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weijianzhg/youtube-summariser-qwen3.5-4b

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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YouTube Summariser Qwen3.5 4B

This is a Tuned Tensor fine-tune of Qwen/Qwen3.5-4B for generating concise Markdown summaries of YouTube transcripts.

It is intended for local/offline use with the `youtube-summariser` CLI and similar transcript summarization workflows.

Training Details

—Base model: Qwen/Qwen3.5-4B
—Run ID: 728676d4-e2b9-44b4-8958-af6cebeddc19
—Precision: bf16
—Max sequence length: 2048
—Training rows: 82
—Final training loss: 4.099260542127821
—Finished: 2026-06-03T08:44:10Z

The downloaded Tuned Tensor archive SHA-256 was:

text

6690a070de3888b61b91f351a3cd3c6efb361e9ae1d59aa84abfdf7976795b39

Usage

Install recent local model dependencies first. This checkpoint was exported with Transformers 5.6.2 metadata.

bash

pip install 'transformers>=5.6.2' torch accelerate safetensors huggingface_hub

Download locally for the CLI:

bash

huggingface-cli download weijianzhg/youtube-summariser-qwen3.5-4b \
  --local-dir ./youtube-summariser-qwen3.5-4b

youtube-summariser 'https://youtu.be/VIDEO_ID' \
  --local-model ./youtube-summariser-qwen3.5-4b

For long videos, use the CLI's automatic long-video mode so full transcripts are chunked and synthesized instead of truncated:

bash

youtube-summariser 'https://youtu.be/VIDEO_ID' \
  --local-model ./youtube-summariser-qwen3.5-4b \
  --summary-strategy auto

Limitations

This is a small task-specific fine-tune trained on a small dataset. It is useful for testing local YouTube summarization behavior, but long videos should be summarized with a chunked/map-reduce workflow rather than a single prompt. Generated summaries may omit details or make mistakes; check important claims against the original transcript.