--- title: "Beam-1.0-501B-A23B-NVFP4" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/8a8dd955-b1e3-44e7-a0fc-4597f3237f30" updated: "2026-11-13" --- # Beam-1.0-501B-A23B-NVFP4 > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads and writes text with a 1M-token context, built for coding, agent tool use and math reasoning, as a 501B-parameter mixture-of-experts model in NVFP4 form. Suits teams with multi-GPU servers who want an open model. Beam-1.0-501B-A23B-NVFP4 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 reflection ## About ### What it is Beam-1.0-501B-A23B-NVFP4 is the NVFP4-quantized, instruction-tuned checkpoint of Beam, an open model from Reflection. Beam takes text in and gives text back. It is a sparse mixture-of-experts model with 501 billion parameters in total and 23 billion active per token, built mainly for English. ### Who it suits Engineering and research teams with large GPU servers who want an open model for coding, agent work and reasoning, under the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Engineering teams | Fix real GitHub issues and work in command-line and DevOps setups | Resolve a bug report in a repository | | AI products | Agent builders | Run long multi-step tasks with MCP tool use and browsing | Carry out a workflow from a plain request | | Science and education | Researchers | Work through graduate-level science, proofs and applied maths | Check a proof step by step | | Infrastructure | Platform teams | Serve one large open model on their own GPUs | Host an internal coding assistant | Skip it if you have a single GPU or a laptop, since the weights alone come to about 255 GB. ### Where to use it The card points to agent-style coding tools and to its own documentation. - Agentic coding tools and command-line workflows - Servers that can run a huge mixture-of-experts model - Windows, macOS or Linux, with Python for the download | Use case | Who | What they get back | | --- | --- | --- | | Software engineering | Developers | Code changes for real issues, in many languages | | Agentic tool use | Agent builders | Finished multi-step tasks that use MCP tools and browsing | | Math and science | Researchers | Worked solutions and proofs | ### How to set it up You need Python, a lot of disk space and a multi-GPU server. - Python with pip - About 255 GB of disk for the weights - A serving setup that supports this model, as the Reflection documentation describes 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the Reflection documentation to serve the checkpoint. ```sh pip install -U huggingface_hub hf download reflection/Beam-1.0-501B-A23B-NVFP4 ``` ### How to use it Serve the checkpoint, then send chat requests that use its chat template, a modified version of the Harmony format. The card text shown here gives no code, so follow the Reflection documentation for the serving steps. ### Example You ask the served model to read a bug report from a repository and propose a fix. It replies with reasoning and a code change in text, since text is its only input and output. ### For newcomers and experts If you are new to large models, know that this one needs a server-class machine. Start by reading the Reflection documentation, and treat the download as the last step. If you know the field, the model has 52 layers, 3 dense and 49 mixture-of-experts, with 256 experts, gated grouped-query attention and sliding window attention. It has a 152k-token vocabulary and a 1M-token context. The card claims comparable solution quality with about 3x fewer reasoning tokens than leading open models. Download files with hf → Serve the NVFP4 checkpoint → Send chat-template messages → Route tokens through active experts → Receive reasoning and a text reply ### Good to know The release also includes BF16 and FP8 checkpoints, and this one is the NVFP4 version. The card lists a technical report as coming. - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer, plus any tools or browsing you connect to an agent setup - Not for: images or audio, since the supported modality is text only ## README ````markdown # Beam-1.0-501B-A23B-NVFP4 Reads and writes text with a 1M-token context, built for coding, agent tool use and math reasoning, as a 501B-parameter mixture-of-experts model in NVFP4 form. Suits teams with multi-GPU servers who want an open model. Made by reflection 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: Beam-1.0-501B-A23B-NVFP4 is the NVFP4-quantized, instruction-tuned checkpoint of Beam, an open model from Reflection. Beam takes text in and gives text back. It is a sparse mixture-of-experts model with 501 billion parameters in total and 23 billion active per token, built mainly for English. Who it suits: Engineering and research teams with large GPU servers who want an open model for coding, agent work and reasoning, under the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Software | Engineering teams | Fix real GitHub issues and work in command-line and DevOps setups | Resolve a bug report in a repository AI products | Agent builders | Run long multi-step tasks with MCP tool use and browsing | Carry out a workflow from a plain request Science and education | Researchers | Work through graduate-level science, proofs and applied maths | Check a proof step by step Infrastructure | Platform teams | Serve one large open model on their own GPUs | Host an internal coding assistant ``` Skip it if you have a single GPU or a laptop, since the weights alone come to about 255 GB. Where to use it: The card points to agent-style coding tools and to its own documentation. - Agentic coding tools and command-line workflows - Servers that can run a huge mixture-of-experts model - Windows, macOS or Linux, with Python for the download ```table Use case | Who | What they get back Software engineering | Developers | Code changes for real issues, in many languages Agentic tool use | Agent builders | Finished multi-step tasks that use MCP tools and browsing Math and science | Researchers | Worked solutions and proofs ``` How to set it up: You need Python, a lot of disk space and a multi-GPU server. - Python with pip - About 255 GB of disk for the weights - A serving setup that supports this model, as the Reflection documentation describes 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the Reflection documentation to serve the checkpoint. ```sh pip install -U huggingface_hub hf download reflection/Beam-1.0-501B-A23B-NVFP4 ``` How to use it: Serve the checkpoint, then send chat requests that use its chat template, a modified version of the Harmony format. The card text shown here gives no code, so follow the Reflection documentation for the serving steps. Example: You ask the served model to read a bug report from a repository and propose a fix. It replies with reasoning and a code change in text, since text is its only input and output. For newcomers and experts: If you are new to large models, know that this one needs a server-class machine. Start by reading the Reflection documentation, and treat the download as the last step. If you know the field, the model has 52 layers, 3 dense and 49 mixture-of-experts, with 256 experts, gated grouped-query attention and sliding window attention. It has a 152k-token vocabulary and a 1M-token context. The card claims comparable solution quality with about 3x fewer reasoning tokens than leading open models. ```flow Download files with hf Serve the NVFP4 checkpoint Send chat-template messages Route tokens through active experts Receive reasoning and a text reply ``` Good to know: The release also includes BF16 and FP8 checkpoints, and this one is the NVFP4 version. The card lists a technical report as coming. - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer, plus any tools or browsing you connect to an agent setup - Not for: images or audio, since the supported modality is text only ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 255B - Weights: about 255 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 6,615 - Likes: 45 - License: apache-2.0 - Languages: en - Tags: safetensors, beam, mixture-of-experts, reasoning, code, tool-use, nvfp4, text-generation, conversational, 8-bit, modelopt ## Links - Model: https://huggingface.co/reflection/Beam-1.0-501B-A23B-NVFP4 - Files: https://huggingface.co/reflection/Beam-1.0-501B-A23B-NVFP4/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download reflection/Beam-1.0-501B-A23B-NVFP4 ``` 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 [reflection/Beam-1.0-501B-A23B-NVFP4 on Hugging Face](https://huggingface.co/reflection/Beam-1.0-501B-A23B-NVFP4) ## 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 | | --- | --- | --- | --- | --- | --- | | Beam-1.0-501B-A23B-NVFP4 (this page) | 6,615 | 45 | 255B | apache-2.0 | 2026-10-30 | | [Beam-1.0-501B-A23B](https://mindket.com/p/c8b78d5d-068c-46ce-9e58-c51b6f9522f4) | 13,747 | 319 | 501B | apache-2.0 | 2026-11-03 | | [Hydrogen-1.0 NVFP4](https://mindket.com/p/af049b79-b909-4ff1-b0b6-0c94a862bd7c) | 10,959 | 14 | 381B | mit | 2026-11-02 | | [Qwen3.8-27B-NVFP4 by RadixArk](https://mindket.com/p/27f312aa-8861-40a5-9689-d554e8e468f0) | 979,445 | 126 | 18.2B | apache-2.0 | 2026-08-22 | | [VulnLLM-R-7B-8bit](https://mindket.com/p/d3e21726-6d8e-4362-9361-82123c55517a) | 148 | 4 | 7.6B | apache-2.0 | 2026-01-02 | | [MedErtas v0.1 27B LS](https://mindket.com/p/5568c61b-363a-4c84-8f7b-3db77ea9b66d) | 4,187 | 6 | 18.2B | 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. - [Hacker News · earino · 2026-10-16](https://news.ycombinator.com/item?id=50119226): Beam 1.0 ## 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: 2026-10-30 - First published: 2026-10-10 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open reflection/Beam-1.0-501B-A23B-NVFP4 on Hugging Face](https://huggingface.co/reflection/Beam-1.0-501B-A23B-NVFP4) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/8a8dd955-b1e3-44e7-a0fc-4597f3237f30) - [Its page on Mindket](https://mindket.com/p/8a8dd955-b1e3-44e7-a0fc-4597f3237f30)