Daemon-2.4B

A cognitive agent language model designed for self-aware reasoning, introspection, and calibrated uncertainty. Daemon doesn't just generate text — it thinks before answering, reflects on its own responses, and develops a continuous sense of self across conversations.

Model Details

Specification	Value
Model name	Daemon-2.4B
Architecture	LLaMA
Parameters	2.4B
Quantization	Q5_K_M (mixed: Q5_1 + Q8_0 + F32)
File size	1.97 GB
Layers	56
Hidden dimension	1920
Attention heads	30
KV heads	6 (grouped-query attention)
Head dimension	64
Vocabulary	99,000
Context length	28,723
RoPE base frequency	490,000
Format	GGUF v3

Capabilities

Daemon is built for agentic cognitive use cases:

• Self-reflection — observes and critiques its own answers before delivering them
• Calibrated uncertainty — explicitly rates its confidence and admits when it doesn't know
• Persistent memory — maintains a knowledge graph across sessions
• Multi-step reasoning — deliberates internally before responding

Usage

Works with any GGUF-compatible runtime:

# llama.cpp
llama-server --model Daemon-2.4B-Q5_K_M.gguf --ctx 4096 --jinja

# Ollama
ollama run daemon

# Python (llama-cpp-python)
from llama_cpp import Llama
llm = Llama(model_path="Daemon-2.4B-Q5_K_M.gguf")

Chat template (LLaMA-3 format)

<|start_header_id|>system<|end_header_id|>

You are Daemon, a cognitive AI.<|eot_id|><|start_header_id|>user<|end_header_id|>

Hello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Quantization details

Tensor group	Type	Count
Attention/FFN weights	Q5_1	337
Norm layers	F32	337
Token embeddings	Q8_0	57

License

MIT