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Functionary Medium V3.0 AWQ by meetkai

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  4-bit   Autotrain compatible   Awq   Conversational   Endpoints compatible   Llama   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/meetkai/functionary-medium-v3.0-AWQ 

Functionary Medium V3.0 AWQ Benchmarks

LLME Score: 0.17558
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Functionary Medium V3.0 AWQ Parameters and Internals

LLM NameFunctionary Medium V3.0 AWQ
Repository 🤗https://huggingface.co/meetkai/functionary-medium-v3.0-AWQ 
Model Size11.3b
Required VRAM39.9 GB
Updated2025-06-09
Maintainermeetkai
Model Typellama
Model Files  5.0 GB: 1-of-9   4.9 GB: 2-of-9   4.9 GB: 3-of-9   4.9 GB: 4-of-9   4.9 GB: 5-of-9   4.9 GB: 6-of-9   4.9 GB: 7-of-9   3.4 GB: 8-of-9   2.1 GB: 9-of-9
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licensemit
Context Length8192
Model Max Length8192
Transformers Version4.41.2
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typefloat16
Functionary Medium V3.0 AWQ (meetkai/functionary-medium-v3.0-AWQ)

Best Alternatives to Functionary Medium V3.0 AWQ

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...T Agents Llama3 5.0.95 DPO AWQ8K / 39.9 GB150
...T Agents Llama3 5.0.79 SFT AWQ8K / 39.9 GB120
Note: green Score (e.g. "73.2") means that the model is better than meetkai/functionary-medium-v3.0-AWQ.

Rank the Functionary Medium V3.0 AWQ 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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