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Apricot Wildflower 20 GGUF by TheBloke

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Apricot Wildflower 20 GGUF is an open-source language model by TheBloke. Features: LLM, VRAM: 3.1GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.11.

Base model:crumb/apricot-wildf... Base model:quantized:crumb/apr...   Gguf   Mistral   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/TheBloke/apricot-wildflower-20-GGUF 

Apricot Wildflower 20 GGUF Benchmarks

LLME Score: 0.1087
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 GGUF (TheBloke/apricot-wildflower-20-GGUF)
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Apricot Wildflower 20 GGUF Parameters and Internals

Model Type 
mistral
Additional Notes 
The quantized versions of the model in GGUF format were provided by TheBloke using hardware from Massed Compute. GGUF supports various UIs and libraries and is compatible with versions of llama.cpp from August 27, 2023 onwards.
Training Details 
Data Sources:
Openwebtext2
Methodology:
Fine-tuned for 1k steps with a combined lm loss and distillation loss on Openwebtext2 with a >=20 reddit score filter with training logits from Mixtral.
Input Output 
Input Format:
{prompt}
LLM NameApricot Wildflower 20 GGUF
Repository 🤗https://huggingface.co/TheBloke/apricot-wildflower-20-GGUF 
Model NameApricot Wildflower 20
Model CreatorMaxine
Base Model(s)  Apricot Wildflower 20   crumb/apricot-wildflower-20
Required VRAM3.1 GB
Updated2026-05-08
MaintainerTheBloke
Model Typemistral
Model Files  3.1 GB   3.8 GB   3.5 GB   3.2 GB   4.1 GB   4.4 GB   4.1 GB   5.0 GB   5.1 GB   5.0 GB   5.9 GB   7.7 GB
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureAutoModel
Licenseapache-2.0

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GOAT Llama3.1 V0.10K / 0.2 GB143
Note: green Score (e.g. "73.2") means that the model is better than TheBloke/apricot-wildflower-20-GGUF.

Rank the Apricot Wildflower 20 GGUF Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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