ChessLlama by Q-bert

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  Autotrain compatible   Chess   Chess-engines   Chessllama Dataset:q-bert/elite-chess-gam...   Endpoints compatible   Llama   Region:us   Safetensors
Model Card on HF ๐Ÿค—: https://huggingface.co/Q-bert/ChessLlama 

ChessLlama Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
ChessLlama (Q-bert/ChessLlama)

ChessLlama Parameters and Internals

Model Type 
Llama Architecture
Additional Notes 
This pre-trained model has been trained on the Llama architecture with the games of grand master chess players.
Training Details 
Data Sources:
Q-bert/Elite-Chess-Games
Methodology:
Fully trained from scratch with random weights using Hugging Face Trainer for 1200 steps.
Model Architecture:
Llama Architecture
Input Output 
Input Format:
Universal Chess Interface (UCI)
Accepted Modalities:
text
LLM NameChessLlama
Repository ๐Ÿค—https://huggingface.co/Q-bert/ChessLlama 
Model Size142.6m
Required VRAM0.6 GB
Updated2025-02-05
MaintainerQ-bert
Model Typellama
Model Files  0.6 GB
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.42.4
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size2010
Torch Data Typefloat32

Rank the ChessLlama 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 v20241227