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Llama 2 70B Hf by meta-llama

 »  All LLMs  »  meta-llama  »  Llama 2 70B 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-2-70b-hf 

Llama 2 70B Hf Benchmarks

MMLU Pro: 30.2
GPQA: 7.05
MUSR: 9.78
BBH: 35.9
IFEval: 24.07 vs 88 (so35)-72.6%
ARC: 67.32 vs 96.7 (so35)-30.4%
HellaSwag: 87.33 vs 95.3 (gpt4)-8.4%
MMLU: 69.83 vs 88.3 (so35)-20.9%
TruthfulQA: 44.92 vs 59 (gpt4)-23.9%
WinoGrande: 83.74 vs 87.5 (gpt4)-4.3%
GSM8K: 54.06 vs 96.4 (so35)-43.9%
MATH Lvl 5: 2.87
LLME Score: 0.3152
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 2 70B Hf (meta-llama/Llama-2-70b-hf)

Llama 2 70B 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 70B Hf
Repository 🤗https://huggingface.co/meta-llama/Llama-2-70b-hf 
Model Size70b
Required VRAM138 GB
Updated2024-12-22
Maintainermeta-llama
Model Typellama
Model Files  9.8 GB: 1-of-15   9.8 GB: 2-of-15   10.0 GB: 3-of-15   9.8 GB: 4-of-15   9.8 GB: 5-of-15   9.8 GB: 6-of-15   10.0 GB: 7-of-15   9.8 GB: 8-of-15   9.8 GB: 9-of-15   9.8 GB: 10-of-15   10.0 GB: 11-of-15   9.8 GB: 12-of-15   9.8 GB: 13-of-15   9.5 GB: 14-of-15   0.5 GB: 15-of-15   9.8 GB: 1-of-15   9.8 GB: 2-of-15   10.0 GB: 3-of-15   9.8 GB: 4-of-15   9.8 GB: 5-of-15   9.8 GB: 6-of-15   10.0 GB: 7-of-15   9.8 GB: 8-of-15   9.8 GB: 9-of-15   9.8 GB: 10-of-15   10.0 GB: 11-of-15   9.8 GB: 12-of-15   9.8 GB: 13-of-15   9.5 GB: 14-of-15   0.5 GB: 15-of-15
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.32.0.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 70B Hf

Model
Likes
Downloads
VRAM
Llama 2 70B GGUF31185129 GB
Tulu 2 DPO 70B 4.0bpw H6 EXL211635 GB
Tulu 2 70B 6.0bpw H6 EXL211552 GB
Tulu 2 DPO 70B 5.0bpw H6 EXL221643 GB
Tulu 2 DPO 70B 2.4bpw H6 EXL211621 GB
Llama 2 70B GPTQ83155635 GB
Llama 2 70B AWQ1454836 GB

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Note: green Score (e.g. "73.2") means that the model is better than meta-llama/Llama-2-70b-hf.

Rank the Llama 2 70B 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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Release v20241217
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