Qwen2.5 7B Instruct Turbo
Instruction-tuned 7.61B Qwen2.5 causal LLM with 131K context, RoPE, SwiGLU, RMSNorm, and advanced attention mechanisms.
LLM
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://aitradestore.com/api/v1/chat/completions"
-H "Authorization: Bearer $AITRADESTORE_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 aitradestore import AiTradeStore client = AiTradeStore()
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 AiTradeStore from "ai-tradestore"; const aitradestore = new AiTradeStore(); const response = await aitradestore.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
