unsloth/GLM-5.3-Flash-GGUF
Text Generation • 321B • Updated • 45.9k • 300
Jeremy Haschal
JermemyHaschal
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Running Qwen3.8-27B Q4_K_S on an RTX 3060 12GB with 96K Context + MTP (~10 t/s)
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#61 opened 15 days ago
by 
Hjx2
The model is really slow due to unclear reason
🚀 2
19
#32 opened 16 days ago
by 
SNMetamorph
Reasoning harder to read?
2
#52 opened 16 days ago
by 
BewunderterAdler
First!
❤️🔥 19
4
#1 opened 17 days ago
by 
mtecnic
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model 18 days ago
1-bit Qwen3.8 GGUF output example!
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#4 opened 18 days ago
by 
danielhanchen
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models about 1 month ago
35b vs 27b?
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#2 opened about 1 month ago
by JermemyHaschal
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Knowledge loss?
#4 opened about 2 months ago
by JermemyHaschal
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reacted to pranavupadhyaya52's post with 🤗🔥 about 2 months ago
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After several weeks of experimenting, debugging, and iterating, I am excited to share WikiSmartBotLM.
WikiSmartBotLM is a compact decoder only language model built from the ground up as an educational and practical project. The goal was not simply to train another language model, but to create one that is easy to understand, modify, and experiment with while following many of the architectural ideas used in modern LLMs.
The model is built on a custom Transformer architecture featuring Rotary Positional Embeddings, RMSNorm, SwiGLU feed forward layers, grouped query attention, and an efficient autoregressive decoder optimized for local inference.
The repository includes the complete model implementation, configuration files, tokenizer integration, training pipeline, inference scripts, checkpoint conversion utilities, and examples that demonstrate how each component works together. Whether you want to understand the forward pass, train your own model, or build applications on top of WikiSmartBotLM, everything is designed to be approachable.
You can directly run the model via the models Huggingfaces space, which I've included in the post.
Model Repository: pranavupadhyaya52/Wiki-SmartBotLM-Instruct
I hope WikiSmartBotLM becomes a useful resource for anyone who enjoys learning by building. Feedback, issues, feature requests, and contributions are always welcome.
https://huggingface.co/spaces/pranavupadhyaya52/WikiSmartBot
WikiSmartBotLM is a compact decoder only language model built from the ground up as an educational and practical project. The goal was not simply to train another language model, but to create one that is easy to understand, modify, and experiment with while following many of the architectural ideas used in modern LLMs.
The model is built on a custom Transformer architecture featuring Rotary Positional Embeddings, RMSNorm, SwiGLU feed forward layers, grouped query attention, and an efficient autoregressive decoder optimized for local inference.
The repository includes the complete model implementation, configuration files, tokenizer integration, training pipeline, inference scripts, checkpoint conversion utilities, and examples that demonstrate how each component works together. Whether you want to understand the forward pass, train your own model, or build applications on top of WikiSmartBotLM, everything is designed to be approachable.
You can directly run the model via the models Huggingfaces space, which I've included in the post.
Model Repository: pranavupadhyaya52/Wiki-SmartBotLM-Instruct
I hope WikiSmartBotLM becomes a useful resource for anyone who enjoys learning by building. Feedback, issues, feature requests, and contributions are always welcome.
https://huggingface.co/spaces/pranavupadhyaya52/WikiSmartBot
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