Qwen3-2507

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What Qwen3-2507 is

Qwen3-4B-Instruct-2507 Highlights We introduce the updated version of the Qwen3-4B non-thinking mode, named Qwen3-4B-Instruct-2507, featuring the following key enhancements: - Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage. - Substantial gains in long-tail knowledge coverage across multiple languages. - Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. - Enhanced capabilities in 256K long-context understanding. Model Overview Qwen3-4B-Instruct-2507 has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 4.0B - Number of Paramaters (Non-Embedding): 3.6B - Number of Layers: 36 - Number of Attention Heads (GQA): 32 for Q and 8 for KV - Context Length: 262,144 natively. NOTE: This model supports only non-thinking mode and does not generate <think</think blocks in its output. Meanwhile, specifying enablethinking=False is no longer required. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation. Performance : For reproducibility, we report the win rates evaluated by GPT-4.1. Quickstart The code of Qwen3 has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers. With transformers<4.51.0, you will encounter the following error: The following contains a code snippet illustrating how to use the model generate content based on given inputs. For deployment, you can use sglang=0.4.6.post1 or vllm=0.8.5 or to create an OpenAI-compatible API endpoint: - SGLang: - vLLM: Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as 32,768. For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. Agentic Use Qwen3 excels in tool calling capabilities. We recommend using Qwen-Agent to make the best use of agentic ability of Qwen3. Qwe

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys qwen3-2507 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (qwen3-2507 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3-2507","messages":[{"role":"user","content":"Hello"}]}'

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