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Go Bruins GPTQ by TheBloke

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  4-bit   Autotrain compatible   Dataset:intel/orca dpo pairs   En   Gptq   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/go-bruins-GPTQ 

Go Bruins GPTQ Benchmarks

LLME Score: 0.14037
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Go Bruins GPTQ (TheBloke/go-bruins-GPTQ)

Go Bruins GPTQ Parameters and Internals

Model Type 
mistral, text-generation
Supported Languages 
en (high)
Training Details 
Data Sources:
Intel/orca_dpo_pairs
Methodology:
Direct Preference Optimization (DPO)
Model Architecture:
Q-bert/MetaMath-Cybertron-Starling
Input Output 
Input Format:
{prompt}
LLM NameGo Bruins GPTQ
Repository 🤗https://huggingface.co/TheBloke/go-bruins-GPTQ 
Model NameGo Bruins
Model CreatorRyan Witzman
Base Model(s)  Go Bruins   rwitz/go-bruins
Model Size1.2b
Required VRAM4.2 GB
Updated2025-04-30
MaintainerTheBloke
Model Typemistral
Model Files  4.2 GB
Supported Languagesen
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureMistralForCausalLM
Licensemit
Context Length32768
Model Max Length32768
Transformers Version4.35.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/go-bruins-GPTQ.

Rank the Go Bruins GPTQ 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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Release v20241227
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