Qwen2-7B-Instruct

Qwen2-7B is a fully functional language model with 7 billion parameters, designed to deliver high performance across a wide range of tasks. The model features 28 layers, utilizes 28 attention heads in total with 4 key-value heads, striking an optimal balance between performance and memory efficiency. Its architecture incorporates all modern enhancements, including Grouped Query Attention (GQA), Dual Chunk Attention with YARN, and optimized Rotary Positional Embedding (RoPE) mechanisms.

Trained on the same high-quality 7-trillion-token dataset as larger models in the series, Qwen2-7B delivers strong performance across diverse knowledge domains and achieves competitive results on standard benchmarks. It supports an extended context window of 128K tokens and demonstrates exceptional multilingual capabilities.

A key advantage of Qwen2-7B is its ability to run efficiently on mid-range GPUs, making advanced AI capabilities accessible to a broader range of users and organizations. The model is well-suited for application development, large document analysis, research, and educational purposes. Additionally, it serves as an excellent base model for fine-tuning on domain-specific tasks, offering a strong trade-off between capability and resource requirements.


Announce Date: 24.07.2024
Parameters: 7B
Context: 33K
Layers: 28
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.41.2
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen2-7B-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 Qwen2-7B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
32,768.0
1 $0.53 29.00 2.611 Launch
teslaa2-2.16.32.160
32,768.0
tensor
2 $0.57 19.04 5.059 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 49.42 3.169 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 3.148 Launch
rtx3080-3.16.64.160
32,768.0
pipeline
3 $1.43 2.995 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 14.480 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 7.264 Launch
rtx3080-4.16.64.160
32,768.0
tensor
4 $1.82 7.768 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 90.36 32.446 Launch
h100-1.16.64.160
32,768.0
1 $3.83 106.65 32.409 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 39.722 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 73.034 Launch
h200-1.16.128.160
32,768.0
1 $4.74 64.278 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 136.698 Launch

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