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Breeze 7B Instruct V1.0 AWQ by YC-Chen

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Breeze 7B Instruct V1.0 AWQ is an open-source language model by YC-Chen. Features: 7b LLM, VRAM: 4.6GB, Context: 32K, Quantized, Instruction-Based, LLM Explorer Score: 0.14, Arc: 61.4, HellaSwag: 82.6, MMLU: 62.1, GSM8K: 43.1.

  4-bit   Awq   Conversational   Endpoints compatible   Instruct   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/YC-Chen/Breeze-7B-Instruct-v1_0-AWQ 

Breeze 7B Instruct V1.0 AWQ Benchmarks

ARC: 61.35 vs 96.7 (so35)-36.6%
HellaSwag: 82.59 vs 95.3 (gpt4)-13.3%
MMLU: 62.11 vs 88.3 (so35)-29.7%
GSM8K: 43.14 vs 96.4 (so35)-55.2%
LLME Score: 0.13921
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Breeze 7B Instruct V1.0 AWQ (YC-Chen/Breeze-7B-Instruct-v1_0-AWQ)
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Breeze 7B Instruct V1.0 AWQ Parameters and Internals

LLM NameBreeze 7B Instruct V1.0 AWQ
Repository 🤗https://huggingface.co/YC-Chen/Breeze-7B-Instruct-v1_0-AWQ 
Base Model(s)  Breeze 7B Instruct V1.0   MediaTek-Research/Breeze-7B-Instruct-v1_0
Model Size7b
Required VRAM4.6 GB
Updated2026-05-13
MaintainerYC-Chen
Model Typemistral
Instruction-BasedYes
Model Files  4.6 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size61952
Torch Data Typefloat16

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Mistral 7B Instruct V0.2 AWQ32K / 4.2 GB50
Mistral 7B Instruct V0.2 AWQ32K / 4.2 GB21214552
Mistral 7B Instruct V0.3 AWQ32K / 4.2 GB35489
...reeze 7B 32K Instruct V1.0 AWQ32K / 4.7 GB90
Breeze 7B Instruct V1.0 AWQ32K / 4.6 GB50
Mistral 7B Instruct V0.1 AWQ32K / 4.2 GB44912
Mistral 7B Instruct V0.3 AWQ32K / 4.2 GB4041
Mistral 7B Instruct V0.3 AWQ32K / 4.2 GB50
...Instruct V0.2 AWQ 4bit Smashed32K / 4.2 GB81
Mistral 7B Instruct 32K AWQ32K / 4.2 GB201
Note: green Score (e.g. "73.2") means that the model is better than YC-Chen/Breeze-7B-Instruct-v1_0-AWQ.

Rank the Breeze 7B Instruct V1.0 AWQ 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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