LLM EXPLORER
 LLM Explorer: A Curated Large Language Model Directory and Analytics
Dark Theme
  LLM  List    LLM Hosting    LLM Leaderboards    Blog    Newsfeed    Advertise

Meta Llama 3 8B Instruct 4bit by ibibek

 »  All LLMs  »  ibibek  »  Meta Llama 3 8B Instruct 4bit   URL Share it on

  85   
  4-bit   4bit   Autotrain compatible   Bitsandbytes   Conversational   En   Endpoints compatible   Facebook   Instruct   Llama   Llama-3   Meta   Pytorch   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/ibibek/Meta-Llama-3-8B-Instruct-4bit 

Meta Llama 3 8B Instruct 4bit Benchmarks

ARC: 58.45 vs 96.7 (so35)-39.6%
HellaSwag: 78.87 vs 95.3 (gpt4)-17.2%
MMLU: 66.72 vs 88.3 (so35)-24.4%
TruthfulQA: 52.54 vs 59 (gpt4)-10.9%
WinoGrande: 71.98 vs 87.5 (gpt4)-17.7%
GSM8K: 52.46 vs 96.4 (so35)-45.6%
LLME Score: 0.19902
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Meta Llama 3 8B Instruct 4bit (ibibek/Meta-Llama-3-8B-Instruct-4bit)

Meta Llama 3 8B Instruct 4bit Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
commercial, research
Applications:
dialogue use cases
Primary Use Cases:
instruction-tuned for assistant-like chat
Limitations:
Only available in English
Considerations:
Developers can fine-tune for other languages if compliance is maintained.
Additional Notes 
Aimed at openness, inclusivity, and helpfulness while maintaining ethical considerations in AI deployment.
Supported Languages 
en (English)
Training Details 
Data Sources:
publicly available online data
Data Volume:
15T+ tokens
Methodology:
Pretrained and instruction tuned with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
8000
Hardware Used:
Meta's Research SuperCluster, third-party cloud compute
Model Architecture:
Auto-regressive language model using an optimized transformer architecture
Safety Evaluation 
Methodologies:
red teaming, adversarial evaluations
Risk Categories:
CBRNE, Cybersecurity, Child Safety
Responsible Ai Considerations 
Fairness:
Designed to serve diverse backgrounds and perspectives
Transparency:
Active involvement in safety standardization and transparency
Accountability:
Developers are responsible for ensuring safety in their specific use cases
Mitigation Strategies:
Implemented safety mitigation techniques; Purple Llama tools provided for developers.
Input Output 
Input Format:
Text input
Accepted Modalities:
text
Output Format:
Text and code
Performance Tips:
Use transformers or 'llama3' codebase for best performance.
Release Notes 
Version:
3.0
Date:
April 18, 2024
Notes:
Release of Meta Llama 3 family in 8B and 70B sizes.
LLM NameMeta Llama 3 8B Instruct 4bit
Repository 🤗https://huggingface.co/ibibek/Meta-Llama-3-8B-Instruct-4bit 
Base Model(s)  ... Llama 3 8B Instruct FineTuned   jknottllm/Meta-Llama-3-8B-Instruct-FineTuned
Model Size8b
Required VRAM5.8 GB
Updated2025-02-22
Maintaineribibek
Model Typellama
Instruction-BasedYes
Model Files  4.7 GB: 1-of-2   1.1 GB: 2-of-2
Supported Languagesen
Quantization Type4bit
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.38.1
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typefloat16

Best Alternatives to Meta Llama 3 8B Instruct 4bit

Best Alternatives
Context / RAM
Downloads
Likes
...B Instruct Gradient 1048K 4bit1024K / 4.5 GB212
...B Instruct Gradient 1048K 8bit1024K / 8.6 GB71
...truct Gradient 1048K Bpw6 EXL21024K / 6.7 GB102
...truct Gradient 1048K Bpw5 EXL21024K / 5.8 GB70
Llama 3 8B Instruct 1048K 4bit1024K / 4.5 GB1225
Llama 3 8B Instruct 1048K 8bit1024K / 8.6 GB2817
... Gradient 1048K 8.0bpw H8 EXL21024K / 8.6 GB83
...ct Gradient 1048K Bpw2.25 EXL21024K / 3.4 GB51
Llama 3 8B Instruct 262K 2bit256K / 2.5 GB71
...B Instruct 262k V2 EXL2 6.0bpw256K / 6.7 GB111
Note: green Score (e.g. "73.2") means that the model is better than ibibek/Meta-Llama-3-8B-Instruct-4bit.

Rank the Meta Llama 3 8B Instruct 4bit 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  

What open-source LLMs or SLMs are you in search of? 43470 in total.

Email us: info@extractum.io. Our Privacy Policy  | Terms and Conditions  | Suggest an improvement.
Our Social Media →  
Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
  Support LLM Explorer