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Apricot Wildflower 20 AWQ by TheBloke

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  4-bit   Awq Base model:crumb/apricot-wildf... Base model:quantized:crumb/apr...   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/apricot-wildflower-20-AWQ 

Apricot Wildflower 20 AWQ Benchmarks

LLME Score: 0.12467
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Apricot Wildflower 20 AWQ (TheBloke/apricot-wildflower-20-AWQ)
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Apricot Wildflower 20 AWQ Parameters and Internals

Model Type 
mistral, low-bit quantization
Additional Notes 
AWQ models supported on Linux and Windows with NVidia GPUs only. macOS users advised to use GGUF models.
Training Details 
Data Sources:
Openwebtext2, Mixtral training logits
Methodology:
Finetuned for 1k steps using a combined LM loss and distillation loss, with a >=20 Reddit score filter.
Model Architecture:
Mistral-7b
LLM NameApricot Wildflower 20 AWQ
Repository 🤗https://huggingface.co/TheBloke/apricot-wildflower-20-AWQ 
Model NameApricot Wildflower 20
Model CreatorMaxine
Base Model(s)  Apricot Wildflower 20   crumb/apricot-wildflower-20
Model Size7.2b
Required VRAM4.2 GB
Updated2026-02-15
MaintainerTheBloke
Model Typemistral
Model Files  4.2 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.2
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/apricot-wildflower-20-AWQ.

Rank the Apricot Wildflower 20 AWQ 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 v20241124
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