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Llama 3 70B Fp16 by casperhansen

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Llama 3 70B Fp16 is an open-source language model by casperhansen. Features: 70b LLM, VRAM: 141.9GB, Context: 8K, License: other, Quantized, HF Score: 65.6, LLM Explorer Score: 0.15, Arc: 68.9, HellaSwag: 86.9, MMLU: 69.4, TruthfulQA: 53.7, WinoGrande: 83, GSM8K: 31.8.

  Autotrain compatible   Conversational   En   Endpoints compatible   Facebook   Fp16   Llama   Llama-3   Meta   Pytorch   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/casperhansen/llama-3-70b-fp16 

Llama 3 70B Fp16 Benchmarks

ARC: 68.94 vs 96.7 (so35)-28.7%
HellaSwag: 86.9 vs 95.3 (gpt4)-8.8%
MMLU: 69.37 vs 88.3 (so35)-21.4%
TruthfulQA: 53.67 vs 59 (gpt4)-9%
WinoGrande: 82.95 vs 87.5 (gpt4)-5.2%
GSM8K: 31.77 vs 96.4 (so35)-67%
LLME Score: 0.15023
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 3 70B Fp16 (casperhansen/llama-3-70b-fp16)
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Llama 3 70B Fp16 Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
commercial, research
Applications:
assistant-like chat
Primary Use Cases:
English text and code generation
Limitations:
Out-of-scope for languages other than English without compliance, Risk of misuse if violated acceptable use policy
Considerations:
Developers should tune model for safety based on specific applications.
Additional Notes 
Focused on safe and inclusive text generation practices. Special measures for sensitive applications implemented.
Supported Languages 
en (proficient)
Training Details 
Data Sources:
publicly available online data
Data Volume:
15T+ tokens pretraining, 10M human-annotated examples fine-tuning
Methodology:
Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
8000
Training Time:
7.7M GPU hours
Hardware Used:
H100-80GB GPUs
Model Architecture:
Auto-regressive language model with transformer architecture
Safety Evaluation 
Methodologies:
red teaming, adversarial evaluations
Findings:
Residual risks remain; improved model helpfulness and reduced false refusals compared to Llama 2.
Risk Categories:
child safety, cybersecurity
Ethical Considerations:
Residual risks highlighted
Responsible Ai Considerations 
Fairness:
Emphasis on inclusivity and openness
Transparency:
Open source license and transparency on safety standards
Accountability:
Meta and developers accountable for use adhering to license terms
Mitigation Strategies:
Llama Guard 2 and Code Shield tools for safety
Input Output 
Input Format:
Text input only
Accepted Modalities:
text
Output Format:
Text and code generation
Performance Tips:
Use Reinforcement Learning with Human Feedback for optimal tuning.
Release Notes 
Version:
Meta Llama 3
Date:
April 18, 2024
Notes:
Initial release with enhanced helpfulness and safety measures.
LLM NameLlama 3 70B Fp16
Repository 🤗https://huggingface.co/casperhansen/llama-3-70b-fp16 
Base Model(s)  LLaMA 2 Wizard 70B QLoRA   v2ray/LLaMA-2-Wizard-70B-QLoRA
Model Size70b
Required VRAM141.9 GB
Updated2025-10-29
Maintainercasperhansen
Model Typellama
Model Files  4.6 GB: 1-of-30   4.7 GB: 2-of-30   5.0 GB: 3-of-30   5.0 GB: 4-of-30   4.7 GB: 5-of-30   4.7 GB: 6-of-30   4.7 GB: 7-of-30   5.0 GB: 8-of-30   5.0 GB: 9-of-30   4.7 GB: 10-of-30   4.7 GB: 11-of-30   4.7 GB: 12-of-30   5.0 GB: 13-of-30   5.0 GB: 14-of-30   4.7 GB: 15-of-30   4.7 GB: 16-of-30   4.7 GB: 17-of-30   5.0 GB: 18-of-30   5.0 GB: 19-of-30   4.7 GB: 20-of-30   4.7 GB: 21-of-30   4.7 GB: 22-of-30   5.0 GB: 23-of-30   5.0 GB: 24-of-30   4.7 GB: 25-of-30   4.7 GB: 26-of-30   4.7 GB: 27-of-30   5.0 GB: 28-of-30   5.0 GB: 29-of-30   2.1 GB: 30-of-30
Supported Languagesen
Quantization Typefp16
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.40.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16

Quantized Models of the Llama 3 70B Fp16

Model
Likes
Downloads
VRAM
NorskGPT Llama 3 70B Adapter750 GB

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Rank the Llama 3 70B Fp16 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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