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MedPaxTral 2x7b by skuma307

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MedPaxTral 2x7b is an open-source language model by skuma307. Features: 10.3b LLM, VRAM: 20.7GB, Context: 32K, License: apache-2.0, MoE, LLM Explorer Score: 0.12.

  Autotrain compatible   Conversational   En   Endpoints compatible   Medical   Mixtral   Moe   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/skuma307/MedPaxTral-2x7b 

MedPaxTral 2x7b Benchmarks

LLME Score: 0.11926
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
MedPaxTral 2x7b (skuma307/MedPaxTral-2x7b)
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MedPaxTral 2x7b Parameters and Internals

Model Type 
text generation
Additional Notes 
A medical Model of Experts developed by amalgamating three leading medical models: BioMistral, Meditron, and Medalpaca using the MergeKit library.
Supported Languages 
en ()
LLM NameMedPaxTral 2x7b
Repository 🤗https://huggingface.co/skuma307/MedPaxTral-2x7b 
Model Size10.3b
Required VRAM20.7 GB
Updated2025-02-04
Maintainerskuma307
Model Typemixtral
Model Files  2.0 GB: 1-of-11   2.0 GB: 2-of-11   2.0 GB: 3-of-11   2.0 GB: 4-of-11   2.0 GB: 5-of-11   2.0 GB: 6-of-11   2.0 GB: 7-of-11   2.0 GB: 8-of-11   2.0 GB: 9-of-11   2.0 GB: 10-of-11   0.7 GB: 11-of-11
Supported Languagesen
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size32000
Torch Data Typefloat16

Best Alternatives to MedPaxTral 2x7b

Best Alternatives
Context / RAM
Downloads
Likes
Medical MoE 2x7b32K / 20.7 GB60
IbuMedTrial 2x7B32K / 20.7 GB70
MedPaxTral 2x7b32K / 20.7 GB71
Note: green Score (e.g. "73.2") means that the model is better than skuma307/MedPaxTral-2x7b.

Rank the MedPaxTral 2x7b 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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