Models / Chat / Refuel LLM-2 API
Refuel LLM-2 API
Chat
47B model optimized for data tasks such as classification, structured data extraction, and more.
Try our Refuel LLM-2 API
New
Refuel LLM-2 API Usage
Endpoint
togethercomputer/Refuel-Llm-V2
RUN INFERENCE
curl -X POST "https://api.together.xyz/v1/chat/completions" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "togethercomputer/Refuel-Llm-V2",
"messages": [
{
"role": "user",
"content": "Clean this address: 251 Rhd Is St, SF, CA 94107"
}
],
"stream": true
}'JSON RESPONSE
RUN INFERENCE
from together import Together
client = Together()
response = client.chat.completions.create(
model="togethercomputer/Refuel-Llm-V2",
messages=[
{
"role": "user",
"content": "Clean this address: 251 Rhd Is St, SF, CA 94107"
}
],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="")
JSON RESPONSE
RUN INFERENCE
import Together from "together-ai";
const together = new Together();
const stream = await together.chat.completions.create({
model: "togethercomputer/Refuel-Llm-V2-Small",
messages: [
{
role: "user",
content: "Clean this address: 251 Rhd Is St, SF, CA 94107"
}
],
stream: true
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0].delta.content || "");
}
JSON RESPONSE
Model Provider:
Refuel
Type:
Chat
Variant:
Parameters:
47B
Deployment:
✔️ Serverless
Quantization
Context length:
16K
Pricing:
$0.60
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Quickstart docs
Quickstart docs
How to use Refuel LLM-2
Model details
Prompting Refuel LLM-2
Applications & Use Cases
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