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
Thank you for 140,000+ downloads. 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
What's New in v2
This is the 10x update to the model that started it all. Same architecture, same no-adapter-needed experience, 10x the training data.
| 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 |
| Downloads | 140,000+ | You're early |
| 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.
For a 7B model running locally on a laptop, this is what's possible when you train with the right data.
Quickstart
pip install mlx-vlm
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
model, processor = load("deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-mlx-4bit")
prompt = apply_chat_template(processor, model.config,
"Write a Python function that implements binary search with error handling.",
num_images=0)
output = generate(model, processor, prompt, max_tokens=2048)
print(output.text)
CLI
mlx_lm.generate \
--model deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-mlx-4bit \
--prompt "Design a rate limiter service with Redis backend" \
--max-tokens 2048
The Story
On April 2, 2026, Google released Gemma 4. Its gemma4 architecture wasn't supported by any training framework — not mlx-lm, not mlx-vlm, not Unsloth, not HuggingFace transformers.
On April 9, we shipped the first working fine-tune. Seven days. We built custom Gemma 4 support into our training framework, figured out that Gemma 4 must be treated as a VLM even for text-only training, and shipped.
Unsloth published their Gemma 4 training guide on July 18 — three months later.
140,000+ people downloaded v1. Zero community issues. No adapter needed. Just download and run.
v2 is our thank you. 10x the training data, production coding capabilities, deep reasoning traces, and the same frictionless experience.
How It Was Made
Training: DeadByDawn101/unsloth-mlx — our fork with Gemma 4 support we added ourselves. FastVisionModel routes text-only training through the VLM pipeline, handling multimodal weight management automatically while LoRA touches only text transformer layers.
Fusing: Custom surgical fuse script. Dequantize each LoRA-targeted weight from the 4-bit base, add the scaled LoRA delta (lora_b.T @ lora_a.T * scale), requantize back to 4-bit. Same format, same key count, same config. No adapter needed.
Hardware: M4 Max 128GB MacBook Pro. 2 hours 4 minutes training time.
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 for speculative decoding |
| ravenx-training-gym | Harbor-native security benchmark |
Other Formats
| Format | Link |
|---|---|
| v1 MLX (Opus only) | gemma-4-E4B-Agentic-Opus-Reasoning-GeminiCLI-mlx-4bit |
| v2 LoRA adapters | gemma-4-E4B-v2-Sol-FABLE5-lora |
| GGUF (v1) | gemma-4-E4B-Agentic-Opus-Reasoning-GeminiCLI-GGUF |
About RavenX AI Labs
Security AI infrastructure company. San Jose, CA. 200K+ HF downloads. 24+ 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