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Phi 3 Mini 128K Instruct AWQ by jsincn

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Phi 3 Mini 128K Instruct AWQ is an open-source language model by jsincn. Features: 3.8b LLM, VRAM: 2.3GB, Context: 128K, License: mit, Quantized, Instruction-Based, LLM Explorer Score: 0.14.

  4-bit   Awq   Conversational   Custom code   Endpoints compatible   Instruct   Phi3   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/jsincn/phi-3-mini-128k-instruct-awq 

Phi 3 Mini 128K Instruct AWQ Benchmarks

LLME Score: 0.13527
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 3 Mini 128K Instruct AWQ (jsincn/phi-3-mini-128k-instruct-awq)
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Phi 3 Mini 128K Instruct AWQ Parameters and Internals

Additional Notes 
The model is quantized using AWQ via AutoAWQ. Utilizes a TGI docker image where hosting fails due to fallback issues with AutoModel. Recommended to host using vLLM with an appropriate trust-remote-code flag. Code should be validated despite being sourced from Microsoft.
LLM NamePhi 3 Mini 128K Instruct AWQ
Repository 🤗https://huggingface.co/jsincn/phi-3-mini-128k-instruct-awq 
Model Size3.8b
Required VRAM2.3 GB
Updated2026-04-07
Maintainerjsincn
Model Typephi3
Instruction-BasedYes
Model Files  2.3 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length131072
Model Max Length131072
Transformers Version4.41.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than jsincn/phi-3-mini-128k-instruct-awq.

Rank the Phi 3 Mini 128K Instruct 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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