Go Bruins V2 AWQ by TheBloke

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  4-bit   Autotrain compatible   Awq Dataset:athirdpath/dpo pairs-r...   Dataset:intel/orca dpo pairs   En   Mistral   Quantized   Region:us   Safetensors

Go Bruins V2 AWQ Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Go Bruins V2 AWQ (TheBloke/go-bruins-v2-AWQ)

Go Bruins V2 AWQ Parameters and Internals

Model Type 
text-generation
Supported Languages 
en (proficient)
Training Details 
Data Sources:
athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW, Intel/orca_dpo_pairs
Methodology:
Direct Preference Optimization (DPO)
Hardware Used:
Massed Compute
Input Output 
Input Format:
{prompt}
Accepted Modalities:
text
Output Format:
text
LLM NameGo Bruins V2 AWQ
Repository ๐Ÿค—https://huggingface.co/TheBloke/go-bruins-v2-AWQ 
Model NameGo Bruins v2
Model CreatorRyan Witzman
Base Model(s)  Go Bruins V2   rwitz/go-bruins-v2
Model Size1.2b
Required VRAM4.2 GB
Updated2025-02-22
MaintainerTheBloke
Model Typemistral
Model Files  4.2 GB
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licensemit
Context Length32768
Model Max Length32768
Transformers Version4.35.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/go-bruins-v2-AWQ.

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227