quantized_by, pipeline_tag, license_link, language, base_model_relation, base_model, license_name, tags, license

quantized_by	pipeline_tag	license_link	language	base_model_relation	base_model	license_name	tags	license
bartowski	text-generation	LICENSE	ar zh en fr de hi id it ja ko pl pt ru es th vi	quantized	LiquidAI/LFM2.5-2.6B	lfm1.0	liquid lfm2.5 edge	other

Llamacpp imatrix Quantizations of LFM2.5-2.6B by LiquidAI

Using llama.cpp release b10262 for quantization.

Original model: https://huggingface.co/LiquidAI/LFM2.5-2.6B

Model details:

• Parameter count: 3B
• Input support: text
• MTP: no
• imatrix: yes - details

How to run

Prompt format

<|startoftext|><|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 (1.68GB) - usually a good mix of size and performance. Download instructions available here

Available files:

Filename	Quant type	File Size	Split	Description
LiquidAI_LFM2.5-2.6B-bf16.gguf	bf16	5.40GB	false	Full BF16 weights.
LiquidAI_LFM2.5-2.6B-Q8_0.gguf	Q8_0	2.87GB	false	Extremely high quality, generally unneeded but max available quant.
LiquidAI_LFM2.5-2.6B-Q6_K_L.gguf	Q6_K_L	2.30GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
LiquidAI_LFM2.5-2.6B-Q6_K.gguf	Q6_K	2.24GB	false	Very high quality, near perfect, recommended.
LiquidAI_LFM2.5-2.6B-Q5_K_L.gguf	Q5_K_L	2.01GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
LiquidAI_LFM2.5-2.6B-Q5_K_M.gguf	Q5_K_M	1.95GB	false	High quality, recommended.
LiquidAI_LFM2.5-2.6B-Q5_K_S.gguf	Q5_K_S	1.90GB	false	High quality, recommended.
LiquidAI_LFM2.5-2.6B-Q4_1.gguf	Q4_1	1.75GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
LiquidAI_LFM2.5-2.6B-Q4_K_L.gguf	Q4_K_L	1.75GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf	Q4_K_M	1.68GB	false	Good quality, default size for most use cases, recommended.
LiquidAI_LFM2.5-2.6B-Q4_K_S.gguf	Q4_K_S	1.61GB	false	Slightly lower quality with more space savings, recommended.
LiquidAI_LFM2.5-2.6B-Q4_0.gguf	Q4_0	1.60GB	false	Legacy format, kept for compatibility with older tools.
LiquidAI_LFM2.5-2.6B-IQ4_NL.gguf	IQ4_NL	1.60GB	false	Similar to IQ4_XS, but slightly larger.
LiquidAI_LFM2.5-2.6B-IQ4_XS.gguf	IQ4_XS	1.52GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
LiquidAI_LFM2.5-2.6B-Q3_K_XL.gguf	Q3_K_XL	1.52GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
LiquidAI_LFM2.5-2.6B-Q3_K_L.gguf	Q3_K_L	1.45GB	false	Lower quality but usable, good for low RAM availability.
LiquidAI_LFM2.5-2.6B-Q3_K_M.gguf	Q3_K_M	1.37GB	false	Low quality.
LiquidAI_LFM2.5-2.6B-IQ3_M.gguf	IQ3_M	1.29GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
LiquidAI_LFM2.5-2.6B-Q3_K_S.gguf	Q3_K_S	1.28GB	false	Low quality, not recommended.
LiquidAI_LFM2.5-2.6B-IQ3_XS.gguf	IQ3_XS	1.23GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
LiquidAI_LFM2.5-2.6B-Q2_K_L.gguf	Q2_K_L	1.17GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
LiquidAI_LFM2.5-2.6B-IQ3_XXS.gguf	IQ3_XXS	1.13GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
LiquidAI_LFM2.5-2.6B-Q2_K.gguf	Q2_K	1.10GB	false	Very low quality but surprisingly usable.
LiquidAI_LFM2.5-2.6B-IQ2_M.gguf	IQ2_M	1.02GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.

Download a specific file:

hf download bartowski/LiquidAI_LFM2.5-2.6B-GGUF --include "LiquidAI_LFM2.5-2.6B-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/LiquidAI_LFM2.5-2.6B-GGUF --include "LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf" --local-dir ./

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/LiquidAI_LFM2.5-2.6B-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 b10262 - 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

imatrix

All quants made using imatrix option with dataset from here. The imatrix is available here: LiquidAI_LFM2.5-2.6B-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?

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