What are the hardware requirements for Phi 3 Mini 4K Instruct Bnb 4bit?

Phi 3 Mini 4K Instruct Bnb 4bit requires approximately 2.3 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Phi 3 Mini 4K Instruct Bnb 4bit and how large is it?

Phi 3 Mini 4K Instruct Bnb 4bit is developed by unsloth, a model with 3.9b 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 4K Instruct Bnb 4bit?

Phi 3 Mini 4K Instruct Bnb 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Phi 3 Mini 4K Instruct Bnb 4bit by unsloth — VRAM 2.3GB, 4K context

Name: Phi 3 Mini 4K Instruct Bnb 4bit
Author: unsloth

Phi 3 Mini 4K Instruct Bnb 4bit is an open-source language model by unsloth. Features: 3.9b LLM, VRAM: 2.3GB, Context: 4K, License: mit, Quantized, Instruction-Based, LLM Explorer Score: 0.21.

4-bit 4bit Bitsandbytes Conversational En Endpoints compatible Instruct Mistral Phi Phi3 Quantized Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/unsloth/Phi-3-mini-4k-instruct-bnb-4bit

Phi 3 Mini 4K Instruct Bnb 4bit Parameters and Internals

Model Type

text generation, multimodal

Use Cases

Areas:

research, commercial applications

Applications:

language generation, chat models

Primary Use Cases:

ShareGPT ChatML, Vicuna templates

Limitations:

unsuitable for non-text inputs

Additional Notes

Using Unsloth for fine-tuning models like Phi-3.5 enhances performance by reducing memory usage by 50% and finetuning 2x faster.

Training Details

Methodology:

fine-tuning

Hardware Used:

Colab Tesla T4, Kaggle 2x T4s

Input Output

Input Format:

text

Accepted Modalities:

text

Output Format:

generated text

LLM Name	Phi 3 Mini 4K Instruct Bnb 4bit
Repository 🤗	https://huggingface.co/unsloth/Phi-3-mini-4k-instruct-bnb-4bit
Model Size	3.9b
Required VRAM	2.3 GB
Updated	2026-07-27
Maintainer	unsloth
Model Type	mistral
Instruction-Based	Yes
Model Files	2.3 GB
Supported Languages	en
Quantization Type	4bit
Model Architecture	MistralForCausalLM
License	mit
Context Length	4096
Model Max Length	4096
Transformers Version	4.44.2
Tokenizer Class	LlamaTokenizer
Padding Token	<\|placeholder6\|>
Vocabulary Size	32064
Torch Data Type	bfloat16

Rank the Phi 3 Mini 4K Instruct Bnb 4bit 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

Quantized Models of the Phi 3 Mini 4K Instruct Bnb 4bit

Model	Likes	Downloads	VRAM
Wandaa V02 Merged	0	28	7 GB
...ct Bnb 4bit 16 4 100 1 Nonmath	1	86	7 GB
...ent V56 3 W Unknown 5 Lr 0.002	0	7	7 GB
Phi 3 Mini 4K Python	1	632	7 GB
... Mini 4K Instruct Bnb 4bit Ita	0	1181	7 GB
...Word Problems 200K Model 16bit	0	3879	7 GB
... Mini 4K Instruct Bnb 4bit Ita	0	1527	7 GB
... 4K Instruct Matter 0.1 Slim A	2	17	7 GB
Phired	0	12	7 GB
...rser User V9b Epoch 7 Lr 0.002	0	6	7 GB

Best Alternatives to Phi 3 Mini 4K Instruct Bnb 4bit

Best Alternatives	Context / RAM	Downloads	Likes
...3 Mini 4K Instruct V0 Bnb 4bit	4K / 2.3 GB	13	3
Phi 4K Gsm8k	4K / 2.3 GB	10	0
Phi 3 Autocomplete Search	4K / 2.3 GB	5	0
Small Mistral 1	4K / 2.3 GB	5	0
Hf	4K / 2.3 GB	12	0
MediChat Medium	4K / 2.3 GB	6	1
Recophi3 4bit	4K / 2.3 GB	6	0
Phi3 MeetingQA 4bit	4K / 2.3 GB	6	0
...Spydaz Web AI ChatQA 002 4 BIT	512K / 4.1 GB	24	0
Spydaz Web AI 02 4 BIT	32K / 4.1 GB	28	0