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OpenhermesTrial by Jaredquek

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  Autotrain compatible Base model:mistralai/mistral-7... Base model:quantized:mistralai...   Chatml   Conversational   Distillation   En   Endpoints compatible   Finetuned   Gguf   Gpt4   Instruct   Mistral   Pytorch   Quantized   Region:us   Sharded   Synthetic data
Model Card on HF 🤗: https://huggingface.co/Jaredquek/OpenhermesTrial 

OpenhermesTrial Benchmarks

LLME Score: 0.13274
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
OpenhermesTrial (Jaredquek/OpenhermesTrial)
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OpenhermesTrial Parameters and Internals

Model Type 
text generation, multimodal
Use Cases 
Areas:
research, commercial applications
Primary Use Cases:
code generation, text generation
Additional Notes 
Quantized models of different formats such as GGUF, GPTQ, and AWQ are available.
Supported Languages 
en (fluent)
Training Details 
Data Sources:
GPT-4 generated data, open datasets
Data Volume:
1,000,000 entries
Methodology:
Fine-tuned on a dataset with a ratio of 7-14% code instructions
Model Architecture:
Mistral Fine-tune
Input Output 
Input Format:
ChatML
Accepted Modalities:
text
Output Format:
text
Performance Tips:
Use ChatML format for better performance.
LLM NameOpenhermesTrial
Repository 🤗https://huggingface.co/Jaredquek/OpenhermesTrial 
Base Model(s)  mistralai/Mistral-7B-v0.1   mistralai/Mistral-7B-v0.1
Model Size7b
Required VRAM14.4 GB
Updated2025-08-11
MaintainerJaredquek
Model Typemistral
Model Files  7.7 GB   9.9 GB: 1-of-2   4.5 GB: 2-of-2
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.34.0.dev0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32002
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Jaredquek/OpenhermesTrial.

Rank the OpenhermesTrial 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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