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
There are no public endpoints for this model yet.

Private server

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

  • maximize endpoint performance,
  • enable full context for long sequences,
  • ensure top-tier security for data processing in an isolated, dedicated environment,
  • use custom weights, such as fine-tuned models or LoRA adapters.

Recommended server configurations for hosting 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 10.379 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 108.23 18.697 Launch
rtx3080-1.16.32.160
32,768.0
1 $0.57 5.133 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 19.758 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 42.903 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 27.991 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 183.47 78.354 Launch
h100-1.16.64.160
32,768.0
1 $3.83 78.281 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 92.906 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 160.011 Launch
h200-1.16.128.160
32,768.0
1 $4.74 142.018 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 287.339 Launch

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