Qwen2.5-3B

Qwen2.5-3B features 3 billion parameters, 36 layers, and a 16/2 attention head architecture, delivering a significant performance leap while maintaining reasonable resource requirements. The model supports a 32K-token context window and up to 8K-token generation, enabling it to handle moderately complex tasks with extended contexts.

The uniqueness of Qwen2.5-3B lies in its return to the product line after being absent from the Qwen2 series, effectively filling the crucial gap between 1.5B and 7B models. This size proves particularly valuable for resource-constrained scenarios where the 7B version might be excessive, yet higher performance than the 1.5B variant is required. The model demonstrates substantially improved capabilities in understanding complex instructions, multi-step reasoning, and working with structured data.

Notably, this model is distributed under the Qwen Research License, which may impose certain restrictions on commercial use. However, Qwen2.5-3B is ideally suited for research projects, prototyping, and developing specialized solutions where licensing flexibility for research purposes is essential. The model performs exceptionally well in data analysis tasks, technical documentation processing, educational applications, and serves as an excellent base for creating domain-specific models through fine-tuning.


Announce Date: 19.09.2024
Parameters: 3B
Context: 32K
Attention Type: Full Attention
VRAM requirements: 2.5 GB using 4 bits quantization
Developer: Alibaba
Transformers Version: 4.43.1
License: qwen

Public endpoint

Use our pre-built public endpoints to test inference and explore Qwen2.5-3B capabilities.
Model Name Context Type GPU TPS 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 configurations for hosting Qwen2.5-3B

Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
rtx2080ti-1.16.32.160 16 32768 160 1 $0.41 Launch
teslat4-1.16.16.160 16 16384 160 1 $0.46 Launch
teslaa10-1.16.32.160 16 32768 160 1 $0.53 Launch
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
rtx3090-1.16.24.160 16 24576 160 1 $0.88 Launch
rtx4090-1.16.32.160 16 32768 160 1 $1.15 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch
Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
rtx2080ti-1.16.32.160 16 32768 160 1 $0.41 Launch
teslat4-1.16.16.160 16 16384 160 1 $0.46 Launch
teslaa10-1.16.32.160 16 32768 160 1 $0.53 Launch
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
rtx3090-1.16.24.160 16 24576 160 1 $0.88 Launch
rtx4090-1.16.32.160 16 32768 160 1 $1.15 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch
Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
rtx2080ti-1.16.32.160 16 32768 160 1 $0.41 Launch
teslat4-1.16.16.160 16 16384 160 1 $0.46 Launch
teslaa10-1.16.32.160 16 32768 160 1 $0.53 Launch
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
rtx3090-1.16.24.160 16 24576 160 1 $0.88 Launch
rtx4090-1.16.32.160 16 32768 160 1 $1.15 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch

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