bartowski/INTELLECT-1-Instruct-GGUF
Llamacpp imatrix Quantizations of INTELLECT-1-Instruct
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b4222">b4222</a> for quantization.
Original model: https://huggingface.co/PrimeIntellect/INTELLECT-1-Instruct
All quants made using imatrix option with dataset from here
Run them in LM Studio
Prompt format
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>Download a file (not the whole branch) from below:
Embed/output weights
Some of these quants (Q3KXL, Q4KL etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Downloading using huggingface-cli
<details> <summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
pip install -U "huggingface_hub[cli]"Then, you can target the specific file you want:
huggingface-cli download bartowski/INTELLECT-1-Instruct-GGUF --include "INTELLECT-1-Instruct-Q4_K_M.gguf" --local-dir ./If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
huggingface-cli download bartowski/INTELLECT-1-Instruct-GGUF --include "INTELLECT-1-Instruct-Q8_0/*" --local-dir ./You can either specify a new local-dir (INTELLECT-1-Instruct-Q8_0) or download them all in place (./)
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Q40X_X information
New: Thanks to efforts made to have online repacking of weights in this PR, you can now just use Q4_0 if your llama.cpp has been compiled for your ARM device.
Similarly, if you want to get slightly better performance, you can use IQ4NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 44 for now. The loading time may be slower but it will result in an overall speed incrase.
<details> <summary>Click to view Q40X_X information</summary> These are NOT for Metal (Apple) or GPU (nvidia/AMD/intel) offloading, only ARM chips (and certain AVX2/AVX512 CPUs).
If you're using an ARM chip, the Q40XX quants will have a substantial speedup. Check out Q4044 speed comparisons on the original pull request
To check which one would work best for your ARM chip, you can check AArch64 SoC features (thanks EloyOn!).
If you're using a CPU that supports AVX2 or AVX512 (typically server CPUs and AMD's latest Zen5 CPUs) and are not offloading to a GPU, the Q408_8 may offer a nice speed as well:
<details> <summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
Q408_8 offers a nice bump to prompt processing and a small bump to text generation
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Which file should I choose?
<details> <summary>Click here for details</summary>
A great write up with charts showing various 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.
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 'QXKX', like Q5KM.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
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 IQXX, like IQ3M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
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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