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Llama 2 7B GPTQ by localmodels

 »  All LLMs  »  localmodels  »  Llama 2 7B GPTQ   URL Share it on

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Llama 2 7B GPTQ is an open-source language model by localmodels. Features: 7b LLM, VRAM: 3.9GB, Context: 2K, Quantized, LLM Explorer Score: 0.08.

  4bit   Endpoints compatible   Gptq   Llama   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/localmodels/Llama-2-7B-GPTQ 

Llama 2 7B GPTQ Benchmarks

LLME Score: 0.08435
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 2 7B GPTQ (localmodels/Llama-2-7B-GPTQ)
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Llama 2 7B GPTQ Parameters and Internals

Model Type 
auto-regressive, generative text model
Use Cases 
Areas:
Commercial and research applications in English
Applications:
Natural language generation tasks, Assistant-like chat
Primary Use Cases:
Pretrained models can be adapted for various tasks
Limitations:
Use in languages other than English, Violates laws or regulations
Considerations:
Specific formatting required to get expected features for chat.
Additional Notes 
Carbon footprint of pretraining is offset by Meta’s program.
Supported Languages 
languages_zero_shot (/0/), proficiency_level (/0/), default_language (/0/)
Training Details 
Data Sources:
A new mix of publicly available online data
Data Volume:
2 trillion tokens
Methodology:
Pretrained using auto-regressive architecture and fine-tuned with supervised learning and reinforcement learning with human feedback.
Context Length:
4096
Training Time:
January 2023 - July 2023
Hardware Used:
Meta's Research Super Cluster, third-party cloud compute
Model Architecture:
Optimized transformer architecture
Safety Evaluation 
Methodologies:
Evaluation on standard academic benchmarks
Findings:
Outperform open-source chat models on most benchmarks tested, Par with some closed-source models
Ethical Considerations:
Before deploying applications, developers should perform safety testing tailored to specific applications.
Responsible Ai Considerations 
Fairness:
Testing covers English scenarios, cannot predict nor cover all scenarios.
Accountability:
Developers should perform safety testing tailored to specific applications.
Mitigation Strategies:
Tuned with reinforcement learning with human feedback for alignment.
Input Output 
Input Format:
Text only
Accepted Modalities:
text
Output Format:
Text only
Performance Tips:
Bigger models (70B) use Grouped-Query Attention for improved scalability.
LLM NameLlama 2 7B GPTQ
Repository 🤗https://huggingface.co/localmodels/Llama-2-7B-GPTQ 
Base Model(s)  Llama 2 7B PetGPT   sudip2003/llama-2-7b-PetGPT
Model Size7b
Required VRAM3.9 GB
Updated2026-01-09
Maintainerlocalmodels
Model Typellama
Model Files  3.9 GB
GPTQ QuantizationYes
Quantization Typegptq|4bit
Model ArchitectureLlamaForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.30.2
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than localmodels/Llama-2-7B-GPTQ.

Rank the Llama 2 7B GPTQ 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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