Sorazium/Qwen3.8-Max-Distillation

Qwen3.8-Max Distillation

A quality-filtered derivative of Qwen3.8-Max Distillation 50K by r0b0tlab, prepared for local training and fine-tuning on consumer hardware.

This repository takes the original 49,772-example dataset and produces a substantially smaller training set focused on coding, reasoning, instruction following, and tool use.

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Terms and provenance notice — not cleared for unrestricted commercial/model-training use.

This dataset was generated through Alibaba Cloud Model Studio. The current Alibaba Cloud International Product Terms, §4.48(d)(v), restrict using Model Studio, its hosted models, or their outputs to train or develop products or services that compete with Alibaba Cloud or its affiliates unless expressly authorized.

In addition, at least 12,106 rows (24.3%) of the original dataset originate from explicitly noncommercial sources: Evol-Instruct-Code (CC BY-NC-SA 4.0) and SciQ (CC BY-NC 3.0). Other sources have heterogeneous attribution and share-alike obligations.

Public availability of the upstream repository is not a grant of rights.

Before training, redistribution, or commercial use, review the agreement applicable to your account/region/plan and every upstream source license.

See PROVENANCE.md for the detailed provenance and terms notice.

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Overview

For this project, the objective was not to preserve the entire original distribution. Instead, the dataset was filtered to create a smaller and more focused training corpus for a student model where training compute and training time are limited.

The final dataset contains:

Particulars	Original	Final
Examples	49,772	21,402
Completion tokens	47,646,032	15,526,829
Rows retained	100%	43.00%
Tokens retained	100%	32.59%

The filtering therefore removes 57.00% of the examples and 67.41% of the completion tokens while increasing the measured quality of the retained data:

Metric	Original	Final	Change
Row-weighted quality	8.5923	9.0315	+0.4392
Token-weighted quality	8.1170	8.9981	+0.8810

The quality score is a heuristic quality indicator, not a guarantee of factual correctness.

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Why was this dataset filtered?

The original dataset is a broad mixture intended to cover multiple capabilities. For this project, the intended student model is focused more heavily on coding, reasoning, instruction following, and tool use.

Training on the entire dataset would require approximately 47.6 million completion tokens.

Instead, the filtering process was designed to:

1. Remove the entire math domain.
2. Remove low-quality core code/reasoning examples.
3. Remove easy core examples.
4. Apply separate quality thresholds to instruction-following data.
5. Apply a separate quality threshold to tool-use data.
6. Preserve useful examples from the smaller instruction and tool-use domains.
7. Reduce the overall training-token requirement.
8. Increase the concentration of higher-scoring examples.

The final dataset contains 21,402 examples and 15.53 million completion tokens.

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Why was mathematics excluded?

Mathematics was deliberately excluded from this derivative dataset.

The original dataset contains 16,597 math examples, representing approximately 33.3% of the complete dataset.

Math is also responsible for approximately 12.88 million completion tokens in the original dataset.

The purpose of this derivative is to concentrate training on the capabilities currently targeted by the project:

• Coding
• General reasoning
• Instruction following
• Tool use

Keeping the math data would substantially increase the amount of training work while allocating a large portion of the training corpus to a capability that is not the primary focus of this particular training run.

What we gain by excluding math

• A substantially smaller training corpus.
• Lower total training-token requirements.
• Less training compute.
• Less training time.
• A greater concentration of coding and reasoning examples.
• More training capacity devoted to the intended target capabilities.

What we lose by excluding math

This is an intentional trade-off.

Removing all math examples means the resulting dataset provides substantially less direct mathematical supervision.

Potentially reduced coverage includes:

• Mathematical problem solving.
• Symbolic manipulation.
• Mathematical reasoning patterns.
• Math-specific chain-of-thought examples.
• Math benchmark-style problem solving.

Therefore, this dataset should not be considered a math-oriented training dataset.

The exclusion of math is a specialization decision for this particular training objective, not a claim that mathematical data is inherently undesirable.

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Filtering methodology

The original dataset was first analyzed to establish its domain, quality, difficulty, token, and source distributions. We then tested different quality thresholds and applied separate filters to each relevant domain.

For the final dataset:

• Math was excluded entirely.
• Code and reasoning were limited to medium/hard examples with quality_score >= 8.4.
• Instruction-following examples were retained at quality_score >= 7.5.
• Tool-use examples were retained at quality_score >= 6.5.
• The original train/validation/test boundaries were preserved.
• Each split was deterministically shuffled with seed 3407.

The final result is a smaller dataset of 21,402 examples and 15.53M completion tokens, focused on coding, reasoning, instruction following, and tool use.

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Python scripts

original-dataset-analsis.py

Analyzes the original dataset and reports its:

• Domain distribution
• Difficulty distribution
• Quality distribution
• Completion-token statistics
• Source distribution
• Train/validation/test split sizes
• Per-domain quality and token statistics

This establishes the baseline used for filtering.

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filter-original-dataset.py

Applies the final filtering strategy to the original dataset.

It:

• Excludes math.
• Filters code/reasoning by domain, difficulty, and quality.
• Applies separate thresholds to instruction and tool-use data.
• Preserves the original dataset splits.
• Shuffles each split with seed 3407.
• Saves the filtered Parquet files and dataset summary.

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analyze-pruned-dataset.py

Analyzes the resulting filtered dataset and compares it against the original dataset.

It verifies:

• Examples retained/removed
• Tokens retained/removed
• Quality changes
• Token-length statistics
• Final domain distribution
• Final dataset statistics