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Nepali Agriculture QA by Hemg

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  4-bit   4bit   Autotrain compatible Base model:quantized:unsloth/m... Base model:unsloth/mistral-7b-...   Bitsandbytes   En   Endpoints compatible   Mistral   Quantized   Region:us   Safetensors   Sft   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/Hemg/Nepali-Agriculture-QA 

Nepali Agriculture QA Benchmarks

LLME Score: 0.20342
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Nepali Agriculture QA (Hemg/Nepali-Agriculture-QA)

Nepali Agriculture QA Parameters and Internals

Model Type 
text-generation-inference, transformers, unsloth, mistral, trl, sft
Additional Notes 
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
LLM NameNepali Agriculture QA
Repository 🤗https://huggingface.co/Hemg/Nepali-Agriculture-QA 
Base Model(s)  Mistral 7B Bnb 4bit   unsloth/mistral-7b-bnb-4bit
Model Size7b
Required VRAM4.1 GB
Updated2025-01-15
MaintainerHemg
Model Typemistral
Model Files  4.1 GB
Supported Languagesen
Quantization Type4bit
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.41.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than Hemg/Nepali-Agriculture-QA.

Rank the Nepali Agriculture QA 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 v20241227
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