AlexAtomic/qwen35-4b-GGUF overview

---

license: apache-2.0

license_link: https://huggingface.co/Qwen/Qwen3.5-4B/blob/main/LICENSE

thumbnail: https://huggingface.co/AlexAtomic/qwen35-4b-GGUF/resolve/main/hero.png

base_model:

• Qwen/Qwen3.5-4B

base_model_relation: quantized

quantized_by: AlexAtomic

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• qwen
• qwen3
• gguf
• imatrix
• quantized
• llama.cpp

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<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AlexAtomic/qwen35-4b-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/AlexAtomic/qwen35-4b-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/AlexAtomic/qwen35-4b-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>

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<img src="https://huggingface.co/AlexAtomic/qwen35-4b-GGUF/resolve/main/hero.png" alt="Qwen3.5 4B" style="width:420px; max-width:100%; height:auto; margin-bottom:0.6em;"/>

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<a href="https://huggingface.co/Qwen/Qwen3.5-4B"><strong>Base model: Qwen/Qwen3.5-4B</strong></a>

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Qwen3.5 4B, self-quantized to GGUF by Atomic Chat. Built straight from Qwen's original weights with a per-tensor importance matrix. Runs fully offline.

Highlights

• Efficient hybrid architecture combining Gated Delta Networks with sparse Mixture-of-Experts for high-throughput inference at low latency and cost.
• Unified vision-language foundation trained with early fusion on multimodal tokens (these GGUF quants cover the text path).
• 262,144-token native context, extensible up to ~1,010,000 tokens.
• Global linguistic coverage: Qwen reports support for 201 languages and dialects.
• Thinking and instruct modes, each with its own recommended sampling presets.
• Full quant ladder with an importance matrix on every quant over calibration_datav3.

> [!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 Qwen3.5 4B chat template is applied. Without it the model can emit malformed turns.

Model Overview

| Property | Value |

|---|---|

| Base model | Qwen/Qwen3.5-4B |

| Total parameters | 4B |

| Layers | 32 |

| Context length | 262,144 native, extensible up to ~1,010,000 |

| Architecture | Causal LM with vision encoder; hybrid Gated DeltaNet + Gated Attention, hidden dim 2560, trained with MTP |

| This repo | GGUF quants (imatrix), text path |

<img src="https://huggingface.co/AlexAtomic/qwen35-4b-GGUF/resolve/main/benchmark.png" alt="Qwen3.5 4B benchmark scores" style="width:100%; max-width:900px;"/>

Scores are Qwen's published results for the base Qwen/Qwen3.5-4B. Quantization preserves the large majority of this; Q4_K_M and up sit within a point or two of full precision.

Choosing a quant

| Quant | Size | Notes |

|---|---|---|

| Q2_K | 1.9 GB | Smallest. Minimal RAM, clear quality drop. |

| IQ3_M | 2.2 GB | Beats Q3 at similar size thanks to imatrix. Best low-RAM pick. |

| Q3_K_M | 2.3 GB | Low quality but usable. |

| Q3_K_L | 2.4 GB | A step above Q3_K_M. |

| IQ4_XS | 2.5 GB | Excellent quality for size. Recommended low-bit. |

| Q4_K_S | 2.6 GB | Compact Q4, fast. |

| Q4_K_M | 2.7 GB | Recommended default. Best balance of size, speed and quality. |

| UD-Q4_K_XL | 2.9 GB | Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint. |

| Q5_K_S | 3.0 GB | Higher quality. |

| Q5_K_M | 3.1 GB | Higher quality, low loss. |

| Q6_K | 3.5 GB | Near lossless. |

| Q8_0 | 4.5 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 Qwen3.5 4B locally with:

• Atomic Chat: the easiest path. Open the app, search AlexAtomic/qwen35-4b-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AlexAtomic/qwen35-4b-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AlexAtomic/qwen35-4b-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 |

| min_p | 0.0 |

| presence_penalty | 1.5 |

| repetition_penalty | 1.0 |

Qwen's recommended Instruct (non-thinking) settings. Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0.

Run in llama.cpp

git clone https://github.com/ggerganov/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 AlexAtomic/qwen35-4b-GGUF:UD-Q4_K_XL \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download Qwen/Qwen3.5-4B (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over calibration_datav3 (100 chunks).
4. Quantize the full ladder with --imatrix.
5. UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Released by Qwen under the Apache 2.0 license. Quantized by Atomic Chat.