| --- |
| 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 |
| --- |
| |
| <p align="center"> |
| <img src="./agens-banner.png" alt="Agens Pilot — by Blockway" width="820"> |
| </p> |
|
|
| <p align="center"> |
| <b>A Cantonese-first, balanced agent model — built on Qwen3.8-27B by <a href="https://blockway.io">Blockway</a>, Hong Kong</b><br> |
| <i>Connecting technologies & scenarios to create a trusted future</i> |
| </p> |
|
|
| <p align="center"> |
| <img src="https://img.shields.io/badge/Base-Qwen3.8--27B-6f42c1"> |
| <img src="https://img.shields.io/badge/License-Apache%202.0-2ea043"> |
| <img src="https://img.shields.io/badge/%E5%BB%A3%E6%9D%B1%E8%A9%B1-native-e87abc"> |
| <img src="https://img.shields.io/badge/Context-1M%20tokens-22d3ee"> |
| <img src="https://img.shields.io/badge/Modality-Text%20%2B%20Vision-0ea5e9"> |
| <img src="https://img.shields.io/badge/Builds-BF16%20%7C%20FP8%20%7C%20INT4-475569"> |
| <a href="https://x.com/blockwaylink"><img src="https://img.shields.io/badge/X-%40blockwaylink-000000?logo=x&logoColor=white"></a> |
| </p> |
|
|
| <p align="center"> |
| 🤗 <a href="https://huggingface.co/Blockway/Agens-Pilot">BF16</a> · |
| <a href="https://huggingface.co/Blockway/Agens-Pilot-FP8">FP8</a> · |
| <a href="https://huggingface.co/Blockway/Agens-Pilot-Int4">INT4</a> |
| · 🧬 Base: <a href="https://huggingface.co/Qwen/Qwen3.8-27B">Qwen/Qwen3.8-27B</a> |
| </p> |
|
|
| --- |
|
|
| ## 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) | |
|
|
| <sub>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: <code>materials/compare_agens_vs_base/</code>; the raw prompt set and responses are available on request. Measured 2026-08-29. N is small; treat as |
| directional.</sub> |
| |
| 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 <path-to-variant> \ |
| --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`). |
| |
| <details> |
| <summary>transformers (bf16, text example)</summary> |
| |
| ```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)) |
| ``` |
| </details> |
| |
| --- |
| |
| ## 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} |
| } |
| ``` |
| |
| <p align="center"> |
| <b>Blockway</b> · 博睿鏈科有限公司 · Hong Kong · <a href="https://blockway.io">blockway.io</a> · <a href="https://x.com/blockwaylink">@blockwaylink</a><br> |
| <i>Connecting technologies & scenarios to create a trusted future</i> |
| </p> |
| |
