---
library_name: transformers
tags:
  - pytorch
  - bfloat16
  - quantization
---

# TCompress Model Benchmark Report
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# TCompress Model Benchmark Report

**Quantization Type:** QAT (Quantization Aware Training)
**Precision:** BF16
**Evaluation Dataset:** Salesforce/wikitext (wikitext-2-raw-v1)

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## Performance Metrics

| Metric | Result |
| :--- | :--- |
| **Total Tokens Evaluated** | 1,000,000 |
| **Latency (Mean)** | 58.12 ms |
| **Throughput** | 17,206.3 tok/s |
| **Peak GPU Memory** | 1,174.4 MB |

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## Model Accuracy

| Variant | Agreement vs Base | Flipped Tokens |
| :--- | :--- | :--- |
| **TCompress (bf16)** | 94.92% | 50,829 |

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## Storage & Packaging

| Asset | Size |
| :--- | :--- |
| **Model Weight File** | 298.0 MB |

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### Implementation Details
This model has been optimized via Training-Aware Quantization to maintain high fidelity (94.92% agreement) with the base FP32 architecture while significantly reducing memory footprint and maximizing throughput on CUDA-enabled hardware.