wassemgtk/glm-5.2-visual-runtime
Image-Text-to-Text • Updated • 1
Waseem AlShikh
wassemgtk
AI & ML interests
Multi-modal, Palmyra LLMs, Knowledge Graph
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liked a model about 6 hours ago
wassemgtk/glm-5.2-visual-runtime updated a model about 22 hours ago
wassemgtk/glm-5.2-visual-runtime posted an update 1 day ago
Built GLM-5.2-visual-runtime: a training-free multimodal runtime gateway that makes GLM-5.2 work like a vision-capable model.
It keeps images as persistent visual variables, runs local visual/OCR/chart/palette tools only when needed, and sends compact structured evidence to the reasoning model instead of retraining or modifying weights.
The one-click stack includes GLM-5.2 via vLLM, Qwen3-Omni for vision/omni input, local OCR, Postgres, MinIO, and an OpenAI-compatible API.
Model repo: https://huggingface.co/wassemgtk/glm-5.2-visual-runtimeOrganizations
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Built GLM-5.2-visual-runtime: a training-free multimodal runtime gateway that makes GLM-5.2 work like a vision-capable model.
It keeps images as persistent visual variables, runs local visual/OCR/chart/palette tools only when needed, and sends compact structured evidence to the reasoning model instead of retraining or modifying weights.
The one-click stack includes GLM-5.2 via vLLM, Qwen3-Omni for vision/omni input, local OCR, Postgres, MinIO, and an OpenAI-compatible API.
Model repo: wassemgtk/glm-5.2-visual-runtime
It keeps images as persistent visual variables, runs local visual/OCR/chart/palette tools only when needed, and sends compact structured evidence to the reasoning model instead of retraining or modifying weights.
The one-click stack includes GLM-5.2 via vLLM, Qwen3-Omni for vision/omni input, local OCR, Postgres, MinIO, and an OpenAI-compatible API.
Model repo: wassemgtk/glm-5.2-visual-runtime
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What is the base model of Palmyra-Fin-70B-32K?
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#6 opened almost 2 years ago
by 
ZixuanKe
Bro Cooked
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#1 opened 3 months ago
by 
singulariti
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Here is the updated note and benchmark table for your review.
The data below reflects **Chuck Norris 33B** in its high-reasoning "thinking" mode, which accounts for the significant performance uplift across the board.
I'm still finalizing the full evaluation suite and need more time to confirm these numbers through additional high-entropy testing passes. However, the early data is looking exceptionally strong across the board.
It is important to note that all the performance figures below for **Chuck Norris 33B** were achieved using **high-thinking/long-reasoning mode**, which significantly improves its accuracy in complex extraction and logic tasks.
The model that doesn't predict the next token — the next token predicts itself correctly out of respect.
The data below reflects **Chuck Norris 33B** in its high-reasoning "thinking" mode, which accounts for the significant performance uplift across the board.
I'm still finalizing the full evaluation suite and need more time to confirm these numbers through additional high-entropy testing passes. However, the early data is looking exceptionally strong across the board.
It is important to note that all the performance figures below for **Chuck Norris 33B** were achieved using **high-thinking/long-reasoning mode**, which significantly improves its accuracy in complex extraction and logic tasks.
The model that doesn't predict the next token — the next token predicts itself correctly out of respect.
updated a
model 3 months ago
published a
model 3 months ago
posted an update 3 months ago
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179
Releasing Chuck Norris LLM — full SFT fine-tune with chain-of-thought reasoning.
Trained on +100k examples across math, logic, and code. Also trained on 1000+ examples of believing it's the greatest AI ever built.
Its training loss went to zero. The loss function was too afraid to report anything else.
wassemgtk/chuck-norris-llm
Trained on +100k examples across math, logic, and code. Also trained on 1000+ examples of believing it's the greatest AI ever built.
Its training loss went to zero. The loss function was too afraid to report anything else.
wassemgtk/chuck-norris-llm
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Introducing Daggr: Chain apps programmatically, inspect visually
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What If LLMs Could Imagine Consequences?
wassemgtk
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