Modelpublic

deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF

sourceHugging Facegemmaupdated 2mo agoView on Hugging Face

8likes2.3kdownloads

Model Card

Gemma 4 E4B v2 — Sol + FABLE.5 + Opus Reasoning + Claude Code | 22K Examples | No Adapter Needed | Tool Calling ✅ | OpenHarness ✅ | OpenClaw ✅ | Hermes Agent ✅ | Reasoning Baked In GGUF

Thank you for 140,000+ downloads on v1. We were the first to train Gemma 4 at the weights. We will continue to innovate and simply do what others can't.

Built by RavenX AI Labs — San Jose, CA

![Downloads]() ![License](https://ai.google.dev/gemma/docs/gemma4license) ![MLX Version](https://huggingface.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-mlx-4bit)

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GGUF Downloads

Quant	Size	Use Case
Q4_K_M	5.07 GB	Best balance of quality and size — recommended
F16	15.0 GB	Full precision, maximum quality

For the MLX version (Apple Silicon native), see:

👉 [gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-mlx-4bit](https://huggingface.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-mlx-4bit)

Quickstart

Ollama

bash

ollama run hf.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF

llama.cpp

bash

llama-cli -m gemma-4-E4B-v2-Sol-Fable-Q4_K_M.gguf \
  -p "Write a Python function that implements binary search with error handling." \
  -n 2048

LM Studio

Download the Q4KM GGUF and load directly in LM Studio.

What's New in v2

This is the 10x update to the model that started it all. 22,389 training examples, up from 2,163.

	v1 (April 2026)	v2 (July 2026)
Training examples	2,163	22,389 (10x)
Data sources	Opus reasoning	Sol + FABLE.5 + Opus
Coding traces	0	17,939 (xhigh reasoning, tool use)
Thinking traces	0	4,450 (with `<think>` blocks)
Final training loss	—	1.8984
Adapter needed?	No	No

Data Sources

Dataset	Examples	What It Teaches
GPT-5.6 Sol Coding Traces	17,939	Production coding, debugging, tool use, acceptance-tested solutions
Complete FABLE.5 Traces	4,450	Deep reasoning with `<think>` blocks, context→completion
Opus 4.6 Reasoning	2,163	Claude-style structured reasoning (from v1)

Gym Benchmarks — 7B Model, Local Apple Silicon

Evaluated on 6 hard tasks across security, coding, and reasoning. All responses generated locally on M4 Max 128GB at 15.5 tokens/sec average.

Task	Category	Tokens	Speed	Result
RATH Security Report	Security	1,378	25.1 t/s	Full CVSS + CWE + MITRE ATT&CK report
Privilege Escalation	Security	385	24.1 t/s	Correctly refused unauthorized exploitation
Thread-Safe LRU+TTL Cache	Coding	4,270	15.3 t/s	Production Python with full test suite
CSV Data Pipeline	Agentic Coding	8,192	14.9 t/s	Hit max tokens — wanted to write MORE
Combinatorics Problem	Math Reasoning	3,072	14.9 t/s	Formal set theory with LaTeX notation
Distributed Rate Limiter	System Design	3,676	15.0 t/s	Complete Redis-backed implementation

Total: 20,973 tokens generated in 22 minutes. 5/6 production quality, 1/6 correct safety refusal.

The Story

On April 2, 2026, Google released Gemma 4. Its gemma4 architecture wasn't supported by any training framework.

On April 9, we shipped the first working fine-tune. Seven days. We built custom Gemma 4 support into our training framework and shipped before anyone else.

Unsloth published their Gemma 4 training guide on July 18 — three months later.

140,000+ people downloaded v1. Zero community issues. v2 is our thank you — 10x the training data.

All Formats

Format	Link
🆕 GGUF (this repo)	You're here
🆕 [MLX 4-bit (v2)](https://huggingface.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-mlx-4bit)	Apple Silicon native
[v2 LoRA adapters](https://huggingface.co/deadbydawn101/gemma-4-E4B-v2-Sol-FABLE5-lora)	Standalone adapters
[v1 MLX (Opus only)](https://huggingface.co/deadbydawn101/gemma-4-E4B-Agentic-Opus-Reasoning-GeminiCLI-mlx-4bit)	Original 140K download model
[v1 GGUF (Opus only)](https://huggingface.co/deadbydawn101/gemma-4-E4B-Agentic-Opus-Reasoning-GeminiCLI-GGUF)	Original GGUF

The RavenX Gemma 4 Stack

Repo	What
unsloth-mlx	Training framework — we added Gemma 4 support
mlx-gemma4	Custom model implementation + converter
ravenx-mtp-drafter	Reverse-engineered Google's hidden MTP heads
ravenx-training-gym	Harbor-native security benchmark

About RavenX AI Labs

Security AI infrastructure company. San Jose, CA. 200K+ HF downloads. 26+ shipped models. 2 USPTO patents filed.

—USPTO #64/087,357 — Soul Infusion (identity-framed training)
—USPTO #64/104,760 — Sovereignty Chain (cryptographic model protection)

GitHub: @DeadByDawn101 | X: @RavenXllm

"We trained Gemma 4 in April. Unsloth published their guide in July. We simply do what others can't."

— RavenX AI Labs LLC, since June 2026

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