| --- |
| base_model: google/gemma-4-E4B-it |
| library_name: peft |
| pipeline_tag: text-generation |
| tags: |
| - gemma4 |
| - lora |
| - peft |
| - reasoning |
| - math |
| - agent |
| - tool-use |
| - function-calling |
| - multimodal |
| - russian |
| - unsloth |
| license: apache-2.0 |
| language: |
| - ru |
| - en |
| datasets: |
| - open-r1/OpenR1-Math-220k |
| - nvidia/Nemotron-Post-Training-Dataset-v2 |
| - nvidia/Nemotron-SFT-Agentic-v2 |
| - HuggingFaceTB/smoltalk2 |
| - Vikhrmodels/GrandMaster-PRO-MAX |
| --- |
| |
| <div align="center"> |
| <img src="cerberusbanner.png" alt="Cerberus-E4B Banner" width="100%" style="border-radius: 10px; margin-bottom: 20px;"> |
| </div> |
|
|
| # Cerberus-E4B: Multi-Task Reasoning & Agentic Model |
|
|
| **Cerberus-E4B** is an open-weights, multi-task reasoning and agentic language model fine-tuned on top of Google's [`gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it) architecture (~7.5B total parameters with frozen vision and audio towers, 4B active language backbone). |
|
|
| Cerberus is designed for advanced bilingual step-by-step reasoning (English & Russian), precise function calling via native Gemma 4 tool schemas, and versatile dual-channel generation. |
|
|
| Looking for quantized GGUF weights? |
| 👉 **[`Cerberus-E4B-GGUF`](https://huggingface.co/rrlabs-ai/Cerberus-E4B-GGUF)** |
|
|
| --- |
|
|
| ## Highlights & Capabilities |
|
|
| * **Dual-Channel Generation:** Seamlessly toggles between long, deliberate step-by-step thought traces (`<|channel>thought\n...<channel|>`) and concise, direct responses (`instant`). |
| * **Advanced Mathematical Reasoning:** Shows a **+16.7%** accuracy improvement over the Google Gemma 4 base model on challenging competition-level mathematics (MATH-500). |
| * **Native Tool Calling (Agentic):** Trained with strict adherence to Gemma 4's native function calling protocol (`<|turn>model\n<|tool_call>...<|call:function_name{...}>`), ensuring structured execution for multi-turn agents. |
| * **Deep Russian Language Alignment:** Native-grade Russian math and logical reasoning without falling back to clumsy machine translation. |
| * **Multimodal Architecture Compatibility:** Trained with LoRA targeting the language reasoning backbone while keeping vision (ViT SigLIP, 280 soft tokens) and audio (Conformer, 16 kHz) encoders intact for drop-in GGUF `mmproj` support. |
|
|
| --- |
|
|
| ## Benchmark Results (vs. Base Model) |
|
|
| All evaluations were conducted in greedy / deterministic mode comparing Cerberus-E4B directly against `google/gemma-4-E4B-it`: |
|
|
| | Benchmark | Base Model (`Gemma-4-E4B-it`) | Cerberus-E4B | Delta (vs. Base) | Generation Mode | |
| | :--- | :---: | :---: | :---: | :--- | |
| | **MATH-500** | 43.3% | **60.0%** | **+16.7%** | Thinking Mode (`<thought>`) | |
| | **MATH-500** | 35.0% | **48.0%** | **+13.0%** | Instant Mode (Direct calculation) | |
| | **MMLU-Redux** | 62.0% | **63.0%** | **+1.0%** | Multi-discipline academic knowledge | |
| | **IFEval** | 90.8% | **90.5%** | **Parity (~90%)** | Complex system constraint following | |
|
|
| --- |
|
|
| ## Quickstart (Hugging Face Transformers) |
|
|
| Cerberus-E4B is released as an official PEFT LoRA adapter for `google/gemma-4-E4B-it`. |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| base_model_id = "google/gemma-4-E4B-it" |
| adapter_model_id = "rrlabs-ai/Cerberus-E4B" |
| |
| # Load tokenizer and base model |
| tokenizer = AutoTokenizer.from_pretrained(base_model_id) |
| base_model = AutoModelForCausalLM.from_pretrained( |
| base_model_id, |
| torch_dtype=torch.bfloat16, |
| device_map="auto" |
| ) |
| |
| # Apply Cerberus LoRA adapter |
| model = PeftModel.from_pretrained(base_model, adapter_model_id) |
| |
| # 1. Thinking Mode Prompt (Step-by-step reasoning) |
| prompt = "Find the number of positive integers n <= 1000 such that gcd(n, 36) = 1." |
| formatted_input = f"<|turn>user\n{prompt}<turn|>\n<|turn>model\n<|channel>thought\n" |
| |
| inputs = tokenizer(formatted_input, return_tensors="pt").to(model.device) |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=2048, |
| temperature=0.8, |
| top_p=0.95, |
| do_sample=True |
| ) |
| |
| response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False) |
| print(response) |
| ``` |
|
|
| --- |
|
|
| ## Recommended Generation Presets |
|
|
| To ensure optimal generation quality across different tasks: |
|
|
| * **For Deliberate Reasoning (`Thinking` mode):** |
| * Sampling: `do_sample=True`, `temperature=0.8`, `top_p=0.95`, `top_k=64`. |
| * For `llama.cpp` / GGUF: Enable the dynamic DRY sampler (`--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2`) to eliminate n-gram loops during long thinking traces. |
| * **For Direct Answers, Code & Function Calling (`Instant` mode):** |
| * Sampling: Greedy decoding (`do_sample=False`, `temperature=0.0`) for maximum determinism and syntax reliability. |
|
|
| --- |
|
|
| ## GGUF & Quantized Models |
|
|
| Looking for standalone quantized GGUF weights for local execution on llama.cpp, LM Studio, or Ollama? |
| Pre-quantized GGUF files (including `Q4_K_M`, `Q5_K_M`, `Q8_0`, `BF16`, and multimodal `mmproj`) are available at: |
| 👉 **[`rrlabs-ai/Cerberus-E4B-GGUF`](https://huggingface.co/rrlabs-ai/Cerberus-E4B-GGUF)** |
|
|
| --- |
|
|
| ## 📱 Recommended Web & Mobile Interface: rrchat |
|
|
| Cerberus-E4B pairs seamlessly with **[rrchat](https://github.com/leftik1/rrchat)** — an open-source, mobile-ready AI chat interface: |
|
|
| > **Use your local PC's llama.cpp from your phone.** Clean mobile-ready AI chat interface with vision, web search, and privacy. |
|
|
| * 🌐 **Project Repository:** [github.com/leftik1/rrchat](https://github.com/leftik1/rrchat) |
| * 📷 **Camera & Vision Ready:** Upload photos or snapshots directly from your mobile camera and get instant multimodal visual analysis from Cerberus-E4B. |
| * 🔍 **Web Search & Privacy:** Built-in web search capabilities while keeping your inference and chat history 100% private and hosted on your local hardware. |
|
|
| --- |
|
|
| ## Dataset Attribution & Licensing |
|
|
| Cerberus was trained on a carefully curated, decontaminated multi-source dataset. In accordance with dataset terms and open research best practices, we gratefully acknowledge the following data creators: |
|
|
| 1. **NVIDIA Nemotron:** |
| * Contains synthetic data derived from [`nvidia/Nemotron-Post-Training-Dataset-v2`](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2) and [`nvidia/Nemotron-SFT-Agentic-v2`](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2). |
| * *Attribution Notice:* This work contains content derived from NVIDIA Nemotron Post-Training and SFT Agentic datasets, developed by NVIDIA Corporation and licensed under the NVIDIA Open Synthetic Data License. |
| 2. **Hugging Face Open-R1:** |
| * Mathematical reasoning traces sourced from [`open-r1/OpenR1-Math-220k`](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) (Apache-2.0 License). |
| 3. **Hugging Face SmolTalk:** |
| * General dialogue and structured conversation samples from [`HuggingFaceTB/smoltalk2`](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2) (Apache-2.0 License). |
| 4. **Vikhr Models (GrandMaster):** |
| * Russian-language STEM and complex instructions sourced from [`Vikhrmodels/GrandMaster-PRO-MAX`](https://huggingface.co/datasets/Vikhrmodels/GrandMaster-PRO-MAX) (Apache-2.0 License). |
| 5. **Base Model:** |
| * Built on [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it), developed by Google DeepMind and governed by the Gemma Terms of Use. |
|
|
| --- |
|
|
| ## License |
|
|
| This model adapter is released under the **[Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0)**. Use of the underlying base model remains subject to the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). |
|
|
