Models / Chat / Qwen2.5 7B Instruct Turbo
Qwen2.5 7B Instruct Turbo
LLM
Instruction-tuned 7.61B Qwen2.5 causal LLM with 131K context, RoPE, SwiGLU, RMSNorm, and advanced attention mechanisms.
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API Usage
How to use Qwen2.5 7B Instruct TurboModel CardPrompting Qwen2.5 7B Instruct TurboApplications & Use CasesAPI Usage
Endpoint
Qwen/Qwen2.5-7B-Instruct-Turbo
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": "Qwen/Qwen2.5-7B-Instruct-Turbo",
"messages": [{"role": "user", "content": "What are some fun things to do in New York?"}]
}'
JSON RESPONSE
RUN INFERENCE
from together import Together
client = Together()
response = client.chat.completions.create(
model="Qwen/Qwen2.5-7B-Instruct-Turbo",
messages=[{"role": "user", "content": "What are some fun things to do in New York?"}],
)
print(response.choices[0].message.content)
JSON RESPONSE
RUN INFERENCE
import Together from "together-ai";
const together = new Together();
const response = await together.chat.completions.create({
messages: [{"role": "user", "content": "What are some fun things to do in New York?"}],
model: "Qwen/Qwen2.5-7B-Instruct-Turbo",
});
console.log(response.choices[0].message.content)
JSON RESPONSE
Model Provider:
Qwen
Type:
Chat
Variant:
Instruct
Parameters:
7B
Deployment:
✔ Serverless
Quantization
FP8
Context length:
131,072
Pricing:
$0.30
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Quickstart docs
Quickstart docs
How to use Qwen2.5 7B Instruct Turbo
Model details
Prompting Qwen2.5 7B Instruct Turbo
Applications & Use Cases
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