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fnlp/
Llama-2-7B-MLA-d_kv_16

Name: Llama-2-7B-MLA-d_kv_16
Availability: InStock

safetensors

llama

text-generation

HuggingFaceTB/smollm-corpus

2502.14837

meta-llama/Llama-2-7b-hf

finetune

apache-2.0

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Research Paper "Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs"

Inference

Step 1: Download the monkey patch file.

wget https://raw.githubusercontent.com/JT-Ushio/MHA2MLA/refs/heads/main/src/mha2mla/monkey_patch.py

Step 2(Option): For MHA2MLA models using Partial-RoPE 2-nrom method, Download the qk_2-norm file. Take qk_tensor_7B.pth as an example:

wget https://github.com/JT-Ushio/MHA2MLA/raw/refs/heads/main/utils/qk_tensor_7B.pth

Step 3: Download the MHA2MLA models and run inference. Take fnlp/Llama-2-7B-MLA-d_kv_16 as an example:

python

import torch
from transformers import AutoConfig, AutoTokenizer, LlamaForCausalLM
from monkey_patch import infer_monkey_patch

model_name = "fnlp/Llama-2-7B-MLA-d_kv_16"

# Monkey Patch: MHA -> MLA
config = AutoConfig.from_pretrained(model_name)
if "RoPE" in config:
    config.RoPE["qk_tensor_path"] = "qk_tensor_7B.pth"  # Configuration for Specific Models
    infer_monkey_patch(config.RoPE)

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.bfloat16).cuda()

# Generate
text = "Which American-born Sinclair won the Nobel Prize for Literature in 1930?"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
generation_kwargs = {"do_sample": False, "use_cache": True, "max_new_tokens": 128}
output = model.generate(**inputs, **generation_kwargs)

print(tokenizer.decode(output[0], skip_special_tokens=True))
# - Sinclair Lewis

Citation

@misc{ji2025economicalinferenceenablingdeepseeks,
      title={Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs}, 
      author={Tao Ji and Bin Guo and Yuanbin Wu and Qipeng Guo and Lixing Shen and Zhan Chen and Xipeng Qiu and Qi Zhang and Tao Gui},
      year={2025},
      eprint={2502.14837},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.14837}, 
}