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license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
library_name: transformers
pipeline_tag: any-to-any
base_model: google/gemma-4-12B-it
tags:
- gemma4
- multimodal
- text-generation
- image-text-to-text
- audio-text-to-text
- video-text-to-text
- long-context
- lora
- bf16
- llama-cpp
- zh
- en
model_name: Anrn-12B-R1
---
# Anrn 12B R1
**Anrn 12B R1** is a 12B dense, natively multimodal Gemma 4 derivative distributed as a byte-identical BF16 base plus a project LoRA adapter applied at a tested scale of `0.45`. The release supports text, image, audio, and video inputs through the native Transformers service and includes a ready-to-run llama.cpp text deployment.
> **Release layout:** this repository intentionally keeps the original BF16 base and the trained adapter separate rather than destructively merging them. The included launchers compose both parts at runtime.
## Benchmark results
| Benchmark | Anrn 12B R1 |
|---|---:|
| MMLU-Pro | **74.2** |
| GPQA Diamond | **77.8** |
| AIME 2026 (no tools) | **69.6** |
| LiveCodeBench v6 | **68.3** |
| Codeforces Elo | **1521** |
| BigBench Extra Hard | **48.2** |
| Tau2 Average | **63.2** |
| MMMU Pro | **62.1** |
| MRCR v2 (128K) | **42.6** |
The Anrn values above come from the project's August 2026 evaluation run. The comparison values shown in the chart are reproduced from the published benchmark results used by that report; values marked with `~` are approximate.
## Model details
| Field | Value |
|---|---|
| Architecture | Gemma 4 Unified, dense decoder-only multimodal model |
| Parameters | 11.95B |
| Base model | `google/gemma-4-12B-it` |
| Native precision | BF16 |
| Adaptation | LoRA, rank 8, alpha 16 |
| Tested adapter scale | `0.45` |
| Native modalities | Text, image, audio, video input; text output |
| Tested service context | 131,072 tokens |
| Model configuration maximum | 262,144 positions |
| Languages | Multilingual; optimized project behavior in Chinese and English |
| Default prompt | Project constitution embedded in `system_prompt.md` |
## Repository structure
```text
model/ Original BF16 Gemma 4 12B weights and processor files
adapter/ Anrn R1 PEFT/LoRA adapter
llama-model/ Q5_K_M GGUF base, Q8_0 GGUF LoRA, and embedded chat template
llama-runtime/ Verified Windows CUDA llama.cpp runtime
assets/ Benchmark figure
start.ps1 Native multimodal OpenAI-compatible service
serve.py Native service implementation
Start-Llama-Service.bat
One-click llama.cpp text Web UI
```
## Quick start: native multimodal service
Requirements: Windows, Python, a recent CUDA-capable NVIDIA GPU, and PowerShell.
```powershell
python -m pip install -r requirements.txt
.\start.ps1 -Port 8091
```
The launcher loads `model/`, mounts `adapter/` at scale `0.45`, injects `system_prompt.md`, uses PyTorch SDPA for long-context inference, and automatically offloads language layers to system memory when required. The tested configuration used approximately 14 GB of GPU memory.
OpenAI-compatible endpoint:
```text
POST http://127.0.0.1:8091/v1/chat/completions
```
Example health check:
```powershell
Invoke-RestMethod http://127.0.0.1:8091/health
```
## Quick start: llama.cpp Web UI
On Windows, double-click:
```text
Start-Llama-Service.bat
```
The bootstrap checks the bundled llama.cpp runtime and, if it is missing, downloads a matching official Windows CUDA/CPU build. It then starts the text Web UI with:
- 131,072-token context
- LoRA scale `0.45`
- Flash Attention
- Q8_0 K/V cache
- the project system prompt embedded in the Jinja chat template
Web UI: `http://127.0.0.1:8091`
Use `Stop-Llama-Service.bat` to stop it. The llama.cpp route is text-only; use `start.ps1` for native image, audio, and video inputs.
## Prompt behavior
`system_prompt.md` contains the release's default project constitution. The native server injects it before caller-provided system content. A request may set:
```json
{"use_default_system_prompt": false}
```
to disable the default prompt for that request. The llama.cpp prompt is embedded in `llama-model/chat_template.prompt30k.jinja`.
## Verification
The release contains machine-readable verification artifacts covering:
- native text, image, audio, and video loading
- prompt hash and injection behavior
- llama.cpp health, 128K context, and live LoRA scale
- identity smoke tests
- package checksums and rollback scripts
Primary records: `verification.json`, `llama-bootstrap-verification.json`, `prompt30k-verification.json`, and `SHA256SUMS.txt`.
## Limitations
- Benchmark scores are sensitive to prompt templates, decoding parameters, judge versions, and harness revisions.
- The BF16 base is large; CPU offload improves accessibility but reduces generation speed.
- The included llama.cpp path is an auxiliary quantized text deployment and does not expose the native multimodal input path.
- Users should evaluate the model for their target language, domain, and deployment policy before production use.
## License
Apache 2.0. See the [Gemma 4 license information](https://ai.google.dev/gemma/docs/gemma_4_license) and the upstream model terms.
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