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Komt Llama 2 7B Chat Hf by davidkim205

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Model Card on HF 🤗: https://huggingface.co/davidkim205/komt-Llama-2-7b-chat-hf 

Komt Llama 2 7B Chat Hf Benchmarks

LLME Score: 0.10036
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Komt Llama 2 7B Chat Hf (davidkim205/komt-Llama-2-7b-chat-hf)
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Komt Llama 2 7B Chat Hf Parameters and Internals

Model Type 
auto-regressive, transformer
Use Cases 
Areas:
Commercial applications, Research
Applications:
Natural language generation tasks
Primary Use Cases:
Assistant-like chat
Limitations:
May produce inaccurate, biased, or objectionable outputs., Limited testing beyond English usage.
Considerations:
Developers should conduct their own safety testing tailored to their applications.
Additional Notes 
For enhanced Korean language performance focus, use the multi-task instruction fine-tuning approach.
Supported Languages 
Korean (Enhanced), English (Fully Supported)
Training Details 
Data Sources:
Korean multi-task instruction dataset, publicly available online data, human-annotated examples
Data Volume:
2 trillion tokens
Methodology:
Supervised fine-tuning (SFT) with multi-task instruction, reinforcement learning with human feedback (RLHF) for alignment
Context Length:
4000
Hardware Used:
A100-80GB GPUs, Meta's Research Super Cluster and production clusters
Model Architecture:
Auto-regressive transformer architecture
Safety Evaluation 
Methodologies:
Internal evaluations library, TruthfulQA, Toxigen
Findings:
Models scored high in truthfulness and low toxicity, Chat models scored higher on safety benchmarks compared to pretrained variants
Risk Categories:
Misinformation, Bias, Toxicity
Ethical Considerations:
Designed to minimize toxic outputs, not fully tested in non-English languages.
Responsible Ai Considerations 
Fairness:
Careful attention to bias features in model outputs and team evaluations.
Transparency:
Public model cards and research papers provide documentation and results.
Accountability:
Meta and developers hold shared responsibility for outputs within usage bounds.
Mitigation Strategies:
Use of human feedback for reinforcement learning, supervision on model outputs, transparency in benchmark results.
Input Output 
Input Format:
Text instructions
Accepted Modalities:
Text
Output Format:
Text generation
Performance Tips:
Use optimized configurations for memory-efficient model execution.
LLM NameKomt Llama 2 7B Chat Hf
Repository 🤗https://huggingface.co/davidkim205/komt-Llama-2-7b-chat-hf 
Model Size7b
Required VRAM27 GB
Updated2025-09-23
Maintainerdavidkim205
Model Typellama
Model Files  9.9 GB: 1-of-3   9.9 GB: 2-of-3   7.2 GB: 3-of-3
Supported Languagesen ko
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.28.0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than davidkim205/komt-Llama-2-7b-chat-hf.

Rank the Komt Llama 2 7B Chat Hf 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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