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Qwen2.5 72B Instruct

accounts/fireworks/models/qwen2p5-72b-instruct

ServerlessLLMTunableChat

Qwen2.5 are a series of decoder-only language models developed by Qwen team, Alibaba Cloud, available in 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B sizes, and base and instruct variants.

Serverless API

Qwen2.5 72B Instruct is available via Fireworks' serverless API, where you pay per token. There are several ways to call the Fireworks API, including Fireworks' Python client, the REST API, or OpenAI's Python client.

See below for easy generation of calls and a description of the raw REST API for making API requests. See the Querying text models docs for details.

Try it

API Examples

Generate a model response using the chat endpoint of qwen2p5-72b-instruct. API reference

import requests
import json

url = "https://api.fireworks.ai/inference/v1/chat/completions"
payload = {
  "model": "accounts/fireworks/models/qwen2p5-72b-instruct",
  "max_tokens": 4096,
  "top_p": 1,
  "top_k": 40,
  "presence_penalty": 0,
  "frequency_penalty": 0,
  "temperature": 0.6,
  "messages": [
    {
      "role": "user",
      "content": "Hello, how are you?"
    }
  ]
}
headers = {
  "Accept": "application/json",
  "Content-Type": "application/json",
  "Authorization": "Bearer <API_KEY>"
}
requests.request("POST", url, headers=headers, data=json.dumps(payload))

Fine-tuning

Qwen2.5 72B Instruct can be fine-tuned on your data to create a model with better response quality. Fireworks uses low-rank adaptation (LoRA) to train a model that can be served efficiently at inference time.

See the Fine-tuning guide for details.

Fine-tune this model

On-demand deployments

On-demand deployments allow you to use Qwen2.5 72B Instruct on dedicated GPUs with Fireworks' high-performance serving stack with high reliability and no rate limits.

See the On-demand deployments guide for details.

Deploy this Base model