初始化项目，由ModelHub XC社区提供模型

Model: positron-ai/microsoft_phi-4-ingest-best-gptq
Source: Original Platform

README.md

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+---
+base_model: microsoft/phi-4
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+tags:
+- 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.