shreyvish5678/qwen3_5-a2d-stage1-sft

Qwen3.5 A2D Stage 1 SFT

Curated general supervised fine-tuning corpus for Qwen3.5 text-only A2D BD3LM experiments.

Files

• —train-*.parquet: canonical training split data (54 shard(s)).
• —stage1_sft_metadata.json: curation counts and source-level metadata.
• —metadata.json: duplicate of the stage metadata for quick inspection.

Loading

from datasets import load_dataset
dataset = load_dataset("parquet", data_files={"train": "train-*.parquet"})

Curation Details

Stage 1 SFT Dataset

Canonical artifact: /Users/shreyvishen/Projects/local_inference/auto-to-diff/datasets/stage1_sft.parquet Training export: /Users/shreyvishen/Projects/local_inference/auto-to-diff/datasets/stage1_sft_train.jsonl Metadata: /Users/shreyvishen/Projects/local_inference/auto-to-diff/datasets/stage1_sft_metadata.json

Stage 1 Filter Logic

• —Tulu-3 examples are scored for reasoning, coding, math, multi-turn structure, source affinity, and response length.
• —SmolTalk examples are scored for category balance, difficulty, quality, rewardmodelscore, conversation_tokens, and response length.
• —OpenThoughts examples are included in full after normalization into the shared messages schema.
• —Deduplication uses an exact normalized conversation fingerprint across all merged sources.
• —Parquet is the canonical artifact; JSONL is a derived training-ready export.

Schema

• —messages: list of {role, content, reasoning_content} structs compatible with Qwen chat templating.
• —source_dataset, source_split, source_id, source_name: provenance fields.
• —selection_bucket, category, difficulty, quality, reward_model_score, conversation_tokens: curation metadata.
• —num_turns, assistant_chars, reasoning_chars, selection_score, dedup_fingerprint: filtering and audit metadata.

Sources

• —allenai/tulu-3-sft-mixture
• —HuggingFaceTB/smoltalk (config: smol-magpie-ultra)
• —open-thoughts/OpenThoughts-114k