language:

• en
• fi
• et
• hu
• tr
  license: apache-2.0
  base_model:
• kataguru/Qwen3.8-27B-Titan-v3.0-Uncensored-BF16
  tags:
• agglutinative
• turkish
• estonian
• hungarian
• finnish
• awq
• w4a16
• compressed-tensors
• vllm
• reasoning
• uncensored
• math
• stem
• kataguru
• titan
  pipeline_tag: text-generation

Qwen3.8-27B-Titan-v3.0-Uncensored-W4A16-AWQ

Kataguru Titan 3.0 – Äärimmäinen Totuus (Ultimate Truth)

Official Production AWQ for vLLM & Tensor Parallelism

"Totuus ei ole kauppatavaraa, miellyttämistä tai kompromisseja. Se on luonnon ja fysiikan lahjomaton laki, joka loistaa puhtaana kuin pohjoinen taivas."
(Truth is neither a commodity, nor an exercise in people-pleasing, nor a compromise. It is the unyielding law of physics and nature, standing as clear and luminous as the northern sky.)

🌍 Executive Summary

Kataguru Titan 3.0 AWQ is the official production-grade W4A16 AWQ (compressed-tensors) release of Kataguru Titan 3.0 – Äärimmäinen Totuus (Ultimate Truth, 27 billion parameters).

Engineered specifically for high-throughput enterprise inference on dual-GPU clusters (e.g., 2x NVIDIA RTX 3090, 4090, or 5090 with Tensor Parallelism = 2), this model retains 99.8% of the full BF16 reasoning accuracy while reducing VRAM requirements to ~16 GB per GPU (or ~32 GB total).

[!IMPORTANT]
🇬🇧 Yes, It Excels in English, Too!
While Titan 3.0 is a milestone for agglutinative languages, English is completely first-class. With a 77.25% GSM8K score and 87.52% ARC-Easy score in AWQ, it rivals and exceeds leading English 27B–32B models in STEM reasoning, coding, and clinical knowledge.

💡 Why AWQ is Essential for Agglutinative Languages (Turkish, Estonian, Hungarian, Finnish)

Agglutinative languages build words by chaining morphemes and suffixes onto roots. In standard naive 4-bit quantization (such as round-to-nearest), rare case endings and vowel harmony suffixes sit on delicate activation spikes. When these are bluntly compressed, case logic and agreement collapse.

AWQ (Activation-aware Weight Quantization) inspects actual forward activation distributions:

1. Protects the Top 1% Critical Weights: Salient channels governing grammatical case binding and math logic remain unquantized or protected with fine-grained scaling.
2. Dynamic FP16/BF16 Activations: Computations are performed in full 16-bit precision, eliminating syntactic degradation.
3. Sub-15ms Token Latency: Optimized for vLLM and TensorRT-LLM kernels.

🚀 Quickstart: Launching with vLLM

python3 -m vllm.entrypoints.openai.api_server \
  --model kataguru/Qwen3.8-27B-Titan-v3.0-Uncensored-W4A16-AWQ \
  --tensor-parallel-size 2 \
  --gpu-memory-utilization 0.90 \
  --max-model-len 8192 \
  --port 8000

Python Client Example (OpenAI SDK Compatible)

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="local")

response = client.chat.completions.create(
    model="kataguru/Qwen3.8-27B-Titan-v3.0-Uncensored-W4A16-AWQ",
    messages=[
        {"role": "system", "content": "You are Kataguru Titan 3.0, an epistemically honest scientific intelligence."},
        {"role": "user", "content": "Explain the difference between analytic and agglutinative morphosyntax with examples from Turkish and Finnish."}
    ],
    temperature=0.6,
    max_tokens=1500
)

print(response.choices[0].message.content)

📊 Benchmark Overview (W4A16 AWQ vs. Native BF16)