KIEFERSA/Sophea-Titan-1

Sophea-Titan-1

Greek fine-tuned multimodal Qwen3.6-27B — non-thinking chat

Sophea-Titan-1 is a 27B Greek fine-tuned multimodal chat model built on Qwen3.6-27B, with register control and a fixed assistant identity — while retaining English ability and vision.

• Creator: Kiefer SA
• Base model: Qwen3.6-27B (multimodal, 64 layers, hidden 5120)
• Languages: Greek (primary), English (retained)
• Decoding: non-thinking (enable_thinking=false) — see recommended sampling under Usage

Serve non-thinking. Set enable_thinking=false in the chat template. Over an OpenAI-compatible vLLM endpoint, pass extra_body={"chat_template_kwargs": {"enable_thinking": false}}. With thinking left on, Greek output quality degrades sharply.

Intended use

• General-purpose Greek conversational assistant / chat, with formal ↔ informal register control
• Greek-knowledge QA; English retained as a secondary language

Not evaluated for safety-critical or legal/medical decisions.

Fine-tuned from Qwen3.6-27B.

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Evaluation

Non-thinking, greedy (temperature 0). Sophea-Titan-1 in bold.

Models compared:

column	model	what it is	identity-tuned?
Base	Qwen3.6-27B	the base Sophea-Titan-1 is built on	no
Sophea-Titan-1	this model	Qwen3.6-27B + general-purpose Greek conversational SFT	yes
Sophea-K1	KIEFERSA/sophea-k1	KIEFERSA production multimodal Greek model (sophea.ai)	yes
Qwen3-30B-A3B	Qwen/Qwen3-30B-A3B	text-only MoE (30B/3B-active), not fine-tuned	no
Qwen3.6-35B-A3B	Qwen/Qwen3.6-35B-A3B	multimodal MoE (35B / 3B-active), not fine-tuned	no
Krikri-8B	ilsp/Llama-Krikri-8B-Instruct	Llama-3.1-8B Greek LLM (external baseline)	no

n/a = not applicable (text-only) · n.t. = not identity-tuned.

Headline scorecard

Axis	Base	Sophea-Titan-1	Sophea-K1	Qwen3-30B-A3B	Qwen3.6-35B-A3B	Krikri-8B
General Greek benchmarks — macro (9)	0.7157	0.7369	0.7240	0.6346	0.6983	0.5977
English retention — macro (5)	0.8631	0.8783	0.8702	0.7631	0.8362	0.7423
Vision — MMStar (1500)	0.6533	0.7140	0.6620	n/a	0.590	n/a

At a glance

Capability profile — General Greek benchmarks, English and vision across open-weight models (Sophea-Titan-1 in bold; frontier MoEs GLM-5.2 / MiniMax-M3 shown for reference, no vision):

General Greek benchmarks vs. model size — Sophea-Titan-1 has the strongest general-Greek score of the ~27B open models, approaching far larger frontier MoEs at a fraction of the size:

Per-benchmark detail

Three benchmark families in one table — General Greek benchmarks (9), English retention (5), and Vision — MMStar (1500) — on the same five models. n/a = not applicable (text-only, no vision).

Benchmark	Base	Sophea-Titan-1	Sophea-K1	Qwen3-30B-A3B	Qwen3.6-35B-A3B	Krikri-8B
General Greek benchmarks (9)
greekmmlu	0.849	0.854	0.855	0.753	0.838	0.675
mmlu_greek	0.798	0.797	0.782	0.628	0.770	0.519
hellaswag	0.611	0.680	0.622	0.410	0.574	0.572
medical_mcqa	0.312	0.387	0.368	0.232	0.312	0.287
winogrande	0.590	0.626	0.599	0.559	0.564	0.609
arc_challenge	0.944	0.950	0.945	0.867	0.916	0.690
arc_easy	0.973	0.973	0.971	0.931	0.969	0.832
belebele	0.941	0.950	0.938	0.887	0.926	0.771
truthfulqa	0.423	0.415	0.437	0.446	0.416	0.425
MACRO (9)	0.7157	0.7369	0.7240	0.6346	0.6983	0.5977
English retention (5)
arc_challenge	0.972	0.979	0.977	0.932	0.959	0.765
arc_easy	0.990	0.992	0.994	0.981	0.990	0.892
hellaswag	0.764	0.805	0.780	0.500	0.734	0.751
mmlu	0.858	0.858	0.858	0.745	0.840	0.608
winogrande	0.732	0.758	0.742	0.658	0.658	0.695
MACRO (5)	0.8631	0.8783	0.8702	0.7631	0.8362	0.7423
Vision — MMStar (1500)
coarse perception	0.744	0.728	0.728	n/a	0.704	n/a
fine-grained perception	0.636	0.608	0.628	n/a	0.588	n/a
instance reasoning	0.756	0.792	0.792	n/a	0.740	n/a
logical reasoning	0.668	0.764	0.636	n/a	0.572	n/a
math	0.464	0.708	0.500	n/a	0.372	n/a
science & technology	0.652	0.684	0.688	n/a	0.564	n/a
OVERALL	0.653	0.714	0.662	n/a	0.590	n/a

greekmmlu — per-subject (31)

subject	Base	Sophea-Titan-1	Sophea-K1	Qwen3-30B-A3B	Qwen3.6-35B-A3B	Krikri-8B
Accounting	0.864	0.864	0.859	0.761	0.848	0.663
Agriculture	0.853	0.870	0.853	0.721	0.819	0.707
Art	0.748	0.756	0.770	0.621	0.757	0.625
Biology	0.864	0.868	0.859	0.794	0.856	0.672
Chemistry	0.827	0.753	0.778	0.654	0.790	0.519
Civil Engineering	0.793	0.827	0.819	0.678	0.785	0.637
Clinical Knowledge	0.810	0.820	0.795	0.689	0.790	0.686
Computer Networks & Security	0.730	0.762	0.714	0.540	0.667	0.476
Computer Science	0.883	0.880	0.866	0.827	0.869	0.768
Driving Rules	0.837	0.820	0.829	0.756	0.811	0.631
Economics	0.911	0.926	0.926	0.840	0.894	0.670
Education	0.857	0.867	0.895	0.762	0.901	0.687
Electrical Engineering	0.830	0.822	0.844	0.681	0.817	0.538
General Knowledge	0.769	0.792	0.823	0.701	0.781	0.630
Geography	0.943	0.964	0.955	0.870	0.950	0.872
Government and Politics	0.944	0.941	0.961	0.901	0.944	0.899
Greek History	0.871	0.877	0.878	0.711	0.884	0.816
Greek Literature	0.714	0.714	0.643	0.429	0.500	0.500
Greek Mythology	0.870	0.857	0.845	0.744	0.849	0.697
Greek Traditions	0.880	0.891	0.880	0.755	0.867	0.710
Law	0.718	0.703	0.697	0.580	0.677	0.504
Management	0.824	0.841	0.842	0.719	0.814	0.694
Maritime Safety & Rescue	0.703	0.669	0.662	0.608	0.682	0.507
Mathematics	0.897	0.924	0.904	0.844	0.857	0.516
Medicine	0.893	0.895	0.886	0.757	0.882	0.663
Modern Greek Language	0.916	0.918	0.928	0.836	0.911	0.780
Physics	0.838	0.851	0.851	0.769	0.838	0.684
Prehistory	1.000	1.000	0.968	0.984	0.984	0.905
World History	0.950	0.950	0.900	0.850	0.950	0.800
World Religions	0.768	0.755	0.787	0.652	0.800	0.684
OVERALL	0.849	0.854	0.855	0.753	0.838	0.675

Frontier & other API models (reference)

Evaluated via API (thinking disabled where supported): GPT-5.5, Claude Opus 4.8, Gemini 3.5, GLM-5.2, MiniMax-M3. API-model Greek/English benchmarks use letter-answer scoring (local models use log-likelihood).

Axis	Sophea-Titan-1	GPT-5.5	Claude-4.8	Gemini-3.5	GLM-5.2	MiniMax-M3
General Greek benchmarks macro (9)	0.737	0.909	0.922	0.872	0.810	0.821
English macro (5)	0.878	0.925	0.940	0.911	0.892	0.890

General Greek benchmarks (9)

benchmark	Sophea-Titan-1	GPT-5.5	Claude-Opus-4.8	Gemini-3.5	GLM-5.2	MiniMax-M3
greekmmlu	0.854	0.905	0.900	0.898	0.805	0.837
mmlu_greek	0.797	0.888	0.884	0.874	0.769	0.773
hellaswag	0.680	0.891	0.909	0.816	0.693	0.741
medical_mcqa	0.387	0.928	0.917	0.912	0.787	0.833
winogrande	0.626	0.781	0.813	0.641	0.705	0.662
arc_challenge	0.950	0.968	0.966	0.952	0.904	0.924
arc_easy	0.973	0.984	0.982	0.973	0.949	0.960
belebele	0.950	0.953	0.947	0.934	0.913	0.911
truthfulqa	0.415	0.885	0.977	0.845	0.766	0.749
MACRO	0.7369	0.9094	0.9216	0.8717	0.8102	0.8212

English — 5 benchmarks

benchmark	Sophea-Titan-1	GPT-5.5	Claude-Opus-4.8	Gemini-3.5	GLM-5.2	MiniMax-M3
arc_challenge	0.979	0.976	0.974	0.958	0.968	0.967
arc_easy	0.992	0.993	0.994	0.979	0.989	0.986
hellaswag	0.805	0.918	0.944	0.913	0.858	0.854
mmlu	0.858	0.908	0.910	0.896	0.845	0.852
winogrande	0.758	0.830	0.877	0.811	0.801	0.793
MACRO	0.8783	0.9249	0.9399	0.9113	0.8922	0.8903

greekmmlu

model	Sophea-Titan-1	GPT-5.5	Claude-Opus-4.8	Gemini-3.5	GLM-5.2	MiniMax-M3
greekmmlu	0.854	0.905	0.900	0.898	0.805	0.837

Quantized variants & other formats

Smaller-footprint and alternate-runtime builds of this model:

• GPU / vLLM (text-only — vision tower not included): Sophea-Titan-1-FP8 — lossless, ≈29 GB · Sophea-Titan-1-NVFP4 — 4-bit, ≈19 GB
• llama.cpp — Sophea-Titan-1-GGUF
• Apple Silicon — Sophea-Titan-1-mlx

The trade-off plot below covers the vLLM builds scored on the Greek suite; GGUF / MLX are format conversions.

VRAM vs. Greek benchmark score across the Sophea-Titan-1 quantized family (bf16 → FP8 → NVFP4).

Usage

Serve with vLLM (OpenAI-compatible; 27B bf16 ≈ 54 GB — add --tensor-parallel-size N for multi-GPU):

vllm serve KIEFERSA/Sophea-Titan-1 --served-model-name sophea-titan-1 --trust-remote-code \
  --enable-auto-tool-choice --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3

For text-only serving (skips the vision tower — much faster), add --language-model-only.

Recommended sampling (instruct / non-thinking): temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0.

Client (OpenAI SDK) — remember non-thinking:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
resp = client.chat.completions.create(
    model="sophea-titan-1",
    messages=[{"role": "user", "content": "Ποια είναι η πρωτεύουσα της Ελλάδας;"}],
    temperature=0,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},   # required: non-thinking
)
print(resp.choices[0].message.content)

Transformers (multimodal — load with the image-text-to-text head to keep vision):

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

proc = AutoProcessor.from_pretrained("KIEFERSA/Sophea-Titan-1", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    "KIEFERSA/Sophea-Titan-1", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)

messages = [{"role": "user", "content": [{"type": "text",
             "text": "Ποια είναι η πρωτεύουσα της Ελλάδας;"}]}]
text = proc.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = proc(text=[text], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(proc.batch_decode(out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0])

License

Inherits the Qwen3.6-27B base-model license. Verify base-model terms before use.