Dystrio Sculpt structurally compresses transformer models, producing dense models that load with standard transformers — no custom code, no new ops, no deployment friction.

This is the Experimental tier of Qwen2.5 3B Instruct.

Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("dystrio/Qwen2.5-3B-Instruct-sculpt-experimental", torch_dtype="bfloat16", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("dystrio/Qwen2.5-3B-Instruct-sculpt-experimental")

inputs = tokenizer("The future of AI inference is", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Benchmark Results

All tiers compiled from Qwen2.5 3B Instruct on A100 80GB, bf16:

Model	PPL	PPL Ratio	Weights (GB)	Chat Prefill TPS	RAG TTFT p95 (ms)	Decode TPS
Baseline	14.0033	1.0	5.748009	22079.6	75.483	59.4
sculpt-default	15.5137	1.1079	5.220665	23360.8	73.544	59.0
sculpt-production	18.9373	1.3523	4.956993	24367.4	69.025	59.0
sculpt-throughput	22.8847	1.6342	4.640587	25556.6	68.084	59.0
sculpt-experimental	31.3266	2.2371	4.165977	26731.9	66.499	59.5

Key Metrics (this model)

Metric	Value
Weights memory	4.165977 GB (28% smaller)
PPL ratio	2.2371
Chat prefill TPS	26731.9 (+21%)
RAG TTFT p95	66.499 ms (-12%)
Decode TPS	59.5 (flat)
Parameters	2.24B

All Sculpt Tiers

Tier	HuggingFace	Size	PPL Ratio	Use Case
default	dystrio/Qwen2.5-3B-Instruct-sculpt-default	5.220665 GB	1.1079	Zero-regret: quality preserved, smaller footprint
production	dystrio/Qwen2.5-3B-Instruct-sculpt-production	4.956993 GB	1.3523	Practical savings with modest quality tradeoff
throughput	dystrio/Qwen2.5-3B-Instruct-sculpt-throughput	4.640587 GB	1.6342	Maximum usable compression for speed/edge
experimental	dystrio/Qwen2.5-3B-Instruct-sculpt-experimental 👈 this model	4.165977 GB	2.2371	Boundary exploration, maximum structural compression

What is Dystrio Sculpt?

Dystrio Sculpt compiles transformer models into smaller, faster variants. Output models:

Are dense (not sparse) — standard architecture, fewer parameters
Load with standard HuggingFace Transformers — no custom code needed
Require no custom kernels and no runtime changes
Work as a one-step compile before deployment
Stack with quantization (AWQ, GPTQ, GGUF) for compound savings

Compatibility

✅ HuggingFace Transformers
✅ vLLM
✅ TGI (Text Generation Inference)
✅ llama.cpp / GGUF conversion
✅ AWQ / GPTQ quantization
✅ Any framework that loads standard safetensors

Benchmark Environment

GPU: NVIDIA A100-SXM4-80GB
dtype: bf16
Torch: 2.10.0+cu128
Transformers: 5.3.0
Deterministic: True
Single-GPU, standard HuggingFace Transformers, no custom kernels.

Metric Definitions

PPL ratio: WikiText-103 perplexity relative to baseline. <1.0 = quality improved.
Prefill TPS: Tokens per second during prompt encoding (higher = faster).
TTFT p95: Time to first token at 95th percentile (lower = faster).
Decode TPS: Tokens per second during generation (higher = faster).
Weights (GB): Model parameter memory (deterministic, runtime-independent).

Citation

bibtex

@misc{dystrio_sculpt_2026,
  title={Dystrio Sculpt: Structural Compilation for Transformer LLMs},
  author={Dystrio},
  year={2026},
  url={https://huggingface.co/dystrio}
}

Downstream Benchmarks (lm-eval)

Evaluated with lm-eval-harness on A100-80GB, bf16, zero-shot.

Benchmark	Baseline	This Model	Delta
ARC-Challenge	0.4573	0.2944	-0.1629
HellaSwag	0.5635	0.3860	-0.1775
MMLU	0.6545	0.4080	-0.2465
TruthfulQA MC2	0.5874	0.4534	-0.1340