Zebra-Llama is a family of hybrid large language models (LLMs) proposed by AMD that composes Multi-head Latent Attention (MLA) and Mamba2 for KV cache compression and computational efficiency. Thus combination achieves Transformer-level accuracy with near-State Space Model (SSM) efficiency. While standard Transformers are limited by the quadratic complexity of self-attention and the large memory footprint of their key-value (KV) cache, Zebra-Llama offers a practical and scalable solution.

This model, Zebra-Llama-1B-4MLA-12M2-DPO, is created by efficiently adapting the pre-trained Llama-3.2-1B-Instruct model conducted post-training on AMD Instinct™ MI300X GPUs. This training approach bypasses the need for costly pre-training from scratch.

Figure 1: Comparing 8B-scale models on average LM Harness score vs. KV cache size. Zebra-Llama (green) matches or exceeds baselines with smaller KV cache and fewer training tokens. Circle and square sizes indicate training tokens (billions for post-training, trillions for pre-training).

Key Takeaways

Announcing Zebra-Llama, a family of highly efficient 1B, 3B, and 8B hybrid models created by post-training adaptation of existing state-of-the-art Transformers.
Extreme KV Cache Compression: Zebra-Llama dramatically reduces the KV cache size to 2%-4% of the original Llama model while preserving 100% of its average zero-shot performance on LM Harness tasks.
Efficient Hybrid Architecture: Zebra-Llama strategically combines Multi-head Latent Attention (MLA) layers, which compress the KV cache, and Mamba2 (SSM) layers, which eliminate the KV cache entirely, to balance memory usage and performance.
Novel Post-Training Pipeline: Zebra-Llama employs an efficient post-training pipeline featuring refined weight initialization, Intermediate Layer Distillation (ILD) for knowledge transfer, and a sensitivity-aware strategy (SMART) for optimal hybrid composition.

Model Composition Pipeline

The Zebra-Llama models are not trained from scratch. Instead, they are composed from powerful pre-trained Transformers through a lightweight and efficient pipeline. The creation of this model followed these stages:

Stage	Action	Description
1. Base Model	Llama-3.2-1B-Instruct	The starting point is a high-quality, pre-trained Transformer model.
2. Initialization	Structured Weight Mapping	Pure Mamba2 and MLA models are initialized from the base model's weights using structured mapping techniques (SVD for MLA, reinterpretation for Mamba2).
3. Refinement	Intermediate Layer Distillation (ILD)	The internal representations of the Mamba2 and MLA models are aligned with the base model's layers on a small dataset to ensure a strong starting point.
4. Composition	SMART Layer Selection	A hybrid architecture is composed using the SMART (Sensitivity Measure-Aware Replacement of Transformer layers) strategy to optimally place each layer type.
5. SFT	End-to-End Knowledge Distillation	The composed hybrid model is fine-tuned via knowledge distillation, using an 8B model as a teacher to transfer rich, pre-trained knowledge.
6. Alignment	Direct Preference Optimization (DPO)	In the final stage, DPO is used to align the model's preferences, with the distilled student model itself serving as the reference model for stability.

Training Data

Stage	Dataset	License
ILD/SFT	https://huggingface.co/datasets/teknium/OpenHermes-2.5	Refer source materials
ILD/SFT	https://huggingface.co/datasets/tomg-group-umd/GenQA	CC BY-NC 4.0
ILD/SFT	https://huggingface.co/datasets/BAAI/Infinity-Instruct	CC BY-SA 4.0
DPO	https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized	MIT
DPO	https://huggingface.co/datasets/HuggingFaceH4/orca_dpo_pairs	MIT
DPO	https://huggingface.co/datasets/JunxiongWang/llama3-ultrafeedback-armorm	MIT

Getting Started

Installation

git clone https://github.com/AMD-AIG-AIMA/AMD-Hybrid-Models.git

Then follow the installation instruction in AMD-AIG-AIMA/AMD-Hybrid-Models repo.

Example Usage

Once the installation completed, we can try the following code for a quick test

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from hybrid.hybrid_wrapper import HybridModelWrapper

checkpoint = "amd/Zebra-Llama-1B-4MLA-12Mamba-DPO"

model = HybridModelWrapper.from_pretrained(checkpoint, torch_dtype=torch.bfloat16).cuda()
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model.eval()

# Format the prompt using the chat template
prompt = [{"role": "user", "content": "What are the benefits of hybrid language models?"}]
input_ids = tokenizer.apply_chat_template(
    prompt,
    add_generation_prompt=True,
    return_tensors='pt'
).cuda()

# Generate a response
tokens = model.generate(
    input_ids, 
    max_new_tokens=256,
    temperature=0.7,
    do_sample=True,
    eos_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(tokens[0], skip_special_tokens=False))

Model details

Model	KV Size	Param	Index of MLA layers	r_kv	r_q	d_rope	d_nope
Llama-3.2-1B-Instruct	100%	1.24B	-	-	-	-	-
Zebra-Llama-1B-8MLA-8M2	7.81%	1.27B	[0,2,4,6,8,10,12,14]	128	1344	32	32
Zebra-Llama-1B-4MLA-12M2	3.91%	1.28B	[0,5,10,14]	128	1344	32	32

Benchmark results

Zebra-Llama was evaluated on the Language Model Harness benchmark for zero-shot tasks and compared against its base model and other post-training methods. The results demonstrate that Zebra-Llama provides a superior balance of performance and efficiency.

Tasks	Metric	Llama-3.2-1B-Instruct	Zebra-Llama-1B-4MLA-12M2-SFT	Zebra-Llama-1B-4MLA-12M2-DPO	Zebra-Llama-1B-8MLA-8M2-SFT	Zebra-Llama-1B-8MLA-8M2-DPO
arc_challenge	acc	0.3575 (±0.0140)	0.3507 (±0.0139)	0.3976 (±0.0143)	0.3456 (±0.0139)	0.3951 (±0.0143)
	acc_norm	0.3797 (±0.0142)	0.3908 (±0.0143)	0.4232 (±0.0144)	0.3797 (±0.0142)	0.4249 (±0.0144)
arc_easy	acc	0.6843 (±0.0095)	0.7054 (±0.0094)	0.7226 (±0.0092)	0.7092 (±0.0093)	0.7239 (±0.0092)
	acc_norm	0.6351 (±0.0099)	0.6536 (±0.0098)	0.6696 (±0.0097)	0.6641 (±0.0097)	0.6726 (±0.0096)
hellaswag	acc	0.4506 (±0.005)	0.4272 (±0.0049)	0.4399 (±0.005)	0.4366 (±0.0049)	0.4527 (±0.0050)
	acc_norm	0.6077 (±0.0049)	0.5691 (±0.0049)	0.5893 (±0.0049)	0.5816 (±0.0049)	0.6061 (±0.0049)
mmlu	acc	0.4609 (±0.0918)	0.3739 (±0.0736)	0.3791 (±0.0742)	0.3940 (±0.0779)	0.3909 (±0.0756)
- humanities	acc	0.4397 (±0.0763)	0.3456 (±0.0583)	0.3443 (±0.0634)	0.3694 (±0.0709)	0.3700 (±0.0684)
- other	acc	0.5204 (±0.0868)	0.4184 (±0.0746)	0.4081 (±0.0707)	0.4300 (±0.0747)	0.4258 (±0.0737)
- social_sciences	acc	0.5109 (±0.0843)	0.4098 (±0.0758)	0.4303 (±0.0709)	0.4348 (±0.0749)	0.4283 (±0.0727)
- stem	acc	0.3850 (±0.09)	0.3375 (±0.0730)	0.3527 (±0.077)	0.3555 (±0.0776)	0.3511 (±0.0746)
openbookqa	acc	0.244 (±0.0192)	0.2800 (±0.0201)	0.302 (±0.0206)	0.2480 (±0.0193)	0.3000 (±0.0205)
	acc_norm	0.35 (±0.0214)	0.3700 (±0.0216)	0.406 (±0.022)	0.3800 (±0.0217)	0.4180 (±0.0221)
piqa	acc	0.7405 (±0.0102)	0.7214 (±0.0105)	0.7252 (±0.0104)	0.7252 (±0.0104)	0.7280 (±0.0104)
	acc_norm	0.7437 (±0.0102)	0.7225 (±0.0104)	0.7296 (±0.0104)	0.7269 (±0.0104)	0.7296 (±0.0104)
pubmedqa	acc	0.602 (±0.0219)	0.5760 (±0.0221)	0.566 (±0.0222)	0.5940 (±0.0220)	0.5860 (±0.0220)
race	acc	0.3809 (±0.015)	0.3445 (±0.0147)	0.377 (±0.015)	0.3694 (±0.0149)	0.3866 (±0.0151)
winogrande	acc	0.5967 (±0.0138)	0.5785 (±0.0139)	0.5888 (±0.0138)	0.6125 (±0.0137)	0.6133 (±0.0137)

Conclusion

Zebra-Llama demonstrates a practical and scalable framework for composing highly efficient hybrid models from existing pre-trained Transformers. By intelligently combining MLA and Mamba2 layers, this approach drastically reduces memory requirements and improves inference throughput while preserving the strong capabilities of the original model. This work highlights the viability of post-training hybridization as a cost-effective and environmentally sustainable alternative to full retraining, paving the way for the deployment of powerful LLMs in resource-constrained environments.

Bias, Risks, and Limitations

This model is a research artifact and has not been evaluated for safety in production use cases.
The model's performance is dependent on the quality of its pre-trained base model and the teacher model used during distillation. Its capabilities and biases are inherited from these sources.
The model may generate content that is factually inaccurate, biased, or otherwise objectionable. Users should be aware of these risks and implement appropriate safeguards for their applications.
One limitation of this work is the reliance on a strong teacher model for knowledge transfer, which may not always be available. Distillation from a teacher also adds to the resource requirements during the post-training phase.

Citation

If you find this model useful, please consider citing the original paper:

@article{yang2025zebra,
  title={Zebra-Llama: Towards Extremely Efficient Hybrid Models},
  author={Yang, Mingyu and Rezagholizadeh, Mehdi and Li, Guihong and Appia, Vikram and Barsoum, Emad},
  journal={arXiv preprint arXiv:2505.17272},
  year={2025}
}
@article{li2025x,
  title={X-ecomla: Upcycling pre-trained attention into mla for efficient and extreme kv compression},
  author={Li, Guihong and Rezagholizadeh, Mehdi and Yang, Mingyu and Appia, Vikram and Barsoum, Emad},
  journal={arXiv preprint arXiv:2503.11132},
  year={2025}
}