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Bling Phi 2 V0 by llmware

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Bling Phi 2 V0 is an open-source language model by llmware. Features: LLM, VRAM: 5.6GB, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.11.

  Phi   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/llmware/bling-phi-2-v0 

Bling Phi 2 V0 Benchmarks

LLME Score: 0.10667
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Bling Phi 2 V0 (llmware/bling-phi-2-v0)
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Bling Phi 2 V0 Parameters and Internals

Model Type 
Phi-2B
Use Cases 
Areas:
enterprise automation
Applications:
financial services, legal and regulatory industries
Primary Use Cases:
question-answering, key-value extraction, basic summarization
Limitations:
may provide inaccurate or incomplete information
Considerations:
Use with appropriate safeguards and fact-checking mechanisms
Additional Notes 
For fast inference with this model, we recommend [bling-phi-2-gguf](https://huggingface.co/llmware/bling-phi-2-gguf).
Supported Languages 
English (NLP)
Training Details 
Data Sources:
custom instruct datasets
Methodology:
RAG-instruct training
Input Output 
Input Format:
{{text_passage}} + "\n" + {{question/instruction}}
Accepted Modalities:
text
Performance Tips:
Wrap inference entries using the "\n:" structure for optimal results.
LLM NameBling Phi 2 V0
Repository 🤗https://huggingface.co/llmware/bling-phi-2-v0 
Required VRAM5.6 GB
Updated2026-05-20
Maintainerllmware
Model Typephi
Model Files  5.6 GB
Model ArchitecturePhiForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.37.0
Tokenizer ClassCodeGenTokenizer
Vocabulary Size51200
Torch Data Typefloat16
Activation Functiongelu_new

Best Alternatives to Bling Phi 2 V0

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Context / RAM
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Tinyllama Quant2K / 0.8 GB61
Phi1.5 Update 42K /  GB60
Phi 2 Fp82K / 3.3 GB890
Merlin1.12K / 5.6 GB8460
Phi 2 Fp82K / 3.3 GB80
Note: green Score (e.g. "73.2") means that the model is better than llmware/bling-phi-2-v0.

Rank the Bling Phi 2 V0 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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