model
stringclasses 1
value | evaluation_date
stringdate 2025-11-26 10:50:09
2025-11-26 10:50:09
| task_id
stringclasses 7
values | agent_type
stringclasses 1
value | difficulty
stringclasses 3
values | prompt
stringclasses 7
values | success
bool 1
class | tool_called
bool 1
class | correct_tool
bool 1
class | final_answer_called
bool 1
class | response_correct
bool 1
class | tools_used
listlengths 0
0
| steps
int64 0
0
| response
null | error
stringclasses 1
value | trace_id
stringclasses 7
values | execution_time_ms
float64 16.9
35.6
| total_tokens
int64 0
0
| cost_usd
float64 0
0
| enhanced_trace_info
stringclasses 7
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
google/gemma-2-9b-it
|
2025-11-26T10:50:09.375622
|
code_calculator_single
|
code
|
easy
|
What is 234 multiplied by 67?
| false
| false
| false
| false
| true
|
[] | 0
| null |
Error in generating model output:
System role not supported
|
0x668b15f105a1036ceadda60c077c75d0
| 35.606895
| 0
| 0
|
{"trace_id": "0x668b15f105a1036ceadda60c077c75d0", "total_tokens": 0, "duration_ms": 35.606895, "cost_usd": 0.0, "span_count": 2}
|
google/gemma-2-9b-it
|
2025-11-26T10:50:09.375700
|
code_calculator_complex
|
code
|
medium
|
Calculate (450 + 230) * 3, then divide the result by 10
| false
| false
| false
| false
| true
|
[] | 0
| null |
Error in generating model output:
System role not supported
|
0xbe91381409f0af021a7121162775692b
| 17.611018
| 0
| 0
|
{"trace_id": "0xbe91381409f0af021a7121162775692b", "total_tokens": 0, "duration_ms": 17.611018, "cost_usd": 0.0, "span_count": 2}
|
google/gemma-2-9b-it
|
2025-11-26T10:50:09.375768
|
code_weather_with_calc
|
code
|
hard
|
Get the weather in Paris and if the temperature is above 15°C, calculate 15 * 2
| false
| false
| false
| false
| true
|
[] | 0
| null |
Error in generating model output:
System role not supported
|
0x57a093b8ee1815305159699ed185286a
| 17.964015
| 0
| 0
|
{"trace_id": "0x57a093b8ee1815305159699ed185286a", "total_tokens": 0, "duration_ms": 17.964015, "cost_usd": 0.0, "span_count": 2}
|
google/gemma-2-9b-it
|
2025-11-26T10:50:09.375786
|
code_search_calculate
|
code
|
hard
|
Search for the population of Paris, then if you find it's around 2 million, calculate what 2 million divided by 365 is
| false
| false
| false
| false
| true
|
[] | 0
| null |
Error in generating model output:
System role not supported
|
0xffc4ad0407e978a3ec48ee9c8f4b3f3b
| 17.280458
| 0
| 0
|
{"trace_id": "0xffc4ad0407e978a3ec48ee9c8f4b3f3b", "total_tokens": 0, "duration_ms": 17.280458, "cost_usd": 0.0, "span_count": 2}
|
google/gemma-2-9b-it
|
2025-11-26T10:50:09.375802
|
code_list_processing
|
code
|
hard
|
Get weather for Paris, London, and Tokyo, then tell me which cities have temperature above 18°C
| false
| false
| false
| false
| true
|
[] | 0
| null |
Error in generating model output:
System role not supported
|
0xdbb427aec2b2f6e035d4fa380be5733a
| 18.489224
| 0
| 0
|
{"trace_id": "0xdbb427aec2b2f6e035d4fa380be5733a", "total_tokens": 0, "duration_ms": 18.489224, "cost_usd": 0.0, "span_count": 2}
|
google/gemma-2-9b-it
|
2025-11-26T10:50:09.375817
|
shared_basic_weather
|
code
|
easy
|
What's the weather like in Sydney, Australia?
| false
| false
| false
| false
| true
|
[] | 0
| null |
Error in generating model output:
System role not supported
|
0x844a22645c456ae04d25a1a8c7ca9f95
| 16.943822
| 0
| 0
|
{"trace_id": "0x844a22645c456ae04d25a1a8c7ca9f95", "total_tokens": 0, "duration_ms": 16.943822, "cost_usd": 0.0, "span_count": 2}
|
google/gemma-2-9b-it
|
2025-11-26T10:50:09.375834
|
shared_basic_search
|
code
|
easy
|
Search for information about machine learning
| false
| false
| false
| false
| true
|
[] | 0
| null |
Error in generating model output:
System role not supported
|
0x9e346b84ee4b0f766696100b8f971c21
| 16.853963
| 0
| 0
|
{"trace_id": "0x9e346b84ee4b0f766696100b8f971c21", "total_tokens": 0, "duration_ms": 16.853963, "cost_usd": 0.0, "span_count": 2}
|
Tiny Agents. Total Visibility.
SMOLTRACE Evaluation Results
This dataset contains evaluation results from a SMOLTRACE benchmark run.
Dataset Information
| Field | Value |
|---|---|
| Model | google/gemma-2-9b-it |
| Run ID | 22f49e98-ecbb-4030-945b-4fbb7e779352 |
| Agent Type | code |
| Total Tests | 7 |
| Generated | 2025-11-26 10:50:14 UTC |
| Source Dataset | kshitijthakkar/smoltrace-tasks |
Schema
| Column | Type | Description |
|---|---|---|
model |
string | Model identifier |
evaluation_date |
string | ISO timestamp of evaluation |
task_id |
string | Unique test case identifier |
agent_type |
string | "tool" or "code" agent type |
difficulty |
string | Test difficulty level |
prompt |
string | Test prompt/question |
success |
bool | Whether the test passed |
tool_called |
bool | Whether a tool was invoked |
correct_tool |
bool | Whether the correct tool was used |
final_answer_called |
bool | Whether final_answer was called |
response_correct |
bool | Whether the response was correct |
tools_used |
string | Comma-separated list of tools used |
steps |
int | Number of agent steps taken |
response |
string | Agent's final response |
error |
string | Error message if failed |
trace_id |
string | OpenTelemetry trace ID |
execution_time_ms |
float | Execution time in milliseconds |
total_tokens |
int | Total tokens consumed |
cost_usd |
float | API cost in USD |
enhanced_trace_info |
string | JSON with detailed trace data |
Usage
from datasets import load_dataset
# Load the results dataset
ds = load_dataset("YOUR_USERNAME/smoltrace-results-TIMESTAMP")
# Filter successful tests
successful = ds.filter(lambda x: x['success'])
# Calculate success rate
success_rate = sum(1 for r in ds['train'] if r['success']) / len(ds['train']) * 100
print(f"Success Rate: {success_rate:.2f}%")
Related Datasets
This evaluation run also generated:
- Traces Dataset: Detailed OpenTelemetry execution traces
- Metrics Dataset: GPU utilization and environmental metrics
- Leaderboard: Aggregated metrics for model comparison
About SMOLTRACE
SMOLTRACE is a comprehensive benchmarking and evaluation framework for 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
Installation
pip install smoltrace
Citation
If you use SMOLTRACE in your research, please cite:
@software{smoltrace,
title = {SMOLTRACE: Benchmarking Framework for Smolagents},
author = {Thakkar, Kshitij},
url = {https://github.com/Mandark-droid/SMOLTRACE},
year = {2025}
}
Generated by SMOLTRACE
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