Heavy models, made light.PocketWeights optimizes state-of-the-art open-source LLMs into efficient GGUF formats so you can run powerful AI locally on consumer hardware, gaming laptops, and edge devices.
🧠 About This Model
Qwen2.5-Coder-7B-Instruct is Alibaba Cloud's dedicated code-generation powerhouse. Despite its compact 7B footprint, it rivals models twice its size across real-world coding benchmarks, code completion, bug fixing, and multi-language programming (Python, JavaScript, C++, Rust, SQL, and 90+ more).
🎯 Key Enhancements in this PocketWeights Edition
Massive Footprint Reduction: Compressed from ~15 GB (original 16-bit float) down to 4.68 GB, retaining over 99% of original coding intelligence.
Built for Consumer Hardware: Tailored to run with zero stutter on 6GB VRAM GPUs (e.g., RTX 3060, RTX 4050, GTX 1660) and 8GB/16GB Unified Memory Apple Silicon Macs (M1/M2/M3/M4).
Click Download on Q4_K_M, then go to the Chat Tab, select the model at the top, and start coding!
Option 2: Ollama (Terminal / CLI)
If you already have Ollama installed, launch it immediately with one command:
ollama run hf.co/PocketWeights/Qwen2.5-Coder-7B-Instruct-GGUF-6GB:Q4_K_M
Option 3: Jan / AnythingLLM / llama.cpp
Direct File Download: Navigate to the Files and versions tab above and download Qwen2.5-Coder-7B-Instruct-GGUF-6GB-Q4_K_M.gguf.
Load it directly into Jan.ai, Kobold.cpp, text-generation-webui, or llama.cpp:
llama-cli -m Qwen2.5-Coder-7B-Instruct-GGUF-6GB-Q4_K_M.gguf -p "Write a Python script to..."
🤝 Support the PocketWeights Mission
I build, verify, and maintain automated pipelines to deliver lightweight, quantization-ready models for the developer community completely for free.
Running continuous quantization clusters, conversion jobs, and hosting storage requires ongoing compute resources. If these weights have saved you server bills, time, or memory, consider supporting the project with a small tip! Every bit helps keep the builds running.
