Qwen2.5-3B-Instruct

Qwen2.5 is the latest series of Qwen large language models. Qwen2.5 releases a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:

Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
Long-context Support up to 128K tokens and can generate up to 8K tokens.
Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

Model Conversion Contributor: APLUX

Model Stats:

Input sequence length for Prompt Processor: 128
Maximum context length: 4096
Quantization Type: w4 + w8 (few layers) with fp16 activations and w4a16 + w8a16 (few layers) are supported
Supported languages: English.
TTFT: Time To First Token is the time it takes to generate the first response token. This is expressed as a range because it varies based on the length of the prompt. The lower bound is for a short prompt (up to 128 tokens, i.e., one iteration of the prompt processor) and the upper bound is for a prompt using the full context length (4096 tokens).
Response Rate: Rate of response generation after the first response token.

Model Details

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
Number of Parameters: 3.09B
Number of Paramaters (Non-Embedding): 2.77B
Number of Layers: 36
Number of Attention Heads (GQA): 16 for Q and 2 for KV
Context Length: Full 32,768 tokens and generation 8192 tokens

For more details, please refer to our blog, GitHub, and Documentation.