Qwen3.8-27B-Human-KO API & Inference Endpoint

Measurements (2026-09-01, temperature and conditions stated)

Table with columns: Axis, base, This model
Axis	base	This model
Human-likeness pairwise (LLM judge, n=175)	1.1% win	94.9% win (tie 4.0%, position-swap consistency 96%)
Free-generation bullet-list rate (n=200)	97.5%	2.0%
CJK contamination, T=0, n=3,369	2.55%	0.33% (true error rate 0.24%)
HumanEval (paired n=58)	93.85%	+3.03pp
Instruction following	81%	−3.0pp (statistically equivalent)
KMMLU (n=300)	55.67%	−2.0pp — minimum detectable difference is 11.4pp, so no verdict is possible

Honest caveats:

The pairwise judge (Qwen3.8-27B, T=0, position-swapped) has not completed human-label calibration (P1.5). The figures above are "win rate on the judge's axis," not human evaluation itself.
The KMMLU row is not "no regression" — it is "a regression on the order of −1pp cannot be detected with this sample."
The floor for CJK contamination is not 0. A rare Hanja character with no standalone token in the vocabulary can be assembled via byte fallback (the measured residual 0.24% is mostly the single-character class kept for gloss preservation).

Usage

python
from vllm import LLM
llm = LLM(model="ThakiCloud/Qwen3.8-27B-Human-KO", dtype="bfloat16", trust_remote_code=True)

The tokenizer, vocabulary size, and architecture are identical to base (standard load). The model can read Hanja but cannot generate Chinese-word tokens.

Limitations

The default tendency is toward short answers. If you need a long, structured document, specify length and format in the prompt.
The measurements above center on the Korean conversation axis. The English and math axes have not been measured with a sufficient sample.
The safety axis inherits base's alignment as-is (no separate safety training).

License

Apache-2.0 (base: Qwen/Qwen3.8-27B). Distributed together with LICENSE, NOTICE, and DATA_PROVENANCE.md.

Measurements (2026-09-01, temperature and conditions stated)

Table with columns: Axis, base, This model
Axis	base	This model
Human-likeness pairwise (LLM judge, n=175)	1.1% win	94.9% win (tie 4.0%, position-swap consistency 96%)
Free-generation bullet-list rate (n=200)	97.5%	2.0%
CJK contamination, T=0, n=3,369	2.55%	0.33% (true error rate 0.24%)
HumanEval (paired n=58)	93.85%	+3.03pp
Instruction following	81%	−3.0pp (statistically equivalent)
KMMLU (n=300)	55.67%	−2.0pp — minimum detectable difference is 11.4pp, so no verdict is possible

Honest caveats:

The pairwise judge (Qwen3.8-27B, T=0, position-swapped) has not completed human-label calibration (P1.5). The figures above are "win rate on the judge's axis," not human evaluation itself.
The KMMLU row is not "no regression" — it is "a regression on the order of −1pp cannot be detected with this sample."
The floor for CJK contamination is not 0. A rare Hanja character with no standalone token in the vocabulary can be assembled via byte fallback (the measured residual 0.24% is mostly the single-character class kept for gloss preservation).

Usage

python
from vllm import LLM
llm = LLM(model="ThakiCloud/Qwen3.8-27B-Human-KO", dtype="bfloat16", trust_remote_code=True)

The tokenizer, vocabulary size, and architecture are identical to base (standard load). The model can read Hanja but cannot generate Chinese-word tokens.

Limitations

The default tendency is toward short answers. If you need a long, structured document, specify length and format in the prompt.
The measurements above center on the Korean conversation axis. The English and math axes have not been measured with a sufficient sample.
The safety axis inherits base's alignment as-is (no separate safety training).

License

Apache-2.0 (base: Qwen/Qwen3.8-27B). Distributed together with LICENSE, NOTICE, and DATA_PROVENANCE.md.

Qwen3.8-27B-Human-KO

README

Measurements (2026-09-01, temperature and conditions stated)

Usage

Limitations

License

Explore FriendliAI today

README

Measurements (2026-09-01, temperature and conditions stated)

Usage

Limitations

License