What are the hardware requirements for Phi 3 Autocomplete Search?

Phi 3 Autocomplete Search 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 Autocomplete Search and how large is it?

Phi 3 Autocomplete Search is developed by rexoscare, 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 Autocomplete Search?

Phi 3 Autocomplete Search is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Phi 3 Autocomplete Search by rexoscare — VRAM 2.3GB, 4K context

Name: Phi 3 Autocomplete Search
Author: rexoscare

Phi 3 Autocomplete Search is an open-source language model by rexoscare. Features: 3.9b LLM, VRAM: 2.3GB, Context: 4K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

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

Model Card on HF 🤗: https://huggingface.co/rexoscare/phi-3-autocomplete-search

Phi 3 Autocomplete Search Parameters and Internals

Model Type

text-generation-inference, transformers

Additional Notes

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

Supported Languages

en (proficient)

LLM Name	Phi 3 Autocomplete Search
Repository 🤗	https://huggingface.co/rexoscare/phi-3-autocomplete-search
Base Model(s)	unsloth/phi-3-mini-4k-instruct-bnb-4bit unsloth/phi-3-mini-4k-instruct-bnb-4bit
Model Size	3.9b
Required VRAM	2.3 GB
Updated	2026-08-05
Maintainer	rexoscare
Model Type	mistral
Instruction-Based	Yes
Model Files	2.3 GB
Supported Languages	en
Quantization Type	4bit
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.41.2
Tokenizer Class	LlamaTokenizer
Padding Token	<\|placeholder6\|>
Vocabulary Size	32064
Torch Data Type	bfloat16

Rank the Phi 3 Autocomplete Search 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

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Note: green Score (e.g. "73.2") means that the model is better than rexoscare/phi-3-autocomplete-search.