AtomicChat/Qwen2.5-14B-Instruct-GGUF overview

---

license: apache-2.0

license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/blob/main/LICENSE

thumbnail: https://huggingface.co/AtomicChat/Qwen2.5-14B-Instruct-GGUF/resolve/main/hero.png

base_model:

• Qwen/Qwen2.5-14B-Instruct

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• qwen2.5
• qwen
• gguf
• llama.cpp
• quantized

---

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<img src="https://huggingface.co/AtomicChat/Qwen2.5-14B-Instruct-GGUF/resolve/main/hero.png" alt="Qwen2.5 14B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

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<a href="https://huggingface.co/Qwen/Qwen2.5-14B-Instruct"><strong>Base model: Qwen/Qwen2.5-14B-Instruct</strong></a>

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Qwen2.5 14B, 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

• 14.8B parameters: the weights this repo quantizes.
• Context length: 32,768 tokens (32K), as published by Qwen.
• 48 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 14B chat template is applied. Without it the model can emit malformed turns.

Model Overview

| Property | Value |

|---|---|

| Base model | Qwen/Qwen2.5-14B-Instruct |

| Parameters | 14.8B |

| Layers | 48 |

| 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, 40 attention heads over 8 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 | 9.0 GB | Recommended default. Best balance of size, speed and quality. |

| UD-Q4_K_XL | 9.6 GB | Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint. |

| Q5_K_M | 10.5 GB | Higher quality, low loss. |

| Q6_K | 12.1 GB | Near lossless, noticeably lighter than Q8_0. |

| Q8_0 | 15.7 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 14B locally with:

• Atomic Chat: the easiest path. Open the app, search AtomicChat/Qwen2.5-14B-Instruct-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/Qwen2.5-14B-Instruct-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/Qwen2.5-14B-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/Qwen2.5-14B-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/Qwen2.5-14B-Instruct-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download Qwen/Qwen2.5-14B-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.