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Llama 2 7B Hf by meta-llama

 »  All LLMs  »  meta-llama  »  Llama 2 7B Hf   URL Share it on

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  Arxiv:2307.09288   Autotrain compatible   En   Endpoints compatible   Facebook   Llama   Llama2   Meta   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/meta-llama/Llama-2-7b-hf 

Llama 2 7B Hf Benchmarks

MMLU Pro: 9.57
GPQA: 2.24
MUSR: 3.76
BBH: 10.35
IFEval: 25.19 vs 88 (so35)-71.4%
ARC: 53.07 vs 96.7 (so35)-45.1%
HellaSwag: 77.74 vs 95.3 (gpt4)-18.4%
MMLU: 43.8 vs 88.3 (so35)-50.4%
TruthfulQA: 38.98 vs 59 (gpt4)-33.9%
WinoGrande: 74.59 vs 87.5 (gpt4)-14.8%
GSM8K: 5.38 vs 96.4 (so35)-94.4%
MATH Lvl 5: 1.28
LLME Score: 0.37741
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 2 7B Hf (meta-llama/Llama-2-7b-hf)

Llama 2 7B Hf Parameters and Internals

Model Type 
Generative text
Use Cases 
Areas:
Commercial, Research
Applications:
Assistant-like chat
Primary Use Cases:
Natural language generation tasks
Limitations:
Use cases not covered extensively in languages other than English.
Considerations:
Developers should ensure the responsible use of models.
Additional Notes 
Tuned models optimized for dialogue. High carbon footprint during pretraining offset by Meta's sustainability program. Modeled potential relationships between text sequences to predict next items in sequences safely and effectively.
Supported Languages 
English (Primary language for intended use)
Training Details 
Data Sources:
Publicly available online data
Data Volume:
2 trillion tokens
Methodology:
Uses a mix of publicly available online data. Fine-tuned using Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF).
Context Length:
4000
Hardware Used:
A100-80GB (TDP of 350-400W)
Model Architecture:
Optimized transformer architecture
Responsible Ai Considerations 
Fairness:
Model may produce inaccurate, biased, or objectionable outputs.
Transparency:
Transparency measures are in place for users.
Accountability:
Developers should perform safety testing tailored to specific applications.
Mitigation Strategies:
Safety testing and tuning recommended by Meta before deployment.
Input Output 
Input Format:
Text only
Accepted Modalities:
Text
Output Format:
Text only
Performance Tips:
Specific formatting needs for chat versions, including the use of `INST` and `<>` tags, `BOS` and `EOS` tokens, and appropriate whitespace management.
LLM NameLlama 2 7B Hf
Repository 🤗https://huggingface.co/meta-llama/Llama-2-7b-hf 
Model Size7b
Required VRAM13.5 GB
Updated2024-12-21
Maintainermeta-llama
Model Typellama
Model Files  10.0 GB: 1-of-2   3.5 GB: 2-of-2   10.0 GB: 1-of-2   3.5 GB: 2-of-2
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.31.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

Quantized Models of the Llama 2 7B Hf

Model
Likes
Downloads
VRAM
Llama 2 7B GPTQ80560113 GB
Llama 2 7B GGUF178151682 GB
Llama 2 7B AWQ16166593 GB
Llama 2 7B Hf Codealpaca 4bit100 GB
Nb Sau 7B 8K Step100k11913 GB
Llama 2 7B GGML2192572 GB
Llama 2 Medical Consultation3210 GB

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Lloma Step40064K / 13.5 GB800

Rank the Llama 2 7B Hf Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217
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