---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation
- causal-lm
- linear-attention
- rwkv
- reka
- knowledge-distillation
- multilingual
languages:
- mul
---
# HRWKV7-Reka-Flash3-Preview
> I'm simply exploring the possibility of linearizing existing Transformer models.
> It's still far from perfect,
> but I hope you'll bear with me as I continue this journey. :)
## Paper and Project Details
This model is part of the research presented in the paper [RADLADS: Rapid Attention Distillation to Linear Attention Decoders at Scale](https://huggingface.co/papers/2505.03005).
The main codebase for the RADLADS project can be found at: [https://github.com/recursal/RADLADS-paper](https://github.com/recursal/RADLADS-paper)
### Model Description
HRWKV7-Reka-Flash3-Preview is an experimental hybrid architecture model that combines RWKV v7's linear attention mechanism with Group Query Attention (GQA) layers. Built upon the Reka-flash3 21B foundation, this model replaces most Transformer attention blocks with RWKV blocks while strategically maintaining some GQA layers to enhance performance on specific tasks.
- **Developed by:** OpenMOSE
- **Model type:** Hybrid Linear-Attention Language Model
- **Language(s):** Multilingual (inherited from Reka-flash3 21B)
- **License:** Apache-2.0
- **Base Model:** Reka-flash3 21B(https://huggingface.co/RekaAI/reka-flash-3)
- **Year:** 2025
### Architecture Specifications
- **Architecture:** RWKV v7 based "hxa079" Architecture + Group Query Attention Hybrid
- **Total Layers:** 44 layers (L44D6114)
- 38 RWKV layers (with Rope)
- 6 GQA layers (No Rope, No Position Embeddings)
- **Hidden Dimension:** 6144
- **Training Context Window:** 4096 tokens
- **Inference Context Window** 32768+
- **Training Strategy** Following RADLADS method based knowledge distillation
## Technical Innovation
### RWKV "hxa079" Architecture
The model implements several key improvements over standard RWKV architectures:
1. **Token Shift Removal**: In order to effectively inherit the teacher model weights, we removed the residual connection one token ago.
2. **GroupNorm Removal**: Helps improve training stability issues
3. **k_first Introduction**: Experimentally adopted the approach of residually connecting k layers in layer 0.
### Hybrid Design Benefits
- **Linear Attention Inference**: RWKV blocks enable O(1) memory complexity during inference, and the hybrid approach reduces the KVCache to 1/7 of full GQA.
- **Enhanced Needle Tasks**: Strategic placement of GQA layers significantly improves performance on needle-in-haystack retrieval tasks, addressing a known limitation of pure linear attention models
- **Implicit Position Encoding**: Interestingly, the model achieves better performance when RoPE (Rotary Position Embedding) is not applied to GQA layers, suggesting that RWKV blocks provide implicit positional encoding capabilities
## Intended Use
This is an **experimental research model** designed to explore hybrid architectures combining linear and quadratic attention mechanisms. It is intended for:
- Research into efficient attention mechanisms
- Benchmarking hybrid architecture performance
- Exploring linear attention limitations and solutions
- Academic and industrial R&D purposes
## Limitations
- **Experimental Status**: This model is in experimental stages and may exhibit unexpected behaviors
- **Context Window**: Limited to 4096 tokens during training, though RWKV architecture theoretically supports longer sequences
- **Performance Variability**: As a hybrid model, performance may vary significantly across different task types
## Training Details
- **Training Context Window:** 4096 tokens
- **Training GPU** AMD MI300X x 1(takes 68hrs)
- **Training Strategy** 8bit MLP Quant, frozen emb,mlp,head, Deepspeed Stage1
- **Base Model Initialization:** Weights initialized from Reka-flash3 21B
- **Architecture Conversion:** Transformer attention blocks systematically replaced with RWKV blocks, except for 6 strategically placed GQA layers
## Evaluation
Performance evaluation is ongoing. The model shows promising results in:
- Maintaining base model capabilities while achieving linear attention efficiency
- Significantly improved needle-in-haystack task performance compared to pure RWKV architectures
- Competitive performance on standard language modeling benchmarks
## Usage with Hugging Face Transformers
This model can be loaded and used with the `transformers` library. Ensure you have `transformers` installed: `pip install transformers`.
When loading, remember to set `trust_remote_code=True` because of the custom architecture.
```python
from transformers import pipeline, AutoTokenizer
import torch
model_name = "OpenMOSE/HRWKV7-Reka-Flash3-Preview" # Replace with the actual model ID if different
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
pipe = pipeline(
"text-generation",
model_name,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16, # or torch.float16 depending on your GPU and model precision
device_map="auto",
trust_remote_code=True,
)
text = "The quick brown fox jumps over the lazy "
result = pipe(text, max_new_tokens=20, do_sample=True, top_p=0.9, temperature=0.7)[0]["generated_text"]
print(result)
```
## Run with RWKV-Infer (as provided by original authors)
- RWKV-Infer now support hxa079
```bash
curl http://127.0.0.1:9000/loadmodel -X POST -H "Content-Type: application/json" -d '{"model_filename":"/home/client/Projects/llm/hxa079-reka-flash3-stage2-hybrid.pth","model_viewname":"RWKV HXA079 L38T6 Reka Flash3","model_strategy":"int8","adapter_filename":"","adapter_mode":"", "template":"rekaflash3", "endtoken":"
","default_temperature":"0.2", "default_top_p":"0.3", "rope_theta":"8000000.0", "rms_norm_eps":"1e-5"}'
```
## Thank you for Big help :)
- SmerkyG Inspired by RADLADS (https://arxiv.org/abs/2505.03005)
## Training Code
- https://github.com/OpenMOSE/RWKVInside (still buggy)
## Model Card Contact
OpenMOSE - 2025
---
*Note: This is an experimental model. Performance characteristics and behaviors may differ from both pure RWKV and standard Transformer architectures. Users should thoroughly evaluate the model for their specific use cases.*
## Citation
If you use this code or find our work valuable, please consider citing RADLADS:
```bibtex
@misc{goldstein2025radladsrapidattentiondistillation,
title={RADLADS: Rapid Attention Distillation to Linear Attention Decoders at Scale},
author={Daniel Goldstein and Eric Alcaide and Janna Lu and Eugene Cheah},
year={2025},
eprint={2505.03005},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.03005},
}
```