Channel
@xsoau
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
500subscribers
+0 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003238287534 |
|---|---|
| Type | Channel |
| Username | @xsoau |
| Created | Between 1 October 2025 and 31 January 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 9 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/xsoau |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 19:45 | 500 | no change |
| 8 Aug 2026, 19:32 | 500 | first reading |
20 posts held, back to 7 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 8 August 2026 |
|---|---|
| Posts held | 20 (7 August 2026 – 8 August 2026) |
| Views total | 180 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 19:45 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Lifetime counters from Telegram’s own channel header, read 8 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
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🗂 模型名字：DeepSeek-R1 📡 来源：HuggingFace ⭐ Stars：13.5k 📌 项目简介 DeepSeek-R1是深度求索推出的开源推理模型，专为复杂逻辑推理、数学计算及代码生成设计。通过强化学习优化，其在多方面基准测试中表现卓越，显著提升了模型的推理深度与准确性。 ⚙️ 引用技术 采用先进的强化学习对齐技术，优化思维链推理过程。模型在训练阶段专注于复杂任务处理，结合大量高质量推理数据微调，显著提升了解决高难度数学问题和代码调试的能力。 🔥 应用场景 适用于需要深度逻辑分析的场景，如复杂数学解题、高级代码编写与调试、科学计算辅助以及逻辑谜题解答。特别适合对推理精度要求极高的企业级应用和研究开发场景。 🏷️ #推理模型 #开源AI #强化学习
🗂 模型名字：Qwen2.5-7B-Instruct 📡 来源：HuggingFace ⭐ Stars：1.5k 📌 项目简介 Qwen2.5-7B-Instruct是通义千问系列最新推出的70亿参数指令微调版本模型。它旨在提供卓越的对话理解、代码生成及多语言处理能力，适合各类离线部署场景，平衡了性能与资源消耗。 ⚙️ 引用技术 基于多语言预训练技术，采用岗位混合专家（MoE）架构优化推理效率。支持8K上下文窗口，强化了对复杂逻辑推理、数学计算及代码指令遵循的能力，适配多种主流训练框架。 🔥 应用场景 广泛应用于智能客服、代码辅助编写、文本摘要生成及多轮对话系统。特别适用于对资源敏感但需高性能的端侧部署或私有云环境，提升垂直行业AI应用落地效率。 🏷️ #开源大语言模型 #指令微调 #Qwen
🗂 模型名字：Qwen3.5-9B 📡 来源：HuggingFace ⭐ Stars：1.8k 📌 项目简介 Qwen3.5-9B是通义千问推出的最新一代轻量级大语言模型。作为9B参数规模的先进成果，它在保持高效推理速度的同时，显著提升了逻辑推理与多语言处理能力，是平衡性能与资源消耗的优质选择。 ⚙️ 引用技术 基于先进的Transformer架构，采用混合注意力机制与MoE（混合专家）技术。模型使用大规模高质量多语言语料预训练，并经过深度指令微调，支持上下文窗口扩展，实现高效的知识压缩与推理加速。 🔥 应用场景 适用于边缘设备部署、实时交互式AI应用及轻量化本地私有化部署。特别适合对推理延迟敏感的业务场景，如智能客服、代码辅助编写、短文本生成及移动端AI助手，降低算力成本并提升响应速度。 🏷️ #大语言模型 #轻量级AI #通义千问
🗂 模型名字：Qwen3-8B 📡 来源：HuggingFace ⭐ Stars：1.3k 📌 项目简介 Qwen3-8B是阿里通义千问团队推出的最新8B参数规模开源大语言模型。该模型延续了Qwen系列的高性能表现，针对复杂逻辑推理、代码编写及多语言交互进行了深度优化，旨在提供高效且通用的文本生成与服务能力。 ⚙️ 引用技术 模型基于Transformer架构，采用混合注意力机制与MoE（专家混合）结构以优化计算效率。底层支持长上下文窗口处理，并经过大规模高质量语料训练，强化了数学推导、代码生成及多轮对话的逻辑一致性处理能力。 🔥 应用场景 适用于智能客服机器人、代码辅助编写工具、自动化文档生成及多语言翻译场景。特别适合在资源受限的边缘设备或需要低延迟响应的业务环境中部署，为企业级应用提供轻量级且高精度的AI解决方案。 🏷️ #开源大语言模型 #阿里通义千问 #8B参数
🗂 模型名字：xlm-roberta-base 📡 来源：HuggingFace ⭐ Stars：878 📌 项目简介 XLM-RoBERTa是Facebook AI开发的预训练Transformer模型，专为多语言自然语言处理设计，支持100多种语言，显著提升跨语言任务表现。 ⚙️ 引用技术 基于RoBERTa架构，采用自注意力机制和多语言掩码语言建模训练。使用大规模多语言语料库进行预训练，能够有效捕捉不同语言间的语义关联和上下文信息。 🔥 应用场景 适用于机器翻译、多语言文本分类、情感分析、命名实体识别及问答系统。特别在多语言资源稀缺场景下，能实现高质量的零样本或少样本迁移学习。 🏷️ #多语言 #自然语言处理 #Transformer
🗂 模型名字：Qwen2.5-VL-72B-Instruct-AWQ 📡 来源：HuggingFace ⭐ Stars：73 📌 项目简介 该项目是阿里通义千问Qwen2.5-VL-72B-Instruct模型的AWQ量化版本，旨在降低显存占用并加速推理过程，同时保持较高的多模态理解性能。 ⚙️ 引用技术 采用AWQ（Activation-aware Weight Quantization）技术，对72B参数的视觉语言模型进行4比特量化，优化内存带宽利用率，支持高效部署在消费级GPU上。 🔥 应用场景 适用于需要高性能多模态交互的应用场景，如复杂图像理解、文档分析、视觉问答及基于视觉内容的智能客服系统，特别适合资源受限环境下的模型部署。 🏷️ #多模态模型 #模型量化 #视觉语言
🗂 模型名字：Qwen2.5-Omni-7B 📡 来源：HuggingFace ⭐ Stars：1.9k 📌 项目简介 Qwen2.5-Omni-7B是阿里通义千问最新推出的全模态大模型，支持音频、视频、文本和图像的统一处理。该模型具备极高的理解与生成能力，能够实现多模态内容的深度融合与交互。 ⚙️ 引用技术 基于先进的Transformer架构，采用多模态对齐技术，统一处理音频、视频、文本及图像数据。支持流式输出与低延迟响应，具备强大的跨模态理解能力。 🔥 应用场景 适用于智能客服、多语言视频摘要、实时语音翻译、多模态教育辅助及创意内容生成等场景。能有效处理复杂的多模态输入，提供流畅的人机交互体验。 🏷️ #全模态大模型 #通义千问 #多模态交互
🗂 模型名字：Gemma 3 1B 📡 来源：HuggingFace ⭐ Stars：1.1k 📌 项目简介 Gemma 3 1B是Google推出的轻量级基础模型，经指令微调强化理解与生成能力。其小巧体积适合端侧部署，在保持高效计算的同时提供优秀的多语言交互体验。 ⚙️ 引用技术 采用高效Transformer架构，针对低资源环境优化。支持多语言处理与复杂指令遵循，具备低延迟推理特性。结合量化技术，可在移动端和边缘设备上流畅运行。 🔥 应用场景 适用于手机助手、智能家居控制、实时翻译及边缘计算场景。凭借低算力需求，可在资源受限设备上实现本地化AI服务，保障数据隐私并降低云端依赖。 🏷️ #Gemma系列 #端侧推理 #轻量级模型
🗂 模型名字：Qwen3-32B 📡 来源：HuggingFace ⭐ Stars：728 📌 项目简介 Qwen3-32B是通义千问系列推出的320亿参数大语言模型，旨在提供强大的自然语言处理能力，支持多轮对话与复杂逻辑推理，兼顾高性能与资源效率。 ⚙️ 引用技术 基于先进Transformer架构，采用混合注意力机制与高效训练策略。模型经过海量高质量数据预训练，优化了长文本理解与代码生成能力，支持多语言交互。 🔥 应用场景 适用于智能客服、内容创作辅助、代码开发助手、数据分析等场景。特别适合对推理复杂度要求较高且需平衡部署成本的中小企业及开发者社区应用。 🏷️ #大语言模型 #通义千问 #NLP
Showing the 12 most recent of 20 posts we hold for @xsoau. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank — 982,191 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 8 August 2026 — this entry's latest reading, not the date you are reading this.
“AI模型快讯” (@xsoau), 500 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/xsoau.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.