> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Cerebras
> Use Weave to trace and log LLM calls made via the Cerebras Cloud SDK
Weave automatically tracks and logs LLM calls made via the [Cerebras Cloud SDK](https://inference-docs.cerebras.ai/introduction).
## Traces
Tracking LLM calls is crucial for debugging and performance monitoring. Weave helps you do this by automatically capturing traces for the Cerebras Cloud SDK.
Here's an example of how to use Weave with Cerebras:
```python lines theme={null}
import os
import weave
from cerebras.cloud.sdk import Cerebras
# Initialise the weave project
weave.init("cerebras_speedster")
# Use the Cerebras SDK as usual
api_key = os.environ["CEREBRAS_API_KEY"]
model = "llama3.1-8b" # Cerebras model
client = Cerebras(api_key=api_key)
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": "What's the fastest land animal?"}],
)
print(response.choices[0].message.content)
```
Weave will now track and log all LLM calls made through the Cerebras SDK. You can view the traces in the Weave web interface, including details like token usage and response time.
[
](https://wandb.ai/capecape/cerebras_speedster/weave/traces)
## Wrapping with your own ops
Weave ops offer a powerful way to enhance reproducibility and traceability in your experiments. By automatically versioning your code and capturing inputs and outputs. Here's an example of how you can leverage Weave ops with the Cerebras SDK:
```python lines theme={null}
import os
import weave
from cerebras.cloud.sdk import Cerebras
# Initialise the weave project
weave.init("cerebras_speedster")
client = Cerebras(api_key=os.environ["CEREBRAS_API_KEY"])
# Weave will track the inputs, outputs and code of this function
@weave.op
def animal_speedster(animal: str, model: str) -> str:
"Find out how fast an animal can run"
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": f"How fast can a {animal} run?"}],
)
return response.choices[0].message.content
animal_speedster("cheetah", "llama3.1-8b")
animal_speedster("ostrich", "llama3.1-8b")
animal_speedster("human", "llama3.1-8b")
```
## Create a `Model` for easier experimentation
The [Model](/weave/guides/core-types/models) class in Weave helps you organize and compare different iterations of your app. This is particularly useful when experimenting with Cerebras models. Here's an example:
```python lines theme={null}
import os
import weave
from cerebras.cloud.sdk import Cerebras
# Initialise the weave project
weave.init("cerebras_speedster")
client = Cerebras(api_key=os.environ["CEREBRAS_API_KEY"])
class AnimalSpeedModel(weave.Model):
model: str
temperature: float
@weave.op
def predict(self, animal: str) -> str:
"Predict the top speed of an animal"
response = client.chat.completions.create(
model=self.model,
messages=[{"role": "user", "content": f"What's the top speed of a {animal}?"}],
temperature=self.temperature
)
return response.choices[0].message.content
speed_model = AnimalSpeedModel(
model="llama3.1-8b",
temperature=0.7
)
result = speed_model.predict(animal="cheetah")
print(result)
```
With this setup, you can easily experiment with different models and parameters, all while keeping track of your Cerebras-powered inferences!
[
](https://wandb.ai/capecape/cerebras_speedster/weave/traces)