What are the hardware requirements for Meta Llama 3 8B Instruct Ct2 Int8 Float16?

Meta Llama 3 8B Instruct Ct2 Int8 Float16 requires approximately 8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Meta Llama 3 8B Instruct Ct2 Int8 Float16 and how large is it?

Meta Llama 3 8B Instruct Ct2 Int8 Float16 is developed by avans06, a model with 8b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Meta Llama 3 8B Instruct Ct2 Int8 Float16?

Meta Llama 3 8B Instruct Ct2 Int8 Float16 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Meta Llama 3 8B Instruct Ct2 Int8 Float16 by avans06 — VRAM 8GB

Name: Meta Llama 3 8B Instruct Ct2 Int8 Float16
Author: avans06

Meta Llama 3 8B Instruct Ct2 Int8 Float16 is an open-source language model by avans06. Features: 8b LLM, VRAM: 8GB, License: other, Instruction-Based, LLM Explorer Score: 0.12.

Conversational Ctranslate2 En Endpoints compatible Facebook Float16 Instruct Int8 Llama Llama-3 Meta Pytorch Quantization Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/avans06/Meta-Llama-3-8B-Instruct-ct2-int8_float16

Meta Llama 3 8B Instruct Ct2 Int8 Float16 Parameters and Internals

Model Type

text generation, instruction tuned

Use Cases

Areas:

Commercial use, Research use, Assistant-like chat

Applications:

Natural language generation tasks, Dialogue-focused applications

Primary Use Cases:

Chatbots, Text generation in English

Limitations:

Use in languages other than English requires compliance with license

Considerations:

Developers should perform safety testing tailored to specific applications

Additional Notes

Fine-tuning recommended for specific language support

Supported Languages

English (native)

Training Details

Data Sources:

Publicly available online data, Instruction datasets, Human-annotated examples

Data Volume:

15 trillion+ tokens

Methodology:

Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)

Context Length:

8000

Training Time:

7.7M GPU hours

Hardware Used:

H100-80GB GPUs

Model Architecture:

Optimized transformer architecture with Grouped-Query Attention (GQA)

Safety Evaluation

Methodologies:

Red teaming, Adversarial evaluations, CyberSecEval safety eval suite

Findings:

Reduced residual risks of LLM Systems, Implemented safety mitigations

Risk Categories:

Cybersecurity, Child Safety

Ethical Considerations:

Industry-standard safety benchmarks and risk assessments were conducted

Responsible Ai Considerations

Fairness:

Openness, inclusivity, and helpfulness are core values

Transparency:

Commitment to responsible AI development and open community collaboration

Accountability:

Meta is responsible for maintaining safety guidelines and providing resources

Mitigation Strategies:

Implemented safeguards, refusal optimization, and community feedback loops

Input Output

Input Format:

Text only

Accepted Modalities:

text

Output Format:

Generates text and code only

Release Notes

Version:

Meta-Llama-3-8B-Instruct-ct2-int8_float16

Date:

2024-4

Notes:

Quantized version with int8_float16 for CTranslate2

LLM Name	Meta Llama 3 8B Instruct Ct2 Int8 Float16
Repository 🤗	https://huggingface.co/avans06/Meta-Llama-3-8B-Instruct-ct2-int8_float16
Model Size	8b
Required VRAM	8 GB
Updated	2026-05-05
Maintainer	avans06
Instruction-Based	Yes
Model Files	8.0 GB
Supported Languages	en
Model Architecture	AutoModel
License	other
Tokenizer Class	PreTrainedTokenizerFast

Rank the Meta Llama 3 8B Instruct Ct2 Int8 Float16 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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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Ur Orpo Adapter	0K / 0.2 GB	5	0
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Note: green Score (e.g. "73.2") means that the model is better than avans06/Meta-Llama-3-8B-Instruct-ct2-int8_float16.