What are the hardware requirements for Phi 3.5 Instruct Vul?

Phi 3.5 Instruct Vul requires approximately 7.6 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Phi 3.5 Instruct Vul and how large is it?

Phi 3.5 Instruct Vul is developed by abhinav2024. 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 3.5 Instruct Vul?

Phi 3.5 Instruct Vul is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Phi 3.5 Instruct Vul by abhinav2024 — VRAM 7.6GB, 128K context

Name: Phi 3.5 Instruct Vul
Author: abhinav2024

Phi 3.5 Instruct Vul is an open-source language model by abhinav2024. Features: LLM, VRAM: 7.6GB, Context: 128K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.14.

4bit Conversational En Endpoints compatible Instruct Llama Pytorch Quantized Region:us Sharded Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/abhinav2024/phi-3.5-instruct-vul

Phi 3.5 Instruct Vul Parameters and Internals

Model Type

text-generation-inference

Additional Notes

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

Supported Languages

en (fluent)

LLM Name	Phi 3.5 Instruct Vul
Repository 🤗	https://huggingface.co/abhinav2024/phi-3.5-instruct-vul
Base Model(s)	unsloth/phi-3.5-mini-instruct-bnb-4bit unsloth/phi-3.5-mini-instruct-bnb-4bit
Required VRAM	7.6 GB
Updated	2026-08-02
Maintainer	abhinav2024
Model Type	llama
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-2 2.6 GB: 2-of-2
Supported Languages	en
Quantization Type	4bit
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.44.2
Tokenizer Class	LlamaTokenizer
Padding Token	<\|placeholder6\|>
Vocabulary Size	32064
Torch Data Type	float16

Rank the Phi 3.5 Instruct Vul 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 3.5 Instruct Vul

Best Alternatives	Context / RAM	Downloads	Likes
Slimorca Phi 3.5	128K / 7.6 GB	7	0
...Instruct DPO V2.8.0bpw H8 EXL2	4K / 10.9 GB	1	2
Reverse Instruct	32K / 27 GB	8	3
Codellama Cairo Instruct GGUF	16K / 4.1 GB	32	1
Etri Ones Solar	4K / 42.9 GB	50	0
Phi 3 Orpo V8.16	4K / 7.6 GB	7	0
Airavata GGUF	4K / 4.2 GB	43	2
Finance Chat GGUF	4K / 2.8 GB	340	4
Tinyllama Python	4K / 2.2 GB	13	2
Sakura SOLAR Instruct GGUF	4K / 4.5 GB	41	1

Note: green Score (e.g. "73.2") means that the model is better than abhinav2024/phi-3.5-instruct-vul.