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Speechless Codellama Dolphin Orca Platypus 13B by uukuguy

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Speechless Codellama Dolphin Orca Platypus 13B is an open-source language model by uukuguy. Features: 13b LLM, VRAM: 26GB, Context: 16K, License: llama2, Code Generating, HF Score: 46.7, LLM Explorer Score: 0.13, Arc: 44.8, HellaSwag: 68.6, MMLU: 44, TruthfulQA: 46.3, WinoGrande: 66.9, GSM8K: 9.6.

  Arxiv:2308.12950   Code   Codegen   Dataset:ehartford/dolphin Dataset:garage-baind/open-plat...   Dataset:open-orca/openorca   En   Endpoints compatible   Llama   Llama2   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/uukuguy/speechless-codellama-dolphin-orca-platypus-13b 

Speechless Codellama Dolphin Orca Platypus 13B Benchmarks

ARC: 44.8 vs 96.7 (so35)-53.7%
HellaSwag: 68.6 vs 95.3 (gpt4)-28%
MMLU: 44.03 vs 88.3 (so35)-50.1%
TruthfulQA: 46.28 vs 59 (gpt4)-21.6%
WinoGrande: 66.93 vs 87.5 (gpt4)-23.5%
GSM8K: 9.55 vs 96.4 (so35)-90.1%
LLME Score: 0.13364
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Speechless Codellama Dolphin Orca Platypus 13B (uukuguy/speechless-codellama-dolphin-orca-platypus-13b)
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Speechless Codellama Dolphin Orca Platypus 13B Parameters and Internals

Model Type 
text generation, code synthesis
Use Cases 
Areas:
commercial, research
Applications:
general code synthesis, code understanding, Python programming, instruction following
Primary Use Cases:
code completion, infilling
Limitations:
May produce inaccurate or objectionable responses, Not fully evaluated for non-English languages
Considerations:
Careful safety evaluation before deployment
Additional Notes 
Trained with 400K GPU hours on Meta’s Research Super Cluster.
Supported Languages 
en (high proficiency), programming_languages (high proficiency)
Training Details 
Data Sources:
ehartford/dolphin, Open-Orca/OpenOrca, garage-bAInd/Open-Platypus
Methodology:
Fine-tuned with Dolphin (1% GPT4), Orca (1% GPT4), and Platypus (100%) datasets, using custom training libraries and Meta’s Research Super Cluster.
Training Time:
400K GPU hours
Hardware Used:
A100-80GB GPUs
Model Architecture:
Auto-regressive language model with an optimized transformer architecture
Responsible Ai Considerations 
Mitigation Strategies:
Developers should perform safety testing and tuning tailored to their specific applications of the model. See the Responsible Use Guide at https://ai.meta.com/llama/responsible-user-guide
Input Output 
Input Format:
Models input text only.
Accepted Modalities:
text
Output Format:
Models generate text only.
LLM NameSpeechless Codellama Dolphin Orca Platypus 13B
Repository 🤗https://huggingface.co/uukuguy/speechless-codellama-dolphin-orca-platypus-13b 
Model Size13b
Required VRAM26 GB
Updated2026-04-02
Maintaineruukuguy
Model Typellama
Model Files  9.9 GB: 1-of-3   9.9 GB: 2-of-3   6.2 GB: 3-of-3
Supported Languagesen
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length16384
Model Max Length16384
Transformers Version4.32.1
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32016
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than uukuguy/speechless-codellama-dolphin-orca-platypus-13b.

Rank the Speechless Codellama Dolphin Orca Platypus 13B 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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