Joy Caption Beta One
🖼
1.06k
Generate custom captions, tags, or prompts for any image
Dan PRO
Daankular
AI & ML interests
None yet
Recent Activity
liked a Space about 4 hours ago
fancyfeast/joy-caption-beta-one liked a Space about 12 hours ago
zai-org/CogVideoX-5B-Space liked a model about 13 hours ago
Hcompany/Holo-3.1-9BOrganizations
liked a
Space about 4 hours ago
liked a
Space about 12 hours ago
liked a
model about 13 hours ago
updated a
Space about 14 hours ago
liked a
model 1 day ago
liked a
model 2 days ago
liked a
Space 2 days ago
liked a
model 5 days ago
liked a
Space 5 days ago
liked a
model 5 days ago
updated a
Space 5 days ago
published a
Space 5 days ago
reacted to RiverRider's post with 🔥 5 days ago
Post
4801
SRT-introspect: Live Token-by-Token Readout of LLM Internal Reasoning
I have released SRT-introspect, a new public demonstration that makes the hidden reasoning process of a frozen large language model visible in real time.
The interface runs a Qwen-2.5-7B backbone equipped with the SRT Adapter and Activation Verbalizer. As the model generates each token, the system continuously measures divergence across attention heads, identifies high-signal moments, and translates the corresponding hidden-state object representations into natural-language verbalizations. You see exactly what the model is internally representing at the precise points where its computation is most active, complete with divergence scores, reflexivity estimates, and per-layer traces.
This is not a summary of the final output. It is a direct window into the model’s latent conceptual landscape, showing the dominant training-data attractors that activate even when the prompt asks for first-principles reasoning. The adaptive scheduler concentrates verbalizations precisely where the real internal work occurs, turning what used to be opaque black-box generation into observable, analyzable data.
The result is the clearest public demonstration yet that modern LLMs possess a rich, structured semiotic infrastructure that can now be audited without retraining or fine-tuning.
Try it:
RiverRider/srt-introspect
I have released SRT-introspect, a new public demonstration that makes the hidden reasoning process of a frozen large language model visible in real time.
The interface runs a Qwen-2.5-7B backbone equipped with the SRT Adapter and Activation Verbalizer. As the model generates each token, the system continuously measures divergence across attention heads, identifies high-signal moments, and translates the corresponding hidden-state object representations into natural-language verbalizations. You see exactly what the model is internally representing at the precise points where its computation is most active, complete with divergence scores, reflexivity estimates, and per-layer traces.
This is not a summary of the final output. It is a direct window into the model’s latent conceptual landscape, showing the dominant training-data attractors that activate even when the prompt asks for first-principles reasoning. The adaptive scheduler concentrates verbalizations precisely where the real internal work occurs, turning what used to be opaque black-box generation into observable, analyzable data.
The result is the clearest public demonstration yet that modern LLMs possess a rich, structured semiotic infrastructure that can now be audited without retraining or fine-tuning.
Try it:
RiverRider/srt-introspect
liked a
model 6 days ago
liked a
Space 6 days ago
updated a
bucket 6 days ago
published a
bucket 6 days ago
liked a
Space 6 days ago
liked a
Space 7 days ago
updated a
Space 8 days ago