Iβm curious how it arrived https://github.com/space-bacon/SRT
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RiverRider
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Computational semiotics is empirically proven. It takes three to tango ππͺ©πΊ
Recent Activity
reacted to AxionLab-official's post with π about 3 hours ago
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! reacted to theirpost with π 1 day ago
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.Β
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
https://huggingface.co/spaces/RiverRider/srt-introspect
Repo
https://github.com/space-bacon/SRTOrganizations
replied to AxionLab-official's post about 3 hours ago
reacted to AxionLab-official's post with π about 3 hours ago
Post
3833
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!
- 7 replies
reacted to their post with π 1 day ago
Post
116
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.Β
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
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.Β
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
replied to their post 1 day 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 2 days ago
Post
116
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.Β
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
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.Β
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
reacted to their post with π₯ 3 days ago
Post
2785
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
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 4 days ago
Post
2785
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
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 π₯ 9 days ago
Post
4824
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 modelβs 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 modelβs 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 9 days ago
Post
4824
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 modelβs 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 modelβs 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
reacted to their post with π 11 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
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 15 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
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
reacted to their post with π₯ 15 days ago
Post
414
π§ New Space: MindReader-NLA β ask a frozen LM what it's thinking, in plain English.
A trained Activation Verbalizer (~5β13M params, frozen backbone) over Qwen-2.5-7B, Llama-3.2-3B, and Gemma-2-2B. Three demos in one Space:
Playground β 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 β stream a verbalization per token as the model writes, side-by-side with the generation.
Steer-by-Editing β 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
A trained Activation Verbalizer (~5β13M params, frozen backbone) over Qwen-2.5-7B, Llama-3.2-3B, and Gemma-2-2B. Three demos in one Space:
Playground β 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 β stream a verbalization per token as the model writes, side-by-side with the generation.
Steer-by-Editing β 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
posted an update 17 days ago
Post
414
π§ New Space: MindReader-NLA β ask a frozen LM what it's thinking, in plain English.
A trained Activation Verbalizer (~5β13M params, frozen backbone) over Qwen-2.5-7B, Llama-3.2-3B, and Gemma-2-2B. Three demos in one Space:
Playground β 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 β stream a verbalization per token as the model writes, side-by-side with the generation.
Steer-by-Editing β 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
A trained Activation Verbalizer (~5β13M params, frozen backbone) over Qwen-2.5-7B, Llama-3.2-3B, and Gemma-2-2B. Three demos in one Space:
Playground β 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 β stream a verbalization per token as the model writes, side-by-side with the generation.
Steer-by-Editing β 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
replied to their post 17 days ago
It takes three to tango. ππͺ©πΊ
reacted to their post with ππ₯ 18 days ago
Post
3304
Natural Language Autoencoders: A Window into Latent Structure
I introduced a concise mathematical formulation of the P versus NP question into the SRT-NLA-AV-v1 demonstration:
P vs NP asks whether every problem whose solution can be verified in polynomial time (NP) can also be solved in polynomial time (P). Integer factorization β given N = pΒ·q where p and q are large primes (p < q) β is in NP but widely believed not to be in P.
The resulting activation verbalization (best-of-N, reranked by AR fidelity) surfaced:
βThis article originally appeared in the August 2016 edition of CACM. A new method of proving computational hardness of problems, known as multilinearization, can improve efficiency, reduce complexity and simplify proofs. In this article, I describe multilinearization and its application to several key problems, from the discrete logarithm and factoring to RSA and elliptic-curve discrete logarithms.β
What emerges is not a literal restatement, but a structured articulation of the modelβs internal associations: hardness proofs, algebraic techniques, and the cryptographic implications that orbit this foundational question in computational complexity.
The demo offers a compelling interface for exploring these latent representations.
Explore it here:
RiverRider/srt-nla-av-v1-demo
Recommended: Best-of-N sampling with round-trip evaluation for highest fidelity.
I introduced a concise mathematical formulation of the P versus NP question into the SRT-NLA-AV-v1 demonstration:
P vs NP asks whether every problem whose solution can be verified in polynomial time (NP) can also be solved in polynomial time (P). Integer factorization β given N = pΒ·q where p and q are large primes (p < q) β is in NP but widely believed not to be in P.
The resulting activation verbalization (best-of-N, reranked by AR fidelity) surfaced:
βThis article originally appeared in the August 2016 edition of CACM. A new method of proving computational hardness of problems, known as multilinearization, can improve efficiency, reduce complexity and simplify proofs. In this article, I describe multilinearization and its application to several key problems, from the discrete logarithm and factoring to RSA and elliptic-curve discrete logarithms.β
What emerges is not a literal restatement, but a structured articulation of the modelβs internal associations: hardness proofs, algebraic techniques, and the cryptographic implications that orbit this foundational question in computational complexity.
The demo offers a compelling interface for exploring these latent representations.
Explore it here:
RiverRider/srt-nla-av-v1-demo
Recommended: Best-of-N sampling with round-trip evaluation for highest fidelity.
- 1 reply
posted an update 19 days ago
Post
3304
Natural Language Autoencoders: A Window into Latent Structure
I introduced a concise mathematical formulation of the P versus NP question into the SRT-NLA-AV-v1 demonstration:
P vs NP asks whether every problem whose solution can be verified in polynomial time (NP) can also be solved in polynomial time (P). Integer factorization β given N = pΒ·q where p and q are large primes (p < q) β is in NP but widely believed not to be in P.
The resulting activation verbalization (best-of-N, reranked by AR fidelity) surfaced:
βThis article originally appeared in the August 2016 edition of CACM. A new method of proving computational hardness of problems, known as multilinearization, can improve efficiency, reduce complexity and simplify proofs. In this article, I describe multilinearization and its application to several key problems, from the discrete logarithm and factoring to RSA and elliptic-curve discrete logarithms.β
What emerges is not a literal restatement, but a structured articulation of the modelβs internal associations: hardness proofs, algebraic techniques, and the cryptographic implications that orbit this foundational question in computational complexity.
The demo offers a compelling interface for exploring these latent representations.
Explore it here:
RiverRider/srt-nla-av-v1-demo
Recommended: Best-of-N sampling with round-trip evaluation for highest fidelity.
I introduced a concise mathematical formulation of the P versus NP question into the SRT-NLA-AV-v1 demonstration:
P vs NP asks whether every problem whose solution can be verified in polynomial time (NP) can also be solved in polynomial time (P). Integer factorization β given N = pΒ·q where p and q are large primes (p < q) β is in NP but widely believed not to be in P.
The resulting activation verbalization (best-of-N, reranked by AR fidelity) surfaced:
βThis article originally appeared in the August 2016 edition of CACM. A new method of proving computational hardness of problems, known as multilinearization, can improve efficiency, reduce complexity and simplify proofs. In this article, I describe multilinearization and its application to several key problems, from the discrete logarithm and factoring to RSA and elliptic-curve discrete logarithms.β
What emerges is not a literal restatement, but a structured articulation of the modelβs internal associations: hardness proofs, algebraic techniques, and the cryptographic implications that orbit this foundational question in computational complexity.
The demo offers a compelling interface for exploring these latent representations.
Explore it here:
RiverRider/srt-nla-av-v1-demo
Recommended: Best-of-N sampling with round-trip evaluation for highest fidelity.
- 1 reply
reacted to their post with π about 1 month ago
Post
697
zooL4nD3r v0.1 demo
Translate a passage across 961 learned discourse communities
5 Funny Communities:
Reddit Shitposters
X Doomers & Doomer Memers
AI Waifu Enthusiasts
Flat Earth Discord Trolls
Crypto Degens on Solana
5 Serious Communities:
Semiotic Reflexive Transformer
Academic Philosophers (Peircean)
Cognitive Science Researchers
Policy Wonks / Think Tank Analysts
Legal Scholars (Constitutional Originalists)
RiverRider/zooL4nD3r-demo
Translate a passage across 961 learned discourse communities
5 Funny Communities:
Reddit Shitposters
X Doomers & Doomer Memers
AI Waifu Enthusiasts
Flat Earth Discord Trolls
Crypto Degens on Solana
5 Serious Communities:
Semiotic Reflexive Transformer
Academic Philosophers (Peircean)
Cognitive Science Researchers
Policy Wonks / Think Tank Analysts
Legal Scholars (Constitutional Originalists)
RiverRider/zooL4nD3r-demo
posted an update about 1 month ago
Post
697
zooL4nD3r v0.1 demo
Translate a passage across 961 learned discourse communities
5 Funny Communities:
Reddit Shitposters
X Doomers & Doomer Memers
AI Waifu Enthusiasts
Flat Earth Discord Trolls
Crypto Degens on Solana
5 Serious Communities:
Semiotic Reflexive Transformer
Academic Philosophers (Peircean)
Cognitive Science Researchers
Policy Wonks / Think Tank Analysts
Legal Scholars (Constitutional Originalists)
RiverRider/zooL4nD3r-demo
Translate a passage across 961 learned discourse communities
5 Funny Communities:
Reddit Shitposters
X Doomers & Doomer Memers
AI Waifu Enthusiasts
Flat Earth Discord Trolls
Crypto Degens on Solana
5 Serious Communities:
Semiotic Reflexive Transformer
Academic Philosophers (Peircean)
Cognitive Science Researchers
Policy Wonks / Think Tank Analysts
Legal Scholars (Constitutional Originalists)
RiverRider/zooL4nD3r-demo
reacted to HannesVonEssen's post with π₯ about 1 month ago
Post
238
π£ I made a visualizer for Hugging Face models: https://hfviewer.com
β¨ Simply paste a Hugging Face URL to get an interactive visualization of the architecture!
π The recent Qwen3.6-27B model as an example: https://hfviewer.com/Qwen/Qwen3.6-27B
Feel free to try it out and give me feedback on how it can be improved! β€οΈ
β¨ Simply paste a Hugging Face URL to get an interactive visualization of the architecture!
π The recent Qwen3.6-27B model as an example: https://hfviewer.com/Qwen/Qwen3.6-27B
Feel free to try it out and give me feedback on how it can be improved! β€οΈ
- 1 reply