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Speechless Llama2 Hermes Orca Platypus Wizardlm 13B by uukuguy

 »  All LLMs  »  uukuguy  »  Speechless Llama2 Hermes Orca Platypus Wizardlm 13B   URL Share it on

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  Arxiv:2307.09288   Autotrain compatible Dataset:garage-baind/open-plat...   En   Facebook   Llama   Llama2   Meta   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b 

Speechless Llama2 Hermes Orca Platypus Wizardlm 13B Benchmarks

MMLU Pro: 17.32
GPQA: 2.68
MUSR: 17.75
BBH: 26.79
IFEval: 45.62 vs 88 (so35)-48.2%
ARC: 59.64 vs 96.7 (so35)-38.3%
HellaSwag: 82.7 vs 95.3 (gpt4)-13.2%
MMLU: 58.3 vs 88.3 (so35)-34%
TruthfulQA: 56 vs 59 (gpt4)-5.1%
WinoGrande: 75.37 vs 87.5 (gpt4)-13.9%
GSM8K: 13.12 vs 96.4 (so35)-86.4%
MATH Lvl 5: 2.04
LLME Score: 0.20114
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Speechless Llama2 Hermes Orca Platypus Wizardlm 13B (uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b)
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Speechless Llama2 Hermes Orca Platypus Wizardlm 13B Parameters and Internals

Model Type 
pretrained, fine-tuned
Use Cases 
Areas:
commercial, research
Applications:
assistant-like chat, natural language generation tasks
Primary Use Cases:
chat assistant, language tasks
Limitations:
Limited to English, Possibility of generating unpredicted outputs
Considerations:
Specific formatting needed for chat versions
Supported Languages 
English (optimized for dialogue use cases)
Training Details 
Data Sources:
publicly available online data, publicly available instruction datasets
Data Volume:
2 trillion tokens
Methodology:
Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
4000
Training Time:
January 2023 to July 2023
Hardware Used:
Meta's Research Super Cluster, production clusters, third-party cloud compute
Model Architecture:
auto-regressive language model with optimized transformer architecture
Safety Evaluation 
Methodologies:
internal evaluations, automatic safety benchmarks
Findings:
competitive safety benchmarks compared to open-source models
Risk Categories:
potential for inaccurate, biased or objectionable responses
Ethical Considerations:
Developers should perform safety testing tailored to their applications
Responsible Ai Considerations 
Fairness:
Testing mostly conducted in English
Transparency:
Model outputs cannot be fully predicted
Accountability:
Meta
Mitigation Strategies:
Responsible Use Guide provided
Input Output 
Input Format:
text only
Accepted Modalities:
text
Output Format:
text only
Performance Tips:
Use recommended formatting for chat versions
Release Notes 
Version:
2
Date:
July 2023
Notes:
Improved model safety with community feedback
LLM NameSpeechless Llama2 Hermes Orca Platypus Wizardlm 13B
Repository 🤗https://huggingface.co/uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b 
Model Size13b
Required VRAM26.7 GB
Updated2025-09-23
Maintaineruukuguy
Model Typellama
Model Files  10.3 GB: 1-of-3   9.9 GB: 2-of-3   6.5 GB: 3-of-3
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Context Length4096
Model Max Length4096
Transformers Version4.32.1
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

Quantized Models of the Speechless Llama2 Hermes Orca Platypus Wizardlm 13B

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...rca Platypus WizardLM 13B GGML675 GB

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Rank the Speechless Llama2 Hermes Orca Platypus Wizardlm 13B 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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