Qwen2.5-7B-Instruct-GGUF

<center> <div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;"> <a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen2.5-7B-Instruct-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a> <a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen2.5-7B-Instruct-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a> <a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen2.5-7B-Instruct-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a> </div> <br/> <img src="https://huggingface.co/AtomicChat/Qwen2.5-7B-Instruct-GGUF/resolve/main/hero.png" alt="Qwen2.5 7B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/> <div style="display:flex; justify-content:center; gap:0.5em;"> <a href="https://huggingface.co/Qwen/Qwen2.5-7B-Instruct"><strong>Base model: Qwen/Qwen2.5-7B-Instruct</strong></a> </div> </center>

Qwen2.5 7B, self-quantized to GGUF by Atomic Chat. Built straight from Qwen's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

• 7.6B parameters: the weights this repo quantizes.
• Context length: 32,768 tokens (32K), as published by Qwen.
• 28 layers: Dense decoder, hybrid sliding-window (131072) and global attention.
• Full imatrix ladder: every quant is calibrated with an importance matrix.
• Multilingual support: for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

[!NOTE] These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

[!IMPORTANT] Always pass --jinja so the Qwen2.5 7B chat template is applied. Without it the model can emit malformed turns.

Model Overview

Choosing a quant

[!TIP] Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Qwen2.5 7B locally with:

• Atomic Chat: the easiest path. Open the app, search AtomicChat/Qwen2.5-7B-Instruct-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/Qwen2.5-7B-Instruct-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
• LM Studio / Jan: search the repo id, download any quant.

Best practices

Qwen's recommended sampling configuration for Qwen/Qwen2.5-7B-Instruct.

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/Qwen2.5-7B-Instruct-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download Qwen/Qwen2.5-7B-Instruct (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over our calibration corpus.
4. Quantize the ladder with --imatrix.
5. UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Original model by Qwen, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.