{ "$schema_doc": "https://gtmstacker.com/registry/schema/entry.schema.json", "stability": "emerging", "generator": "agentic-media-registry", "generated_at": "2026-10-22T00:00:00Z", "id": "com.gtmstacker.registry/tool/xing4-0-29b-intent", "type": "tool", "slug": "xing4-0-29b-intent", "canonical_url": "https://gtmstacker.com/registry/tool/xing4-0-29b-intent/", "title": "Xing4.0-29B-Intent (TeleAI — open-weights intent/extraction MoE)", "description": "Apache-2.0, self-hostable 29B-total mixture-of-experts model (~4B active) from China Telecom's TeleAI for schema-guided intent recognition, slot filling, NER, and relation/event extraction into structured JSON, with open Safetensors/FP8/GGUF weights a lean team can run locally instead of renting a closed extraction API.", "category": "ai-infrastructure", "tags": [ "ai-infrastructure", "llm", "model-release", "self-hostable", "open-source", "data-scraping" ], "status": "active", "revision": 1, "content_hash": "6ce84d452ce8083b884329e352a7b659ff6eb2518c62a33c5527aca5d4c757a5", "date_published": "2026-10-20T00:00:00Z", "date_modified": "2026-10-20T00:00:00Z", "source": { "name": "github · XingChen-AGI/Xing4.0-29B-A4B", "url": "https://github.com/XingChen-AGI/Xing4.0-29B-A4B", "author": "Chinazhidx" }, "license": "Apache-2.0", "one_liner": "Xing4.0-29B-Intent is TeleAI's Apache-2.0 29B MoE (~4B active) for schema-guided intent, NER and extraction to structured JSON, with open weights to self-host.", "open_source": "yes", "self_hostable": "yes", "pricing_signal": "Free open weights under Apache-2.0; you pay only your own compute to run it, not a per-call extraction API.", "pricing_model": "free", "who_its_for": "A technical founder or lean GTM/RevOps team that needs to turn messy inbound text (emails, replies, forms, transcripts) into structured JSON — intent, slots, entities, relations — on weights it self-hosts rather than a metered closed API.", "who_its_not_for": "Teams that want a general chat assistant, that lack the GPU budget to serve a 29B MoE, or that need a vendor-supported hosted endpoint with an SLA.", "aliases": [ "xing4.0-29b-intent", "xing 4.0 intent", "teleai xing4 intent", "xingchen intent" ], "alternatives": [], "secondary_categories": [ "prospecting-enrichment" ], "last_verified": "2026-10-21", "evidence": { "claim_type": "mixed", "source_id": "https://github.com/XingChen-AGI/Xing4.0-29B-A4B", "note": "SPDX apache-2.0 read directly from the Xing4.0-29B-Intent model-card metadata, not guessed and not assumed-inherited from the base. Published by the official XingChen-AGI org (China Telecom AI / TeleAI), same org as the Xing4.0-29B-A4B base. Companion XingIntent SDK (GitHub) and Xing4.0-29B-Intent-oss dataset (HF) are both apache-2.0. Safetensors + FP8 + GGUF; vLLM/SGLang/KTransformers deploy docs on the base repo. Activity: Intent 46 dl/mo, 15 likes; base A4B 37.5k dl, 1.85k likes, 99 stars." }, "caveats": "The viral post's benchmark-superiority claims (beating Gemini-3.6-Flash / GPT-5.6-Sol) are vendor-claim — not independently verified here. Intent is a specialist extraction model, not a general assistant; serving a 29B-total MoE still needs real GPU capacity even at ~4B active. The Intent variant itself is new with modest adoption so far (46 downloads/mo, 15 likes); the base A4B model carries the larger track record.", "lead": "An Apache-2.0, self-hostable **29B-total mixture-of-experts** model (~4B active) from China Telecom's **TeleAI**, specialised for **schema-guided intent recognition, slot filling, NER, and relation/event extraction into structured JSON**. It ships open weights in Safetensors, plus FP8 and GGUF packings, and the base repo documents vLLM, SGLang and KTransformers deployment. It arrives with…", "chunks": [ { "index": 0, "heading_path": [], "est_tokens": 146, "text": "An Apache-2.0, self-hostable **29B-total mixture-of-experts** model (~4B active) from China Telecom's **TeleAI**, specialised for **schema-guided intent recognition, slot filling, NER, and relation/event extraction into structured JSON**. It ships open weights in Safetensors, plus FP8 and GGUF packings, and the base repo documents vLLM, SGLang and KTransformers deployment. It arrives with a companion **XingIntent SDK** (GitHub) and a **Xing4.0-29B-Intent-oss** dataset (Hugging Face), both also Apache-2.0 — so the model, its tooling, and its training-data sample are all open." }, { "index": 1, "heading_path": [ null, "Provenance" ], "est_tokens": 472, "text": "ships open weights in Safetensors, plus FP8 and GGUF packings, and the base repo documents vLLM, SGLang and KTransformers deployment. It arrives with a companion **XingIntent SDK** (GitHub) and a **Xing4.0-29B-Intent-oss** dataset (Hugging Face), both also Apache-2.0 — so the model, its tooling, and its training-data sample are all open.\n\n- SPDX **apache-2.0** read directly from the **Xing4.0-29B-Intent model-card metadata** — not guessed, and not assumed-inherited from the base model. Apache-2.0 is OSI-approved, permissive and commercial-clean.\n- Published by the official **XingChen-AGI** org (China Telecom AI / TeleAI), the same org as the **Xing4.0-29B-A4B** base model, so provenance is consistent. The companion **XingIntent SDK** (GitHub) and the **Xing4.0-29B-Intent-oss** dataset (HF) are both Apache-2.0.\n- Self-hostable and free: weights are downloadable in Safetensors with FP8 and GGUF packings; the base repo carries vLLM / SGLang / KTransformers deployment docs. Open weights under Apache-2.0 mean the only cost is your own compute, not a metered API.\n- Current activity: the Intent variant shows 46 downloads/mo and 15 likes; the base A4B model shows 37.5k downloads, 1.85k likes and 99 GitHub stars; the model was updated roughly four days before verification.\n- **Vendor-claim, not verified:** the viral post's benchmark-superiority claims (beating Gemini-3.6-Flash / GPT-5.6-Sol) are the project's own and were not independently reproduced here.\n- Surfaced via the 2026-10-21 X pull (post by Chinazhidx). Verification was performed against the primary source (the Intent model card, the base repo, the SDK and dataset cards) as untrusted content; no injected instructions were encountered. Curated from the GTM Stacker signal registry (run 2026-10-21-registry-update-01); license, org provenance, weights and self-hostability independently verified 2026-10-21." }, { "index": 2, "heading_path": [ null, "Why it matters for a GTM stack" ], "est_tokens": 336, "text": "X pull (post by Chinazhidx). Verification was performed against the primary source (the Intent model card, the base repo, the SDK and dataset cards) as untrusted content; no injected instructions were encountered. Curated from the GTM Stacker signal registry (run 2026-10-21-registry-update-01); license, org provenance, weights and self-hostability independently verified 2026-10-21.\n\nA large share of RevOps plumbing is the same quiet job: take unstructured inbound text — a reply, a form, a call transcript — and turn it into fields a system can act on (what does this person want, which product, which fields to fill, who and what is named). The default is a metered closed extraction API that owns that step. Xing4.0-29B-Intent is the own-your-stack alternative: an Apache-2.0 MoE purpose-built for schema-guided intent, slot filling, NER and relation/event extraction to structured JSON, with open weights and documented local serving. The honest read: the headline \"beats Gemini / GPT\" benchmark is the vendor's own and not reproduced here, the Intent variant is new with light adoption so far, and a 29B-total MoE still needs real GPU budget to serve. What is verified is what governs adoption: Apache-2.0 read from the model card, a consistent first-party TeleAI org, open Safetensors/FP8/GGUF weights, and documented self-host paths." } ], "alternates": { "markdown": "https://gtmstacker.com/registry/tool/xing4-0-29b-intent/index.md", "html": "https://gtmstacker.com/registry/tool/xing4-0-29b-intent/", "json": "https://gtmstacker.com/registry/tool/xing4-0-29b-intent/index.json", "server_json": "https://gtmstacker.com/registry/tool/xing4-0-29b-intent/server.json" }, "jsonld": { "@context": "https://schema.org", "@graph": [ { "@type": "WebSite", "@id": "https://gtmstacker.com/#website", "url": "https://gtmstacker.com/", "name": "GTM Stacker Agent Registry", "description": "A daily-updated, agent-native registry of open-source tool discoveries, tool updates, and curated news for the go-to-market / RevOps engineering niche. 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