Local LLM
Qwen3-Coder
Qwen3-Coder - Alibaba's open code model series for agentic coding, up to 1M tokens of context
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View guide →DeepSeek-Coder-V2 — DeepSeek's open code model family that challenged closed-source code models.
DeepSeek-Coder-V2 is an open-source local llm project by deepseek-ai (deepseek-ai/DeepSeek-Coder-V2 on GitHub). It has 7,014 GitHub stars and 1,108 forks, is released under the MIT license, and was last updated on GitHub on 11 November 2025.
Choose the variant that fits your hardware — a Lite model for a single workstation GPU, or the full model only if you have a multi-GPU server — and your inference stack (Transformers or SGLang).
Clone deepseek-ai/DeepSeek-Coder-V2 from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
There is nothing to build: download the weights from the model's Hugging Face page and load them with Transformers or SGLang using the README's examples.
Run the code-completion example with DeepSeek-Coder-V2-Lite-Base on a short Python snippet, then try the chat example with the Instruct variant.
Evaluated extensively on code generation benchmarks in the repo, positioning it against leading closed models.
Supports fill-in-the-middle style code insertion as well as plain completion, with worked examples for both.
Evaluated on code-fixing tasks, so it can be used to repair broken code, not just write new code.
Instruct (chat) and Base variants, in full and Lite sizes, cover assistant use and raw completion/fine-tuning bases.
The repo also reports mathematical-reasoning and general natural-language evaluations, beyond pure code tasks.
Run with Hugging Face Transformers for simplicity, or SGLang (the recommended path) for efficient serving.
Get the source from the official GitHub repository for DeepSeek-Coder-V2.
git clone https://github.com/deepseek-ai/DeepSeek-Coder-V2.git
cd DeepSeek-Coder-V2Use the README's Hugging Face example to load the Lite base model for code completion.
python -c "from transformers import AutoTokenizer, AutoModelForCausalLM; m='deepseek-ai/DeepSeek-Coder-V2-Lite-Base'; tok=AutoTokenizer.from_pretrained(m, trust_remote_code=True)" # full example in READMEFor the full model, follow the README's SGLang section (BF16, tensor parallelism) to serve it efficiently.
# See README 'Inference with SGLang (recommended)' for the exact serve command and settingsdeepseek-ai (DeepSeek), through the deepseek-ai/DeepSeek-Coder-V2 repository, with models on Hugging Face and an API platform documented in the repo.
Yes — the project is open source under the MIT license as recorded for this listing. You can use, modify and distribute it under the MIT terms.
Running locally needs no API key — just hardware. DeepSeek also offers a hosted API platform if you prefer not to run the weights yourself.
A hosted API is instant and scales for you, billed per token. DeepSeek-Coder-V2 gives you open weights to run, inspect and fine-tune on your own GPUs — full control and no per-token bill, with real hardware requirements, especially for the full model.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — run the code-completion example with DeepSeek-Coder-V2-Lite-Base on a short Python snippet, then try the chat example with the Instruct variant.
Qwen3-Coder - Alibaba's open code model series for agentic coding, up to 1M tokens of context
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