Hermes 2 Pro Mistral 7B AWQ GEMM 4 128 True by FoxEngineAi

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  4-bit   Autotrain compatible   Awq Base model:mistralai/mistral-7... Base model:quantized:mistralai...   Chatml   Conversational   Dataset:teknium/openhermes-2.5   Distillation   Dpo   En   Endpoints compatible   Finetuned   Foxengine   Function calling   Gpt4   Instruct   Json mode   Mistral   Quantized   Region:us   Rlhf   Safetensors   Synthetic data

Hermes 2 Pro Mistral 7B AWQ GEMM 4 128 True Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Hermes 2 Pro Mistral 7B AWQ GEMM 4 128 True (FoxEngineAi/Hermes-2-Pro-Mistral-7B_AWQ-GEMM_4_128_True)

Hermes 2 Pro Mistral 7B AWQ GEMM 4 128 True Parameters and Internals

Model Type 
instruct, finetune, chatml, DPO, RLHF, gpt4, function calling
Additional Notes 
Hermes 2 Pro is designed to excel at Function Calling and JSON Structured Outputs.
Supported Languages 
en (English)
Training Details 
Data Sources:
teknium/OpenHermes-2.5
Input Output 
Input Format:
ChatML
Accepted Modalities:
text
Output Format:
text, JSON
LLM NameHermes 2 Pro Mistral 7B AWQ GEMM 4 128 True
Repository ๐Ÿค—https://huggingface.co/FoxEngineAi/Hermes-2-Pro-Mistral-7B_AWQ-GEMM_4_128_True 
Base Model(s)  mistralai/Mistral-7B-v0.1   mistralai/Mistral-7B-v0.1
Model Size7b
Required VRAM4.2 GB
Updated2025-02-22
MaintainerFoxEngineAi
Model Typemistral
Model Files  4.2 GB
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32032
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than FoxEngineAi/Hermes-2-Pro-Mistral-7B_AWQ-GEMM_4_128_True.

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Instruction Following and Task Automation  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227