Qwen3-12B-20pct-Compressed-14B-EN-V1

Qwen3-12B — 20% Compressed from Qwen3-14B (English · Chat)

This repository is part of the Efficient and Robust AI System (E-AI) Project by Vincent-Daniel Yun, which publicly releases compressed large language models. This model is a compressed edition of Qwen/Qwen3-14B with 8 of 40 transformer layers removed (32 layers remain, ≈12B parameters), then instruction-tuned for chat so it generates coherent responses at lower memory and latency.

🔗 Project: https://www.worldwidedaniel.com/eai-project 📅 Release date: 2026-06-22 · Version: V1

⚠️ Language support — English only. This model is tuned on English data and is English-focused. Other languages (e.g., Korean, Chinese, Japanese) are not officially supported and may produce degraded or broken output. For open-domain factual questions, use retrieval (RAG) — the compressed model can hallucinate specific facts.

✅ What to use this model for

Our evaluations (full benchmarks vs the dense Qwen3-14B and an off-the-shelf Qwen3-8B) show this compressed model is best used as a fast, low-cost discrimination / classification engine — not as a free-form text generator.

✅ Recommended — matches or beats both the dense 14B and Qwen3-8B here:

• Text classification (sentiment, topic, intent routing)
• Safety & content moderation (toxicity / harmfulness detection — and it is less biased than the dense model)
• Reading comprehension & RAG reading (including multilingual reading)
• Medical / biomedical domain QA (MedQA, MedMCQA, PubMedQA, clinical knowledge, anatomy)
• Natural language inference, paraphrase / duplicate detection, word-sense
• Preference scoring / LLM-as-judge

❌ Not recommended — use the full model instead:

• Open-ended generation / chat, multi-step chain-of-thought reasoning, long-context tasks, math, and broad commonsense reasoning.

About E-AI

Modern AI is powerful but heavy. State-of-the-art models are enormous and their inference is slow — still far from human intuition, and far too slow and unreliable to trust in urgent, high-stakes moments.

Two obstacles stand between today's models and AI we can trust in the field. Individually, each model is too large and too slow to run where it is actually needed. Collectively, when many models or agents work together, a single faulty or adversarial member can quietly derail the whole system. E-AI attacks both — making every model lightweight and fast, and keeping teams of agents reliable even when some of them fail.

I started the E-AI (Efficient-AI) project to build compact yet powerful AI that can assist people in disaster scenarios — responding to dangerous accidents quickly and reliably when every second counts.

Method

The pruning method and the recovery method used to build this model are proprietary, undisclosed methods created by Vincent-Daniel Yun and are not released. The compressed model is then instruction-tuned for chat (distilled from the base model). Only the resulting model is shared here.

Results (measured)

All numbers below were measured by us. PPL is on a 2048-token context (lower is better); downstream tasks and MMLU are 0-shot accuracy via lm-eval-harness (higher is better).

Task-suitability evaluation (when to use this model)

Beyond standard benchmarks, we measured where this 12B compressed model is competitive with — or stronger than — the full Qwen3-14B (dense) and an off-the-shelf Qwen3-8B, on discrimination / classification tasks (full test sets; lm-eval-harness / likelihood scoring, 0-shot).

Real-world discriminative use-cases (vs dense 14B and Qwen3-8B)

Tasks where this 12B model outperforms Qwen3-8B (full test sets)

MMLU professional/STEM subjects where this model also beats Qwen3-8B: Anatomy +0.059, Electrical Engineering +0.055, Machine Learning +0.045, College Physics +0.029, plus College Medicine, College Biology, and Clinical Knowledge.

Takeaway. This model is well suited as a fast, lightweight discrimination engine — text classification, safety / content moderation, reading comprehension, medical-domain QA, and preference scoring — where it matches or exceeds both the dense 14B and a same-tier 8B model. (For open-ended long-form generation and broad commonsense reasoning, the dense model remains preferable.)

Model family — pick your size

All sizes in this release (click to open each model). Memory is measured peak inference (fp16 and 4-bit, batch 4 × 2048, single 48 GB GPU).

Compressed + Quantization — GPU memory vs dense

How little GPU memory each option needs relative to the original dense fp16 model (lower is better; combine compression with 4-bit for the largest savings).

Efficiency (measured, fp16, batch 4 × 2048, single 48 GB GPU)

Quantization

4-bit (and other) quantization can be used with this model — it is a standard Qwen3 architecture, so bitsandbytes 4-bit / 8-bit loading and other PTQ methods apply on top of the compression. Verified: this model loads and generates correctly in 4-bit, with peak inference memory ~12.25 GB (vs 13.9 GB for the dense model in 4-bit, and 33.5 GB for the dense model in fp16).

from transformers import AutoModelForCausalLM, BitsAndBytesConfig
m = AutoModelForCausalLM.from_pretrained(
    "daniel-eai/Qwen3-12B-20pct-Compressed-14B-EN-V1", trust_remote_code=True, device_map="cuda",
    quantization_config=BitsAndBytesConfig(load_in_4bit=True))

Usage — Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

m = AutoModelForCausalLM.from_pretrained(
    "daniel-eai/Qwen3-12B-20pct-Compressed-14B-EN-V1", trust_remote_code=True, dtype=torch.float16, device_map="cuda")
tok = AutoTokenizer.from_pretrained("daniel-eai/Qwen3-12B-20pct-Compressed-14B-EN-V1", trust_remote_code=True)

ids = tok("The capital of France is", return_tensors="pt").to("cuda")
print(tok.decode(m.generate(**ids, max_new_tokens=20)[0]))

trust_remote_code=True is required: the model ships a small custom decoder layer in modeling_qwen3_recovered.py.

Usage — vLLM

vLLM uses its own model implementations, so the custom decoder layer is loaded via a tiny plugin (provided in this repo under vllm_plugin/). Install it once, then serve normally:

pip install ./vllm_plugin   # from a checkout of this repo's vllm_plugin/ folder

from vllm import LLM, SamplingParams
llm = LLM(model="daniel-eai/Qwen3-12B-20pct-Compressed-14B-EN-V1", trust_remote_code=True, dtype="float16")
print(llm.generate(["The capital of France is"], SamplingParams(max_tokens=20))[0].outputs[0].text)

Other backends: TGI / SGLang / llama.cpp each use their own model graphs and would need an analogous custom decoder layer; they are not supported out of the box.

License

Apache-2.0, inherited from the base model Qwen/Qwen3-14B.

Acknowledgements

Sincere thanks to Prof. Sai Praneeth Karimireddy (University of Southern California) for his invaluable advice and feedback throughout this work, and to Prof. Sunwoo Lee (Inha University) for his guidance and support. We are also grateful to Alibaba (the Qwen team) for openly releasing the Qwen3-14B base model that made this work possible.