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

🤗 On Hugging Facetext-generationapache-2.030 GBGGUF✓ HF checksums availableupdated today
Magnet

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

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.