What are the hardware requirements for Phi 2 Nf4 Fp16 Upscaled?

Phi 2 Nf4 Fp16 Upscaled requires approximately 5.6 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Phi 2 Nf4 Fp16 Upscaled and how large is it?

Phi 2 Nf4 Fp16 Upscaled is developed by arnavgrg, a model with 2.8b 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 2 Nf4 Fp16 Upscaled?

Phi 2 Nf4 Fp16 Upscaled is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Phi 2 Nf4 Fp16 Upscaled by arnavgrg — VRAM 5.6GB, 2K context

Name: Phi 2 Nf4 Fp16 Upscaled
Author: arnavgrg

Phi 2 Nf4 Fp16 Upscaled is an open-source language model by arnavgrg. Features: 2.8b LLM, VRAM: 5.6GB, Context: 2K, License: apache-2.0, Quantized, LLM Explorer Score: 0.11.

Custom code Endpoints compatible Fp16 Phi Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/arnavgrg/phi-2-nf4-fp16-upscaled

Phi 2 Nf4 Fp16 Upscaled Parameters and Internals

Model Type

text generation

Additional Notes

This is an upscaled fp16 variant of the original Phi-2 base model using nf4 4-bit quantization. The quantization operation to nf4 is not lossless, affecting the model's performance compared to the original model.

Input Output

Performance Tips:

To use this model, you can just load it via `transformers` in fp16.

LLM Name	Phi 2 Nf4 Fp16 Upscaled
Repository 🤗	https://huggingface.co/arnavgrg/phi-2-nf4-fp16-upscaled
Model Size	2.8b
Required VRAM	5.6 GB
Updated	2026-07-31
Maintainer	arnavgrg
Model Type	phi
Model Files	5.0 GB: 1-of-2 0.6 GB: 2-of-2
Quantization Type	fp16
Model Architecture	PhiForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.37.0
Tokenizer Class	CodeGenTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	51200
Torch Data Type	float16

Rank the Phi 2 Nf4 Fp16 Upscaled 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 2 Nf4 Fp16 Upscaled

Best Alternatives	Context / RAM	Downloads
Bnb DPO 8bit	2K / 3 GB	7
Bnb DPO 8bit	2K / 3 GB	7
Phi 2 4bit 64rank	2K / 5.6 GB	13
MFANN3bv0.24	128K / 11.1 GB	6
MFANN3b	128K / 11.1 GB	86
MFANN3bv1.4	128K / 11.1 GB	8
MFANN Phigments Slerp V3.2	128K / 5.6 GB	6
MFANN3bv1.3	128K / 11.1 GB	14
MFANN3bv0.23	128K / 11.1 GB	10
MFANN3bv1.1	128K / 11.1 GB	10

Note: green Score (e.g. "73.2") means that the model is better than arnavgrg/phi-2-nf4-fp16-upscaled.