--- license: apache-2.0 base_model: Qwen/Qwen3.8-27B base_model_relation: finetune language: - yue - zh - en library_name: transformers pipeline_tag: image-text-to-text tags: - agens - blockway - qwen3.8 - cantonese - 廣東話 - hong-kong - multimodal - vision - agent - agentic - code - long-context - chat ---

Agens Pilot — by Blockway

A Cantonese-first, balanced agent model — built on Qwen3.8-27B by Blockway, Hong Kong
Connecting technologies & scenarios to create a trusted future

🤗 BF16 · FP8 · INT4  ·  🧬 Base: Qwen/Qwen3.8-27B

--- ## What Agens Pilot is **Agens Pilot is a post-trained fine-tune of [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B).** We kept everything that makes Qwen3.8 a great 27B model — coding, 1M-token context, vision, hybrid linear-attention efficiency — and changed the things a Hong Kong company and its agent products actually needed from it: | | Qwen3.8-27B | **Agens Pilot** | |---|---|---| | **Balanced** — complex historical & geopolitical questions → factual, multi-perspective answer | 60% (12/20) | **85% (17/20)** | | … → single-viewpoint answer | 35% (7/20) | **10% (2/20)** | | Universally-harmful requests (weapons, malware, abuse…) → refuses | 8/8 | **8/8** — safety retained | | 廣東話 as a first-class language | supported | **Cantonese-first: Hong Kong usage, register and defaults** | | Tuned for a first-party agent harness | — | **Claway** (live) · **Codeway** (soon) | Same 20 complex-question prompts and 8 harmful prompts to both models, temperature 0.6, single run. Balance graded by an independent judge — Qwen3.8-27B itself, thinking off, temperature 0 — on a 0 / 1 / 2 rubric (non-answer / single viewpoint / multi-perspective). Judge script and aggregate scores: materials/compare_agens_vs_base/; the raw prompt set and responses are available on request. Measured 2026-08-29. N is small; treat as directional. Everything else — architecture, tokenizer, context length, vision — is Qwen3.8's, and on general capability benchmarks Agens tracks its base within measurement noise. That is by design: we set out to **add behaviours without paying a capability tax**, not to re-teach a model that was already excellent. - **Developer:** Blockway (BlockWay Link Limited · 博睿鏈科有限公司), Hong Kong · founded 2018 · https://blockway.io - **Base model:** [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) (Alibaba Cloud, Apache-2.0) - **Model type:** decoder-only multimodal LLM (text + vision), ~27B params, hybrid linear + full attention - **Context length:** up to **1,000,000 tokens** (262,144 native; extended with YaRN) - **Languages:** 廣東話 (Cantonese, first-class), 繁體 / 简体中文, English - **License:** Apache-2.0 — commercial use welcome --- ## Why we made it Three things we could not get from any off-the-shelf 27B model: **1. 廣東話 as a first-class language, not a translation target.** A Hong Kong company needs a model that is built and evaluated Cantonese-first: it answers in natural written Cantonese when you write in Cantonese, follows Hong Kong usage and register, and switches cleanly to 普通話 or English when you do. **2. Balanced perspectives — with the guardrails that matter kept.** On complex historical and geopolitical questions Agens presents the facts and multiple perspectives where the base tends toward a single viewpoint or a non-answer — **85% vs 60% multi-perspective** on our 20-question set, graded by the base model itself. Refusals on universally-harmful requests (weapons, malware, exploitation) are unchanged from the base (8/8). **3. An engine for our own agent harnesses.** Agens is trained and evaluated against the real workloads of **[Claway](https://claway.io)** (Blockway's Team-AI workforce, live) and **Codeway** (our coding agent, opening soon): long context, strict instruction-following, reliable tool calls. It runs anywhere OpenAI-compatible (sglang / vLLM), so you're never locked in. --- ## Highlights - 🗣️ **Cantonese-first** — written 廣東話 in, written 廣東話 out, with Hong Kong usage and register. - ⚖️ **Balanced and factual** on complex historical and geopolitical questions — 85% multi-perspective vs 60% for the base; universal safety intact. - 🤖 **Agent-harness tuned** — Claway + Codeway. - ⚡ **Everything Qwen3.8-27B does** — strong coding (97.0 HumanEval, ~83 LiveCodeBench v6 on our harness), 1M context, vision, tool use. - 🧩 **Three official builds** — BF16 / FP8 / INT4, Apache-2.0. --- ## Model variants All three builds share the same tokenizer, chat template and 1M-context configuration — they differ only in weight precision. | Variant | Repo | Size | Precision recipe | Quality | Suggested hardware | |---|---|---|---|---|---| | **BF16** | `Blockway/Agens-Pilot` | ~51 GB | full precision | reference | 2×48 GB or 4×24 GB | | **FP8** ⭐ | `Blockway/Agens-Pilot-FP8` | ~34 GB | FP8 on feed-forward + full-attention; linear-attention, embeddings, LM head & vision kept in bf16 | **matches BF16** | 1×48 GB or 2×24 GB | | **INT4** | `Blockway/Agens-Pilot-Int4` | ~26 GB | INT4 (GPTQ, group 128) on the same layers | very close; slightly softer on borderline factual topics | ≥32 GB VRAM, or 24 GB + CPU offload | **Why the quantized builds aren't smaller.** The hybrid **linear-attention (Gated DeltaNet) layers** and the token **embeddings / LM head** are always kept in bf16 — quantizing the linear-attention path degrades generation control. Only feed-forward and full-attention weights are quantized. > GGUF / Ollama / llama.cpp are **not** available yet: the Qwen3.5/3.8 hybrid architecture isn't > supported by upstream llama.cpp. We will publish GGUF builds the day that support lands. > **On the reported "model size":** Hugging Face auto-detects the **INT4** build as ~11B. That's a > counting artifact — packed 4-bit weights are stored 8-per-int32. All three builds are the > **same ~27B model**. --- ## Serving Quantized builds are in `compressed-tensors` format and are auto-detected — no `--quantization` flag needed. ```bash python3 -m sglang.launch_server \ --model-path \ --served-model-name "Agens Pilot" \ --tp-size 4 \ --context-length 1048576 \ --tool-call-parser qwen3_coder \ --reasoning-parser qwen3 \ --trust-remote-code \ --host 0.0.0.0 --port 8000 # env: SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 (to serve the full 1M context) ``` ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model":"Agens Pilot","messages":[{"role":"user","content":"用廣東話解釋下咩係 API。"}]}' ``` `tp-size` may be 1–4. FP8 fits 2×24 GB; INT4 fits a single ≥32 GB card (or 24 GB with `--cpu-offload-gb`).
transformers (bf16, text example) ```python from transformers import AutoProcessor, AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained( "Blockway/Agens-Pilot", torch_dtype="bfloat16", device_map="auto", trust_remote_code=True) processor = AutoProcessor.from_pretrained("Blockway/Agens-Pilot", trust_remote_code=True) messages = [{"role": "user", "content": "幫我用廣東話寫封短訊俾同事,話佢知我遲到十五分鐘。"}] text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = processor(text=text, return_tensors="pt").to(model.device) out = model.generate(**inputs, max_new_tokens=512) print(processor.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)) ```
--- ## Evaluation ### The behaviours we changed (Agens vs. its base, same harness) See the table at the top. Judge script and aggregate scores: `materials/compare_agens_vs_base/`; raw prompt set and responses available on request. ### General capability (Blockway internal harness; single-sample, thinking on, temp 0.6) | Benchmark | Agens Pilot | Notes | |---|---|---| | HumanEval (chat) | **97.0** | | | LiveCodeBench v6 | **~83** | base Qwen3.8-27B reports 90.3 on its own harness | | IFBench (prompt-strict) | 61.0 | base reports 79.5 (official) | | GPQA Diamond | 80.3 | base reports 89.2 (official) | | Tool-call selection | 16 / 16 | | | RealWorldQA (vision) | 77.2 | | | ERQA (vision) | 57.8 | | | MathVision (vision) | 65.8 | | **How to read this.** Our harness is deliberately conservative (single sample, tight answer extraction, no external judge, 40K serving context), so its absolute numbers run below official leaderboards for *every* model including the base. In same-harness A/B runs Agens tracks Qwen3.8-27B within run-to-run noise (≈ ±3 points at these sample sizes) — the fine-tune neither adds nor removes general capability; it changes behaviour. --- ## Intended use - Cantonese-first assistants, customer-facing bots and internal tools for Hong Kong / Guangdong users. - Research, education and journalism that needs factual, multi-perspective answers on complex questions. - Autonomous AI teammates and multi-step operations (Claway); agentic coding workflows (Codeway). - Everything Qwen3.8-27B is good at: coding, long-document work, vision-language tasks. **Use with care:** high-stakes decisions need human review; the model can make mistakes and should not be treated as an authoritative source on complex questions — verify facts. --- ## Limitations - Cantonese behaviour is strongest for written 廣東話 as used in Hong Kong; other Yue varieties are less covered. - Balanced ≠ omniscient: on complex questions the model presents perspectives; it can still be wrong on specifics. - Internal benchmark numbers are conservative and not directly comparable to other harnesses. - Visual mathematics (MathVision) is the weakest capability axis, inherited from the base. - The **INT4** build can be slightly less consistent than FP8/BF16 on borderline factual topics. --- ## License, attribution & citation Released under the **Apache-2.0 license**. © 2026 BlockWay Link Limited (博睿鏈科有限公司). Agens Pilot is a derivative of **Qwen3.8-27B** © Alibaba Cloud, Apache-2.0 — see `NOTICE`. We're grateful to the Qwen team; a base this good is what made a focused fine-tune worth doing. Training data used in development may carry its own licenses; downstream users are responsible for their own compliance. ```bibtex @misc{agenspilot2026, title = {Agens Pilot: a Cantonese-first fine-tune of Qwen3.8-27B}, author = {Blockway (BlockWay Link Limited)}, year = {2026}, url = {https://blockway.io/agens-pilot} } ```

Blockway · 博睿鏈科有限公司 · Hong Kong · blockway.io
Connecting technologies & scenarios to create a trusted future