AtomicChat/Qwen2.5-7B-Instruct-GGUF

Base model: Qwen/Qwen2.5-7B-Instruct

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

Property	Value
Base model	`Qwen/Qwen2.5-7B-Instruct`
Parameters	7.6B
Layers	28
Sliding window	131072 tokens
Context length	32,768 tokens (32K)
Vocabulary	152,064
Modalities	Text
Architecture	Dense decoder, hybrid sliding-window (131072) and global attention, 28 attention heads over 4 KV heads, `Qwen2ForCausalLM`
This repo	GGUF quants (imatrix). Quants: `Q4_K_M`, `UD-Q4_K_XL`, `Q5_K_M`, `Q6_K`, `Q8_0`

Choosing a quant

Quant	Size	Notes
`Q4_K_M`	4.7 GB	Recommended default. Best balance of size, speed and quality.
`UD-Q4_K_XL`	5.1 GB	Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.
`Q5_K_M`	5.4 GB	Higher quality, low loss.
`Q6_K`	6.3 GB	Near lossless, noticeably lighter than Q8_0.
`Q8_0`	8.1 GB	Effectively lossless, reference quality.

[!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

Parameter	Value
temperature	0.7
top_p	0.8
top_k	20
repetition_penalty	1.05

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

Download Qwen/Qwen2.5-7B-Instruct (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus.
Quantize the ladder with --imatrix.
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.