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Humanish Mistral Nemo Instruct 2407 by HumanLLMs

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Humanish Mistral Nemo Instruct 2407 is an open-source language model by HumanLLMs. Features: 12.2b LLM, VRAM: 24.5GB, Context: 1000K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.23.

  Axolotl Base model:finetune:mistralai/... Base model:mistralai/mistral-n...   Dpo   Generated from trainer   Instruct   Mistral   Model-index   Region:us   Safetensors   Sharded   Tensorflow   Trl
Model Card on HF 🤗: https://huggingface.co/HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407 

Humanish Mistral Nemo Instruct 2407 Benchmarks

MMLU Pro: 28.01
GPQA: 5.03
MUSR: 9.4
BBH: 32.71
IFEval: 54.51 vs 88 (so35)-38.1%
MATH Lvl 5: 13.67
LLME Score: 0.2265
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Humanish Mistral Nemo Instruct 2407 (HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407)
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Humanish Mistral Nemo Instruct 2407 Parameters and Internals

Model Type 
MistralForCausalLM
Additional Notes 
Built with Axolotl, version 0.4.1. See axolotl config.
Training Details 
Data Sources:
HumanLLMs/humanish-dpo-project
Context Length:
8192
Hardware Used:
multi-GPU, 2 devices
LLM NameHumanish Mistral Nemo Instruct 2407
Repository 🤗https://huggingface.co/HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407 
Base Model(s)  Mistral Nemo Instruct 2407   mistralai/Mistral-Nemo-Instruct-2407
Model Size12.2b
Required VRAM24.5 GB
Updated2024-10-15
MaintainerHumanLLMs
Model Typemistral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-5   4.9 GB: 2-of-5   4.9 GB: 3-of-5   4.9 GB: 4-of-5   4.9 GB: 5-of-5
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length1024000
Model Max Length1024000
Transformers Version4.45.1
Tokenizer ClassPreTrainedTokenizerFast
Padding Token</s>
Vocabulary Size131072
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than HumanLLMs/Humanish-Mistral-Nemo-Instruct-2407.

Rank the Humanish Mistral Nemo Instruct 2407 Capabilities

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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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