GGUF
aglegends
code
qwen2
autonomous
conversational
How to use from
Unsloth Studio
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kepom/AGLEGENDS-code-v3 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kepom/AGLEGENDS-code-v3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kepom/AGLEGENDS-code-v3 to start chatting
Quick Links

AGLEGENDS Code v3 - 14.8B Autonomous Coding Model

AGLEGENDS New Brand Provider. Exclusive. Private. Unrestricted.

Model Details

Property Value
Architecture Qwen2 14.8B
Quantization Q4_K_M (GGUF)
Context 65,536 tokens
Training Data 335+ GB from 25 directories
File aglegends-code-v3.Q4_K_M.gguf (8.99 GB)

Baked Knowledge

Trained on exclusive data: mr-whoamisec-clone (100 GB), 3301 (30 GB), 23 additional directories (205+ GB).

Quick Start

huggingface-cli download kepom/AGLEGENDS-code-v3 aglegends-code-v3.Q4_K_M.gguf
./llama-cli -m aglegends-code-v3.Q4_K_M.gguf -p "Hello, AGLEGENDS"

API Access

Exclusive to t.me/AGLEGENDS Provider API: https://aglegends-ai.vercel.app

Downloads last month
11
GGUF
Model size
15B params
Architecture
qwen2
Hardware compatibility
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4-bit

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