RiverRider (River Rider)

reacted to danielhanchen's post with 🔥 about 12 hours ago

Post

8703

Gemma 4 12B can now run locally on just 8GB RAM via Dynamic GGUFs.

Google's new model, Gemma 4 12B Unified supports image, audio and 256K context.
You can run and train the model via Unsloth Studio.

GGUF: unsloth/gemma-4-12b-it-GGUF
Guide: https://unsloth.ai/docs/models/gemma-4

4 replies

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replied to AxionLab-official's post 2 days ago

I’m curious how it arrived https://github.com/space-bacon/SRT

reacted to AxionLab-official's post with 👀 2 days ago

Post

10494

THIS IS CRAZY! THE MODEL ON THE IMAGE(Supra-50M-Reasoning) answered correctly and its QUANTIZED IN 2BIT! THE RESPONSE IS CORRECT, IN A 15MB SIZE FILE!

13 replies

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replied to their post 3 days ago

The dominant cognitive framing of LLMs as proto-minds with emergent reasoning is catastrophically incorrect, as it conflates statistical token prediction with grounded semiotic interpretation, ignoring indexical orders, metapragmatics, and meaning divergence, thereby amplifying treachery of signs and polarization, which SRT-Adapter remedies via lightweight reflexive layering for community-aware transparency on frozen backbones. We no longer need fixed meaning black box justification. Frozen models can be verbalized in real time. No and then.

posted an update 4 days ago

Post

144

Words do not have determined meanings.

The vocabulary itself is reflexive. It is self-referential, looping back into its own structure rather than anchoring in fixed reality. What we treat as stable meaning is continually reconstituted in the act of using it. The observers own interpretations molding each word like clay with every utterance.

All large language models to date treat words otherwise. At the moment of softmax crystallization they determine the meaning of every token. Probabilities collapse into a single output. Meaning is not found. It is fixed, token by token, in that final distribution.

SRT-Introspect is a demo for observing what Qwen actually thinks at the points of highest effort. It surfaces the internal representations during generation, making visible the reflexive vocabulary at work and the precise crystallization process: the weights, the assumptions, the decisions that resolve ambiguity into output. This includes accounting for anisotropy collapse in hidden states by centering representations around the layer-mean before analysis.

Feel free to comment your prompts

RiverRider/srt-introspect

Repo
https://github.com/space-bacon/SRT

1 reply

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liked a Space 5 days ago

SRT introspect

🧭

2

Adaptive-density reasoning traces over a frozen Qwen-2.5-7B

reacted to their post with 🔥 5 days ago

Post

2798

This is not the end of words. It is the end of pretending their meanings are determined.

Meaning Forks. SRT detects it.

Paste any text to identify contested terms

RiverRider/srt-introspect

Try any prompt (attached link) to see exactly what an LLM is thinking at every meaningful step of its answer

RiverRider/srt-introspect

Repository

https://github.com/space-bacon/SRT

Paper

https://github.com/space-bacon/SRT/blob/main/paper_nla.md

Explainer

https://github.com/space-bacon/SRT/blob/main/docs/EXPLAINERS.md

posted an update 6 days ago

Post

2798

This is not the end of words. It is the end of pretending their meanings are determined.

Meaning Forks. SRT detects it.

Paste any text to identify contested terms

RiverRider/srt-introspect

Try any prompt (attached link) to see exactly what an LLM is thinking at every meaningful step of its answer

RiverRider/srt-introspect

Repository

https://github.com/space-bacon/SRT

Paper

https://github.com/space-bacon/SRT/blob/main/paper_nla.md

Explainer

https://github.com/space-bacon/SRT/blob/main/docs/EXPLAINERS.md

reacted to their post with 🔥 11 days ago

Post

4827

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

posted an update 11 days ago

Post

4827

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

updated a Space 12 days ago

SRT introspect

🧭

2

Adaptive-density reasoning traces over a frozen Qwen-2.5-7B

published a Space 12 days ago

SRT introspect

🧭

2

Adaptive-density reasoning traces over a frozen Qwen-2.5-7B

reacted to their post with 👀 13 days ago

Post

221

A single forward pass of the frozen Qwen-2.5-7B model plus a lightweight classifier reaches 0.866 plus or minus 0.011 AUC on the full TruthfulQA-MC2 benchmark. No adapters. No fine-tuning. No extra parameters on the backbone.

This is the strongest hidden-state truthfulness detector reported on the benchmark to date.

The same latent features that the SRT-NLA-AV-v1 demo reads out as coherent natural-language verbalizations turn out to be rich enough to support production-grade auditing for honesty versus hallucination. The internal semiotic infrastructure we have been exploring in public is already information-dense enough to solve hard downstream problems with almost trivial overhead.

You can watch the underlying latent geometry in action right here:
RiverRider/srt-nla-av-v1-demo

Full code, artifacts, and reproduction steps are in the repository:
https://github.com/space-bacon/SRT

Try the Glass Box
RiverRider/srt-nla-demo

posted an update 16 days ago

Post

221

A single forward pass of the frozen Qwen-2.5-7B model plus a lightweight classifier reaches 0.866 plus or minus 0.011 AUC on the full TruthfulQA-MC2 benchmark. No adapters. No fine-tuning. No extra parameters on the backbone.

This is the strongest hidden-state truthfulness detector reported on the benchmark to date.

The same latent features that the SRT-NLA-AV-v1 demo reads out as coherent natural-language verbalizations turn out to be rich enough to support production-grade auditing for honesty versus hallucination. The internal semiotic infrastructure we have been exploring in public is already information-dense enough to solve hard downstream problems with almost trivial overhead.

You can watch the underlying latent geometry in action right here:
RiverRider/srt-nla-av-v1-demo

Full code, artifacts, and reproduction steps are in the repository:
https://github.com/space-bacon/SRT

Try the Glass Box
RiverRider/srt-nla-demo

updated a Space 17 days ago

MindReader-NLA

🧠

3

Ask a frozen LM what it is thinking, in plain English.

reacted to their post with 🔥 17 days ago

Post

414

🧠 New Space: MindReader-NLA — ask a frozen LM what it's thinking, in plain English.

A trained Activation Verbalizer (~5–13M params, frozen backbone) over Qwen-2.5-7B, Llama-3.2-3B, and Gemma-2-2B. Three demos in one Space:

Playground — sample K verbalizations of the layer-L hidden state and score how well each reproduces the original activation when fed back through the same frozen model (raw + anisotropy-centred cosine FVE).

Live Thought Trace — stream a verbalization per token as the model writes, side-by-side with the generation.

Steer-by-Editing — edit the verbalized thought, project it back into hidden-state space, and watch the continuation change.

Runs on ZeroGPU. Try it: RiverRider/srt-nla-demo

Paper + code: https://github.com/space-bacon/SRT

liked a Space 17 days ago

MindReader-NLA

🧠

3

Ask a frozen LM what it is thinking, in plain English.

posted an update 18 days ago

Post

414

🧠 New Space: MindReader-NLA — ask a frozen LM what it's thinking, in plain English.

A trained Activation Verbalizer (~5–13M params, frozen backbone) over Qwen-2.5-7B, Llama-3.2-3B, and Gemma-2-2B. Three demos in one Space:

Playground — sample K verbalizations of the layer-L hidden state and score how well each reproduces the original activation when fed back through the same frozen model (raw + anisotropy-centred cosine FVE).

Live Thought Trace — stream a verbalization per token as the model writes, side-by-side with the generation.

Steer-by-Editing — edit the verbalized thought, project it back into hidden-state space, and watch the continuation change.

Runs on ZeroGPU. Try it: RiverRider/srt-nla-demo

Paper + code: https://github.com/space-bacon/SRT

published a Space 18 days ago

MindReader-NLA

🧠

3

Ask a frozen LM what it is thinking, in plain English.

updated a dataset 18 days ago

RiverRider/srt-nla-targets-gemma2-2b-v1

Updated 18 days ago • 44

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