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Phi 3 Mini 4K Instruct Cinder With 16bit GGUF by Josephgflowers

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Phi 3 Mini 4K Instruct Cinder With 16bit GGUF is an open-source language model by Josephgflowers. Features: 3.8b LLM, VRAM: 7.7GB, Context: 4K, License: mit, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

  16bit   Code   Conversational   Custom code   En   Endpoints compatible   Gguf   Instruct   Phi3   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Josephgflowers/Phi-3-mini-4k-instruct-Cinder-with-16bit-GGUF 

Phi 3 Mini 4K Instruct Cinder With 16bit GGUF Benchmarks

LLME Score: 0.13189
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 4K Instruct Cinder With 16bit GGUF (Josephgflowers/Phi-3-mini-4k-instruct-Cinder-with-16bit-GGUF)
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Phi 3 Mini 4K Instruct Cinder With 16bit GGUF Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
commercial, research
Primary Use Cases:
Memory/compute constrained environments, Latency bound scenarios, Strong reasoning (code, math, and logic)
Limitations:
Models are not specifically designed for all downstream purposes and have limitations in scope.
Considerations:
Developers should evaluate use cases for safety and fairness before deployment.
Supported Languages 
en (primary)
Training Details 
Data Volume:
3.3 trillion tokens
Methodology:
Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO)
Context Length:
4096
Training Time:
7 days
Hardware Used:
512 H100-80G GPUs
Model Architecture:
dense decoder-only Transformer
Responsible Ai Considerations 
Fairness:
These models can potentially behave in ways that are unfair, unreliable, or offensive. They may over- or under-represent groups of people or reinforce stereotypes.
Transparency:
Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations.
Accountability:
Developers are responsible for the use of the model and ensuring compliance with relevant laws.
Mitigation Strategies:
Despite safety post-training, developers should assess outputs for context and use available safety classifiers.
Input Output 
Input Format:
Chat format with roles and system instructions.
Accepted Modalities:
Text
Output Format:
Text response to input
Performance Tips:
For optimal use, ensure proper formatting of chat prompts.
LLM NamePhi 3 Mini 4K Instruct Cinder With 16bit GGUF
Repository 🤗https://huggingface.co/Josephgflowers/Phi-3-mini-4k-instruct-Cinder-with-16bit-GGUF 
Model Size3.8b
Required VRAM7.7 GB
Updated2026-03-30
MaintainerJosephgflowers
Model Typephi3
Instruction-BasedYes
Model Files  7.6 GB   5.0 GB: 1-of-2   2.7 GB: 2-of-2   0.0 GB   0.0 GB   0.0 GB
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf|16bit
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length4096
Model Max Length4096
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 Josephgflowers/Phi-3-mini-4k-instruct-Cinder-with-16bit-GGUF.

Rank the Phi 3 Mini 4K Instruct Cinder With 16bit GGUF 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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