Add SMOLTRACE dataset card

README.md

@@ -1,41 +1,142 @@
 ---
-dataset_info:
-  features:
-  - name: run_id
-    dtype: string
-  - name: service_name
-    dtype: string
-  - name: co2_emissions_gco2e
-    dtype: float64
-  - name: power_cost_usd
-    dtype: float64
-  - name: gpu_utilization_percent
-    dtype: float64
-  - name: gpu_memory_used_mib
-    dtype: float64
-  - name: gpu_memory_total_mib
-    dtype: float64
-  - name: gpu_temperature_celsius
-    dtype: float64
-  - name: gpu_power_watts
-    dtype: float64
-  - name: timestamp
-    dtype: string
-  - name: timestamp_unix_nano
-    dtype: string
-  - name: gpu_id
-    dtype: string
-  - name: gpu_name
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 6290
-    num_examples: 40
-  download_size: 7688
-  dataset_size: 6290
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: agpl-3.0
+tags:
+  - smoltrace
+  - smolagents
+  - evaluation
+  - benchmark
+  - llm
+  - agents
 ---
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/Mandark-droid/SMOLTRACE/main/.github/images/Logo.png" alt="SMOLTRACE Logo" width="400"/>
+
+  <h3>Tiny Agents. Total Visibility.</h3>
+
+  <p>
+    <a href="https://github.com/Mandark-droid/SMOLTRACE"><img src="https://img.shields.io/badge/GitHub-SMOLTRACE-blue?logo=github" alt="GitHub"></a>
+    <a href="https://pypi.org/project/smoltrace/"><img src="https://img.shields.io/pypi/v/smoltrace?color=green" alt="PyPI"></a>
+    <a href="https://github.com/Mandark-droid/SMOLTRACE#readme"><img src="https://img.shields.io/badge/docs-readme-orange" alt="Documentation"></a>
+  </p>
+</div>
+
+---
+
+
+# SMOLTRACE GPU & Environmental Metrics
+
+This dataset contains time-series GPU metrics and environmental impact data from a SMOLTRACE benchmark run.
+
+## Dataset Information
+
+| Field | Value |
+|-------|-------|
+| **Model** | `Kiy-K/Fyodor-Q3-8B-Instruct` |
+| **Run ID** | `job_6f7ee8e4` |
+| **Total Samples** | 40 |
+| **Generated** | 2025-11-25 13:19:56 UTC |
+| **GPU Metrics** | Available |
+
+## Schema
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `run_id` | string | Unique run identifier |
+| `timestamp` | string | ISO timestamp of measurement |
+| `timestamp_unix_nano` | string | Unix nanosecond timestamp |
+| `service_name` | string | Service identifier |
+| `gpu_id` | string | GPU device ID |
+| `gpu_name` | string | GPU model name |
+| `gpu_utilization_percent` | float | GPU compute utilization (0-100%) |
+| `gpu_memory_used_mib` | float | GPU memory used (MiB) |
+| `gpu_memory_total_mib` | float | Total GPU memory (MiB) |
+| `gpu_temperature_celsius` | float | GPU temperature (°C) |
+| `gpu_power_watts` | float | GPU power consumption (W) |
+| `co2_emissions_gco2e` | float | Cumulative CO2 emissions (gCO2e) |
+| `power_cost_usd` | float | Cumulative power cost (USD) |
+
+## Environmental Impact
+
+SMOLTRACE tracks environmental metrics to help you understand the carbon footprint of your AI workloads:
+
+- **CO2 Emissions**: Calculated based on GPU power consumption and regional carbon intensity
+- **Power Cost**: Estimated electricity cost based on configurable rates
+
+## Usage
+
+```python
+from datasets import load_dataset
+import pandas as pd
+
+# Load metrics
+ds = load_dataset("YOUR_USERNAME/smoltrace-metrics-TIMESTAMP")
+
+# Convert to DataFrame for analysis
+df = pd.DataFrame(ds['train'])
+
+# Plot GPU utilization over time
+import matplotlib.pyplot as plt
+plt.plot(df['timestamp'], df['gpu_utilization_percent'])
+plt.xlabel('Time')
+plt.ylabel('GPU Utilization (%)')
+plt.title('GPU Utilization During Evaluation')
+plt.show()
+
+# Get total environmental impact
+total_co2 = df['co2_emissions_gco2e'].max()
+total_cost = df['power_cost_usd'].max()
+print(f"Total CO2: {total_co2:.4f} gCO2e")
+print(f"Total Cost: ${total_cost:.6f}")
+```
+
+## Related Datasets
+
+This evaluation run also generated:
+- **Results Dataset**: Pass/fail outcomes for each test case
+- **Traces Dataset**: Detailed OpenTelemetry execution traces
+- **Leaderboard**: Aggregated metrics for model comparison
+
+---
+
+## About SMOLTRACE
+
+**SMOLTRACE** is a comprehensive benchmarking and evaluation framework for [Smolagents](https://huggingface.co/docs/smolagents) - HuggingFace's lightweight agent library.
+
+### Key Features
+- Automated agent evaluation with customizable test cases
+- OpenTelemetry-based tracing for detailed execution insights
+- GPU metrics collection (utilization, memory, temperature, power)
+- CO2 emissions and power cost tracking
+- Leaderboard aggregation and comparison
+
+### Quick Links
+- [GitHub Repository](https://github.com/Mandark-droid/SMOLTRACE)
+- [PyPI Package](https://pypi.org/project/smoltrace/)
+- [Documentation](https://github.com/Mandark-droid/SMOLTRACE#readme)
+- [Report Issues](https://github.com/Mandark-droid/SMOLTRACE/issues)
+
+### Installation
+
+```bash
+pip install smoltrace
+```
+
+### Citation
+
+If you use SMOLTRACE in your research, please cite:
+
+```bibtex
+@software{smoltrace,
+  title = {SMOLTRACE: Benchmarking Framework for Smolagents},
+  author = {Thakkar, Kshitij},
+  url = {https://github.com/Mandark-droid/SMOLTRACE},
+  year = {2025}
+}
+```
+
+---
+
+<div align="center">
+  <sub>Generated by <a href="https://github.com/Mandark-droid/SMOLTRACE">SMOLTRACE</a></sub>
+</div>