Qwen2.5 Coder · 7B
Code generation, repair, and reasoning in a compact open-weight coding model.
What do you want to do?
Choose a task. Get the software, files, settings, and steps to run it.
Open a guide for its software, hardware requirements, and setup steps.
At a glance
- Parameters
- 7.61B
- License
- Apache 2.0
- Disk space
- 4.7 GB
- Software
- Ollama, llama.cpp
Will it run on your machine?
Save your machine to see a personalized rating and its reasoning.
Add my hardwareWays to run it
Ollama · Windows, macOS, Linux
Ollama lists a 4.7 GB package; runtime and context need extra memory.
ollama run qwen2.5-coder:7bExplore its uses
Keep exploring
Mistral Small 4 · 119B
Mistral Small 4 119B A6B is an Apache-2.0 licensed hybrid model from mistralai that combines general instruction, reasoning, and Devstral-style capabilities in one model. It uses a Mixture-of-Experts architecture with 119B total parameters, 6.5B activated per token, 128 experts with 4 active, and a 256k context length. It supports multimodal input (text and image) with text output, multilingual support across dozens of languages, system prompts, function calling, JSON output, and configurable reasoning effort. The model card also mentions optional efficiency resources: an eagle head for speculative decoding and an NVFP4 checkpoint. It is tagged for vLLM and offered in safetensors format.
Qwen3-Coder-Next · 80B A3B
Qwen3-Coder-Next is an open-weight text-generation model from Qwen/Qwen3-Coder-Next, licensed under apache-2.0. It is designed specifically for coding agents and local development. The model has 80B total parameters with 3B activated parameters, a 262,144-token native context length, and a hybrid architecture combining Gated DeltaNet, Gated Attention, and Mixture-of-Experts layers. It supports only non-thinking mode and does not generate thinking blocks.
Mellum2.1 Thinking
A 12B-total, 2.5B-active open-weight thinking model for coding and tool use. Start with JetBrains’ official GGUF on a suitable desktop, or serve the BF16 checkpoint with vLLM on a larger GPU.