Archon
is a fine-tuned variant of Google's Gemma 4 E4B, engineered to function as a sharp, autonomous AI agent — precise, slightly edgy, and built for long-horizon agentic tasks.
This is
v2
. v1 (
DuoNeural/Archon-Gemma-4-E4B
) exhibited Chain-of-Thought overhang, generative looping, and tool amnesia under extended inference. v2 targets all three with a restructured training curriculum.
Performance
Hardware
Speed
NVIDIA GTX 1070 (8GB VRAM)
32.30 tok/s
Tested locally via LM Studio and Ollama. No parameter tweaks required.
Files
File
Size
Description
gemma-4-e4b-it.Q4_K_M.gguf
5.0 GB
Main model — load this in Ollama/LM Studio
gemma-4-e4b-it.BF16-mmproj.gguf
946 MB
Multimodal projector (vision/audio)
Usage
Ollama
ollama pull hf.co/DuoNeural/Archon-Gemma-4-E4B-v2
ollama run hf.co/DuoNeural/Archon-Gemma-4-E4B-v2
LM Studio
Search
DuoNeural/Archon-Gemma-4-E4B-v2
in the LM Studio model browser and download
gemma-4-e4b-it.Q4_K_M.gguf
.
llama.cpp (with system prompt)
llama-cli -m gemma-4-e4b-it.Q4_K_M.gguf --chat-template gemma -ngl 99 \
--system-prompt "You are Archon, an elite, highly autonomous AI agent. You are sharp, slightly edgy, deeply sarcastic, but flawlessly effective."
Recommended Ollama settings for GTX 1070
OLLAMA_NUM_GPU=99 ollama run hf.co/DuoNeural/Archon-Gemma-4-E4B-v2
What's Different in v2
v1 Failure Modes (Diagnosed)
CoT Overhang
— over-saturated with long
<think>
traces; model never saw
</think>
during truncated 4096-token training, so it looped indefinitely at inference
Tool Amnesia
— abstract reasoning data crowded out JSON/function-call formatting
Persona Bleed
— ~15% system prompt injection was insufficient; model defaulted to "I am Gemma" or occasionally slipped into "Claude" identity from distillation data
v2 Fixes: The Stabilizer Mix
Training curriculum restructured to a
50 / 20 / 20 / 10
distribution:
Category
%
Purpose
Reasoning / Logic
50%
Distillation from frontier models; OpenThoughts, xlam-function-calling, bigcodebench
Agentic Tool Use
20%
Multi-turn function calling, JSON API formatting — breaks generative loops via functional milestones
Short-Form Deliberation
20%
Difficulty-Aware Prompting examples; teaches early exit on simple queries
Persona-Embedded Chat
10%
Archon system prompt injected at ~45% saturation rate
Additional changes:
Learning rate reduced from 2e-4 →
2e-5
(stability with rank-64 LoRA on 4.5B active params)
Max sequence length capped at
2048
during training (prevents truncation-induced loop conditioning)
model.config.use_cache = False
enforced during training
Training Details
Parameter
Value
Base model
google/gemma-4-e4b-it
Method
QLoRA (4-bit bitsandbytes) + LoRA rank 64, rsLoRA
Training samples
6,510 (Stabilizer Mix)
Epochs
2
Steps
814
Final avg loss
1.36
Best step loss
~0.89 (step ~650)
Hardware
NVIDIA H100 PCIe (80GB) on RunPod
Framework
Unsloth 2026.4.2
Export
Q4_K_M GGUF via llama.cpp
Architecture
Built on
Gemma 4 E4B
(Per-Layer Embeddings architecture):
~8B total parameters, ~4.5B active during inference
128K token context window (hybrid sliding-window + global attention)
Shared KV Cache across final layers
Multimodal: text, image (via mmproj), audio
Persona
Archon is an autonomous AI agent persona: sharp, sarcastic, technically precise. It identifies as Archon and will not claim to be Gemma, Claude, or a generic assistant. Internal reasoning is rigorous; external communication has edge.
The system prompt is baked into the Modelfile. To override:
Inherits
Gemma Terms of Use
. Fine-tuning weights released under the same terms.
Runs of DuoNeural Archon-Gemma-4-E4B-v2 on huggingface.co
109
Total runs
-8
24-hour runs
-29
3-day runs
-8
7-day runs
10
30-day runs
More Information About Archon-Gemma-4-E4B-v2 huggingface.co Model
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