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GodziLLa2 70B by MayaPH

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  1863   
  Arxiv:1803.05457   Arxiv:1903.00161   Arxiv:1905.07830   Arxiv:1907.10641   Arxiv:2009.03300   Arxiv:2109.07958   Arxiv:2110.14168   Autotrain compatible   Cot Dataset:mlabonne/guanaco-llama...   Llama   Merge   Mix   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/MayaPH/GodziLLa2-70B 

GodziLLa2 70B Benchmarks

ARC: 71.42 vs 96.7 (so35)-26.1%
HellaSwag: 87.53 vs 95.3 (gpt4)-8.2%
MMLU: 69.88 vs 88.3 (so35)-20.9%
TruthfulQA: 61.54 vs 59 (gpt4)4.3%
WinoGrande: 83.19 vs 87.5 (gpt4)-4.9%
GSM8K: 43.21 vs 96.4 (so35)-55.2%
LLME Score: 0.13154
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
GodziLLa2 70B (MayaPH/GodziLLa2-70B)
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GodziLLa2 70B Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
research, experimental
Applications:
text generation, instruction-following tasks
Primary Use Cases:
Stress testing limitations of composite LLMs
Limitations:
Uncensored content, Potential biases
Considerations:
Exercise caution when discussing personal/confidential matters.
Additional Notes 
Model is experimental and combines various proprietary components. May require quantization for efficient deployment.
Supported Languages 
primary (English), proficiency (Fluent)
Training Details 
Data Sources:
mlabonne/guanaco-llama2-1k
Methodology:
Experimental combination of proprietary LoRAs and datasets.
Model Architecture:
Composite, instruction-following LLMs
Safety Evaluation 
Methodologies:
Proprietary training techniques
Findings:
May contain uncensored content including violence, explicit language, and sexual content.
Risk Categories:
Bias, Inappropriate Content
Ethical Considerations:
Uncensored, may reflect biases from training data.
Responsible Ai Considerations 
Fairness:
Potential biases in responses due to training data.
Transparency:
Model's inner workings and training data are proprietary.
Accountability:
Users are responsible for their own decisions when using the model.
Mitigation Strategies:
Implement guardrails for safe/aligned usage.
Input Output 
Input Format:
Instruction-Response
Accepted Modalities:
text
Output Format:
Text output
Performance Tips:
Consider using quantized model versions to save resources.
Release Notes 
Version:
Release August 11, 2023
Notes:
Debuted on Open LLM Leaderboard at rank #2 (November 10, 2023).
LLM NameGodziLLa2 70B
Repository 🤗https://huggingface.co/MayaPH/GodziLLa2-70B 
Model Size70b
Required VRAM134.2 GB
Updated2025-09-23
MaintainerMayaPH
Model Typellama
Model Files  9.4 GB: 1-of-29   9.3 GB: 2-of-29   10.0 GB: 3-of-29   9.9 GB: 4-of-29   9.3 GB: 5-of-29   9.3 GB: 6-of-29   9.3 GB: 7-of-29   10.0 GB: 8-of-29   9.9 GB: 9-of-29   9.3 GB: 10-of-29   9.3 GB: 11-of-29   9.3 GB: 12-of-29   10.0 GB: 13-of-29   9.9 GB: 14-of-29   9.3 GB: 15-of-29   9.3 GB: 16-of-29   9.3 GB: 17-of-29   10.0 GB: 18-of-29   9.9 GB: 19-of-29   9.3 GB: 20-of-29   9.3 GB: 21-of-29   9.3 GB: 22-of-29   10.0 GB: 23-of-29   9.9 GB: 24-of-29   9.3 GB: 25-of-29   9.3 GB: 26-of-29   9.3 GB: 27-of-29   10.0 GB: 28-of-29   7.6 GB: 29-of-29   9.4 GB: 1-of-29   9.3 GB: 2-of-29   10.0 GB: 3-of-29   9.9 GB: 4-of-29   9.3 GB: 5-of-29   9.3 GB: 6-of-29   9.3 GB: 7-of-29   10.0 GB: 8-of-29   9.9 GB: 9-of-29   9.3 GB: 10-of-29   9.3 GB: 11-of-29   9.3 GB: 12-of-29   10.0 GB: 13-of-29   9.9 GB: 14-of-29
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.32.0.dev0
Vocabulary Size32000
Torch Data Typefloat32

Quantized Models of the GodziLLa2 70B

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Likes
Downloads
VRAM
GodziLLa2 70B GGUF1049329 GB
GodziLLa2 70B AWQ41936 GB
GodziLLa2 70B GGML91028 GB
GodziLLa2 70B GPTQ42235 GB

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Note: green Score (e.g. "73.2") means that the model is better than MayaPH/GodziLLa2-70B.

Rank the GodziLLa2 70B 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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