---

library_name: gguf

license: gemma

license_link: https://ai.google.dev/gemma/docs/gemma_4_license

pipeline_tag: text-generation

tags:

- gguf

- gemma4

- quantized

- reasoning

- chain-of-thought

- sol

- fable

- opus

- sft

- fused

- ravenx

- tool-calling

- function-calling

- agentic

- coding

- ollama

- llama-cpp

- lm-studio

base_model: deadbydawn101/gemma-4-E4B-mlx-4bit

base_model_relation: finetune

language:

- en

datasets:

- greghavens/gpt-5.6-sol-coding-and-debugging-traces

- Crownelius/Complete-FABLE.5-traces-2M

- Crownelius/Opus-4.6-Reasoning-2100x-formatted

---

<div align="center">

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]()



</div>

---

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

---

Quickstart

Ollama

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

llama.cpp

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 Q4_K_M 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) | Apple Silicon native |

| v2 LoRA adapters | Standalone adapters |

| v1 MLX (Opus only) | Original 140K download model |

| v1 GGUF (Opus only) | 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

---

<div align="center">

"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

</div>