What are the hardware requirements for Phi 3 Mini Instruct 16bit?

Phi 3 Mini Instruct 16bit requires approximately 7.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Phi 3 Mini Instruct 16bit and how large is it?

Phi 3 Mini Instruct 16bit is developed by pinkyprakash, a model with 3.8b 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 Phi 3 Mini Instruct 16bit?

Phi 3 Mini Instruct 16bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Phi 3 Mini Instruct 16bit by pinkyprakash — VRAM 7.6GB

Name: Phi 3 Mini Instruct 16bit
Author: pinkyprakash

Phi 3 Mini Instruct 16bit is an open-source language model by pinkyprakash. Features: 3.8b LLM, VRAM: 7.6GB, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

4bit Conversational En Endpoints compatible Instruct Lora Mistral Quantized Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/pinkyprakash/Phi-3-Mini-Instruct_16bit

Phi 3 Mini Instruct 16bit Parameters and Internals

Model Type

text-generation-inference, transformers, unsloth, mistral, trl, sft

Additional Notes

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

LLM Name	Phi 3 Mini Instruct 16bit
Repository 🤗	https://huggingface.co/pinkyprakash/Phi-3-Mini-Instruct_16bit
Base Model(s)	unsloth/phi-3-mini-4k-instruct-bnb-4bit unsloth/phi-3-mini-4k-instruct-bnb-4bit
Model Size	3.8b
Required VRAM	7.6 GB
Updated	2026-07-30
Maintainer	pinkyprakash
Instruction-Based	Yes
Model Files	0.1 GB 5.0 GB: 1-of-2 2.6 GB: 2-of-2
Supported Languages	en
Quantization Type	4bit
Model Architecture	AutoModelForCausalLM
License	apache-2.0
Model Max Length	4096
Is Biased	none
Tokenizer Class	LlamaTokenizer
Padding Token	<\|placeholder6\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|v_proj\|up_proj\|gate_proj\|o_proj\|down_proj\|k_proj
LoRA Alpha	16
LoRA Dropout	0
R Param	16

Rank the Phi 3 Mini Instruct 16bit 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

Best Alternatives to Phi 3 Mini Instruct 16bit

Best Alternatives	Context / RAM	Downloads	Likes
Kittylm Phi 4 Mini	0K / 0.1 GB	244	0
...ct Bnb 4bit 16 4 100 1 Nonmath	0K / 7.6 GB	86	1
Followup V6	0K / 7.6 GB	0	0

Note: green Score (e.g. "73.2") means that the model is better than pinkyprakash/Phi-3-Mini-Instruct_16bit.