--- 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 ---
# 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](https://github.com/DeadByDawn101) — San Jose, CA [![Downloads](https://img.shields.io/badge/v1_downloads-140%2C000%2B-brightgreen)]() [![License](https://img.shields.io/badge/license-Gemma-green)](https://ai.google.dev/gemma/docs/gemma_4_license) [![MLX Version](https://img.shields.io/badge/MLX_version-available-blue)](https://huggingface.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-mlx-4bit)
--- ## 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 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 `` blocks)** | | **Final training loss** | — | **1.8984** | | **Adapter needed?** | No | **No** | ### Data Sources | Dataset | Examples | What It Teaches | |---------|----------|----------------| | [GPT-5.6 Sol Coding Traces](https://huggingface.co/datasets/greghavens/gpt-5.6-sol-coding-and-debugging-traces) | 17,939 | Production coding, debugging, tool use, acceptance-tested solutions | | [Complete FABLE.5 Traces](https://huggingface.co/datasets/Crownelius/Complete-FABLE.5-traces-2M) | 4,450 | Deep reasoning with `` blocks, context→completion | | [Opus 4.6 Reasoning](https://huggingface.co/datasets/Crownelius/Opus-4.6-Reasoning-2100x-formatted) | 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](https://github.com/DeadByDawn101/unsloth-mlx) 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](https://github.com/DeadByDawn101/unsloth-mlx) | Training framework — we added Gemma 4 support | | [mlx-gemma4](https://github.com/DeadByDawn101/mlx-gemma4) | Custom model implementation + converter | | [ravenx-mtp-drafter](https://github.com/DeadByDawn101/ravenx-mtp-drafter) | Reverse-engineered Google's hidden MTP heads | | [ravenx-training-gym](https://github.com/DeadByDawn101/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](https://github.com/DeadByDawn101) | X: [@RavenXllm](https://x.com/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*