What are the hardware requirements for Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix?

Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix requires a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix?

Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix by amd — 128K context

Name: Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix
Author: amd

Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix is an open-source language model by amd. Features: LLM, Context: 128K, License: mit, Quantized, Instruction-Based.

Amd Awq Base model:microsoft/phi-3.5-m... Base model:quantized:microsoft... Code Conversational Custom code Endpoints compatible Instruct Multilingual Onnx Phi3 Quantized Region:us Ryzenai-npu

Model Card on HF 🤗: https://huggingface.co/amd/Phi-3.5-mini-instruct-awq-g128-int4-asym-bf16-onnx-ryzen-strix

Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix Parameters and Internals

LLM Name	Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix
Repository 🤗	https://huggingface.co/amd/Phi-3.5-mini-instruct-awq-g128-int4-asym-bf16-onnx-ryzen-strix
Base Model(s)	microsoft/Phi-3.5-mini-instruct microsoft/Phi-3.5-mini-instruct
Updated	2026-08-09
Maintainer	amd
Model Type	phi3
Instruction-Based	Yes
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	Phi3ForCausalLM
License	mit
Context Length	131072
Model Max Length	131072
Transformers Version	4.43.3
Tokenizer Class	LlamaTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	32064
Torch Data Type	bfloat16

Rank the Phi 3.5 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix 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 Mini Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix

Best Alternatives	Context / RAM	Downloads	Likes
...nt4 Asym Bf16 Onnx Ryzen Strix	4K / GB	9	2
...m 128K Instruct 8.0bpw H8 EXL2	128K / 13.4 GB	7	4
...m 128K Instruct 6.0bpw H6 EXL2	128K / 10.7 GB	7	3
...dium 128K Instruct 8 0bpw EXL2	128K / 13.4 GB	4	1
...m 128K Instruct 3.0bpw H6 EXL2	128K / 5.6 GB	5	0
...m 128K Instruct 5.0bpw H6 EXL2	128K / 8.9 GB	5	0
...28K Instruct Ov Fp16 Int4 Asym	128K / 2.5 GB	9	0
...128K Instruct HQQ 2bit Smashed	128K / 1.4 GB	8	0
...128K Instruct HQQ 4bit Smashed	128K / 2.9 GB	7	0
NuExtract Bpw6 EXL2	4K / 3 GB	4	1

Note: green Score (e.g. "73.2") means that the model is better than amd/Phi-3.5-mini-instruct-awq-g128-int4-asym-bf16-onnx-ryzen-strix.