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Qwen1.5 0.5B Chat Mistral by Minami-su

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Qwen1.5 0.5B Chat Mistral is an open-source language model by Minami-su. Features: 0.5b LLM, VRAM: 1.2GB, Context: 32K, License: other, HF Score: 28.8, LLM Explorer Score: 0.18, Arc: 25.5, HellaSwag: 26.4, MMLU: 23.1, TruthfulQA: 49.1, WinoGrande: 48.7.

  Conversational   En   Mistral   Pytorch   Qwen   Qwen1.5   Qwen2   Region:us   Zh
Model Card on HF 🤗: https://huggingface.co/Minami-su/Qwen1.5-0.5B-Chat_mistral 

Qwen1.5 0.5B Chat Mistral Benchmarks

ARC: 25.51 vs 96.7 (so35)-73.6%
HellaSwag: 26.41 vs 95.3 (gpt4)-72.3%
MMLU: 23.08 vs 88.3 (so35)-73.9%
TruthfulQA: 49.06 vs 59 (gpt4)-16.8%
WinoGrande: 48.7 vs 87.5 (gpt4)-44.3%
LLME Score: 0.17601
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen1.5 0.5B Chat Mistral (Minami-su/Qwen1.5-0.5B-Chat_mistral)
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Qwen1.5 0.5B Chat Mistral Parameters and Internals

Model Type 
text-generation
Additional Notes 
Modifications were made to the Mistral version for compatibility with Qwen1.5-0.5B-Chat. The model can load in 4-bit precision.
Input Output 
Input Format:
JSON (role-based messages)
Accepted Modalities:
text
Output Format:
text
Performance Tips:
The model can be loaded in 4-bit precision for performance optimization.
LLM NameQwen1.5 0.5B Chat Mistral
Repository 🤗https://huggingface.co/Minami-su/Qwen1.5-0.5B-Chat_mistral 
Model Size0.5b
Required VRAM1.2 GB
Updated2026-04-06
MaintainerMinami-su
Model Typemistral
Model Files  1.2 GB
Supported Languagesen zh
Model ArchitectureMistralForCausalLM
Licenseother
Context Length32768
Model Max Length32768
Transformers Version4.34.0.dev0
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typebfloat16
Errorsreplace

Best Alternatives to Qwen1.5 0.5B Chat Mistral

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Mistral 0.5B 40K40K / 1.2 GB52
Mistral 0.5B Base4K / 2.1 GB50
Note: green Score (e.g. "73.2") means that the model is better than Minami-su/Qwen1.5-0.5B-Chat_mistral.

Rank the Qwen1.5 0.5B Chat Mistral 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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