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GPT Neo FineTuned by yashmathur0310

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GPT Neo FineTuned is an open-source language model by yashmathur0310. Features: 125.2m LLM, VRAM: 0.5GB, Context: 2K, Fine-Tuned, LLM Explorer Score: 0.14.

  Autotrain compatible   Endpoints compatible   Finetuned   Gpt neo   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/yashmathur0310/GPT-Neo-FineTuned 

GPT Neo FineTuned Benchmarks

LLME Score: 0.13911
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
GPT Neo FineTuned (yashmathur0310/GPT-Neo-FineTuned)
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GPT Neo FineTuned Parameters and Internals

Model Type 
text classification, sentiment analysis
Use Cases 
Areas:
Customer feedback analysis, Social media monitoring
Applications:
E-commerce review analysis, Film industry sentiment tracking
Primary Use Cases:
Sentiment analysis for product reviews, Automated social media sentiment responses
Limitations:
Not effective for sarcasm, Limited non-English language support
Considerations:
Punctuation and context complexity can affect accuracy.
Additional Notes 
Integration examples provided for web applications.
Supported Languages 
English (Fluent), Spanish (Moderate)
Training Details 
Data Sources:
Amazon reviews, Yelp reviews, IMDB reviews
Data Volume:
500,000 records
Methodology:
Fine-tuned with labeled sentiment data
Context Length:
2048
Training Time:
72 hours
Hardware Used:
8 NVIDIA A100 GPUs
Model Architecture:
Transformer-based
Safety Evaluation 
Methodologies:
Adversarial testing, Bias testing
Findings:
Handles explicit language neutrally, Occasionally polarized in political contexts
Risk Categories:
Bias, Misinformation
Ethical Considerations:
Ensure diverse dataset representation to reduce bias.
Responsible Ai Considerations 
Fairness:
Model outputs should be checked for bias, particularly in culturally sensitive contexts.
Transparency:
Implement interpretability features for sentiment predictions.
Accountability:
Developers are responsible for auditing and maintaining model output accuracy.
Mitigation Strategies:
Regular audits and updates with diverse datasets.
Input Output 
Input Format:
Prompts should be prefixed with '[Q]' and use curly braces for input.
Accepted Modalities:
text
Output Format:
Structured JSON with sentiment classification and confidence score.
Performance Tips:
Provide clear and contextually balanced sentences for improved accuracy.
Release Notes 
Version:
v1.0
Date:
2023-10-15
Notes:
Initial release of the fine-tuned sentiment model.
Version:
v1.1
Date:
2023-10-22
Notes:
Improved handling of ambiguous language cases.
LLM NameGPT Neo FineTuned
Repository 🤗https://huggingface.co/yashmathur0310/GPT-Neo-FineTuned 
Model Size125.2m
Required VRAM0.5 GB
Updated2025-09-23
Maintaineryashmathur0310
Model Typegpt_neo
Model Files  0.5 GB
Model ArchitectureGPTNeoForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.41.2
Tokenizer ClassGPT2Tokenizer
Vocabulary Size50257
Torch Data Typefloat32
Activation Functiongelu_new
Errorsreplace

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Instruction Following and Task Automation  
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