bartowski/dolphin-2.9.1-llama-3-70b-GGUF

---

license: llama3 base_model: meta-llama/Meta-Llama-3-70B tags:

• generatedfromtrainer
• axolotl

model-index:

• name: out

results: [] datasets:

• cognitivecomputations/Dolphin-2.9
• teknium/OpenHermes-2.5
• m-a-p/CodeFeedback-Filtered-Instruction
• cognitivecomputations/dolphin-coder
• cognitivecomputations/samantha-data
• microsoft/orca-math-word-problems-200k
• Locutusque/function-calling-chatml
• internlm/Agent-FLAN

quantized_by: bartowski pipeline_tag: text-generation

---

Llamacpp imatrix Quantizations of dolphin-2.9.1-llama-3-70b

Using llama.cpp release b2965 for quantization.

Original model: https://huggingface.co/cognitivecomputations/dolphin-2.9.1-llama-3-70b

All quants made using imatrix option with dataset from here

Prompt format

<|im_start|>system
{systemprompt}<|imend|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Description
dolphin-2.9.1-llama-3-70b-Q80.gguf	Q80	74.97GB	Extremely high quality, generally unneeded but max available quant.
dolphin-2.9.1-llama-3-70b-Q6K.gguf	Q6K	57.88GB	Very high quality, near perfect, recommended.
dolphin-2.9.1-llama-3-70b-Q5KM.gguf	Q5K_M	49.94GB	High quality, recommended.
dolphin-2.9.1-llama-3-70b-Q5KS.gguf	Q5K_S	48.65GB	High quality, recommended.
dolphin-2.9.1-llama-3-70b-Q4KM.gguf	Q4K_M	42.52GB	Good quality, uses about 4.83 bits per weight, recommended.
dolphin-2.9.1-llama-3-70b-Q4KS.gguf	Q4K_S	40.34GB	Slightly lower quality with more space savings, recommended.
dolphin-2.9.1-llama-3-70b-IQ4NL.gguf	IQ4NL	40.05GB	Decent quality, slightly smaller than Q4KS with similar performance recommended.
dolphin-2.9.1-llama-3-70b-IQ4XS.gguf	IQ4XS	37.90GB	Decent quality, smaller than Q4KS with similar performance, recommended.
dolphin-2.9.1-llama-3-70b-Q3KL.gguf	Q3K_L	37.14GB	Lower quality but usable, good for low RAM availability.
dolphin-2.9.1-llama-3-70b-Q3KM.gguf	Q3K_M	34.26GB	Even lower quality.
dolphin-2.9.1-llama-3-70b-IQ3M.gguf	IQ3M	31.93GB	Medium-low quality, new method with decent performance comparable to Q3KM.
dolphin-2.9.1-llama-3-70b-IQ3S.gguf	IQ3S	30.91GB	Lower quality, new method with decent performance, recommended over Q3KS quant, same size with better performance.
dolphin-2.9.1-llama-3-70b-Q3KS.gguf	Q3K_S	30.91GB	Low quality, not recommended.
dolphin-2.9.1-llama-3-70b-IQ3XS.gguf	IQ3XS	29.30GB	Lower quality, new method with decent performance, slightly better than Q3KS.
dolphin-2.9.1-llama-3-70b-IQ3XXS.gguf	IQ3XXS	27.46GB	Lower quality, new method with decent performance, comparable to Q3 quants.
dolphin-2.9.1-llama-3-70b-Q2K.gguf	Q2K	26.37GB	Very low quality but surprisingly usable.
dolphin-2.9.1-llama-3-70b-IQ2M.gguf	IQ2M	24.11GB	Very low quality, uses SOTA techniques to also be surprisingly usable.
dolphin-2.9.1-llama-3-70b-IQ2S.gguf	IQ2S	22.24GB	Very low quality, uses SOTA techniques to be usable.
dolphin-2.9.1-llama-3-70b-IQ2XS.gguf	IQ2XS	21.14GB	Very low quality, uses SOTA techniques to be usable.
dolphin-2.9.1-llama-3-70b-IQ2XXS.gguf	IQ2XXS	19.09GB	Lower quality, uses SOTA techniques to be usable.
dolphin-2.9.1-llama-3-70b-IQ1M.gguf	IQ1M	16.75GB	Extremely low quality, not recommended.
dolphin-2.9.1-llama-3-70b-IQ1S.gguf	IQ1S	15.34GB	Extremely low quality, not recommended.

Downloading using huggingface-cli

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/dolphin-2.9.1-llama-3-70b-GGUF --include "dolphin-2.9.1-llama-3-70b-Q4KM.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/dolphin-2.9.1-llama-3-70b-GGUF --include "dolphin-2.9.1-llama-3-70b-Q80.gguf/*" --local-dir dolphin-2.9.1-llama-3-70b-Q80

You can either specify a new local-dir (dolphin-2.9.1-llama-3-70b-Q8_0) or download them all in place (./)

Which file should I choose?

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:

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 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.

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