Activefastllm Open Source

Qwen2.5 1.5B

by Alibaba· Released September 2024· Cutoff June 2024

Qwen2.5 1.5B is a compact language model from Alibaba's Qwen2.5 series, designed for efficient deployment on edge devices and resource-constrained environments. It offers strong performance for its size, with support for multiple languages and a 32K token context window.

Official Site 🤗 Hugging Face

Input cost

Free (open source)

Output cost

Free (open source)

Context window

32K tokens

Max output

8192 tokens

Modalities

text

Parameters

1.5B

License

Apache-2.0

Capabilities

Text GenerationCode GenerationMultilingual SupportFunction CallingStreamingJSON Mode

Best For

Lightweight, efficient text generation and coding tasks on devices with limited compute.

Strengths

Strong performance for a 1.5B parameter model
Efficient inference on CPU and mobile
Multilingual capabilities
Open source with permissive license

Limitations

Smaller context window (32K) compared to larger Qwen models
Limited reasoning and complex task capability
Not suitable for tasks requiring deep domain expertise

Use Cases

On-device chatbots

Code completion in IDEs

Text summarization for mobile apps

Language translation on edge devices

Educational tools

Lightweight content generation

IoT device interaction

Improvements Over Previous Model

Increased context window from 32K to 32K (same as Qwen2)
Improved multilingual support with more languages
Better coding performance on HumanEval and MBPP
Enhanced instruction following and safety
More efficient architecture for faster inference

Back to all models

Activefastllm Open Source

Qwen2.5 1.5B

by Alibaba· Released September 2024· Cutoff June 2024

Official Site 🤗 Hugging Face

Input cost

Free (open source)

Output cost

Free (open source)

Context window

32K tokens

Max output

8192 tokens

Modalities

text

Parameters

1.5B

License

Apache-2.0

Capabilities

Text GenerationCode GenerationMultilingual SupportFunction CallingStreamingJSON Mode

Best For

Lightweight, efficient text generation and coding tasks on devices with limited compute.

Strengths

Strong performance for a 1.5B parameter model
Efficient inference on CPU and mobile
Multilingual capabilities
Open source with permissive license

Limitations

Smaller context window (32K) compared to larger Qwen models
Limited reasoning and complex task capability
Not suitable for tasks requiring deep domain expertise

Use Cases

On-device chatbots

Code completion in IDEs

Text summarization for mobile apps

Language translation on edge devices

Educational tools

Lightweight content generation

IoT device interaction

Improvements Over Previous Model

Increased context window from 32K to 32K (same as Qwen2)
Improved multilingual support with more languages
Better coding performance on HumanEval and MBPP
Enhanced instruction following and safety
More efficient architecture for faster inference

Back to all models