> ## Documentation Index
> Fetch the complete documentation index at: https://docs.monostate.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Benchmarking
> Mide y compara el rendimiento del modelo
# Benchmarking
Evalúa y compara el rendimiento del modelo.
## Evaluación Rápida
### Usando Evaluación Mejorada
```bash theme={null}
aitraining llm --train \
--model google/gemma-3-270m \
--data-path ./data \
--project-name my-model \
--use-enhanced-eval \
--eval-metrics "perplexity,accuracy"
```
### Solo Evaluación (Sin Entrenamiento)
```bash theme={null}
python -c "
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained('./my-model')
tokenizer = AutoTokenizer.from_pretrained('./my-model')
# Compute perplexity on test data
# ...
"
```
## Métricas
### Métricas Disponibles
| Metric | Description | Use Case |
| ------------ | ------------------------- | -------------- |
| `perplexity` | Language modeling quality | LLMs |
| `accuracy` | Classification accuracy | Classification |
| `f1` | F1 score | Classification |
| `bleu` | Translation quality | Seq2Seq |
| `rouge` | Summarization quality | Seq2Seq |
### Evaluación Personalizada
La evaluación mejorada se ejecuta en tu división de validación durante el entrenamiento:
```bash theme={null}
aitraining llm --train \
--model google/gemma-3-270m \
--data-path ./data \
--valid-split validation \
--project-name my-model \
--use-enhanced-eval \
--eval-metrics "perplexity,accuracy"
```
La evaluación mejorada usa los datos de validación especificados por `--valid-split`. Para evaluar en un conjunto de prueba separado después del entrenamiento, usa el LM Evaluation Harness o scripts personalizados mostrados a continuación.
## Velocidad de Inferencia
### Prueba de Rendimiento
```python theme={null}
# benchmark_speed.py
import time
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def benchmark(model_path, num_samples=100, max_tokens=50):
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
if torch.cuda.is_available():
model = model.cuda()
prompt = "The quick brown fox"
inputs = tokenizer(prompt, return_tensors="pt")
if torch.cuda.is_available():
inputs = {k: v.cuda() for k, v in inputs.items()}
# Warmup
for _ in range(5):
model.generate(**inputs, max_new_tokens=max_tokens)
# Benchmark
start = time.time()
for _ in range(num_samples):
model.generate(**inputs, max_new_tokens=max_tokens)
elapsed = time.time() - start
tokens_per_second = (num_samples * max_tokens) / elapsed
print(f"Throughput: {tokens_per_second:.2f} tokens/sec")
print(f"Latency: {elapsed/num_samples*1000:.2f} ms/sample")
benchmark("./my-model")
```
### Uso de Memoria
```python theme={null}
# benchmark_memory.py
import torch
from transformers import AutoModelForCausalLM
def measure_memory(model_path):
torch.cuda.reset_peak_memory_stats()
model = AutoModelForCausalLM.from_pretrained(model_path)
model = model.cuda()
peak_memory = torch.cuda.max_memory_allocated() / 1024**3
print(f"Peak memory: {peak_memory:.2f} GB")
measure_memory("./my-model")
```
## Comparación de Modelos
### Comparar Múltiples Modelos
```python theme={null}
# compare_models.py
import json
from pathlib import Path
def compare_models(model_paths):
results = []
for path in model_paths:
state_file = Path(path) / "trainer_state.json"
if state_file.exists():
with open(state_file) as f:
state = json.load(f)
results.append({
"model": path,
"best_metric": state.get("best_metric"),
"epoch": state.get("epoch"),
})
# Sort by best_metric (typically eval_loss)
results.sort(key=lambda x: x.get("best_metric") or float("inf"))
print("Model Comparison:")
print("-" * 50)
for r in results:
metric = r.get('best_metric')
metric_str = f"{metric:.4f}" if metric else "N/A"
print(f"{r['model']}: best_metric={metric_str}")
compare_models([
"./model-v1",
"./model-v2",
"./model-v3"
])
```
### Comparación W\&B
Al hacer logging en W\&B, compara ejecuciones en el panel:
```bash theme={null}
# Train multiple variants
aitraining llm --train --model modelA --project-name exp-a --log wandb
aitraining llm --train --model modelB --project-name exp-b --log wandb
# Compare in W&B dashboard
```
## Benchmarks Estándar
### LM Evaluation Harness
Para benchmarks estándar como HellaSwag, ARC y MMLU, usa el LM Evaluation Harness después del entrenamiento:
```bash theme={null}
pip install lm-eval
lm_eval --model hf \
--model_args pretrained=./my-model \
--tasks hellaswag,arc_easy,arc_challenge \
--batch_size 8
```
### Tareas de Benchmark Comunes
| Task | Description |
| --------------- | -------------------------------- |
| `hellaswag` | Commonsense reasoning |
| `arc_easy` | Science questions (easy) |
| `arc_challenge` | Science questions (hard) |
| `mmlu` | Multitask language understanding |
| `winogrande` | Commonsense reasoning |
| `truthfulqa` | Truthfulness evaluation |
## Informes
### Generar Informe
```python theme={null}
# generate_report.py
import json
from datetime import datetime
def generate_report(model_path, metrics, benchmark_results):
report = {
"model": model_path,
"date": datetime.now().isoformat(),
"metrics": metrics,
"benchmarks": benchmark_results,
}
with open("benchmark_report.json", 'w') as f:
json.dump(report, f, indent=2)
# Print summary
print(f"\nBenchmark Report - {model_path}")
print("=" * 50)
print(f"Eval Loss: {metrics.get('eval_loss', 'N/A')}")
print(f"Perplexity: {metrics.get('perplexity', 'N/A')}")
for name, score in benchmark_results.items():
print(f"{name}: {score}")
```
## Próximos Pasos
Despliega tu modelo
Ejecuta inferencia