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 AWQ by aspenita

 »  All LLMs  »  aspenita  »  Meta Llama 3 8B Instruct AWQ   URL Share it on

  7   
  4-bit   Autoquant   Autotrain compatible   Awq   Conversational   En   Endpoints compatible   Facebook   Instruct   Llama   Llama-3   Meta   Pytorch   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/aspenita/Meta-Llama-3-8B-Instruct-AWQ 

Meta Llama 3 8B Instruct AWQ Benchmarks

ARC: 59.64 vs 96.7 (so35)-38.3%
HellaSwag: 78.84 vs 95.3 (gpt4)-17.3%
MMLU: 65.06 vs 88.3 (so35)-26.3%
TruthfulQA: 50.17 vs 59 (gpt4)-15%
WinoGrande: 74.98 vs 87.5 (gpt4)-14.3%
GSM8K: 57.32 vs 96.4 (so35)-40.5%
LLME Score: 0.17076
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 AWQ (aspenita/Meta-Llama-3-8B-Instruct-AWQ)
🌟 Advertise your project 🚀

Meta Llama 3 8B Instruct AWQ Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
commercial, research
Applications:
assistant-like chat
Primary Use Cases:
instruction-tuned models for dialogue
Limitations:
Use must comply with laws and Llama 3 license policy
Considerations:
Developers should perform safety testing and tuning prior to deployment.
Additional Notes 
Models are optimized for helpfulness and safety through RLHF and SFT.
Supported Languages 
primary (English)
Training Details 
Data Sources:
publicly available online data
Data Volume:
15 trillion tokens
Methodology:
pre-trained, instruction-tuned, RLHF
Context Length:
8000
Hardware Used:
Meta's Research SuperCluster, H100-80GB GPUs
Model Architecture:
auto-regressive transformer with Grouped-Query Attention
Safety Evaluation 
Methodologies:
red teaming, adversarial evaluations
Findings:
reduced residual risk via safety mitigations
Risk Categories:
misinformation, cybersecurity, child safety
Ethical Considerations:
developers must assess risks for specific use cases
Responsible Ai Considerations 
Fairness:
Safety benchmarks are transparent and rigorous.
Transparency:
Evaluations and benchmarks are publicly accessible.
Accountability:
Users and developers must adhere to guidelines and policies.
Mitigation Strategies:
Incorporate safeguards like Meta Llama Guard 2 and Code Shield
Input Output 
Input Format:
text
Accepted Modalities:
text
Output Format:
text and code
Performance Tips:
Use with transformers pipeline or llama3 codebase for best results.
Release Notes 
Version:
April 18, 2024
Date:
April 18, 2024
Notes:
Initial release of Llama 3 models, including optimized transformers architecture.
LLM NameMeta Llama 3 8B Instruct AWQ
Repository 🤗https://huggingface.co/aspenita/Meta-Llama-3-8B-Instruct-AWQ 
Base Model(s)  Meta Llama 3 13B Instruct   andrijdavid/Meta-Llama-3-13B-Instruct
Model Size8b
Required VRAM5.8 GB
Updated2025-09-23
Maintaineraspenita
Model Typellama
Instruction-BasedYes
Model Files  4.7 GB: 1-of-2   1.1 GB: 2-of-2
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licensellama3
Context Length8192
Model Max Length8192
Transformers Version4.38.2
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typefloat16

Best Alternatives to Meta Llama 3 8B Instruct AWQ

Best Alternatives
Context / RAM
Downloads
Likes
...8B Instruct Gradient 1048K AWQ1024K / 5.8 GB70
...radient 1048K AWQ 4bit Smashed1024K / 5.8 GB71
...Instruct 262K AWQ 4bit Smashed256K / 5.8 GB54
...ta Llama 3 8B Instruct 64K AWQ64K / 5.8 GB60
... Instruct 8B 32K V0.1 4bit AWQ64K / 5.8 GB80
Llama 3 8B Instruct AWQ8K / 5.8 GB1659825
...eta Llama 3 8B Instruct Hf AWQ8K / 5.8 GB14329
Meta Llama 3 8B Instruct AWQ8K / 5.8 GB12675
Meta Llama 3 8B Instruct AWQ8K / 5.8 GB60
Meta Llama 3 8B Instruct AWQ8K / 5.8 GB2780
Note: green Score (e.g. "73.2") means that the model is better than aspenita/Meta-Llama-3-8B-Instruct-AWQ.

Rank the Meta Llama 3 8B Instruct AWQ 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? 51545 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 v20241124
  Support LLM Explorer