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Law Chat GGUF by TheBloke

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  Arxiv:2309.09530   Base model:adaptllm/law-chat Base model:quantized:adaptllm/...   Dataset:eleutherai/pile   Dataset:gair/lima   Dataset:open-orca/openorca Dataset:wizardlm/wizardlm evol...   En   Gguf   Instruct   Legal   Llama   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/TheBloke/law-chat-GGUF 

Law Chat GGUF Benchmarks

LLME Score: 0.16824
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Law Chat GGUF (TheBloke/law-chat-GGUF)

Law Chat GGUF Parameters and Internals

Model Type 
llama, text-generation
Additional Notes 
This is a specialized chat model, adapted for the legal domain, from LLaMA-2-Chat-7B. It applies techniques from the 'Adapting Large Language Models via Reading Comprehension' paper.
Supported Languages 
en (English)
Input Output 
Input Format:
[INST] <> {system_message} <> {prompt} [/INST]
LLM NameLaw Chat GGUF
Repository 🤗https://huggingface.co/TheBloke/law-chat-GGUF 
Model NameLaw Chat
Model CreatorAdaptLLM
Base Model(s)  Law Chat   AdaptLLM/law-chat
Required VRAM2.8 GB
Updated2025-03-25
MaintainerTheBloke
Model Typellama
Instruction-BasedYes
Model Files  2.8 GB   3.6 GB   3.3 GB   3.0 GB   3.8 GB   4.1 GB   3.9 GB   4.7 GB   4.8 GB   4.7 GB   5.5 GB   7.2 GB
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureAutoModel
Licensellama2

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/law-chat-GGUF.

Rank the Law Chat GGUF 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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Release v20241227
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