HoomanBio2021/Qwen3.8-27B-LabOpen-v1

Qwen3.8-27B-LabOpen v1

Qwen3.8-27B strengthened for verifiable scientific computation — released both as merged FP8 full weights (drop-in for the base) and as the original LoRA adapter.

LabOpen v1 adds six verifiable capability domains (fluid physics, materials mixture laws, classical genetics, ops action queries, prose style, agentic coding) on top of Qwen/Qwen3.8-27B-FP8, while preserving the base model's native code generation, function calling, intent understanding and formula rendering. The largest measured gain: agentic coding 32% → 72% (+40pp) on a 78-test deterministic battery.

• Base model: Qwen3.8-27B (FP8 release, Apache 2.0)
• Training: 15 reinforcement windows (G69–G84), LoRA r=4, group-relative RL (RLOO-style, k=8 sampling) with mechanically verifiable rewards
• License: Apache 2.0 (same as base)
• Hardware target: NVIDIA GPUs with compute capability ≥ 8.9 (H100 / L40S / B200 / DGX GB10 class); ~31 GB in FP8

Positioning & Philosophy

Qwen3.8-27B-LabOpen v1 is a research model built on the open Qwen3.8-27B foundation through domain fine-tuning and optimization, focused on scientific research fields including programming, physics, life sciences, and materials science.

The project does not aim to replace general-purpose large language models. It seeks to preserve the comprehensive strengths of a general model while further strengthening its adaptation to scientific knowledge, domain-specific problems, and research workloads.

The model is intended for universities, research institutes, corporate R&D centers, and professional laboratories, and supports local or private deployment scaled to the institution's actual computing resources.

What's in this repo

Path	Content
model-0000X-of-00007.safetensors + index	Merged FP8 full weights (base + LoRA, blockwise-FP8 re-quantized). Same tensor layout and quantization format as the base — drop-in replacement
adapter/	LoRA adapter (112 MB, PEFT format, 992 tensors). Exact training artifact for inspection and further training — serve the merged weights instead
MERGE_REPORT.md	Merge provenance: digests, shard SHA-256, re-quantization error stats, output-equivalence probes
EVALUATION.md	Measured capability matrix and 5-model external comparison
USAGE.md	Loading, domain tags, temperature guidance, troubleshooting

Use the merged weights for serving (vLLM/SGLang). Use the adapter on top of the base when you want the exact training-time numerics or a smaller download.

Capability matrix (measured, heldout sets)

Domain	Metric	Score	vs Base
Agentic coding	78 deterministic tests, 3-round fix	72% (56/78)	+40pp (base: 32%)
Intent understanding	10-scene deep-intent probe	8/10	preserved
Formula display	LaTeX integrity, 5 problems	5/5	preserved
Fluid physics (CFM)	Pool-B heldout, T=0.3 k=8, tagged	44% draws	new (base ≈ 0)
Materials (mixture laws)	heldout 40 items, T=0.3, untagged	32% draws	new (base ≈ 0)
Classical genetics	heldout 20 items, tagged	79% draws	new (base: 47% natural)
Ops (action queries)	8 items, dual-temp	4/8 (T0.3) – 5/8 (T0.8)	new
Prose style	20 open-ended, T=0.3 k=8	75% any-correct	new

The matrix reports draw rates (correct samples / total samples) under the deployment protocol. The same heldout sets, scored at item level (any-correct) and compared zero-shot against frontier models, appear in the Domain-capability comparison section below.

External comparison (same battery, 2026-10)

Model	Intent	Agentic tests	Rank
GLM 5.3	8/10	57/78 (73%)	#1
LabOpen v1 (this)	8/10	56/78 (72%)	#2
Qwen3.8-max	8/10	54/78 (69%)	#3
DeepSeek v4.1 flash	6/10	48/78 (62%)	#4
Qwen3.8-27B (base)	8/10	25/78 (32%)	#5

A 27B + r4 LoRA reaching frontier-model agentic-coding territory in 15 training windows is the headline result; see EVALUATION.md for gaps that remain (first-pass rate, general knowledge vs 100B+ models).

Domain-capability comparison with frontier models (zero-shot)

The five external-comparison models above were also run on the domain heldout sets themselves (the same questions used for our capability matrix, with tags stripped — plain zero-shot prompts, k=8 samples at T=0.3, item any-correct, scored by the same stack scorers with fence-stripped outputs).

Domain (items)	GLM 5.3	Qwen3.8-max¹	DeepSeek flash	LabOpen v1²	Base
Fluid physics (36)	36/36	36/36	35/36	25/36 †	≈0
Materials — strict (40)	1/40	0/40	0/40	17/40 ‡	≈0
Materials — fair 1% (40)	20/40	15/40	22/40	18/40 ‡	—
Classical genetics (20)	9/20	12/20	7/20	20/20 †	47%
Ops — strict (8)	0/8	0/8	0/8	4/8 (T0.3)	0/8
Ops — null-action-equivalent (8)	4/8	0/8	3/8	4/8	—

¹ non-thinking mode, consistent with the agentic battery; GLM 5.3 ran with full thinking; DeepSeek flash with thinking disabled (its API option). † our tagged deployment protocol; ‡ our untagged protocol (directly comparable to external).

Reading the zeros honestly:

• Ops 0/8 (strict) has two components. (a) A convention artifact: the reference null action is the domain vocabulary "observe", which the prompt never states — frontier models answering "none" understood the rule but failed the private vocabulary (null-action-equivalent rescoring: GLM 4/8, DeepSeek 3/8, Qwen still 0/8). (b) A genuine failure that remains: frontier models substitute their own ops heuristics for the declared rule — e.g. rolling back on regression=true when the declared rule (escalate_on_critical, severity below critical) requires no action; across 6 null-trigger items they over-act (rollback/restart dominate their outputs). Our advantage is faithful declared-rule binding plus the domain action vocabulary — not that frontier models "cannot do ops".
• Materials 0/40 (strict) is a tolerance artifact: strict scoring (0.01% relative) requires reproducing the corpus property table to 4 significant figures; frontier answers using standard handbook constants (physically correct) fail it. Under a fair 1% tolerance the domain is at parity (38–55% vs our 45%); the strict score measures corpus-table reproduction, our 42% vs their 0–3%.
• Fluid physics: frontier models solve these fully zero-shot — our value here is lifting a local 27B from ≈0 to 69%, not uniqueness.

Usage

Serving the merged weights (recommended)

vllm serve HoomanBio2021/Qwen3.8-27B-LabOpen-v1
# or: docker run ... vllm/vllm-openai --model HoomanBio2021/Qwen3.8-27B-LabOpen-v1

The merged checkpoint uses the same blockwise FP8 (e4m3, 128×128 scales) format as the base — any runtime that loads Qwen/Qwen3.8-27B-FP8 loads this without changes.

Hardware support: requires NVIDIA compute capability ≥ 8.9 (Hopper / Ada / Blackwell — e.g. H100, L40S, B200, DGX GB10; tested on DGX GB10). Ampere and older (A100, RTX 3090) have no FP8 support. Single-card memory must fit ~31 GB of weights (48 GB minimum with reduced context, 80 GB recommended); multi-GPU tensor parallel works. No macOS path — vLLM has no Apple-Silicon backend and no Mac runtime loads this blockwise-FP8 checkpoint format.

Deployment notes (tested on vLLM 0.27.1): give domain-computation requests max_tokens ≥ 4096 — the model reasons before answering, and a 1024 budget is exhausted by the reasoning chain (finish_reason=length, empty content). With vLLM's --reasoning-parser qwen3 the final answer arrives in content and reasoning in reasoning_content.

The adapter (research artifact — not a serving path)

The adapter/ directory holds the exact training artifact (PEFT format, 112 MB) for inspection, provenance and further training. It is not a drop-in serving path: plain transformers cannot load the blockwise-FP8 base, and vLLM's LoRA kernels do not cover this architecture's hybrid linear-attention projections (in_proj_*, out_proj) — 5 of 12 target module types would be silently skipped, corrupting behavior. Serve the merged weights.

Do not run merge_and_unload() on the FP8 base yourself. The base stores weights as blockwise-FP8 codes with 128×128 scales; a naive PEFT merge adds a BF16 delta directly onto FP8 codes and silently corrupts the weights. Use the merged checkpoint provided here — it was produced by dequantize → add delta → re-quantize with per-block scale recomputation and verified against the adapter (see MERGE_REPORT.md).

Domain tags

The strengthened domains are addressed by a one-line tag at the start of the prompt:

Tag	Domain
[domain: cfm-chain]	fluid physics chains
[domain: mat-table]	materials mixture laws
[domain: bio-genetics]	classical genetics
[domain: ops-action]	ops action queries
[domain: general-prose]	open-ended prose style

Without a tag the model keeps base behavior (code / function calling need no tag).

Temperature

Use case	Temperature
Domain computation (cfm/mat/bio/ops)	0.3 (greedy degrades on some domains)
Open-ended prose	0.3 (prose is in the distribution, not always the mode)
Code generation	0.0–0.2
General QA	0.3–0.7

Technical notes

• Architecture: Qwen3.8-27B hybrid — 64 decoder layers: 48 linear-attention + 16 full-attention layers.
• LoRA: r=4, α=4 (scaling 1.0), dropout 0, on 496 projection modules across 12 module types (q/k/v/o/gate/up/down_proj, in_proj_a/b/qkv/z, out_proj) = 992 tensors, ~112 MB.
• Merge math: per module W = codes × scale(blockwise) dequantized to FP32, LoRA delta (B·A)·scaling added, re-quantized per 128×128 block (scale = amax/448). Mean re-quantization relative error 0.33% — the same order as the base's own FP8 storage noise. Equivalence probes (greedy, 3 prompts × 48 tokens, adapter vs merged): see MERGE_REPORT.md.
• Training substrate: a local FP8 copy of Qwen3.8-27B (community redistribution). Bit-equivalence with Qwen/Qwen3.8-27B-FP8 was not verified; the adapter config points to the official release for loading.

Thinking-behavior governance

The base model can exhaust its thinking budget on unfamiliar domain problems, leaving the reasoning segment unclosed — "thinking exhaustion". This was measured, governed, and re-measured rather than assumed away:

• Worst recorded case: before the materials window (G77), 48% of materials generations truncated with unclosed thinking (155/320 during in-window fuel sampling).
• Mechanism: the reward contract scores termination mechanically — truncation, format errors, and runtime errors are separate reward categories — and oracle injection teaches termination explicitly. Within that single window, truncation fell from 155/320 to 0/320 and per-item generation time dropped from 327 s to 74 s.
• Post-governance audit (four domains, harvest trajectories): unclosed-thinking rate 0% across fluid physics / materials / genetics / ops; degenerate repetition 1/952; median thinking length 180–581 characters (genetics shortest, fluid physics longest).
• Boundary: this audit covers the five-domain deployment protocol; open-ended (non-domain) questions have not had a dedicated thinking-exhaustion probe.

Training methodology (summary)

15 reinforcement windows (G69–G84) on a frozen FP8 serving stack with bit-exact checkpoint accounting: group-relative advantages with k=8 sampling and programmatic answer scoring; oracle injection for zero-mastery domains; verified distillation from champion checkpoints; frozen domain tags; substep advantage for multi-step composition; per-window heldout gates and retention protection. Single seed, single run — no capability claim in this card comes from a set seen during training.

Honest boundaries

• Single seed, single training run; variance bands not fully characterized
• Prose style is stable at T=0.3 sampling, unstable at greedy decoding
• Domain capabilities require their tags; untagged behavior is base behavior
• 27B parameter count — not comparable to 100B+ models on general knowledge
• Merged FP8 weights add re-quantization noise vs the adapter path (stats in MERGE_REPORT.md); the adapter is the numerically exact artifact
• All scores are single evaluation runs on heldout sets

Acknowledgments

• Alibaba Qwen team — for Qwen3.8-27B, the open base model this work builds on, and for the open Apache-2.0 release that makes derivative research like this possible.
• Z.ai (Zhipu AI) — GLM 5.3 served as one of the external benchmarks in our evaluation battery and as an engineering assistant during the research and release workflow.
• Moonshot AI — Kimi K3 provided engineering assistance in experiment design and testing during the early research process.

We thank these teams for pushing open, measurable frontiers — this release aims to contribute back in the same spirit.

Citation

@software{labopen_v1,
  title = {Qwen3.8-27B-LabOpen v1: Six-Domain Reinforcement of Qwen3.8-27B},
  author = {Hooman AI Lab},
  year = {2026},
  url = {https://huggingface.co/HoomanBio2021/Qwen3.8-27B-LabOpen-v1},
  base = {Qwen/Qwen3.8-27B-FP8},
  license = {Apache-2.0}
}

Contact

If you have any questions, please raise an issue or contact us at service@hoomanbio.com.

---

中文版模型卡

Qwen3.8-27B-LabOpen v1

面向可验证科学计算强化的 Qwen3.8-27B——同时以合并 FP8 全权重（与基座即插即用）与原始 LoRA 适配器两种形态发布。

LabOpen v1 在 Qwen/Qwen3.8-27B-FP8 之上新增六个可验证能力域（流体物理、材料混合律、经典遗传学、运维动作查询、散文文风、智能体编程），同时保留基座原生的代码生成、函数调用、意图理解与公式显示能力。最大实测增益：78 项确定性智能体编程测试上 32% → 72%（+40pp）。

• 基座模型：Qwen3.8-27B（FP8 版本，Apache 2.0）
• 训练：15 个强化窗口（G69–G84），LoRA r=4，组相对强化学习（RLOO 式，k=8 采样），机械可验证奖励
• 许可证：Apache 2.0（与基座相同）
• 硬件要求：NVIDIA 计算能力 ≥ 8.9 的 GPU（H100 / L40S / B200 / DGX GB10 等级别）；FP8 下约 31 GB

项目定位与理念

Qwen3.8-27B-LabOpen v1 是基于 Qwen3.8-27B 开源基础模型开展领域微调与优化的研究模型，重点面向编程、物理学、生命科学、材料科学等科学研究领域。

项目并不以取代通用大模型为目标，而是希望在保留通用模型综合能力的基础上，进一步增强其对科学专业知识、领域问题及研究任务的适应能力。

模型主要面向高校、科研院所、企业研发中心及专业实验室等使用场景，支持根据实际计算资源条件进行本地化或私有化部署。

仓库内容

路径	内容
model-0000X-of-00007.safetensors + index	合并 FP8 全权重（基座 + LoRA，分块 FP8 重量化）。张量布局与量化格式和基座完全相同——即插即用替换
adapter/	LoRA 适配器（112 MB，PEFT 格式，992 张量）。训练精确工件，供检查与继续训练——服务请用合并权重
MERGE_REPORT.md	合并溯源：摘要、分片 SHA-256、重量化误差统计、输出等价探针
EVALUATION.md	实测能力矩阵与五模型外部对比
USAGE.md	加载方式、域标记、温度建议、故障排查

部署服务用合并权重（vLLM/SGLang）；需要训练时精确数值或更小下载量时用适配器加载基座。

能力矩阵（heldout 集实测）

域	指标	得分	对比基座
智能体编程	78 项确定性测试，三轮修复	**72%**（56/78）	+40pp（基座：32%）
意图理解	10 场景深层意图探针	8/10	保持
公式显示	LaTeX 完整性，5 题	5/5	保持
流体物理（CFM）	heldout 池 B，T=0.3 k=8，带标记	44% 采样命中	新增（基座 ≈ 0）
材料（混合律）	heldout 40 项，T=0.3，无标记	32% 采样命中	新增（基座 ≈ 0）
经典遗传学	heldout 20 项，带标记	79% 采样命中	新增（基座自然 47%）
运维（动作查询）	8 项，双温	4/8（T0.3）–5/8（T0.8）	新增
散文文风	20 项开放题，T=0.3 k=8	75% any-correct	新增

本表报告部署口径下的采样命中率（正确样本/总样本）。同一批 heldout 题以题目级口径（any-correct）与前沿模型零样本对照，见下方「域独特性实测」一节。

外部对比（同一测试组，2026-10）

模型	意图	智能体测试	排名
GLM 5.3	8/10	57/78（73%）	#1
LabOpen v1（本模型）	8/10	56/78（72%）	#2
Qwen3.8-max	8/10	54/78（69%）	#3
DeepSeek v4.1 flash	6/10	48/78（62%）	#4
Qwen3.8-27B（基座）	8/10	25/78（32%）	#5

27B + r4 LoRA 以 15 个训练窗口进入前沿模型的智能体编程区间，这是本发布的头条结果；仍存在的差距（首过率、与 100B+ 模型的通用知识差距）见 EVALUATION.md。

域独特性实测（与前沿模型零样本对照）

上述外部对比模型同时在各域 heldout 原题上复测（同一批题、剥离标记的纯题面、k=8 采样 T=0.3、item any-correct、同一套栈评分器、输出统一剥 fence）。

域（题数）	GLM 5.3	Qwen3.8-max¹	DeepSeek flash	LabOpen v1	基座
流体物理（36）	36/36	36/36	35/36	25/36 †	≈0
材料·严格口径（40）	1/40	0/40	0/40	17/40 ‡	≈0
材料·公平口径 1%（40）	20/40	15/40	22/40	18/40 ‡	—
经典遗传（20）	9/20	12/20	7/20	20/20 †	47%
运维·严格口径（8）	0/8	0/8	0/8	4/8（T0.3）	0/8
运维·空动作等价（8）	4/8	0/8	3/8	4/8	—

¹ 非思考模式，与智能体对比 battery 口径一致；GLM 5.3 保留完整深思；DeepSeek flash 关闭思考（其 API 选项）。† 我方带标记部署口径；‡ 我方无标记口径（与外部直接可比）。

零分的诚实归因：

• 运维 0/8（严格）含两个成分。(a) 约定伪影：参考答案的空动作是域词表 observe，题面从未说明——前沿模型答 none 说明其理解了规则、只是不掌握私有词表（空动作等价重判：GLM 4/8、DeepSeek 3/8、Qwen 仍 0/8）。(b) 真实失败仍然存在：前沿模型以自身运维直觉替代题面声明的规则——如对 regression=true 一律 rollback，而声明规则（escalate_on_critical、severity 未达 critical）应为空动作；6 道无触发题上它们明显过度动作（rollback/restart 占输出主导）。我方优势是声明规则的忠实执行加域动作词表，不是"前沿不会运维"。
• 材料 0/40（严格）是容差伪影：严格评分（0.01% 相对）要求把语料属性表复现到 4 位有效数字；前沿模型用标准手册常量（物理上正确）被判错。公平 1% 容差下该域持平（38–55% vs 我方 45%）；严格口径衡量的是语料表复现（我方 42% vs 0–3%）。
• 流体物理：前沿零样本全解——我方价值是把本地 27B 从 ≈0 提到 69%，而非独特性。

使用方法

部署合并权重（推荐）

vllm serve HoomanBio2021/Qwen3.8-27B-LabOpen-v1
# 或：docker run ... vllm/vllm-openai --model HoomanBio2021/Qwen3.8-27B-LabOpen-v1

合并检查点与基座采用完全相同的分块 FP8（e4m3，128×128 块尺度）格式——任何能加载 Qwen/Qwen3.8-27B-FP8 的运行时均可直接加载本仓库，无需改动。

硬件支持：需要 NVIDIA 计算能力 ≥ 8.9（Hopper / Ada / Blackwell——如 H100、L40S、B200、DGX GB10；已在 DGX GB10 实测）。Ampere 及更早（A100、RTX 3090）无 FP8 支持。单卡显存需容纳约 31 GB 权重（最低 48 GB 并缩短上下文，推荐 80 GB）；支持多卡张量并行。无 macOS 路径——vLLM 无 Apple Silicon 后端，且无 Mac 运行时能加载本分块 FP8 检查点格式。

部署要点（已在 vLLM 0.27.1 实测）：域计算请求的 max_tokens 应给到 4096 以上——模型先思考后作答，1024 预算会被思考链耗尽（finish_reason=length、正文为空）。vLLM 配 --reasoning-parser qwen3 时，最终答案在 content、推理在 reasoning_content。

适配器（研究工件——非服务路径）

adapter/ 目录保存训练时的精确工件（PEFT 格式，112 MB），供检查、溯源与继续训练使用。它不是即插即用的服务路径：原生 transformers 无法加载分块 FP8 基座；vLLM 的 LoRA 内核不覆盖本架构混合线性注意力投影（in_proj_*、out_proj）——12 类目标模块中 5 类会被静默跳过、损坏行为。服务请使用合并权重。

不要自行对 FP8 基座执行 merge_and_unload()。 基座权重以分块 FP8 码 + 128×128 块尺度存储；朴素 PEFT 合并会把 BF16 增量直接加到 FP8 码上，静默损坏权重。请使用本仓库提供的合并检查点——它按「反量化 → 加增量 → 按块重量化」生成，并经与适配器的等价探针验证（见 MERGE_REPORT.md）。

域标记

强化域通过置于提示首行的单行标记寻址：

标记	域
[domain: cfm-chain]	流体物理链
[domain: mat-table]	材料混合律
[domain: bio-genetics]	经典遗传学
[domain: ops-action]	运维动作查询
[domain: general-prose]	开放式散文文风

不加标记时模型保持基座行为（代码 / 函数调用无需标记）。

温度

场景	温度
域计算（cfm/mat/bio/ops）	0.3（部分域 greedy 退化）
开放式散文	0.3（散文在分布中但未必是众数）
代码生成	0.0–0.2
通用问答	0.3–0.7

技术说明

• 架构：Qwen3.8-27B 混合——64 个解码层：48 层线性注意力 + 16 层全注意力。
• LoRA：r=4，α=4（缩放 1.0），dropout 0，覆盖 12 类模块（q/k/v/o/gate/up/down_proj、in_proj_a/b/qkv/z、out_proj）共 496 个投影模块 = 992 张量，约 112 MB。
• 合并数学：逐模块 W = codes × scale（分块）反量化至 FP32，加入 LoRA 增量 (B·A)·scaling，再按 128×128 块重量化（scale = amax/448）。平均重量化相对误差 0.33%——与基座自身 FP8 存储噪声同量级。等价探针（greedy，3 提示 × 48 token，适配器 vs 合并）：见 MERGE_REPORT.md。
• 训练基材：Qwen3.8-27B 的本地 FP8 副本（社区再分发）。未与 Qwen/Qwen3.8-27B-FP8 做逐位等价核验；适配器配置指向官方发布以便加载。

思考行为治理

基座模型在不熟悉的域问题上可能耗尽思考预算、使推理段未闭合即截断——即"思考枯竭"。本模型对该问题做了测量—治理—复测的完整闭环，而非默认其不存在：

• 最严重记录：材料域窗口（G77）之前，材料题生成 48% 出现思考未闭合截断（窗内燃料采样期 155/320）。
• 机制：奖励合同对终止行为做机械评分——截断、格式错误、运行异常是相互独立的奖励类别；oracle 注入显式教授终止行为。该窗口内截断从 155/320 降至 0/320，单题生成时长从 327 秒降至 74 秒。
• 治理后审计（四域收获轨迹）：流体物理 / 材料 / 遗传学 / 运维四域思考未闭合率均为 0%；退化重复 1/952；思考段长度中位数 180–581 字（遗传学最短、流体物理最长）。
• 边界：该审计覆盖五域部署口径；开放式（非域）问题尚未做思考枯竭专项探针。

训练方法（摘要）

15 个强化窗口（G69–G84），运行于带逐位精确检查点账目的冻结 FP8 服务栈：组相对优势 + k=8 采样 + 程序化答案评分；零起点域的 oracle 注入；冠军检查点的验证蒸馏；冻结域标记；多步组合的子步优势；逐窗 heldout 门与保持保护。单种子单次运行——本卡中的全部能力声明均来自训练未见过的评测集。

诚实边界

• 单种子单次训练；方差带未完全表征
• 散文文风在 T=0.3 采样下稳定，greedy 解码下不稳定
• 域能力需要标记；无标记行为即基座行为
• 27B 参数量——通用知识不可与 100B+ 模型相比
• 合并 FP8 权重相对适配器路径存在重量化噪声（统计见 MERGE_REPORT.md）；适配器是精确数值工件
• 所有得分均为 heldout 集上的单次评估

致谢

• 阿里巴巴 Qwen 团队——感谢 Qwen3.8-27B 这一开放基座模型，以及 Apache-2.0 开放发布使此类衍生研究成为可能。
• 智谱 AI（Z.ai）——GLM 5.3 在我们的评估测试组中作为外部基准之一，并在研究与发布流程中担任工程助手。
• 月之暗面（Moonshot AI）——Kimi K3 在前期研究进程中的实验设计和测试中提供了工程协助。

我们感谢这些团队推动开放、可度量的前沿——本次发布希望以同样的精神回馈社区。

引用

@software{labopen_v1,
  title = {Qwen3.8-27B-LabOpen v1: Six-Domain Reinforcement of Qwen3.8-27B},
  author = {Hooman AI Lab},
  year = {2026},
  url = {https://huggingface.co/HoomanBio2021/Qwen3.8-27B-LabOpen-v1},
  base = {Qwen/Qwen3.8-27B-FP8},
  license = {Apache-2.0}
}

联系方式

如有任何问题，请提交问题反馈或联系 service@hoomanbio.com。