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Model: dystrio/Qwen2.5-7B-Instruct-sculpt-throughput
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---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
language:
- en
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- dystrio
- sculpt
- pruned
- compressed
- efficient
- dense
- runtime-agnostic
- no-custom-kernels
- hf-drop-in
- drop-in-replacement
- smaller
- faster
- qwen
datasets:
- wikitext
model-index:
- name: Dystrio Sculpt (Qwen2.5-7B-Instruct Throughput)
results:
- task:
type: text-generation
dataset:
name: WikiText-103 (validation)
type: wikitext
metrics:
- name: perplexity
type: perplexity
value: 23.2366
- name: ppl_ratio
type: ppl_ratio
value: 1.8644
---
# dystrio/Qwen2.5-7B-Instruct-sculpt-throughput
> **30% smaller, +34% faster prefill, drop-in replacement. No custom kernels. No runtime changes.**
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 **Throughput** tier of [Qwen 2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
## Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("dystrio/Qwen2.5-7B-Instruct-sculpt-throughput", torch_dtype="bfloat16", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("dystrio/Qwen2.5-7B-Instruct-sculpt-throughput")
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 [Qwen 2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on A100 80GB, bf16:
| Model | PPL | PPL Ratio | Weights (GB) | Chat Prefill TPS | RAG TTFT p95 (ms) | Decode TPS |
|-------|-----|-----------|-------------|------------------|-------------------|------------|
| **Baseline** | 12.4633 | 1.0 | 14.185191 | 11510.6 | 117.869 | 71.1 |
| **sculpt-default** | 12.334 | 0.9896 | 12.964976 | 12352.7 | 110.714 | 72.7 |
| **sculpt-production** | 21.9239 | 1.7591 | 10.596324 | 14700.3 | 95.291 | 73.5 |
| **sculpt-throughput** | 23.2366 | 1.8644 | 9.950328 | 15386.6 | 91.914 | 73.3 |
### Key Metrics (this model)
| Metric | Value |
|--------|-------|
| **Weights memory** | 9.950328 GB (30% smaller) |
| **PPL ratio** | 1.8644 |
| **Chat prefill TPS** | 15386.6 (+34%) |
| **RAG TTFT p95** | 91.914 ms (-22%) |
| **Decode TPS** | 73.3 (flat) |
| **Parameters** | 5.34B |
## All Sculpt Tiers
| Tier | HuggingFace | Size | PPL Ratio | Use Case |
|------|-------------|------|-----------|----------|
| default | [dystrio/Qwen2.5-7B-Instruct-sculpt-default](https://huggingface.co/dystrio/Qwen2.5-7B-Instruct-sculpt-default) | 12.964976 GB | 0.9896 | Zero-regret: quality preserved, smaller footprint |
| production | [dystrio/Qwen2.5-7B-Instruct-sculpt-production](https://huggingface.co/dystrio/Qwen2.5-7B-Instruct-sculpt-production) | 10.596324 GB | 1.7591 | Practical savings with modest quality tradeoff |
| throughput | [dystrio/Qwen2.5-7B-Instruct-sculpt-throughput](https://huggingface.co/dystrio/Qwen2.5-7B-Instruct-sculpt-throughput) 👈 **this model** | 9.950328 GB | 1.8644 | Maximum usable compression for speed/edge |
## 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](https://github.com/EleutherAI/lm-evaluation-harness) on A100-80GB, bf16, zero-shot.
| Benchmark | Baseline | This Model | Delta |
|-----------|:--------:|:----------:|:-----:|
| ARC-Challenge | 0.5282 | 0.3567 | -0.1715 |
| HellaSwag | 0.6204 | 0.4321 | -0.1883 |
| MMLU | 0.7176 | 0.4104 | -0.3072 |
| TruthfulQA MC2 | 0.6475 | 0.4523 | -0.1952 |

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
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"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
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"rope_type": "default"
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"tie_word_embeddings": false,
"transformers_version": "5.3.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
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