bartowski/Muse-Glimmer-30B-GGUF overview

---

quantized_by: bartowski

pipeline_tag: image-text-to-text

license: apache-2.0

base_model: meta-models/Muse-Glimmer-30B

base_model_relation: quantized

---

Llamacpp imatrix Quantizations of Muse-Glimmer-30B by meta-models

Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> commit <a href="https://github.com/ggml-org/llama.cpp/commit/62bf73d25c53">62bf73d25c53</a> for quantization.

Original model: https://huggingface.co/meta-models/Muse-Glimmer-30B

Model details:

• Parameter count: 30B
• Input support: text, image (with mmproj file) - details
• MTP: yes - details
• imatrix: yes - details

How to run

Prompt format

<|begin_of_text|><|start|>system<|message|>{system_prompt}

Reasoning strength: high.

# Valid recipients: "self", "user".<|eot|><|start|>user<|message|>{prompt}<|eot|><|start|>assistant

Don't know which to choose? Grab Q4_K_M (17.31GB) - usually a good mix of size and performance. Download instructions available here

Available files:

| Filename | Quant type | File Size | Split | Description |

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

| Muse-Glimmer-30B-bf16.gguf | bf16 | 55.73GB | true | Full BF16 weights. |

| Muse-Glimmer-30B-Q8_0.gguf | Q8_0 | 29.61GB | false | Extremely high quality, generally unneeded but max available quant. |

| Muse-Glimmer-30B-Q6_K_L.gguf | Q6_K_L | 24.07GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended. |

| Muse-Glimmer-30B-Q6_K.gguf | Q6_K | 23.41GB | false | Very high quality, near perfect, recommended. |

| Muse-Glimmer-30B-Q5_K_L.gguf | Q5_K_L | 20.94GB | false | Uses Q8_0 for embed and output weights. High quality, recommended. |

| Muse-Glimmer-30B-Q5_K_M.gguf | Q5_K_M | 20.11GB | false | High quality, recommended. |

| Muse-Glimmer-30B-Q5_K_S.gguf | Q5_K_S | 19.44GB | false | High quality, recommended. |

| Muse-Glimmer-30B-Q4_K_L.gguf | Q4_K_L | 18.30GB | false | Uses Q8_0 for embed and output weights. Good quality, recommended. |

| Muse-Glimmer-30B-Q4_1.gguf | Q4_1 | 17.83GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |

| Muse-Glimmer-30B-Q4_K_M.gguf | Q4_K_M | 17.31GB | false | Good quality, default size for most use cases, recommended. |

| Muse-Glimmer-30B-Q4_K_S.gguf | Q4_K_S | 16.32GB | false | Slightly lower quality with more space savings, recommended. |

| Muse-Glimmer-30B-Q4_0.gguf | Q4_0 | 16.27GB | false | Legacy format, kept for compatibility with older tools. |

| Muse-Glimmer-30B-IQ4_NL.gguf | IQ4_NL | 16.24GB | false | Similar to IQ4_XS, but slightly larger. |

| Muse-Glimmer-30B-Q3_K_XL.gguf | Q3_K_XL | 15.96GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |

| Muse-Glimmer-30B-IQ4_XS.gguf | IQ4_XS | 15.44GB | false | Decent quality, smaller than Q4_K_S with similar performance, recommended. |

| Muse-Glimmer-30B-Q3_K_L.gguf | Q3_K_L | 14.78GB | false | Lower quality but usable, good for low RAM availability. |

| Muse-Glimmer-30B-Q3_K_M.gguf | Q3_K_M | 13.96GB | false | Low quality. |

| Muse-Glimmer-30B-IQ3_M.gguf | IQ3_M | 13.11GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |

| Muse-Glimmer-30B-Q3_K_S.gguf | Q3_K_S | 12.79GB | false | Low quality, not recommended. |

| Muse-Glimmer-30B-Q2_K_L.gguf | Q2_K_L | 12.35GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |

| Muse-Glimmer-30B-IQ3_XS.gguf | IQ3_XS | 12.32GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |

| Muse-Glimmer-30B-IQ3_XXS.gguf | IQ3_XXS | 11.55GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |

| Muse-Glimmer-30B-Q2_K.gguf | Q2_K | 11.04GB | false | Very low quality but surprisingly usable. |

| Muse-Glimmer-30B-IQ2_M.gguf | IQ2_M | 10.66GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |

| Muse-Glimmer-30B-IQ2_S.gguf | IQ2_S | 10.04GB | false | Low quality, uses SOTA techniques to be usable. |

| Muse-Glimmer-30B-IQ2_XS.gguf | IQ2_XS | 9.58GB | false | Low quality, uses SOTA techniques to be usable. |

| Muse-Glimmer-30B-IQ2_XXS.gguf | IQ2_XXS | 8.92GB | false | Very low quality, uses SOTA techniques to be usable. |

Download a specific file:

hf download bartowski/Muse-Glimmer-30B-GGUF --include "Muse-Glimmer-30B-Q4_K_M.gguf" --local-dir ./

Downloading using the Hugging Face CLI

<details>

<summary>Click to view download instructions</summary>

First, make sure you have the Hugging Face CLI installed:

pip install -U "huggingface_hub[cli]"

Download a specific file:

hf download bartowski/Muse-Glimmer-30B-GGUF --include "Muse-Glimmer-30B-Q4_K_M.gguf" --local-dir ./

The files marked true in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

hf download bartowski/Muse-Glimmer-30B-GGUF --include "Muse-Glimmer-30B-bf16/*" --local-dir ./

You can either specify a new local-dir (Muse-Glimmer-30B-bf16) or download them all in place (./)

</details>

How to run

These quants run with llama.cpp - installable in one line via llama.app:

curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M

llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.

These quants were made from llama.cpp commit 62bf73d25c53 - this model's architecture may be newly supported, so you'll need a build from that commit or a later release to run them.

They also work in: LM Studio · koboldcpp · ramalama · Jan AI · Text Generation Web UI · LoLLMs · Atomic Chat

Multimodal

This model supports image input. Alongside the quants, this repo includes the multimodal projector files mmproj-Muse-Glimmer-30B-f16.gguf and mmproj-Muse-Glimmer-30B-bf16.gguf, which pair with any quant above.

llama.cpp downloads the mmproj automatically when using -hf as shown above; if you're loading files manually, pass it with --mmproj.

MTP (DFlash)

This model has a DFlash draft model. These are not included in the quants themselves - they are provided as separate files in this repo: dflash-Muse-Glimmer-30B-Q4_0.gguf and dflash-Muse-Glimmer-30B-Q8_0.gguf

DFlash acts as a draft model, letting llama.cpp run speculative decoding for faster generation. To use it, add the following flag to your llama.cpp command:

--spec-type draft-dflash

When running with -hf as shown above, llama.cpp should download the DFlash file automatically alongside the model. If you're downloading files manually instead, also grab the dspark file and pass it with -md /path/to/dflash-Muse-Glimmer-30B-Q4_0.gguf.

imatrix

All quants made using imatrix option with dataset from here. The imatrix is available here: Muse-Glimmer-30B-imatrix.gguf.

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

ARM/AVX information

llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in this PR. This once required downloading special Q4_0_4_4/4_8/8_8 files; those are long gone. Online repacking now covers Q4_0, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.

Which file should I choose?

<details>

<summary>Click here for details</summary>

An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Hugging Face can also do this math for you: add your hardware in your Local Apps settings and the model page will show which files fit.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

</details>

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski