finished chat app. A good first step is to run the snippet above with the Arabic question and read the reply. The card describes two training paths for the ALLaM series, pretraining from scratch and continued training from open-weight models, and this release follows the first. It was trained on 5.2 trillion tokens, with a codebase built on NVIDIA Megatron-LM, an average MFU of about 42 percent and bf16-mixed precision. Post-training used 7M instructions and 260K preference pairs, and the context length is 4096 tokens. ```flow Pretrain on 4T English tokens Pretrain on 1.2T Arabic and English tokens Tune on 7M instructions Tune on 260K preference pairs Chat in Arabic or English ``` Good to know: - License: Apache 2.0 in Hugging Face's record, and the card points to its LICENSE file - Cost to run: none named - Data it reaches: the download fetches files from Hugging Face, and the source names nothing else - It is a preview release, and the card asks developers to add safety measures, especially when connecting it to external tools ```chart Title: Pretraining tokens by stage, in trillions English stage: 4 T Arabic and English stage: 1.2 T ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 7B - Weights: about 14 GB in BF16; about 3.5 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 93,626 - Likes: 207 - License: apache-2.0 - Languages: ar, en - Quantized versions: 15 - Fine-tunes: 29 - Tags: transformers, safetensors, llama, text-generation, pytorch, conversational ## Links - Model: https://huggingface.co/humain-ai/ALLaM-7B-Instruct-preview - Files: https://huggingface.co/humain-ai/ALLaM-7B-Instruct-preview/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download humain-ai/ALLaM-7B-Instruct-preview ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [humain-ai/ALLaM-7B-Instruct-preview on Hugging Face](https://huggingface.co/humain-ai/ALLaM-7B-Instruct-preview) ## 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 | | --- | --- | --- | --- | --- | --- | | ALLaM-7B-Instruct-preview (this page) | 93,626 | 207 | 7B | apache-2.0 | 2025-07-14 | | [Kiyo-65M](https://mindket.com/p/41f070ee-1394-4748-845b-ecb0e0d53d8e) | 1,179 | 5 | 65M | apache-2.0 | 2026-10-23 | | [Meta-Llama-3.1-8B-Instruct-GGUF](https://mindket.com/p/51184fc4-b11d-4c65-93f0-2107bafe9b0e) | 322,920 | 433 | 8B | llama3.1 | 2024-12-01 | | [MiniCPM5-2B](https://mindket.com/p/4d384af8-8281-4982-baed-7ea9aab0a3e1) | 1,039,518 | 1,850 | 2.5B | apache-2.0 | 2026-11-09 | | [TinyLlama-1.1B-Chat-v1.0](https://mindket.com/p/e73c1610-865e-48f4-a11d-4f6b640f3ae3) | 963,057 | 1,910 | 1.1B | apache-2.0 | 2024-03-17 | | [SmolLM2-135M-Instruct](https://mindket.com/p/64e0fae8-46ea-40d4-9f5e-7d3ea5c053af) | 841,106 | 474 | 135M | apache-2.0 | 2025-09-22 | Numbers as Mindket last read them, from 2026-11-12 to 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: 2025-07-14 - First published: 2025-02-13 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open humain-ai/ALLaM-7B-Instruct-preview on Hugging Face](https://huggingface.co/humain-ai/ALLaM-7B-Instruct-preview) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/95ed1553-ca4e-4a47-9739-80a0aa93810c) - [Its page on Mindket](https://mindket.com/p/95ed1553-ca4e-4a47-9739-80a0aa93810c)