Llamacpp imatrix Quantizations of firefunction-v2
Using llama.cpp release b3197 for quantization.
Original model: https://huggingface.co/fireworks-ai/firefunction-v2
All quants made using imatrix option with dataset from here
Prompt format
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant with access to functions.
In addition to plain text responses, you can chose to call one or more of the provided functions.
Use the following rule to decide when to call a function:
* if the response can be generated from your internal knowledge (e.g., as in the case of queries like "What is the capital of Poland?"), do so
* if you need external information that can be obtained by calling one or more of the provided functions, generate a function calls
If you decide to call functions:
* prefix function calls with functools marker (no closing marker required)
* all function calls should be generated in a single JSON list formatted as functools[{"name": [function name], "arguments": [function arguments as JSON]},...]
* follow the provided JSON schema. Do not hallucinate arguments or values. Do to blindly copy values from the provided samples
* respect the argument type formatting. E.g., if the type if number and format is float, write value 7 as 7.0
* make sure you pick the right functions that match the user intent
Available functions as JSON spec:
[
{functions}
]
Today is {datetime}.<|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:
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| firefunction-v2-Q8_0.gguf | Q8_0 | 0GB | Extremely high quality, generally unneeded but max available quant. |
| firefunction-v2-Q6_K.gguf | Q6_K | 0GB | Very high quality, near perfect, recommended. |
| firefunction-v2-Q5_K_L.gguf | Q5_K_L | 0GB | Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, recommended. |
| firefunction-v2-Q5_K_M.gguf | Q5_K_M | 49.94GB | High quality, recommended. |
| firefunction-v2-Q4_K_L.gguf | Q4_K_L | 45.27GB | Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, recommended. |
| firefunction-v2-Q4_K_M.gguf | Q4_K_M | 42.52GB | Good quality, uses about 4.83 bits per weight, recommended. |
| firefunction-v2-IQ4_XS.gguf | IQ4_XS | 37.90GB | Decent quality, smaller than Q4_K_S with similar performance, recommended. |
| firefunction-v2-Q3_K_M.gguf | Q3_K_M | 34.26GB | Even lower quality. |
| firefunction-v2-IQ3_M.gguf | IQ3_M | 31.93GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| firefunction-v2-Q3_K_S.gguf | Q3_K_S | 30.91GB | Low quality, not recommended. |
| firefunction-v2-IQ3_XXS.gguf | IQ3_XXS | 27.46GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| firefunction-v2-Q2_K.gguf | Q2_K | 26.37GB | Very low quality but surprisingly usable. |
| firefunction-v2-IQ2_M.gguf | IQ2_M | 24.11GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| firefunction-v2-IQ2_XS.gguf | IQ2_XS | 21.14GB | Lower quality, uses SOTA techniques to be usable. |
| firefunction-v2-IQ2_XXS.gguf | IQ2_XXS | 19.09GB | Lower quality, uses SOTA techniques to be usable. |
| firefunction-v2-IQ1_M.gguf | IQ1_M | 16.75GB | 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/firefunction-v2-GGUF --include "firefunction-v2-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/firefunction-v2-GGUF --include "firefunction-v2-Q8_0.gguf/*" --local-dir firefunction-v2-Q8_0
You can either specify a new local-dir (firefunction-v2-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 '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:
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 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
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