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Meta Muse Glimmer
latestModel IDmeta/muse-glimmer-30b
Meta Muse Glimmer (meta/muse-glimmer-30b) is a multimodal vision-language model served on Qubrid's self-hosted vLLM infrastructure, optimized for vision understanding and chat completions.
32K Tokens ContextFast InferenceCodingFunction CallingMultilingual
Save 20%
$0.31$0.25
/ 1M input tokens
$1.31$1.05
/ 1M output tokens
Cache pricing
/ 1M tokens
Implicit cache
$0.0375$0.030
Request (Non streaming)
from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="meta/muse-glimmer-30b",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=4096,
temperature=1,
top_p=1,
stream=False
)
print(response.choices[0].message.content)from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="meta/muse-glimmer-30b",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=4096,
temperature=1,
top_p=1,
stream=False
)
print(response.choices[0].message.content)Example response
A chat completion API provides a standard way to send conversational input and receive model-generated text in a single request. Key benefits include: • Interoperability: any client can use HTTP with JSON request and response bodies. • Flexibility: system prompts, user messages, and parameters such as temperature and max tokens are easy to configure. • Observability: responses typically include token usage fields for cost and performance tracking. A typical integration sends a POST request with the model name and messages array, then reads the assistant message from the first choice in the response.
Request (Non streaming)
from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="meta/muse-glimmer-30b",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=4096,
temperature=1,
top_p=1,
stream=False
)
print(response.choices[0].message.content)from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="meta/muse-glimmer-30b",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=4096,
temperature=1,
top_p=1,
stream=False
)
print(response.choices[0].message.content)Example response
A chat completion API provides a standard way to send conversational input and receive model-generated text in a single request. Key benefits include: • Interoperability: any client can use HTTP with JSON request and response bodies. • Flexibility: system prompts, user messages, and parameters such as temperature and max tokens are easy to configure. • Observability: responses typically include token usage fields for cost and performance tracking. A typical integration sends a POST request with the model name and messages array, then reads the assistant message from the first choice in the response.
Streaming supported • Function calling supported • See all examples in Playground Open in Playground for streaming, files, and all parameters.
Open in Playground