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Quintellect 10.7B by Walmart-the-bag

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Quintellect 10.7B is an open-source language model by Walmart-the-bag. Features: 10.7b LLM, VRAM: 21.4GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.13, Arc: 65, HellaSwag: 84.5, MMLU: 63.3, GSM8K: 60.6.

  Autotrain compatible   Code   Conversational Dataset:sahil2801/codealpaca-2...   En   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Walmart-the-bag/Quintellect-10.7B 

Quintellect 10.7B Benchmarks

ARC: 65.02 vs 96.7 (so35)-32.8%
HellaSwag: 84.48 vs 95.3 (gpt4)-11.4%
MMLU: 63.28 vs 88.3 (so35)-28.3%
GSM8K: 60.58 vs 96.4 (so35)-37.2%
LLME Score: 0.13347
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Quintellect 10.7B (Walmart-the-bag/Quintellect-10.7B)
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Quintellect 10.7B Parameters and Internals

Model Type 
code generation
Use Cases 
Areas:
coding knowledge accessibility
Primary Use Cases:
coding assistance, code generation
Additional Notes 
This model is designed for coding tasks, specifically for languages like Python and JavaScript. It's beneficial for both standard programming tasks and code creation from scratch.
Supported Languages 
en (proficient)
Input Output 
Input Format:
Alpaca prompt format
Accepted Modalities:
text
Output Format:
text response
LLM NameQuintellect 10.7B
Repository 🤗https://huggingface.co/Walmart-the-bag/Quintellect-10.7B 
Model Size10.7b
Required VRAM21.4 GB
Updated2025-03-15
MaintainerWalmart-the-bag
Model Typemistral
Model Files  6.0 GB: 1-of-4   5.9 GB: 2-of-4   6.0 GB: 3-of-4   3.5 GB: 4-of-4
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.38.2
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 Walmart-the-bag/Quintellect-10.7B.

Rank the Quintellect 10.7B 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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