microsoft_phi-4-ingest-best-gptq/README.md

base_model, library_name, license, pipeline_tag, tags

base_model	library_name	license	pipeline_tag	tags
microsoft/phi-4	transformers	mit	text-generation	positron-ai quantized gptq

Positron AI Quantized Build

This repository contains a Positron AI quantized build of microsoft/phi-4 for tron inference on Positron FPGA-serving infrastructure.

Recommended Use

Use this artifact when you need a GPTQ 4-bit build of microsoft/phi-4 optimized for Positron's tron runtime. This build targets Positron's ingest-serving path, where runtime fidelity and FPGA deployability are prioritized over general-purpose GPU portability.

For general-purpose GPU inference, compare against the original model and other quantized formats before deployment.

Artifact Summary

Field	Value
Base model	microsoft/phi-4
Published artifact	microsoft_phi-4-ingest-best-gptq
Quantization method	GPTQ
Quantization format	gptq
Source precision	n/a
Target runtime	tron
Hardware target	FPGA
Release date	2026-06-30
License	mit

Quantization Details

Field	Value
Weight precision	4-bit
Activation precision	not quantized
Bits	4
Group size	64
Symmetric quantization	true
Activation ordering / desc_act	false
Damp percent	0.05
Calibration dataset	Universal mixed-domain set
Calibration samples	256
Calibration sequence length	4096
MoE experts per token	n/a
Quantization toolchain	GPTQModel 5.8.0, transformers 4.57.6, torch 2.9.1, CUDA 12.8

Validation Results

Metric	Result	Reference	Notes
Mean KL-divergence	0.0183	microsoft/phi-4	Mean across the prompt suite
P95 KL-divergence	0.0753	microsoft/phi-4	Mean of per-prompt 95th-percentile token KL-divergence
Top-1 agreement	0.9523	microsoft/phi-4	Greedy top-1 token agreement
Perplexity / NLL delta	+1.7%	microsoft/phi-4	Same prompt suite as KL-divergence
MMLU mean	pending	n/a	Evaluation pending

KL-divergence measures token-distribution drift between this quantized artifact and the BF16 reference model; lower values indicate closer agreement. It was computed on Positron's tron FPGA-serving path, so treat it as a runtime-specific drift measurement rather than a GPU benchmark.

Evaluation Methodology

Field	Value
Evaluation date	2026-06-30
Evaluation suite	Positron mixed-domain prompt suite
Number of prompts	12
Runtime	tron
Device	FPGA
Pass criteria	Measurement only (no fixed KL-divergence threshold)

Known Limitations

• KL-divergence was measured on a 12-prompt Positron validation suite; treat it as a runtime validation signal, not a broad benchmark.
• Results are specific to the Positron tron FPGA-serving path and may differ from GPU-native inference.
• MMLU evaluation is pending; results will be added when available.

Provenance

This artifact was produced by Positron AI from microsoft/phi-4. The original model license and usage restrictions continue to apply.