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Phi 3.5 Instruct Vul by abhinav2024

 »  All LLMs  »  abhinav2024  »  Phi 3.5 Instruct Vul   URL Share it on

  5   
  4bit   Autotrain compatible   Conversational   En   Endpoints compatible   Instruct   Llama   Pytorch   Quantized   Region:us   Sharded   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/abhinav2024/phi-3.5-instruct-vul 

Phi 3.5 Instruct Vul Benchmarks

LLME Score: 0.17538
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 3.5 Instruct Vul (abhinav2024/phi-3.5-instruct-vul)
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Phi 3.5 Instruct Vul Parameters and Internals

Model Type 
text-generation-inference
Additional Notes 
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Supported Languages 
en (fluent)
LLM NamePhi 3.5 Instruct Vul
Repository 🤗https://huggingface.co/abhinav2024/phi-3.5-instruct-vul 
Base Model(s)  unsloth/phi-3.5-mini-instruct-bnb-4bit   unsloth/phi-3.5-mini-instruct-bnb-4bit
Required VRAM7.6 GB
Updated2025-09-14
Maintainerabhinav2024
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-2   2.6 GB: 2-of-2
Supported Languagesen
Quantization Type4bit
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length131072
Model Max Length131072
Transformers Version4.44.2
Tokenizer ClassLlamaTokenizer
Padding Token<|placeholder6|>
Vocabulary Size32064
Torch Data Typefloat16

Best Alternatives to Phi 3.5 Instruct Vul

Best Alternatives
Context / RAM
Downloads
Likes
Slimorca Phi 3.5128K / 7.6 GB50
...Instruct DPO V2.8.0bpw H8 EXL24K / 10.9 GB52
Reverse Instruct32K / 27 GB63
Codellama Cairo Instruct GGUF16K / 4.1 GB131
Etri Ones Solar4K / 42.9 GB100
Phi 3 Orpo V8.164K / 7.6 GB50
Finance Chat GGUF4K / 2.8 GB12413
Sakura SOLAR Instruct GGUF4K / 4.5 GB8031
Airavata GGUF4K / 4.2 GB482
Tinyllama Python4K / 2.2 GB02
Note: green Score (e.g. "73.2") means that the model is better than abhinav2024/phi-3.5-instruct-vul.

Rank the Phi 3.5 Instruct Vul 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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