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

 »  All LLMs  »  Josephgflowers  »  Phi 3 Mini 4K Instruct Cinder Llamafied With 16bit GGUF   URL Share it on

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  16bit   Autotrain compatible   Code   Conversational Dataset:josephgflowers/just ci...   En   Endpoints compatible   Gguf   Instruct   Llama   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Josephgflowers/Phi-3-mini-4k-instruct-Cinder-llamafied-with-16bit-GGUF 

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

ARC: 52.73 vs 96.7 (so35)-45.5%
HellaSwag: 75.22 vs 95.3 (gpt4)-21.1%
MMLU: 60.92 vs 88.3 (so35)-31%
TruthfulQA: 43.24 vs 59 (gpt4)-26.7%
WinoGrande: 75.22 vs 87.5 (gpt4)-14%
GSM8K: 39.95 vs 96.4 (so35)-58.6%
LLME Score: 0.21594
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 Llamafied With 16bit GGUF (Josephgflowers/Phi-3-mini-4k-instruct-Cinder-llamafied-with-16bit-GGUF)

Phi 3 Mini 4K Instruct Cinder Llamafied With 16bit GGUF Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
Commercial, Research
Applications:
Memory constrained environments, Latency bound scenarios, Reasoning tasks (code, math, logic)
Primary Use Cases:
Language and multimodal model research, Generative AI application development
Limitations:
Inappropriateness for non-English languages, Potential for stereotype reinforcement, Offensive content generation
Considerations:
Models should not be used without prior fairness and safety evaluations.
Supported Languages 
languages_supported (English), proficiency (Primary language)
Training Details 
Data Sources:
Publicly available documents, Newly created synthetic data, High quality chat format supervised data
Data Volume:
3.3 trillion tokens
Methodology:
Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO)
Context Length:
4000
Training Time:
7 days
Hardware Used:
512 H100-80G GPUs
Model Architecture:
Dense decoder-only Transformer model
Responsible Ai Considerations 
Fairness:
Fairness assessments must be conducted by developers.
Transparency:
Developers should inform end-users they are interacting with AI and provide transparency in AI usage.
Accountability:
Developers must adhere to applicable laws and regulations related to their use case.
Mitigation Strategies:
Use-case specific mitigations are needed for accuracy and safety.
Input Output 
Input Format:
Chat format (<|user|> <|assistant|>)
Accepted Modalities:
text
Output Format:
Generated text
LLM NamePhi 3 Mini 4K Instruct Cinder Llamafied With 16bit GGUF
Repository 🤗https://huggingface.co/Josephgflowers/Phi-3-mini-4k-instruct-Cinder-llamafied-with-16bit-GGUF 
Model Size3.8b
Required VRAM7.6 GB
Updated2024-12-22
MaintainerJosephgflowers
Model Typellama
Instruction-BasedYes
Model Files  7.6 GB   7.6 GB: 1-of-1
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf|16bit
Model ArchitectureLlamaForCausalLM
Licensemit
Context Length4096
Model Max Length4096
Transformers Version4.40.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16

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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-llamafied-with-16bit-GGUF.

Rank the Phi 3 Mini 4K Instruct Cinder Llamafied 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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Release v20241217
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