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Apricot Wildflower 20 by crumb

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  Autotrain compatible   Endpoints compatible   Mistral   Model-index   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/crumb/apricot-wildflower-20 

Apricot Wildflower 20 Benchmarks

ARC: 59.64 vs 96.7 (so35)-38.3%
HellaSwag: 81.76 vs 95.3 (gpt4)-14.2%
MMLU: 63.38 vs 88.3 (so35)-28.2%
TruthfulQA: 41.76 vs 59 (gpt4)-29.2%
WinoGrande: 77.9 vs 87.5 (gpt4)-11%
GSM8K: 33.97 vs 96.4 (so35)-64.8%
LLME Score: 0.15474
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 (crumb/apricot-wildflower-20)
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Apricot Wildflower 20 Parameters and Internals

Model Type 
text generation
Additional Notes 
emphasized the casual nature of the project's inception.
Training Details 
Data Sources:
Openwebtext2 with a >=20 reddit score filter
Methodology:
Finetuned for 1k steps with a combined lm loss and distillation loss using training logits from Mixtral.
Input Output 
Accepted Modalities:
text
Output Format:
text
LLM NameApricot Wildflower 20
Repository 🤗https://huggingface.co/crumb/apricot-wildflower-20 
Model Size7b
Required VRAM14.4 GB
Updated2025-09-23
Maintainercrumb
Model Typemistral
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

Quantized Models of the Apricot Wildflower 20

Model
Likes
Downloads
VRAM
Apricot Wildflower 20 GGUF3623 GB
Apricot Wildflower 20 AWQ134 GB
Apricot Wildflower 20 GPTQ114 GB

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Hebrew Mistral 7B 200K256K / 30 GB131615
Astral 256K 7B V2250K / 14.4 GB50
Astral 256K 7B250K / 14.4 GB50
Note: green Score (e.g. "73.2") means that the model is better than crumb/apricot-wildflower-20.

Rank the Apricot Wildflower 20 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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