InferX Endpoint | Qwen2.5-Coder-1.5B-Instruct

Metadata

Name

Qwen2.5-Coder-1.5B-Instruct

Provider

Qwen

Parameter Size

1.50B

GPU Count

1

Context Length

2000

Concurrency

146.64x

Cold Start TTFT

—

Recommended Use Cases

Coding assistant, Code generation

Detailed Intro

testest test

Log In To Use This Endpoint

This public page shows the published endpoint metadata and integration shape. Log in to get a tenant-scoped endpoint URL, inference API key, and the interactive playground. Log in

Integration

Start with the shared setup below, then choose the endpoint mode that fits your workload: token-based shared serving or a tenant-scoped dedicated GPU endpoint.

API Key

<INFERENCE_API_KEY>

An inference API key is required for both endpoint modes. Until one is available, the fields below keep the correct request shape and use a placeholder token.

Token-Based

Use the shared OpenAI-compatible endpoint. The base URL is shared across published endpoints, and usage is billed per token.

API Base URL

https://dev1.inferx.net/endpoints/v1

Model Name

Qwen2.5-Coder-1.5B-Instruct

Sample REST Call

curl https://dev1.inferx.net/endpoints/v1/chat/completions \
  -H "Authorization: Bearer $INFERX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "Qwen2.5-Coder-1.5B-Instruct", "messages": [{"role": "user", "content": "Hello"}]}'

Dedicated GPU

Model Spec

{
    "image": "vllm/vllm-openai:v0.12.0",
    "commands": [
        "--model",
        "Qwen/Qwen2.5-Coder-1.5B-Instruct",
        "--trust-remote-code",
        "--gpu-memory-utilization",
        "0.95",
        "--max-model-len",
        "2000",
        "--tensor-parallel-size=1"
    ],
    "resources": {
        "GPU": {
            "Count": 1,
            "vRam": 13000
        }
    },
    "envs": [],
    "policy": {
        "Obj": {
            "nvidia_replica": 0,
            "min_replica": 0,
            "max_replica": 1,
            "standby_per_node": 1,
            "parallel": 50,
            "queue_len": 100,
            "queue_timeout": 30.0,
            "scalein_timeout": 1.0,
            "scaleout_policy": {
                "WaitQueueRatio": {
                    "wait_ratio": 0.1
                }
            },
            "runtime_config": {
                "graph_sync": false
            }
        }
    },
    "sample_query": {
        "apiType": "text2text",
        "raw_query": false,
        "path": "v1/completions",
        "prompt": "write a quick sort algorithm.",
        "prompts": [
            "Write a Python function that computes Fibonacci numbers. Explain time complexity.",
            "Translate the following Chinese text to English: \u4eca\u5929\u5929\u6c14\u5f88\u597d\u3002",
            "Explain general relativity in simple language.",
            "Write a legal contract clause about liability and indemnification.",
            "Summarize the plot of a fantasy novel involving dragons.",
            "Solve this calculus integral: \u222b x^3 log(x) dx",
            "Generate a JSON schema describing a user profile.",
            "Explain why emojis like \ud83d\ude00\ud83d\udd25\ud83d\ude80 represent byte-level tokens."
        ],
        "dataUrl": "",
        "max_tokens": 0,
        "body": {
            "max_tokens": "1000",
            "model": "Qwen/Qwen2.5-Coder-1.5B-Instruct",
            "stream": "true",
            "temperature": "0"
        },
        "loadingTimeout": 90
    }
}