Qwen/Qwen3.8-27B
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Weaves, Wires, and Morphisms: Formalizing and Implementing the Algebra of Deep Learning updated a dataset about 20 hours ago
bench-labs/slop-classificationOrganizations
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Introducing Unsloth Desktop 🦥
The first desktop app to run and train models locally.
• Open-source. Runs on Mac, Windows and Linux
• Supports MLX, diffusion image/video, audio, GGUF
• Connect Claude Code and Codex to local LLMs
• 50% more accurate, self-healing tool calls + sandboxed code exec
• Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac
• Train models 2× faster with 70% less VRAM
• Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF)
• Use Unsloth’s OpenAI-compatible API and cloud models
• Securely deploy LLMs remotely and access anywhere
Unsloth Desktop is now available on http://unsloth.ai
and GitHub.
GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/desktop
The first desktop app to run and train models locally.
• Open-source. Runs on Mac, Windows and Linux
• Supports MLX, diffusion image/video, audio, GGUF
• Connect Claude Code and Codex to local LLMs
• 50% more accurate, self-healing tool calls + sandboxed code exec
• Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac
• Train models 2× faster with 70% less VRAM
• Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF)
• Use Unsloth’s OpenAI-compatible API and cloud models
• Securely deploy LLMs remotely and access anywhere
Unsloth Desktop is now available on http://unsloth.ai
and GitHub.
GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/desktop
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bench-labs/GCTokenizer-v1 , a multilingual tokenizer which does not require a training corpus
bench-labs developed **GCTokenizer-v1**, which is a multi-lingual tokenizer
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world
General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer
We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world
General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer
We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently
posted an update 4 days ago
Post
2146
bench-labs/GCTokenizer-v1 , a multilingual tokenizer which does not require a training corpus
bench-labs developed **GCTokenizer-v1**, which is a multi-lingual tokenizer
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world
General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer
We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world
General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer
We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently