| --- |
| |
| library_name: transformers |
| license: apache-2.0 |
| license_link: https://ai.google.dev/gemma/docs/gemma_4_license |
| pipeline_tag: text-generation |
| base_model: |
| - google/gemma-4-26B-A4B |
| tags: |
| - text-generation |
| - conversational |
| - moe |
| - gemma4 |
| - openlumen |
| - lumen |
| - aeonthic |
|
|
| --- |
| |
| <div align="center"> |
| <img src=https://huggingface.co/Aeonthic/OpenLumen-2-Pulsar/resolve/main/OpenLumen_banner.png> |
| </div> |
|
|
| # OpenLumen 2 Pulsar |
|
|
| **OpenLumen 2 Pulsar** is the open-weight, text-only FP16 release of the Lumen 2 Pulsar family. |
|
|
| Unlike the primary Lumen 2 Pulsar models, OpenLumen 2 Pulsar does **not** use Lumen QAT and does **not** include a vision encoder. It is distributed as a standard FP16 Transformers model, intended to provide a simple and broadly compatible version of the model for inference, research, experimentation, and further fine-tuning. |
|
|
| Some model weights are shared with **Lumen 2 Pulsar**, but OpenLumen 2 Pulsar should not be considered a bit-for-bit FP16 reconstruction of the quantized Lumen 2 Pulsar release. |
|
|
| ## Model Overview |
|
|
| | Property | OpenLumen 2 Pulsar | |
| | ----------------- | ------------------------ | |
| | Base architecture | Gemma 4 26B-A4B | |
| | Modality | Text only | |
| | Weight format | FP16 | |
| | Lumen QAT | No | |
| | Vision | No | |
| | Model size | ~48 GB | |
| | Framework | Transformers | |
| | Architecture | Mixture-of-Experts | |
| | License | Apache 2.0 / Gemma terms | |
|
|
| OpenLumen 2 Pulsar deliberately prioritizes a conventional FP16 distribution rather than the aggressively optimized low-bit deployment formats used by other Lumen models. |
|
|
| ## Training |
|
|
| **OpenLumen 2 Pulsar** was trained and fine-tuned using **aikar-engine**, Aeonthic's open-source LLM training, Pruning, and inference engine: |
|
|
| https://github.com/DFveloper/aikar-engine |
|
|
| aikar-engine provides the training infrastructure used throughout the Lumen development pipeline, including support for model fine-tuning, quantization-aware training experiments, and other LLM optimization workflows. |
|
|
| The OpenLumen 2 Pulsar checkpoint released here is an FP16, non-QAT model, although the broader Lumen model family and training workflow also make use of aikar-engine's quantization and optimization capabilities. |
|
|
| ## Important: Quantization |
|
|
| OpenLumen 2 Pulsar is **not QAT-trained**. |
|
|
| Naively quantizing the FP16 checkpoint to very low-bit formats may cause substantial degradation. In particular, **Q4_0 post-training quantization is currently known to severely degrade or break model behavior**. |
| |
| This is an important distinction between OpenLumen 2 Pulsar and Lumen 2 Pulsar: the latter uses Lumen's quantization-aware training pipeline, while OpenLumen 2 Pulsar does not. |
| |
| For this reason, **FP16 is currently the recommended format** for reproducing the intended behavior of OpenLumen 2 Pulsar. |
| |
| Other quantization methods and precisions may work, but they should be independently evaluated before deployment. |
| |
| ## Memory Requirements |
| |
| The FP16 checkpoint occupies approximately **48 GB**. |
| |
| Despite the Pulsar name and partial weight correspondence with Lumen 2 Pulsar, the unquantized OpenLumen release has memory requirements closer to **Lumen 2 Magnetic** than to the optimized Lumen 2 Pulsar deployment. |
| |
| Actual runtime memory usage depends on the inference backend, context length, KV cache configuration, batching, and device placement. |
| |
| Multi-GPU inference or CPU offloading may therefore be necessary on systems without sufficient accelerator memory. |
| |
| ## Text-Only Release |
| |
| The original Gemma 4 architecture supports multimodal inputs. OpenLumen 2 Pulsar, however, is intentionally released as a **text-only model**. |
| |
| Vision functionality is not included or supported by this checkpoint. |
| |
| This is intentional rather than a packaging omission. |
| |
| ## Usage |
| |
| Install recent versions of the required libraries: |
| |
| ```bash |
| pip install -U transformers torch accelerate |
| ``` |
| |
| Example usage: |
| |
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| MODEL_ID = "Aeonthic/OpenLumen-2-Pulsar" |
| |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| MODEL_ID, |
| torch_dtype=torch.float16, |
| device_map="auto", |
| ) |
| |
| messages = [ |
| { |
| "role": "user", |
| "content": "Explain speculative decoding in simple terms." |
| } |
| ] |
| |
| inputs = tokenizer.apply_chat_template( |
| messages, |
| tokenize=True, |
| add_generation_prompt=True, |
| return_tensors="pt", |
| ).to(model.device) |
| |
| with torch.inference_mode(): |
| outputs = model.generate( |
| inputs, |
| max_new_tokens=512, |
| ) |
| |
| response = tokenizer.decode( |
| outputs[0][inputs.shape[-1]:], |
| skip_special_tokens=True, |
| ) |
| |
| print(response) |
| ``` |
| |
| Depending on the final Transformers integration and model configuration, loading APIs may differ between versions. Use a recent Transformers release. |
| |
| ## Training Data |
| |
| The training dataset used to produce OpenLumen 2 Pulsar is **not publicly released**. |
| |
| The model weights are provided for use under the applicable license, but the underlying Lumen training and alignment datasets remain proprietary. |
| |
| No release of the training dataset is currently planned. |
| |
| ## Relationship to Lumen 2 Pulsar |
| |
| OpenLumen 2 Pulsar and Lumen 2 Pulsar belong to the same model family, but they target different deployment goals. |
| |
| **Lumen 2 Pulsar** focuses on efficient deployment using Lumen's quantization-aware training and optimization pipeline. |
|
|
| **OpenLumen 2 Pulsar** instead provides a conventional FP16 checkpoint with broad compatibility and without requiring the Lumen QAT stack. |
|
|
| Some weights between the two models are identical, but their numerical representation, deployment characteristics, memory requirements, and low-bit behavior differ substantially. |
|
|
| In short: |
|
|
| > **OpenLumen 2 Pulsar is the FP16, text-only, non-QAT member of the Lumen 2 Pulsar family.** |
|
|
| ## Base Model |
|
|
| OpenLumen 2 Pulsar is based on: |
|
|
| **Google Gemma 4 26B-A4B** |
|
|
| Gemma 4 is developed by Google DeepMind. Please refer to the original Gemma documentation and model card for architecture details, usage conditions, and licensing information. |
|
|
| ## License |
|
|
| OpenLumen 2 Pulsar is released under the **Apache License 2.0**, subject to the applicable Gemma terms. |
|
|
| Please review the Gemma license and usage terms before redistribution or deployment. |
|
|
| ## Citation |
|
|
| OpenLumen 2 Pulsar is derived from Gemma 4. When using this model in academic work, please cite the original Gemma 4 Technical Report: |
|
|
| ```bibtex |
| @misc{gemmateam2026gemma4, |
| title={Gemma 4 Technical Report}, |
| author={Gemma Team}, |
| year={2026}, |
| eprint={2607.02770}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2607.02770}, |
| } |
| ``` |
|
|
| ## Disclaimer |
|
|
| OpenLumen 2 Pulsar is provided as an open-weight research and development release. Model outputs may be inaccurate, biased, or otherwise unsuitable for a particular application. |
|
|
| Users are responsible for evaluating the model for their intended use case and for complying with applicable licenses, laws, and regulations. |
|
|
