DeepakSinghworks/blip-demo

Blip for Generation

Overview

A research-oriented Blip prototype targeting Generation. The included xlarge setup documents defaults and file formats without presenting unverified performance numbers.

Expect rough edges — this is a working sketch, not a polished release.

Repository status

• Single-file implementation; the __main__ block runs a smoke test.
• config.json and training_args.json hold the generated settings.
• model.safetensors is freshly initialized — plumbing, not results.
• No benchmark numbers are reported on purpose.

Limitations

The initialization checkpoint has not been trained or audited for robustness, fairness, or domain transfer. The implementation should be treated as an experimental starting point. Results from a future trained checkpoint must be documented separately from the defaults shipped here.

Architecture

Item	Value
Architecture	Blip
Scale	xlarge
Attention	grouped query
Fusion	cross attention
Activation	gelu
Normalization	scalenorm

Default experiment recipe

The included configuration uses lamb with a linear warmup schedule. These are starting values in the script, not evidence of a completed run. For a meaningful evaluation, train all baselines with the same data exposure, tuning budget, and random seeds.

Citation

If this repository is useful for your work, a link back is appreciated. No formal citation is required yet.

Evaluation guidance

• pick a task dataset such as a task-specific held-out set
• fix a tuning budget in advance
• compare against a capacity-matched baseline
• publish raw logs next to any claim

Files

• pipeline.py — primary artifact
• README.md — this documentation
• config.json — architecture configuration
• training_args.json — default experiment settings
• model.safetensors — initialization checkpoint

License

Released under unknown. Review the source-data terms separately when this repository is used with external datasets.