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Zephyr 7B Beta by HuggingFaceH4

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Zephyr 7B Beta is an open-source language model by HuggingFaceH4. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: mit, HF Score: 59.1, LLM Explorer Score: 0.39, ELO: 1068, Arc: 62, HellaSwag: 84.5, MMLU: 61.1, TruthfulQA: 57.4, WinoGrande: 78.1, GSM8K: 11.4.

  Arxiv:2305.14233   Arxiv:2305.18290   Arxiv:2310.01377   Arxiv:2310.16944 Base model:finetune:mistralai/... Base model:mistralai/mistral-7...   Conversational Dataset:huggingfaceh4/ultracha... Dataset:huggingfaceh4/ultrafee...   Deploy:azure   En   Endpoints compatible   Generated from trainer   Mistral   Model-index   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta 

Zephyr 7B Beta Benchmarks

LMSys ELO: 1068 vs 1272 (so35)-16%
MMLU Pro: 19.79
GPQA: 5.37
MUSR: 7.73
BBH: 21.49
IFEval: 49.5 vs 88 (so35)-43.8%
ARC: 62.03 vs 96.7 (so35)-35.9%
HellaSwag: 84.53 vs 95.3 (gpt4)-11.3%
MMLU: 61.06 vs 88.3 (so35)-30.8%
TruthfulQA: 57.44 vs 59 (gpt4)-2.6%
WinoGrande: 78.06 vs 87.5 (gpt4)-10.8%
GSM8K: 11.37 vs 96.4 (so35)-88.2%
MATH Lvl 5: 2.87
LLME Score: 0.39115
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Zephyr 7B Beta (HuggingFaceH4/zephyr-7b-beta)
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Zephyr 7B Beta Parameters and Internals

Model Type 
text generation, chatbot
Use Cases 
Areas:
chat
Applications:
assistant
Primary Use Cases:
chatbot applications
Limitations:
not aligned to human preferences for safety within the RLHF phase, can produce problematic outputs when prompted
Additional Notes 
Zephyr-7B-β can produce problematic outputs, especially when prompted, due to the lack of human preference alignment and in-the-loop filtering.
Supported Languages 
en (primary)
Training Details 
Data Sources:
HuggingFaceH4/ultrachat_200k, HuggingFaceH4/ultrafeedback_binarized
Methodology:
Direct Preference Optimization (DPO)
Input Output 
Input Format:
structured message input format (role-based behavior)
Accepted Modalities:
text
Output Format:
text generation output
Performance Tips:
Direct Preference Optimization (DPO) improves performance.
LLM NameZephyr 7B Beta
Repository 🤗https://huggingface.co/HuggingFaceH4/zephyr-7b-beta 
Base Model(s)  mistralai/Mistral-7B-v0.1   mistralai/Mistral-7B-v0.1
Model Size7b
Required VRAM14.4 GB
Updated2026-04-14
MaintainerHuggingFaceH4
Model Typemistral
Model Files  1.9 GB: 1-of-8   1.9 GB: 2-of-8   2.0 GB: 3-of-8   1.9 GB: 4-of-8   2.0 GB: 5-of-8   1.9 GB: 6-of-8   2.0 GB: 7-of-8   0.8 GB: 8-of-8   1.9 GB: 1-of-8   1.9 GB: 2-of-8   2.0 GB: 3-of-8   1.9 GB: 4-of-8   2.0 GB: 5-of-8   1.9 GB: 6-of-8   2.0 GB: 7-of-8   0.8 GB: 8-of-8
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licensemit
Context Length32768
Model Max Length32768
Transformers Version4.35.0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

Quantized Models of the Zephyr 7B Beta

Model
Likes
Downloads
VRAM
Zephyr 7B Beta Marlin0374 GB
Dj Zephyr 7B F16 GGUF12414 GB
Dj Zephyr 7B Q5 K M GGUF0105 GB
Zephyr 7B Beta GGUF23120743 GB
Zephyr 7B Beta GGUF06092 GB
Zephyr 7B Beta Marlin01264 GB
Zephyr 7B Beta GGUF0653 GB
Zephyr 7B Beta GPTQ174 GB
Zephyr 7B Beta GPTQ584214 GB
Vietzephyr 7B Lora 8bit070 GB

Best Alternatives to Zephyr 7B Beta

Best Alternatives
Context / RAM
Downloads
Likes
...Nemo Instruct 2407 Abliterated1000K / 24.5 GB25420
MegaBeam Mistral 7B 512K512K / 14.4 GB1144354
SpydazWeb AI HumanAI RP512K / 14.4 GB141
SpydazWeb AI HumanAI 002512K / 14.4 GB181
...daz Web AI ChatML 512K Project512K / 14.5 GB120
MegaBeam Mistral 7B 300K282K / 14.4 GB377916
MegaBeam Mistral 7B 300K282K / 14.4 GB796816
Hebrew Mistral 7B 200K256K / 30 GB131615
Astral 256K 7B V2250K / 14.4 GB60
Astral 256K 7B250K / 14.4 GB50

Rank the Zephyr 7B Beta 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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