Smarter Optimization,
Smarter Optimization,
Smarter Optimization,
Faster Results
Faster Results
Faster Results
Find the best hyperparameters 5x faster
- no extra setup, no wasted trials.
Find the best hyperparameters 5x faster
- no extra setup, no wasted trials.
Find the best hyperparameters 5x faster
- no extra setup, no wasted trials.
$
🤖
🤖
Trusted by Engineers at
Trusted by Engineers at
steev Run, Analyze and Iterate
steev Run, Analyze and Iterate
steev Run, Analyze and Iterate
steev takes the guesswork out of hyperparameter optimization,
finding the best configurations for your models effortlessly
steev takes the guesswork out of hyperparameter optimization,
finding the best configurations for your models effortlessly
steev takes the guesswork out of hyperparameter optimization,
finding the best configurations for your models effortlessly
Run Seamless Experimentation
Run Seamless Experimentation
Run Seamless Experimentation
steev automatically understands your code and detects tunable hyperparameters.
No more manual setups or complex configurations.
steev automatically understands your code and detects tunable hyperparameters.
No more manual setups or complex configurations.
steev automatically understands your code and detects tunable hyperparameters.
No more manual setups or complex configurations.
Steev here!! 👋 Let me take a look at your code...
Awesome! I found 7 key hyperparameters in your code:
• learning_rate: 2e-4
• warmup_steps: 100
• max_steps: 1000
• gradient_accumulation_steps: 4
• lora_rank: 8
• lora_alpha: 32
• max_seq_length: 512
After analyzing your code, I found your model and dataset!
I see you're using the Qwen-7B model and the smoltalk dataset!
Did I get that right? (yes/no): yes
Please review my experimental plan carefully:
BATCH SIZE CONFIG:
- Batch sizes: [1, 2, 4]
- Gradient accumulation: [8, 16, 32]
LEARNING RATE CONFIG:
- Learning rates: [5e-5, 1e-4, 2e-4, 3e-4]
- Warmup ratios: [0.03, 0.05, 0.1]
LoRA CONFIG:
- Rank values: [16, 32, 64, 128]
- Alpha values: [8, 16, 32]
- Dropout rates: [0.05, 0.1, 0.15]
Total experiments to run: 432 combinations
Estimated total experiment time: 144-216 hours
Steev here!! 👋 Let me take a look at your code...
Awesome! I found 7 key hyperparameters in your code:
• learning_rate: 2e-4
• warmup_steps: 100
• max_steps: 1000
• gradient_accumulation_steps: 4
• lora_rank: 8
• lora_alpha: 32
• max_seq_length: 512
After analyzing your code, I found your model and dataset!
I see you're using the Qwen-7B model and the smoltalk dataset!
Did I get that right? (yes/no): yes
Please review my experimental plan carefully:
BATCH SIZE CONFIG:
- Batch sizes: [1, 2, 4]
- Gradient accumulation: [8, 16, 32]
LEARNING RATE CONFIG:
- Learning rates: [5e-5, 1e-4, 2e-4, 3e-4]
- Warmup ratios: [0.03, 0.05, 0.1]
LoRA CONFIG:
- Rank values: [16, 32, 64, 128]
- Alpha values: [8, 16, 32]
- Dropout rates: [0.05, 0.1, 0.15]
Total experiments to run: 432 combinations
Estimated total experiment time: 144-216 hours
Analyze live.
Skip wasted steps
Analyze live.
Skip wasted steps
Analyze live.
Skip wasted steps
Steev monitors your experiments live, skipping inefficient trials to fast-track optimal results.
Steev monitors your experiments live, skipping inefficient trials to fast-track optimal results.
Steev monitors your experiments live, skipping inefficient trials to fast-track optimal results.
Iterate with
Continuous Feedback
Iterate with
Continuous Feedback
Iterate with
Continuous Feedback
steev doesn’t just stop at results but, constantly learns from them.
steev will refine and plan experiments automatically, searching for perfect hyperparameters.
steev doesn’t just stop at results but, constantly learns from them.
steev will refine and plan experiments automatically, searching for perfect hyperparameters.
steev doesn’t just stop at results but, constantly learns from them.
steev will refine and plan experiments automatically, searching for perfect hyperparameters.
Analyze
Analyze
Run
Run
steev
steev
steev
is all you need
is all you need
is all you need
Provide your code and datasets,
Provide your code and datasets,
Provide your code and datasets,
and steev takes care of the rest.
and steev takes care of the rest.
and steev takes care of the rest.
Best results, delivered with a single command.
Best results, delivered with a single command.
Best results, delivered with a single command.
Before & After Meeting steev
Before & After Meeting steev
Before & After
Meeting steev
No more complex scripts, just call steev
No more complex scripts, just call steev
No more complex scripts, just call steev
Before
Before
# !/bin/bash
LEARNING_RATES=(1e-5 3e-5 5e-5)
BATCH_SIZE=(8 16)
LORA_RANKS=(8 16 32)
DROPOUT_RATES=(0.1 0.2 0.3)
for LR in "${LEARNING_RATES[@]}"; do
…
# !/bin/bash
LEARNING_RATES=(1e-5 3e-5 5e-5)
BATCH_SIZE=(8 16)
LORA_RANKS=(8 16 32)
DROPOUT_RATES=(0.1 0.2 0.3)
for LR in "${LEARNING_RATES[@]}"; do
…
$ ./hyperparameter_optimize.sh
$ ./hyperparameter_optimize.sh
After
After
$ steev auto-run train.py
$ steev auto-run train.py
…
💡Steev found the best model weight
with best hyperparameters!💡
…
save report to ./outputs/reports.html
…
💡Steev found the best model weight
with best hyperparameters!💡
…
save report to ./outputs/reports.html
You Speak, steev Works
You Speak, steev Works
You Speak,
steev Works
Tell steev what you need and steev turns them into fully optimized ML experiments.
What you say is what you get. Simple, effective, and results-driven.
Tell steev what you need and steev turns them into fully optimized ML experiments.
What you say is what you get. Simple, effective, and results-driven.
Tell steev what you need and steev turns them into fully optimized ML experiments.
What you say is what you get. Simple, effective, and results-driven.
Loved by world-class Researchers
Loved by world-class Researchers
Loved by world-class Researchers
Many ML researchers empowered their potential with steev
Many ML researchers empowered their potential with steev
Many ML researchers empowered their potential
with steev
@bob
ML Engineer
ex. OpenVINO developer
@steve
ML Engineer
CVPR, ICCV author
@Nataniel
ML Engineer
ex. OpenVINO developer
@Junkyu
series A
startup
on-device AI engineer
computer vision
@mountain
series A
startup
full-stack engineer
2+m MAU service builder
@Jacob
machine vision engineer
Robotics, AR, VR
@Simon
backend developer
3x serial founder
@Selin
ML Engineer
ex. OpenVINO developer
@andrew
ML Engineer
CVPR, ICCV author
@Luis
ML Engineer
ex. OpenVINO developer
@Carmen
series A
startup
on-device AI engineer
computer vision
@bob
ML Engineer
ex. OpenVINO developer
@steve
ML Engineer
CVPR, ICCV author
@Nataniel
ML Engineer
ex. OpenVINO developer
@Junkyu
series A
startup
on-device AI engineer
computer vision
@mountain
series A
startup
full-stack engineer
2+m MAU service builder
@Jacob
machine vision engineer
Robotics, AR, VR
@Simon
backend developer
3x serial founder
@Selin
ML Engineer
ex. OpenVINO developer
@andrew
ML Engineer
CVPR, ICCV author
@Luis
ML Engineer
ex. OpenVINO developer
@Carmen
series A
startup
on-device AI engineer
computer vision
@bob
ML Engineer
ex. OpenVINO developer
@steve
ML Engineer
CVPR, ICCV author
@Nataniel
ML Engineer
ex. OpenVINO developer
@Junkyu
series A
startup
on-device AI engineer
computer vision
@mountain
series A
startup
full-stack engineer
2+m MAU service builder
@Jacob
machine vision engineer
Robotics, AR, VR
@Simon
backend developer
3x serial founder
@Selin
ML Engineer
ex. OpenVINO developer
@andrew
ML Engineer
CVPR, ICCV author
@Luis
ML Engineer
ex. OpenVINO developer
@Carmen
series A
startup
on-device AI engineer
computer vision
100+ Researchers
100+ Researchers
are already joined the waitlist.
steev
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contact@steev.io
contact@steev.io