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bartowski/
vectionlabs_Salience-27B-R5-GGUF

Name: vectionlabs_Salience-27B-R5-GGUF
Rating: 2.5 (3874 reviews)

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Llamacpp imatrix Quantizations of Salience-27B-R5 by vectionlabs

Using llama.cpp release b10419 for quantization.

Original model: https://huggingface.co/vectionlabs/Salience-27B-R5

Model details:

Parameter count: 28B
Input support: text, image (with mmproj file) - details
Speculative decoding: yes (MTP) - details
imatrix: yes - details
Perplexity/KLD measured: no

How to run

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>

Don't know which to choose? Grab Q4_K_M (17.77GB) - usually a good mix of size and performance. Download instructions available here

Available files:

Filename	Quant type	File Size	Split	Description
vectionlabs_Salience-27B-R5-bf16.gguf	bf16	54.66GB	true	Full BF16 weights.
vectionlabs_Salience-27B-R5-Q8_0.gguf	Q8_0	29.12GB	false	Extremely high quality, generally unneeded but max available quant.
vectionlabs_Salience-27B-R5-Q6_K_L.gguf	Q6_K_L	24.08GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
vectionlabs_Salience-27B-R5-Q6_K.gguf	Q6_K	23.46GB	false	Very high quality, near perfect, recommended.
vectionlabs_Salience-27B-R5-Q5_K_L.gguf	Q5_K_L	21.54GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
vectionlabs_Salience-27B-R5-Q5_K_M.gguf	Q5_K_M	20.75GB	false	High quality, recommended.
vectionlabs_Salience-27B-R5-Q5_K_S.gguf	Q5_K_S	19.68GB	false	High quality, recommended.
vectionlabs_Salience-27B-R5-Q4_K_L.gguf	Q4_K_L	18.72GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
vectionlabs_Salience-27B-R5-Q4_1.gguf	Q4_1	17.83GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
vectionlabs_Salience-27B-R5-Q4_K_M.gguf	Q4_K_M	17.77GB	false	Good quality, default size for most use cases, recommended.
vectionlabs_Salience-27B-R5-Q4_K_S.gguf	Q4_K_S	16.71GB	false	Slightly lower quality with more space savings, recommended.
vectionlabs_Salience-27B-R5-Q3_K_XL.gguf	Q3_K_XL	16.39GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
vectionlabs_Salience-27B-R5-Q4_0.gguf	Q4_0	16.35GB	false	Legacy format, kept for compatibility with older tools.
vectionlabs_Salience-27B-R5-IQ4_NL.gguf	IQ4_NL	16.33GB	false	Similar to IQ4_XS, but slightly larger.
vectionlabs_Salience-27B-R5-IQ4_XS.gguf	IQ4_XS	15.57GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
vectionlabs_Salience-27B-R5-Q3_K_L.gguf	Q3_K_L	15.28GB	false	Lower quality but usable, good for low RAM availability.
vectionlabs_Salience-27B-R5-Q3_K_M.gguf	Q3_K_M	14.61GB	false	Low quality.
vectionlabs_Salience-27B-R5-IQ3_M.gguf	IQ3_M	13.90GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
vectionlabs_Salience-27B-R5-Q3_K_S.gguf	Q3_K_S	13.72GB	false	Low quality, not recommended.
vectionlabs_Salience-27B-R5-IQ3_XS.gguf	IQ3_XS	13.33GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
vectionlabs_Salience-27B-R5-Q2_K_L.gguf	Q2_K_L	13.08GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
vectionlabs_Salience-27B-R5-IQ3_XXS.gguf	IQ3_XXS	12.63GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
vectionlabs_Salience-27B-R5-Q2_K.gguf	Q2_K	11.84GB	false	Very low quality but surprisingly usable.
vectionlabs_Salience-27B-R5-IQ2_M.gguf	IQ2_M	10.87GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.
vectionlabs_Salience-27B-R5-IQ2_S.gguf	IQ2_S	10.30GB	false	Low quality, uses SOTA techniques to be usable.
vectionlabs_Salience-27B-R5-IQ2_XS.gguf	IQ2_XS	9.99GB	false	Low quality, uses SOTA techniques to be usable.
vectionlabs_Salience-27B-R5-IQ2_XXS.gguf	IQ2_XXS	9.39GB	false	Very low quality, uses SOTA techniques to be usable.

Download a specific file:

hf download bartowski/vectionlabs_Salience-27B-R5-GGUF --include "vectionlabs_Salience-27B-R5-Q4_K_M.gguf" --local-dir ./

Downloading using the Hugging Face CLI

Click to view download instructions

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

pip install -U "huggingface_hub[cli]"

Download a specific file:

hf download bartowski/vectionlabs_Salience-27B-R5-GGUF --include "vectionlabs_Salience-27B-R5-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/vectionlabs_Salience-27B-R5-GGUF --include "vectionlabs_Salience-27B-R5-bf16/*" --local-dir ./

You can either specify a new local-dir (vectionlabs_Salience-27B-R5-bf16) or download them all in place (./)

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/vectionlabs_Salience-27B-R5-GGUF:Q4_K_M

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

These quants were made with llama.cpp release b10419 - if this model's architecture is newly supported, you'll need that release or newer 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-vectionlabs_Salience-27B-R5-f16.gguf and mmproj-vectionlabs_Salience-27B-R5-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

This model has MTP (Multi-Token Prediction) layers, and they are included in these quants

MTP layers act as a built-in draft model, letting llama.cpp run speculative decoding for faster generation. To use them, add the following flag to your llama.cpp command:

--spec-type draft-mtp

Note: the MTP layers are stored at Q4_0 in the imatrix quants (except for the Q8_0 quant), since imatrix calibration does not exercise them. Q4_0 is chosen for its speed which massively benefits MTP performance.

imatrix

All quants made using imatrix option, with a calibration corpus rendered through this model's own chat template. The corpus pairs plain prose with tool-calling and reasoning conversations (corpus source data), encoded exactly as this model sees them at inference and processed with --parse-special, so chat-format special tokens contribute to the importance matrix. The corpus rendered for this model is included in this repo: vectionlabs_Salience-27B-R5-calibration-v6.txt. The imatrix is available here: vectionlabs_Salience-27B-R5-imatrix.gguf.

Calibration render details

json

{
  "generator": "auto_quant_v2 calibration renderer",
  "recipe": "calibration-v6",
  "model": "Salience-27B-R5",
  "encoder": "chat_template",
  "chunk_size": 512,
  "prose_chunks": 214,
  "tool_chunks": 410,
  "total_chunks": 624,
  "tool_chunk_fraction": 0.657,
  "n_conversations": 137,
  "extension_convs_used": 0,
  "conversation_token_lengths": [
    758,
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  ],
  "warnings": []
}

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?

Click here for details

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.

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