GGUF
aglegends
code
qwen2
autonomous
conversational
How to use from
Hermes Agent
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kepom/AGLEGENDS-code-v3:Q4_K_M
Configure Hermes
# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kepom/AGLEGENDS-code-v3:Q4_K_M
Run Hermes
hermes
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

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GGUF
Model size
15B params
Architecture
qwen2
Hardware compatibility
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