What are the hardware requirements for WiNGPT2 Llama 3 8B Base?

WiNGPT2 Llama 3 8B Base requires approximately 16.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed WiNGPT2 Llama 3 8B Base and how large is it?

WiNGPT2 Llama 3 8B Base is developed by winninghealth, 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 WiNGPT2 Llama 3 8B Base?

WiNGPT2 Llama 3 8B Base is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

WiNGPT2 Llama 3 8B Base by winninghealth — VRAM 16.1GB, 8K context

Name: WiNGPT2 Llama 3 8B Base
Author: winninghealth

WiNGPT2 Llama 3 8B Base is an open-source language model by winninghealth. Features: 8b LLM, VRAM: 16.1GB, Context: 8K, License: apache-2.0, LLM Explorer Score: 0.15.

Base model:finetune:meta-llama... Base model:meta-llama/meta-lla... Deploy:azure En Endpoints compatible Llama Medical Region:us Safetensors Sharded Tensorflow Zh

Model Card on HF 🤗: https://huggingface.co/winninghealth/WiNGPT2-Llama-3-8B-Base

WiNGPT2 Llama 3 8B Base Parameters and Internals

Model Type

text generation

Use Cases

Areas:

Medical industry, Healthcare

Applications:

Medical Q&A, Diagnostic support

Primary Use Cases:

AI doctor consultations, General medical knowledge Q&A

Limitations:

Does not replace professional medical advice.

Considerations:

Users should independently verify information.

Additional Notes

Predominantly designed for medical domain expertise.

Supported Languages

en (Proficient), zh (Proficient)

Training Details

Data Sources:

Medical datasets

Data Volume:

20G for pretraining, 500k samples for finetuning

Methodology:

Continuation of pretraining and finetuning for alignment

Context Length:

8192

Hardware Used:

A100 * 8 GPUs

Model Architecture:

GPT-based architecture

Responsible Ai Considerations

Fairness:

Strives to provide unbiased medical advice.

Transparency:

Model limitations are disclosed.

Accountability:

Users must evaluate provided suggestions.

Mitigation Strategies:

Users are advised to use model outputs as a reference.

Input Output

Input Format:

Custom prompt in format combining user roles with content.

Accepted Modalities:

text

Output Format:

Textual response in conversational format.

Performance Tips:

Ensure compatibility with model specifications for optimal performance.

Release Notes

Version:

WiNGPT2-Llama-3-8B-Base

Date:

2024-04-23

Notes:

Enhanced with multilingual support.

Version:

WiNGPT2-7B-Chat-4bit

Date:

2024-03-05

Notes:

Open-sourced with 4-bit precision model weights.

Version:

WiNGPT2-14B-Base

Date:

2023-12-12

Notes:

Open-sourced with model weights.

LLM Name	WiNGPT2 Llama 3 8B Base
Repository 🤗	https://huggingface.co/winninghealth/WiNGPT2-Llama-3-8B-Base
Base Model(s)	Meta Llama 3 8B meta-llama/Meta-Llama-3-8B
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-05-15
Maintainer	winninghealth
Model Type	llama
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 Languages	en zh
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.40.0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the WiNGPT2 Llama 3 8B Base 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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Note: green Score (e.g. "73.2") means that the model is better than winninghealth/WiNGPT2-Llama-3-8B-Base.