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Hebrew Mistral 7B 200K by yam-peleg

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Hebrew Mistral 7B 200K is an open-source language model by yam-peleg. Features: 7b LLM, VRAM: 30GB, Context: 256K, License: apache-2.0, LLM Explorer Score: 0.17.

  Deploy:azure   En   Endpoints compatible   He   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/yam-peleg/Hebrew-Mistral-7B-200K 

Hebrew Mistral 7B 200K Benchmarks

MMLU Pro: 16.99
GPQA: 0.45
MUSR: 4.42
BBH: 7.67
IFEval: 17.7 vs 88 (so35)-79.9%
MATH Lvl 5: 3.1
LLME Score: 0.17083
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Hebrew Mistral 7B 200K (yam-peleg/Hebrew-Mistral-7B-200K)
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Hebrew Mistral 7B 200K Parameters and Internals

Model Type 
causal language model
Additional Notes 
Model is focused on Hebrew language understanding and generation. It lacks moderation mechanisms.
Supported Languages 
en (full proficiency), he (full proficiency)
Training Details 
Context Length:
200000
Input Output 
Input Format:
Text inputs using AutoTokenizer from the Transformers library.
Accepted Modalities:
text
Output Format:
Generated text responses decoded back from outputs.
LLM NameHebrew Mistral 7B 200K
Repository 🤗https://huggingface.co/yam-peleg/Hebrew-Mistral-7B-200K 
Model Size7b
Required VRAM30 GB
Updated2026-04-23
Maintaineryam-peleg
Model Typemistral
Model Files  4.9 GB: 1-of-7   5.0 GB: 2-of-7   4.8 GB: 3-of-7   5.0 GB: 4-of-7   5.0 GB: 5-of-7   4.2 GB: 6-of-7   1.1 GB: 7-of-7
Supported Languagesen he
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length262144
Model Max Length262144
Transformers Version4.40.1
Tokenizer ClassLlamaTokenizer
Padding Token[PAD]
Vocabulary Size64001
Torch Data Typefloat32

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Astral 256K 7B V2250K / 14.4 GB60
Astral 256K 7B250K / 14.4 GB50
Test001128K / 14.5 GB90
Note: green Score (e.g. "73.2") means that the model is better than yam-peleg/Hebrew-Mistral-7B-200K.

Rank the Hebrew Mistral 7B 200K 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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