Llama 2 7B MHA D Kv 256

Name: Llama 2 7B MHA D Kv 256
Author: fnlp

7.0B

llama

fnlp

Language Model

OTHER

7B params

New

9 downloads

Early-stage

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Edge AI:

Mobile

Laptop

Server

16GB+ RAM

Mobile

Laptop

Server

Quick Summary

Research Paper "Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs" - Step 2(Option): For MHA2MLA mode...

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

7GB+ RAM

Training Data Analysis

🟡 Average (4.8/10)

Researched training datasets used by Llama 2 7B MHA D Kv 256 with quality assessment

Specialized For

general

science

multilingual

reasoning

Training Datasets (4)

common crawl

🔴 2.5/10

general

science

Key Strengths

•Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
•Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
•Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...

Considerations

•Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
•Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...

🔵 6/10

general

multilingual

Key Strengths

•Scale and Accessibility: 750GB of publicly available, filtered text
•Systematic Filtering: Documented heuristics enable reproducibility
•Language Diversity: Despite English-only, captures diverse writing styles

Considerations

•English-Only: Limits multilingual applications
•Filtering Limitations: Offensive content and low-quality text remain despite filtering

wikipedia

🟡 5/10

science

multilingual

Key Strengths

•High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
•Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
•Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...

Considerations

•Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
•Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...

arxiv

🟡 5.5/10

science

reasoning

Key Strengths

•Scientific Authority: Peer-reviewed content from established repository
•Domain-Specific: Specialized vocabulary and concepts
•Mathematical Content: Includes complex equations and notation

Considerations

•Specialized: Primarily technical and mathematical content
•English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Monkey Patch: MHA -> MLApythontransformers

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

model_name = "fnlp/Llama-2-7B-MHA-d_kv_256"

# 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

Monkey Patch: MHA -> MLApythontransformers

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

model_name = "fnlp/Llama-2-7B-MHA-d_kv_256"

# 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

Monkey Patch: MHA -> MLApythontransformers

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

model_name = "fnlp/Llama-2-7B-MHA-d_kv_256"

# 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

- Sinclair Lewistext

@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}, 
}

- Sinclair Lewistext

@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}, 
}

- Sinclair Lewistext

@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}, 
}

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