{ your data }
Capture datasets at the application level, fine tune best in class models, and deploy with our optimized inference engine.
OUR INVESTORS
Training
Fine tuning made as simple as possible, but no simpler.
Some training jobs are harder than others. Use our out-of-the-box training hyperparameters, input your own, or reach out to our support team of ML engineers for task-specific optimizations.
Enqueued
Startup
Execution
Status
09:34:22 AM
0.0s
-
09:34:19 AM
0.0s
0.0s
09:34:15 AM
0.1s
2.2s
09:34:14 AM
0.0s
1.9s
09:34:12 AM
0.2s
1.7s
09:34:10 AM
0.0s
2.1s
Deployment
Easily deploy models for inference.
Each deployment is a custom Relace inference engine, tuned to perform optimally on your specific task. Just request a dedicated deployment on any number of GPUs, or use one of our autoscaling default deployments.
Capture
A competitive edge starts with only a few extra lines of code
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from openai import OpenAI
client = OpenAI(
base_url="https://openai.log.relace.run/v1",
api_key="RELACE_API_KEY",
)
messages = [
{
"role": "system",
"content": "..."
},
{
"role": "user",
"content": "..."
}
]
completion = client.chat.completions.create(
model="gpt-4o",
messages=messages,
extra_body={"relace-metadata": metadata_json}
)from relace import OpenAI client = OpenAI( api_key= "OPENAI_API_KEY", relace_api_key= "RELACE_API_KEY", ) messages = [ { "role": "system", "content": "..." }, {"role": "user", "content": "..." } ] completion = client.chat.completions.create( model="gpt-4o", messages= messages, )
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from anthropic import Anthropic
client = Anthropic(
base_url="https://anthropic.log.relace.run",
api_key="RELACE_API_KEY",
)
system = "..."
prompt = "..."
completion = client.chat.completions.create(
model="claude-3-5-sonnet-latest",
system=system,
messages=prompt,
max_tokens=8192,
extra_body={"relace-metadata": metadata_json}
)import OpenAI from 'relace'; const client = new OpenAI({ apiKey: "OPENAI_API_KEY", relaceApiKey: "RELACE_API_KEY" }); const messages = [ { role: "system", content: "..." }, { role: "user", content: "..." } ]; const completion = await client.chat.completions.create({ model: "gpt-4o", messages: messages });