What are the hardware requirements for Llama 3 8B Instruct Function Calling?

Llama 3 8B Instruct Function Calling requires approximately 2.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3 8B Instruct Function Calling and how large is it?

Llama 3 8B Instruct Function Calling is developed by smangrul, 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 Llama 3 8B Instruct Function Calling?

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

Llama 3 8B Instruct Function Calling by smangrul — VRAM 2.4GB

Name: Llama 3 8B Instruct Function Calling
Author: smangrul

Llama 3 8B Instruct Function Calling is an open-source language model by smangrul. Features: 8b LLM, VRAM: 2.4GB, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.12.

4bit Adapter Base model:adapter:unsloth/lla... Base model:unsloth/llama-3-8b-... Conversational Dataset:generator Endpoints compatible Finetuned Generated from trainer Gguf Instruct Lora Peft Q4 Quantized Region:us Safetensors Sft Tensorboard Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/smangrul/llama-3-8B-instruct-function-calling

Llama 3 8B Instruct Function Calling Parameters and Internals

Model Type

fine-tuned

Additional Notes

Model description, intended uses, limitations, and training and evaluation data information were not provided.

Training Details

Data Sources:

generator

LLM Name	Llama 3 8B Instruct Function Calling
Repository 🤗	https://huggingface.co/smangrul/llama-3-8B-instruct-function-calling
Base Model(s)	Llama 3 8B Instruct Bnb 4bit unsloth/llama-3-8b-Instruct-bnb-4bit
Model Size	8b
Required VRAM	2.4 GB
Updated	2026-07-14
Maintainer	smangrul
Instruction-Based	Yes
Model Files	2.4 GB 4.9 GB 0.0 GB
GGUF Quantization	Yes
Quantization Type	q4\|4bit\|gguf\|q4_k
Model Architecture	Adapter
License	apache-2.0
Is Biased	none
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	k_proj\|o_proj\|down_proj\|up_proj\|q_proj\|v_proj\|gate_proj
LoRA Alpha	32
LoRA Dropout	0
R Param	32

Rank the Llama 3 8B Instruct Function Calling capabilities

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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 smangrul/llama-3-8B-instruct-function-calling.