Hugging Face's logo

open/ acc
community
Activity Feed Request to join this org

AI & ML interests

None defined yet.

Recent Activity
bofenghuang  authored a paper about 1 month ago
Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks
bofenghuang  authored a paper about 1 month ago
Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining
shahrukhx01  authored a paper about 2 months ago
Transformers for molecular property prediction: Domain adaptation efficiently improves performance
View all activity
prithivMLmods 
posted an update 3 days ago
view post
Post
3128
Made a demo for Text/Image-to-3D Video and Image-to-3D Video asset generation using TRELLIS.2. It is paired with Z-Image-Turbo to accelerate the input image preprocessing pipeline, streamlining the Image-to-3D workflow. The generated GLB (GL Transmission Format) files are converted into MP4 (MPEG-4) videos, making them easy to preview and share. Try it now on Hugging Face Spaces.🤗

➠ Image-to-3D-Video-Asset-Generator: prithivMLmods/Image-to-3D-Video-Asset-Generator
➠ collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
➠ github: https://github.com/PRITHIVSAKTHIUR/Image-to-3D-Video-Asset-Generator

⤷ To learn more, visit the app page or the respective model pages.
julien-c 
posted an update 5 days ago
view post
Post
3882
who's working on an NVFP4 version of Kimi-K3?
  • 4 replies
·
Nymbo 
posted an update 8 days ago
view post
Post
5431
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.

CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.

See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2

Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
  • 6 replies
·
RiverRider 
posted an update 8 days ago
view post
Post
156
Train Once, Read Everywhere

Paper title:
Train Once, Read Everywhere: Substrate Invariance of the Linearly Readable Structure in Frozen Language Models

Paper URL:
https://github.com/space-bacon/SRT/blob/main/arxiv_program/paper.md

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

The consolidated findings of the SRT research program are now available.

The program treats frozen production-scale language models as substrates whose internal states carry structure that small, inspectable instruments can read. Results include:

- A ~12 M-parameter adapter that surfaces per-token semiotic signals from a frozen 7 B backbone with zero cross-entropy degradation
- An activation verbalizer that recovers text from single hidden states up to a calibrated paraphrase ceiling
- Linear readout ports spanning dense 3 B models to 94-layer 235 B mixture-of-experts models
- A 22 MB linear head that gives a frozen multimodal chat model image-to-text retrieval performance matching fully trained 2018 dual encoders on the COCO benchmark

The central claim is substrate invariance. The readable structure is a stable property of the model class. A head trained once on one host reads, with no retraining and at most a 42 KB recalibration, across:

- Hosts ten times smaller (31 B → 3 B)
- 4-bit weight precision
- Entirely different silicon and kernels (CUDA/bf16 to Apple Silicon/MLX-Q4)

Deployment tiers differ in latency and cost, never in capability.

All instruments, measurement protocols, invariance evidence, negative results, and artifacts are in the repository.
RiverRider 
posted an update 30 days ago
view post
Post
161
🔮 Gemma-4-31B-it SRT-Sunstone

A read-out that reads images — trained only on words.

A 12.3M side-channel head on a frozen google/gemma-4-31B-it, taught meaning from text alone — to separate discourse communities in the residual stream. It never saw a single picture in training. Hand it a picture and it names what the image means, with zero image training.

Cross-modal transfer. Give it a photo and it lands next to the right words: bicycle → bicycle, rose → flower, dog → pet. It groups images by what they mean, not how they look: image→referent kNN 0.64 against chance 0.10.

Why this is a semiotic read-out, not an image classifier. A classifier is trained on labelled images and learns a fixed map from pixels to a closed label set; it only knows the labels it was shown. This read-out is different in kind. It never saw an image in training — it was trained only on text, to separate discourse communities in the residual stream of a frozen gemma-4-31B. It can interpret a picture because gemma-4 already fuses image and word into one representational stream, and the read-out taps the shared interpretant: the meaning a sign carries, whatever form it arrived in. So it does not classify the image. It tells you what the image means to a system that learned meaning from words — a transfer across modality, from a head that was never cross-trained. That is the result.

Each picture gets two readings: the words it means — the load-bearing evidence — and the discourse it evokes, the nearest of 35 communities it learned from text. Read the second as a flavour, not a category: cars into the automotive community, deer and mushrooms into gardening, cats and dogs into the cozy-domestic communities. Never a class label.

Scored offline through a frozen google/gemma-4-31B-it (62.5 GB, too large to run live)

Try it: RiverRider/srt-sunstone
Model: RiverRider/Gemma-4-31B-it-SRT-Sunstone
Code: https://github.com/space-bacon/SRT
RiverRider 
posted an update about 2 months ago
view post
Post
277
SRT Showcase: Watch a Frozen Model Think, Token by Token

A frozen Qwen-2.5-7B now narrates its own interpretation in real time. SRT Showcase is the most complete public demonstration of computational semiotics to date, running the backbone with the SRT Adapter and Activation Verbalizer. As the model generates, every token is tinted by its predictive effort, and at the highest-effort positions the Verbalizer decodes the hidden state directly into natural language. You see what the model is representing at the exact moment its computation is most active.

Every verbalization is validated, not asserted. Each decoded thought is re-encoded and compared back to the original hidden state, and the reconstruction closely approximates it. The "this is what the model was thinking" claim carries its own fidelity badge. This is grounded introspection, not plausible narration.

The Showcase goes further than the trace. An A/B panel runs the same prompt with SRT injection on and off under an identical seed, so the side-channel's effect is directly observable. A curated gallery walks through confident recall, false premises, misconceptions, reasoning pivots, genuine uncertainty, and safety boundaries. Live entropy and divergence meters track the crystallization process token by token, with per-layer traces and reflexivity estimates on hover.

None of the backbone weights are touched. The entire mechanism is a lightweight reflexive layer over a frozen model, which is why the same read-out heads already port from Qwen-2.5-7B up to a 235B Mixture of Experts. Frozen models can now be verbalized in real time. No retraining. No fine-tuning. No black box.

First request is a brief cold start while ZeroGPU acquires a GPU. Bring your own prompt.

Try it: RiverRider/srt-showcase

Repository: https://github.com/space-bacon/SRT
prithivMLmods 
posted an update about 2 months ago
view post
Post
7897
Wan2.2-I2V-Fast with highly upscaled sequential frame sampling is now available as a Spaces demo, built using Wan2.2-I2V and FLUX.2-Klein. Try the demo using the links below.👇

➠ wan2.2-i2v-fast : prithivMLmods/wan2.2-i2v-fast
➠ github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
➠ collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

⤷ To learn more, visit the app page or the respective model pages.
cfahlgren1 
submitted a paper to Daily Papers about 2 months ago
RiverRider 
posted an update about 2 months ago
view post
Post
208
Real-Time Introspection for Qwen3 235B

An SRT adapter is now available for Qwen3 235B, a large open Mixture of Experts model. This adapter adds a lightweight monitoring layer that provides signals about the models internal state during generation without modifying any of its original weights.

The adapter can indicate whether the model is operating in a more stable or more divergent internal condition. It also estimates how self referential the models processing is at each step and tracks shifts in representation across layers. In addition it produces information about the kind of discourse style the model appears to be using at any moment.

This is the first adapter of its kind released on a model of this scale with fully open weights. It was trained in a read only configuration so the base model remains unchanged. The strongest result is in regime detection where the adapter achieved very high accuracy in distinguishing between different internal operating states on held out data.

For researchers focused on model interpretability this provides a practical way to inspect internal dynamics at frontier scale. For those working in semiotics it offers empirical access to how processes of meaning and interpretation unfold inside large contemporary language models.

Model: RiverRider/srt-adapter-qwen3-235b

Repository and documentation: https://github.com/space-bacon/SRT
RiverRider 
posted an update about 2 months ago
view post
Post
5142
ATTENTION: The SRT-Introspect framework moves past surface-level output commentary by supplying real-time natural language interpretations of a model’s latent states. These verbalizations are validated, not merely asserted, through a round-trip reconstruction procedure. Natural language descriptions derived from hidden activations are passed through an encoder that reconstructs the corresponding activation vector; the recovered vector closely approximates the original. High reconstruction fidelity indicates that the verbalizations encode genuine structural information about the internal state rather than offering plausible but ungrounded speculation.

This validated introspection converts what has often remained a theoretical or post-hoc exercise into a practical instrument for auditing model behavior, diagnosing failure modes, and providing high-level semantic guidance—all without modifying the base model or incurring the costs of fine-tuning. Because the mechanism operates on frozen configurations, it can be applied to production systems where any change to weights or architecture is undesirable. Thank you for your attention.

Run a trace: RiverRider/srt-introspect

Repo: https://github.com/space-bacon/SRT
RiverRider 
posted an update about 2 months ago
view post
Post
3631
This is not a pipe.

Everyone is born a semiotician, no one is born knowing it. Go easy on yourself (and me) for not understanding this yet.

Computational semiotics is now an empirical study.

LLMs are not proto-minds. They are verifiably semiotic infrastructure.

This repository (or attached demo) can show you, in real time, how any frozen model (Qwen for demo) arrives at any answer by reading its latent states directly during generation.

Any questions?

RiverRider/srt-introspect

Repo:

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

Grok insist my intro is condescending … This is certainly true, as is the statement in my condescended opinion. I expect heat for it, let’s think this through?
RiverRider 
posted an update about 2 months ago
view post
Post
204
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
·
RiverRider 
posted an update 2 months ago
view post
Post
2823
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
prithivMLmods 
posted an update 2 months ago
Ritvik19 
authored a paper 2 months ago
prithivMLmods 
posted an update 2 months ago
view post
Post
6291
PiD — Pixel Diffusion Decoder Image Edit Upscale and Image Generation Upscale, an all-in-one demo, is now live on Spaces! Great improvements in realism-based image generation and editing are powered by FLUX.2-Klein, while image generation is paired with Z-Image, and upscaling is enabled by default!

🤗 Space: prithivMLmods/PiD-Image-Upscaler
🔗 Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

🤗 > To learn more, visit the app page or the respective model pages.
RiverRider 
posted an update 2 months ago
view post
Post
4849
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
prithivMLmods 
posted an update 2 months ago
view post
Post
5623
I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached “Space of the Week”! A few Spaces are still topping the list even after many months.

Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.

Thanks for all the community support! 🤗❤️

🔗 Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
  • 4 replies
·
RiverRider 
posted an update 2 months ago
view post
Post
425
🧠 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
rajkumarrawal 
submitted a paper to Daily Papers 2 months ago