Qwen2-1.5B-Instruct

Qwen2-1.5B is a lightweight model with 1.5 billion parameters, offering an optimal balance between performance and resource efficiency. The model includes 28 layers and employs Grouped Query Attention (GQA) with 12 query heads and 2 shared key-value heads, enabling efficient management of the KV-cache memory.

Trained on 7 trillion tokens of high-quality multilingual data, the model demonstrates significantly enhanced capabilities compared to previous versions, particularly in programming and mathematics. It supports a wide range of languages and delivers competitive results on standard benchmarks, while maintaining relatively low computational resource requirements.

Qwen2-1.5B stands out for its versatility and ability to perform efficiently on both consumer-grade hardware and small servers. The model supports a context window of 32K tokens, allowing it to process long documents and support complex conversations. It is ideally suited for information extraction, document analysis, entry-level programming tasks, educational applications, and enterprise chatbots. Qwen2-1.5B is an excellent choice for users seeking a reliable language model for simple tasks without the need for expensive GPU infrastructure.


Announce Date: 24.07.2024
Parameters: 2B
Context: 33K
Layers: 28
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.40.1
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen2-1.5B-Instruct capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU Status Link

API access to Qwen2-1.5B-Instruct endpoints

There are no public endpoints for this model yet.

Private endpoint

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

  • achieve maximum endpoint performance,
  • access the full context window for processing large requests and conversations,
  • ensure maximum security by processing data in a dedicated, isolated environment.

Recommended server configurations for creating a private inference endpoint Qwen2-1.5B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.160
32,768.0
1 $0.38 46.79 10.848 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 108.23 19.063 Launch
rtx3080-1.16.32.160
32,768.0
1 $0.57 152.43 5.556 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 293.00 20.200 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 189.44 20.181 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 43.852 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 28.414 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 183.47 78.777 Launch
h100-1.16.64.160
32,768.0
1 $3.83 78.704 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 93.329 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 160.960 Launch
h200-1.16.128.160
32,768.0
1 $4.74 142.441 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 288.288 Launch

Related models

Need help?

Contact our dedicated neural networks support team at support@immers.cloud or send your request to the sales department at sale@immers.cloud.

We use cookies and web analytics services to ensure the proper functioning of the website, analyze web-site traffic, and improve the quality of our services.
By continuing to use the website, you consent to the Privacy Policy and consent to the processing of cookie files and technical data.