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Gpt2 Large Conversational by Locutusque

 »  All LLMs  »  Locutusque  »  Gpt2 Large Conversational   URL Share it on

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Dataset:crumb/clean-instruct-4... Dataset:locutusque/columnedcha...   Doi:10.57967/hf/1215   En   Endpoints compatible   Gpt2   Instruct   Pytorch   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/Locutusque/gpt2-large-conversational 

Gpt2 Large Conversational Benchmarks

ARC: 26.96 vs 96.7 (so35)-72.1%
HellaSwag: 44.98 vs 95.3 (gpt4)-52.8%
MMLU: 26.33 vs 88.3 (so35)-70.2%
TruthfulQA: 39.6 vs 59 (gpt4)-32.9%
WinoGrande: 56.04 vs 87.5 (gpt4)-36%
GSM8K: 0.08 vs 96.4 (so35)-99.9%
LLME Score: 0.14348
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gpt2 Large Conversational (Locutusque/gpt2-large-conversational)
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Gpt2 Large Conversational Parameters and Internals

Model Type 
Language Modeling
Use Cases 
Areas:
chatbots, virtual assistants, customer service applications
Applications:
text-based and voice-based interfaces
Primary Use Cases:
Generating Conversational Responses
Limitations:
Limited training time on a weak computer, Produces irrelevant or nonsensical responses, Not fine-tuned to remember chat history, Unable to provide follow-up responses, Does not know the answer to many questions
Additional Notes 
Model is deprecated; a better performing version is available.
Supported Languages 
en (proficient)
Training Details 
Data Sources:
Locutusque/ColumnedChatCombined, crumb/Clean-Instruct-440k
Data Volume:
550,000 examples
Methodology:
Fine-tuned using maximum likelihood estimation
Training Time:
687,500 steps
Hardware Used:
1x Nvidia Titan V
Model Architecture:
Transformer-based, multi-layered decoder-only with self-attention mechanisms.
Input Output 
Input Format:
<|USER|> {user prompt} <|ASSISTANT|>
Accepted Modalities:
text
Output Format:
<|USER|> {dataset prompt} <|ASSISTANT|> {dataset output}
LLM NameGpt2 Large Conversational
Repository 🤗https://huggingface.co/Locutusque/gpt2-large-conversational 
Model Size774m
Required VRAM1.6 GB
Updated2026-03-02
MaintainerLocutusque
Model Typegpt2
Instruction-BasedYes
Model Files  1.6 GB   1.6 GB
Supported Languagesen
Model ArchitectureGPT2LMHeadModel
Licenseopenrail
Model Max Length1024
Transformers Version4.30.1
Tokenizer ClassGPT2Tokenizer
Vocabulary Size50260
Torch Data Typebfloat16
Activation Functiongelu_new

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Rank the Gpt2 Large Conversational 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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