MaxDevv/perfectblend-unified

PerfectBlend Unified Multi-Model Collation

A unified collation of 44 open PerfectBlend regenerated datasets across diverse reasoning and non-reasoning language models, aligned onto a single wide schema by exact model input prompts.

Dataset Overview

• Total Rows (Distinct Prompts): 6,866,154
• Total Columns: 46 (1 prompt column + 45 dataset response columns)
• Source Repositories Collated: 44 repositories from the PerfectBlend regeneration initiative
• Format: 64 Snappy-compressed Parquet shards (train-00000-of-00064.parquet .. train-00063-of-00064.parquet)
• Total Dataset Size: ~90 GB compressed

Schema & Field Specifications

prompt

• Type: String (canonical JSON array)
• Format: [{"role": "user"|"system"|"assistant", "content": "..."}]
• Description: Preserves the complete model input context without loss, including system prompts, multi-turn conversational history, and the final user instruction. Generated model completions are excluded from this column.

Dataset Response Columns

Each response column corresponds to one source dataset repository, sanitized using the rule [owner]-[repo] with special characters replaced by hyphens:

• Reasoning Models: Responses are standardized so reasoning traces appear inside <think>...</think> tags, followed by the final answer:

  <think>
  step-by-step reasoning
  </think>
  
  final answer
  

• Non-Reasoning Models: Contains the direct generated completion without <think> delimiters.
• Missing Responses: Represented as null where a model dataset does not contain an evaluation or completion for that specific prompt.

Column Distribution & Non-Null Coverage

Column Name	Non-Null Completions	Coverage %
3podi-perfectblend-qwen4B-regenerated	854,366	12.44%
AQ-MedAI-Kimi-K2-Instruct-open-perfectblend-regenerate	889,954	12.96%
AQ-MedAI-Ling-flash-2-0-open-perfectblend-regenerate	890,640	12.97%
Cornmonster-kimi-k25-perfectblend	390,143	5.68%
Dogacel-open-perfectblend-kimi-linear-regen	882,821	12.86%
JingweiSong-perfectblend-qwen3-8b-regen-no-thinking	890,660	12.97%
Rano23-qwen3-14b-perfectblend-regen	855,439	12.46%
TIE-Pilot-qwen3-4b-perfectblend-deepspec-rollout	890,660	12.97%
alice1001-open-perfectblend-qwen3-4b-regen	849,760	12.38%
alice1001-open-perfectblend-qwen3-8-27b-regen	890,455	12.97%
baseten-admin-gpt-oss120b-generated-perfectblend	890,514	12.97%
baseten-admin-perfectblend-Qwen3-235B-A22B-Instruct-2507-FP8-generated	890,658	12.97%
cm00cm-kimi-k2-7-open-perfectblend-regenerated	850,562	12.39%
frankleeeee-PerfectBlend-Regenerated-Llama-3-1-8B-Instruct	889,502	12.95%
frankleeeee-PerfectBlend-Regenerated-Llama-3-3-70B-Instruct	890,083	12.96%
frankleeeee-PerfectBlend-Regenerated-Llama-4-Maverick-17B-128E-Instruct	890,643	12.97%
frankleeeee-PerfectBlend-Regenerated-Llama-4-Scout-17B-16E-Instruct	884,101	12.88%
heiheiha798-perfectblend-regen-qwen3-8b-non-thinking	890,167	12.96%
jihwan1205-perfectblend-qwen3-8b-regen	888,221	12.94%
kimyuji-perfectblend-qwen3-8b-full-shuffled	878,018	12.79%
langq1225-perfectblend-qwen3-4b-regen	889,485	12.95%
linYD0718-open-perfectblend-qwen3-4b-nonthinking	855,134	12.45%
lukeysong-qwen3-235-regen-perfect-blend	886,822	12.92%
mgoin-open-perfectblend-glm5-2-regen	890,328	12.97%
mocoV3-Open-PerfectBlend-qwen3-5-4B-regenerate	613,192	8.93%
rasyosef-open-perfectblend-gemma-4-E4B-it	26,526	0.39%
rasyosef-open-perfectblend-phi-4-mini-instruct	106,561	1.55%
rasyosef-open-perfectblend-qwen-3-5-2b	98,675	1.44%
rasyosef-open-perfectblend-qwen3-1-7b	103,824	1.51%
rasyosef-open-perfectblend-qwen3-1-7b-nothink	51,850	0.76%
shanjiaz-glm-5-3-flash-open-perfectblend-regen-25pct	82,158	1.20%
shanjiaz-qwen3-5-4b-perfectblend-regen	1,196,729	17.43%
shuaills-perfect-blend-gptoss-20B	87,839	1.28%
sunnysetia-GLM-5-2-PerfectBlend-Regen-Sharded	682,572	9.94%
susun-123-perfectblend-qwen3-4b-regen	663,412	9.66%
xf15-ssd-perfectblend-qwen3-30b-a3b-regen	855,433	12.46%
xf15-ssd-perfectblend-qwen3-4b-regen	855,433	12.46%
xpzhao11-open-perfectblend-qwen38-flash-next-regen	855,462	12.46%
yosefw-open-perfectblend-gemma-4-E2B-it	99,468	1.45%
yosefw-open-perfectblend-llama-3-2-3b-instruct	115,251	1.68%
yosefw-open-perfectblend-qwen-3-1-7b	1,023	0.01%
yosefw-open-perfectblend-qwen-3-5-2b	53,316	0.78%
yuekai-perfectblend-qwen3-omni-sglang	850,109	12.38%
zhuyksir-perfect-blend-gptoss-20B-1M	833,943	12.15%
zhuyksir-perfectblend-Qwen3-235B-A22B-Instruct-2507-FP8-generated	548,472	7.99%

Quick Start & Usage

Hugging Face Datasets

from datasets import load_dataset

# Stream the dataset without downloading all shards at once
dataset = load_dataset("MaxDevv/perfectblend-unified", split="train", streaming=True)

for example in dataset.take(2):
    print("Prompt:", example["prompt"][:200])
    # Inspect specific model outputs
    for col, val in example.items():
        if col != "prompt" and val is not None:
            print(f"  {col}: {val[:150]}...")

DuckDB

SELECT prompt, "3podi-perfectblend-qwen4B-regenerated"
FROM read_parquet('hf://datasets/MaxDevv/perfectblend-unified/data/*.parquet')
WHERE "3podi-perfectblend-qwen4B-regenerated" IS NOT NULL
LIMIT 5;

Polars

import polars as pl

# Scan dataset with predicate and projection pushdown
lf = pl.scan_parquet("data/*.parquet")
sample = lf.select(["prompt", "rasyosef-open-perfectblend-qwen-3-5-2b"]).drop_nulls().limit(5).collect()
print(sample)

Data Sample

{
  "prompt": "[{\"content\": \"If John books a hotel room for 3 nights at a rate of $250 per night and receives a $100 discount, what is the total amount he needs to pay?\", \"role\": \"user\"}]",
  "3podi-perfectblend-qwen4B-regenerated": "John is booking a hotel room for **3 nights** at a rate of **$250 per night**, and he receives a **$100 discount**.\n\n### Step 1: Calculate the total cost without the discount...",
  "Cornmonster-kimi-k25-perfectblend": "<think>\nThe user wants to calculate the total amount John needs to pay for a hotel room.\n1. Cost per night = $250\n2. Number of nights = 3\n3. Total cost before discount = 3 * 250 = $750\n4. Discount = $100\n5. Total to pay = 750 - 100 = $650\n</think>\n\nTo find the total amount John needs to pay, we can break down the calculation into simple steps:\n\n1. **Calculate the cost before discount:**\n   $$\\text{Total cost} = 3 \\text{ nights} \\times \\$250/\\text{night} = \\$750$$\n\n2. **Apply the discount:**\n   $$\\text{Final amount} = \\$750 - \\$100 = \\$650$$\n\nJohn needs to pay **$650**."
}

Extraction & Normalization Reproducibility

• Extracted and normalized from source repositories in perfectblend_datasets.txt.
• Duplicate prompts deduplicated per dataset; cross-model responses aligned on canonical prompt JSON.