ProCreations/grug-35b-mtp-gguf
36B • Updated • 411 • 3
Jeremy Haschal
JermemyHaschal
AI & ML interests
None yet
Recent Activity
liked a model about 23 hours ago
ProCreations/grug-35b-mtp-gguf new activity 1 day ago
ProCreations/grug-35b-mtp-gguf:35b vs 27b? liked a model 12 days ago
unsloth/Ornith-1.0-397B-GGUFOrganizations
None yet
liked a
model about 23 hours ago
35b vs 27b?
1
#2 opened 1 day ago
by JermemyHaschal
liked a
model 12 days ago
upvoted a changelog 21 days ago
Hugging Face Changelog
Filter Models page by Hardware
• 221
Knowledge loss?
#4 opened 21 days ago
by JermemyHaschal
liked a
model 23 days ago
reacted to pranavupadhyaya52's post with 🤗🔥 23 days ago
Post
3625
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
- 1 reply
liked a
model about 1 month ago
CrispASR repo is gone - What to do?
2
#1 opened 4 months ago
by JermemyHaschal
FP8 version
#1 opened 4 months ago
by JermemyHaschal
reacted to albertvillanova's post with 🤗 5 months ago
Post
3041
🚀 TRL v0.29.0 introduces trl-training: an agent-native training skill.
This makes the TRL CLI a structured, agent-readable capability, allowing AI agents to reliably execute training workflows such as:
- Supervised Fine-Tuning (SFT)
- Direct Preference Optimization (DPO)
- Group Relative Policy Optimization (GRPO)
We’re excited to see what the community builds on top of this.
If you’re working on AI agents, alignment research, or scalable RL training infrastructure: give TRL v0.29.0 a try! 🤗
The future of ML tooling is agent-native.
🔗 https://github.com/huggingface/trl/releases/tag/v0.29.0
This makes the TRL CLI a structured, agent-readable capability, allowing AI agents to reliably execute training workflows such as:
- Supervised Fine-Tuning (SFT)
- Direct Preference Optimization (DPO)
- Group Relative Policy Optimization (GRPO)
We’re excited to see what the community builds on top of this.
If you’re working on AI agents, alignment research, or scalable RL training infrastructure: give TRL v0.29.0 a try! 🤗
The future of ML tooling is agent-native.
🔗 https://github.com/huggingface/trl/releases/tag/v0.29.0
reacted to OzTianlu's post with 🤗 5 months ago
Post
3467
O(1) inference is the foundational design of Spartacus-1B-Instruct 🛡️ !
NoesisLab/Spartacus-1B-Instruct
We have successfully replaced the KV-cache bottleneck inherent in Softmax Attention with Causal Monoid State Compression. By defining the causal history as a monoid recurrence, , the entire prefix is lossily compressed into a fixed-size state matrix per head.
The technical core of this architecture relies on the associativity of the monoid operator:
Training: parallel prefix scan using Triton-accelerated JIT kernels to compute all prefix states simultaneously.
Inference: True sequential updates. Memory and time complexity per token are decoupled from sequence length.
Explicit Causality: We discard RoPE and attention masks. Causality is a first-class citizen, explicitly modeled through learned, content-dependent decay gates.
Current zero-shot benchmarks demonstrate that Spartacus-1B-Instruct (1.3B) is already outperforming established sub-quadratic models like Mamba-1.4B and RWKV-6-1.6B on ARC-Challenge (0.3063). Recent integration of structured Chain-of-Thought (CoT) data has further pushed reasoning accuracy to 75%.
The "Spartacus" era is about scaling intelligence, not the memory wall ♾️.
NoesisLab/Spartacus-1B-Instruct
We have successfully replaced the KV-cache bottleneck inherent in Softmax Attention with Causal Monoid State Compression. By defining the causal history as a monoid recurrence, , the entire prefix is lossily compressed into a fixed-size state matrix per head.
The technical core of this architecture relies on the associativity of the monoid operator:
Training: parallel prefix scan using Triton-accelerated JIT kernels to compute all prefix states simultaneously.
Inference: True sequential updates. Memory and time complexity per token are decoupled from sequence length.
Explicit Causality: We discard RoPE and attention masks. Causality is a first-class citizen, explicitly modeled through learned, content-dependent decay gates.
Current zero-shot benchmarks demonstrate that Spartacus-1B-Instruct (1.3B) is already outperforming established sub-quadratic models like Mamba-1.4B and RWKV-6-1.6B on ARC-Challenge (0.3063). Recent integration of structured Chain-of-Thought (CoT) data has further pushed reasoning accuracy to 75%.
The "Spartacus" era is about scaling intelligence, not the memory wall ♾️.
Comparison with Rivermind-Lux-12B-v1b?
4
#1 opened 6 months ago
by JermemyHaschal
reacted to Reubencf's post with 🔥 6 months ago
Post
2225
📢 New release! World_events Dataset now available featuring global events spanning 2023 through 2025
🌍 https://huggingface.co/collections/Reubencf/world-events
🚀 2026 dataset dropping soon
🌍 https://huggingface.co/collections/Reubencf/world-events
🚀 2026 dataset dropping soon
- 1 reply
Joytag no longer works
4
#14 opened 9 months ago
by 
bro123123
ERROR: Could not detect model type of: D:\...\Chroma1-Radiance-v0.1.safetensors
7
#2 opened 11 months ago
by 
Viennar
updated a
model 12 months ago
published a
model 12 months ago