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Llama 3 8B Instruct Gradient 4194K by gradientai

 »  All LLMs  »  gradientai  »  Llama 3 8B Instruct Gradient 4194K   URL Share it on

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  Arxiv:2305.14233   Arxiv:2309.00071   Arxiv:2402.08268   Autotrain compatible   Conversational   En   Endpoints compatible   Instruct   Llama   Llama-3   Meta   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k 

Llama 3 8B Instruct Gradient 4194K Benchmarks

ARC: 53.75 vs 96.7 (so35)-44.4%
HellaSwag: 77.89 vs 95.3 (gpt4)-18.3%
MMLU: 60.1 vs 88.3 (so35)-31.9%
TruthfulQA: 44.82 vs 59 (gpt4)-24%
WinoGrande: 72.53 vs 87.5 (gpt4)-17.1%
GSM8K: 46.1 vs 96.4 (so35)-52.2%
LLME Score: 0.22255
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 3 8B Instruct Gradient 4194K (gradientai/Llama-3-8B-Instruct-Gradient-4194k)

Llama 3 8B Instruct Gradient 4194K Parameters and Internals

Model Type 
text generation, instruction-tuned
Use Cases 
Areas:
Commercial applications, Research
Applications:
Assistant-like chat, Natural language generation tasks
Primary Use Cases:
Dialogue, Instruction following
Limitations:
Limited to English applications, Potential outputs: inaccurate, biased, objectionable
Considerations:
Safety testing and tuning required before deployment
Supported Languages 
English (primary)
Training Details 
Data Sources:
publicly available online data, SlimPajama dataset, UltraChat dataset
Data Volume:
15 trillion tokens (pretraining), 10M human-annotated examples (fine-tuning)
Methodology:
Supervised fine-tuning, Reinforcement learning with human feedback
Context Length:
8000
Hardware Used:
NVIDIA L40S, H100-80GB
Model Architecture:
Auto-regressive transformer with optimized architecture
Safety Evaluation 
Methodologies:
Red teaming, Adversarial evaluations
Risk Categories:
CBRNE, Cybersecurity, Child Safety
Ethical Considerations:
Assess responses related to adversarial risks and CBRNE threats
Responsible Ai Considerations 
Fairness:
Fairness considerations are interwoven with model alignment strategies.
Transparency:
Models openly released for safety evaluation and transparency.
Accountability:
Model developers are accountable for ensuring alignment and safety.
Mitigation Strategies:
Meta Llama Guard and Code Shield as safeguards.
Input Output 
Input Format:
text input
Accepted Modalities:
text
Output Format:
text generation
Performance Tips:
Apply supervised fine-tuning or RLHF for specific applications.
LLM NameLlama 3 8B Instruct Gradient 4194K
Repository 🤗https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k 
Model Size8b
Required VRAM16.1 GB
Updated2025-02-05
Maintainergradientai
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   1.2 GB: 4-of-4
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licensellama3
Context Length1234
Model Max Length1234
Transformers Version4.41.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16

Quantized Models of the Llama 3 8B Instruct Gradient 4194K

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...8B Instruct Gradient 4194K AWQ0795 GB

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Note: green Score (e.g. "73.2") means that the model is better than gradientai/Llama-3-8B-Instruct-Gradient-4194k.

Rank the Llama 3 8B Instruct Gradient 4194K 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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