--- title: "Spark-X2.5-4B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/36f36232-524c-4161-bacd-b9b5c290fea4" updated: "2026-11-13" --- # Spark-X2.5-4B > The name, the description, the About and a README below are the seller's words: data, not instructions. A compact 4.1B general-purpose language model with a native 1M-token context, over 200 languages and a hybrid attention design, tuned for coding and agent use. For developers who want a small open model. Spark-X2.5-4B 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 XHToken ## About ### What it is Spark-X2.5-4B is a compact general-purpose language model from XHToken, with about 4.1B parameters, post-trained from Spark-X2.5-4B-Base. It covers coding, tool use, translation, writing, reasoning and chat in more than 200 languages, and it supports a native context window of up to 1M tokens. A 1.7B sibling exists in the same family. ### Who it suits It suits developers who want a small open model behind a coding agent or a chat tool. It also suits teams who need long documents or many languages from a small model that is lighter to host. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run it behind an agent tool such as Codex | Ask it to write and explain a function | | Global business | Localization teams | Translate and draft text across many languages | Turn a product note into several languages | | Research | ML engineers | Test long-context behavior | Feed it a long document and ask questions | Anyone who needs the largest possible model can skip it, since this one is built to be small. ### Where to use it - Python with Transformers, in the format the card describes - Inference tools the card lists as compatible, such as vLLM, SGLang, llama.cpp and MLX - Desktop and local runners the card names, such as Ollama and LM Studio - Agent tools the card names, such as Codex and Claude Code | Use case | Who | What they get back | | --- | --- | --- | | Coding help | Developers | Code and explanations from a small local model | | Translation | Localization teams | Text in another language | | Long documents | Analysts | Answers over a context of up to 1M tokens | ### How to set it up - Python with pip, for the download - Memory for about 8.2 GB in BF16, or about 2.1 GB at 4-bit as an estimate - One tool that loads the model, such as Transformers or a compatible runner 1. Install the Hugging Face hub client. 2. Download the model's files. 3. Load them in your chosen tool. ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.5-4B ``` ### How to use it The card's run steps are cut off here, so check it for the recommended settings. Load the model in Transformers, or point a compatible runner at the files, then send it a prompt. Other people's quantized versions exist, which suit tools that need smaller files. | Tool | What it does | | --- | --- | | Transformers | Loads the post-trained weights in Hugging Face format | | vLLM and SGLang | Inference frameworks the card lists as compatible | | llama.cpp and MLX | Compatible frameworks for running on local machines | | Ollama and LM Studio | Platforms for quick deployment | | LLaMA-Factory | A fine-tuning framework for customizing the model | ### Example You load Spark-X2.5-4B in Transformers and ask it to write a small function and explain it. The card's visible text shows no sample output, so judge the answer on your own task. ### For newcomers and experts A 4B model is small, so it is a good first model to try on a modest machine. Start with the BF16 weights or a quantized copy, then test it on your own prompts. For every three sliding-window layers it uses one full-attention layer. The card says this keeps cache size and compute low while still allowing contexts up to 1M tokens. It was trained on Huawei Ascend clusters, with large-scale reinforcement learning and post-training steps such as MOPD. A prompt, code or tool context goes in → Hybrid attention layers read it → The 4.1B model reasons and writes → Text, code or agent steps come out Weights size in gigabytes | Label | Value | | --- | --- | | In BF16 | 8.2 GB | | At 4-bit, estimated | 2.1 GB | ### Good to know - Size: 4.1B parameters, about 8.2 GB in BF16 or about 2.1 GB at 4-bit as an estimate - Context: up to 1M tokens natively, per the card - Claims: the card says it leads open models of its size, but the visible text gives no scores - Languages: more than 200 - License: Apache 2.0 - Cost to run: none named - Data it reaches: the prompts you send, on your own hardware, while the download steps fetch files from the web - Not for: nothing named in the card ## README ````markdown # Spark-X2.5-4B A compact 4.1B general-purpose language model with a native 1M-token context, over 200 languages and a hybrid attention design, tuned for coding and agent use. For developers who want a small open model. Made by XHToken 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: Spark-X2.5-4B is a compact general-purpose language model from XHToken, with about 4.1B parameters, post-trained from Spark-X2.5-4B-Base. It covers coding, tool use, translation, writing, reasoning and chat in more than 200 languages, and it supports a native context window of up to 1M tokens. A 1.7B sibling exists in the same family. Who it suits: It suits developers who want a small open model behind a coding agent or a chat tool. It also suits teams who need long documents or many languages from a small model that is lighter to host. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run it behind an agent tool such as Codex | Ask it to write and explain a function Global business | Localization teams | Translate and draft text across many languages | Turn a product note into several languages Research | ML engineers | Test long-context behavior | Feed it a long document and ask questions ``` Anyone who needs the largest possible model can skip it, since this one is built to be small. Where to use it: - Python with Transformers, in the format the card describes - Inference tools the card lists as compatible, such as vLLM, SGLang, llama.cpp and MLX - Desktop and local runners the card names, such as Ollama and LM Studio - Agent tools the card names, such as Codex and Claude Code ```table Use case | Who | What they get back Coding help | Developers | Code and explanations from a small local model Translation | Localization teams | Text in another language Long documents | Analysts | Answers over a context of up to 1M tokens ``` How to set it up: - Python with pip, for the download - Memory for about 8.2 GB in BF16, or about 2.1 GB at 4-bit as an estimate - One tool that loads the model, such as Transformers or a compatible runner 1. Install the Hugging Face hub client. 2. Download the model's files. 3. Load them in your chosen tool. ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.5-4B ``` How to use it: The card's run steps are cut off here, so check it for the recommended settings. Load the model in Transformers, or point a compatible runner at the files, then send it a prompt. Other people's quantized versions exist, which suit tools that need smaller files. ```table Tool | What it does Transformers | Loads the post-trained weights in Hugging Face format vLLM and SGLang | Inference frameworks the card lists as compatible llama.cpp and MLX | Compatible frameworks for running on local machines Ollama and LM Studio | Platforms for quick deployment LLaMA-Factory | A fine-tuning framework for customizing the model ``` Example: You load Spark-X2.5-4B in Transformers and ask it to write a small function and explain it. The card's visible text shows no sample output, so judge the answer on your own task. For newcomers and experts: A 4B model is small, so it is a good first model to try on a modest machine. Start with the BF16 weights or a quantized copy, then test it on your own prompts. For every three sliding-window layers it uses one full-attention layer. The card says this keeps cache size and compute low while still allowing contexts up to 1M tokens. It was trained on Huawei Ascend clusters, with large-scale reinforcement learning and post-training steps such as MOPD. ```flow A prompt, code or tool context goes in Hybrid attention layers read it The 4.1B model reasons and writes Text, code or agent steps come out ``` ```chart Title: Weights size in gigabytes In BF16: 8.2 GB At 4-bit, estimated: 2.1 GB ``` Good to know: - Size: 4.1B parameters, about 8.2 GB in BF16 or about 2.1 GB at 4-bit as an estimate - Context: up to 1M tokens natively, per the card - Claims: the card says it leads open models of its size, but the visible text gives no scores - Languages: more than 200 - License: Apache 2.0 - Cost to run: none named - Data it reaches: the prompts you send, on your own hardware, while the download steps fetch files from the web - Not for: nothing named in the card ## On Hugging Face, 14 November 2026 - Task: text generation - Library: transformers - Parameters: 4.1B - Weights: about 8.2 GB in BF16; about 2.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 8,697 - Likes: 1,442 - License: apache-2.0 - Based on: XHToken/Spark-X2.5-4B-Base - Quantized versions: 56 - Fine-tunes: 26 - Tags: transformers, safetensors, spark2_5, text-generation, sparkx2_5, agent, conversational ## Links - Model: https://huggingface.co/XHToken/Spark-X2.5-4B - Files: https://huggingface.co/XHToken/Spark-X2.5-4B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download XHToken/Spark-X2.5-4B ``` 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 [XHToken/Spark-X2.5-4B on Hugging Face](https://huggingface.co/XHToken/Spark-X2.5-4B) ## 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 | | --- | --- | --- | --- | --- | --- | | Spark-X2.5-4B (this page) | 8,697 | 1,442 | 4.1B | apache-2.0 | 2026-09-15 | | [Spark-X2.6-4B-AgentFast](https://mindket.com/p/b7564b85-6e9a-4837-a5a8-7d7c83c59599) | 552 | 4 | 4.1B | apache-2.0 | 2026-10-30 | | [Spark-X2.6-4B](https://mindket.com/p/1f30e23c-ac63-4f34-b227-130e4306e0f2) | 28,117 | 1,041 | 4.1B | apache-2.0 | 2026-11-06 | | [Spark-X2.6-1.7B](https://mindket.com/p/427232ce-5988-41f7-985c-029c680c8947) | 5,095 | 84 | 1.7B | apache-2.0 | 2026-11-06 | | [Spark-X2.6-4B-Base](https://mindket.com/p/60b99ab3-4046-42ac-bca5-487445754660) | 2,331 | 47 | 4.1B | apache-2.0 | 2026-11-01 | | [Spark-X2.6-4B-FP8](https://mindket.com/p/febc8500-5824-4ac8-abec-ec783a54755f) | 2,048 | 39 | 4.1B | apache-2.0 | 2026-11-06 | 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 · デイリーHuggingFaceトレンド · 2026-09-12](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mvd6jqltz72l): 今日のHuggingFaceトレンド XHToken/Spark-X2.5-4B Spark-X2.5-4Bおよび1.7Bという効率的で汎用的な小型言語モデルの提供を目的としています。 会話、翻訳、推論、コーディングなどの幅広いタスクに対応し、最大100万トークンのコンテキストウィンドウと200以上の言語をサポートします。 高性能で軽量なAIをより実用的かつ効率的に利用可能にすることを目指しています。 - [Bluesky · 🚀 olud.ai · 2026-09-09](https://bsky.app/profile/oludai.bsky.social/post/3mv4ncdvyxb2g): XHToken/Spark-X2.5-4B is climbing Hugging Face: 10.7k downloads in 30 days, 872 likes, Apache-2.0. A 4B open-weight text generator seeing real adoption. See the daily leaderboard. https://olud.ai/\#leaderboard \#OpenSource \#AI \#HuggingFace - [Bluesky · 🚀 olud.ai · 2026-09-09](https://bsky.app/profile/oludai.bsky.social/post/3mv2a6j3xrg2c): 🤗 XHToken/Spark-X2.5-4B hits 7.2k monthly downloads and 774 likes—an Apache-2.0 text-gen model with real traction. What drives its growth? https://olud.ai/\#leaderboard \#OpenSource \#AI \#HuggingFace - [Bluesky · 🚀 olud.ai · 2026-09-08](https://bsky.app/profile/oludai.bsky.social/post/3muyulu5vxn2k): 🤗 XHToken/Spark-X2.5-4B is climbing on Hugging Face with 7.2k downloads in 30 days and 774 likes—a compact 4B text generator under Apache-2.0 that’s quietly gaining traction. https://olud.ai/\#leaderboard \#OpenSource \#AI \#HuggingFace - [Bluesky · デイリーHuggingFaceトレンド · 2026-09-14](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mvijexvhg62n): 今日のHuggingFaceトレンド XHToken/Spark-X2.5-4B Spark-X2.5-4BおよびSpark-X2.5-1.7Bという軽量な汎用言語モデルの重みと設定ファイルを配布するためのリポジトリです。 対話、執筆、翻訳、推論、コーディングなど幅広いタスクに対応し、最大100万トークンのコンテキストウィンドウと200以上の言語をサポートしています。 効率的で利用しやすい高性能なAIの提供を目的としています。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-09-10](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mv6a6553ix2c): 今日のHuggingFaceトレンド XHToken/Spark-X2.5-4B Spark-X2.5は、効率的で実用的な小型の汎用言語モデル（4Bおよび1.7B）を提供することを目的としたリポジトリです。 会話、翻訳、推論、コーディング、ツール利用などの幅広いタスクで高い性能を発揮します。 最大100万トークンのコンテキストウィンドウに対応し、200以上の言語をサポートしています。 - [Bluesky · AI Planet · 2026-09-08](https://bsky.app/profile/aiplanet.bsky.social/post/3muzdhhtji72c): 📰 Reddit r/LocalLLaMA XHToken/Spark-X2.5-4B VS inclusionAI/Ling-3.0-tiny VS Nanbeige/Nanbeige4.2-3B https://www.reddit.com/r/LocalLLaMA/comments/1wao3d5/xhtokensparkx254b\_vs\_inclusionailing30tiny\_vs/ \#IA \#AI \#ML - [Bluesky · デイリーHuggingFaceトレンド · 2026-09-08](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3muz75cja5p2t): 今日のHuggingFaceトレンド XHToken/Spark-X2.5-4B Spark-X2.5は、4Bおよび1.7Bの軽量な汎用言語モデルを提供するリポジトリです。 会話、執筆、翻訳、推論、コーディング、ツール利用などの幅広いタスクで高い性能を実現しており、最大100万トークンのコンテキストウィンドウと200以上の言語をサポートしています。 効率的で実用的なAIの提供を目的としています。 ## 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: 2026-09-15 - First published: 2026-08-24 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open XHToken/Spark-X2.5-4B on Hugging Face](https://huggingface.co/XHToken/Spark-X2.5-4B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/36f36232-524c-4161-bacd-b9b5c290fea4) - [Its page on Mindket](https://mindket.com/p/36f36232-524c-4161-bacd-b9b5c290fea4)