<|system|>{system_prompt}<|system|>
# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools> XML tags:
<tools>{"name": "get_stock_price", "description": "Get the current stock price", "parameters": {"type": "object", "properties": {"symbol": {"type": "string", "description": "The stock symbol, e.g. AAPL, GOOG"}}, "required": ["symbol"]}}</tools>
For each function call, output the function name and arguments within the following XML format:
<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value>...</tool_call>
<|user|>{prompt}<|assistant|>
Don't know which to choose? Grab Q4_K_M (25.35GB) - usually a good mix of size and performance. Download instructions available here
Some of these files were built with a layout computed for this model instead of llama.cpp's standard one-size-fits-all rules. A Q4_K_M is still mostly Q4_K; the extra precision goes to the weights this particular model is most sensitive to. The S, M or L in a name says how much of the model stays at the base precision: about 90 % for S, 70 % for M and 50 % for L. An _L name is simply the large size of its family. Q4_K_L is to Q4_K_M what Q4_K_M is to Q4_K_S; Q6_K_S, Q6_K and Q6_K_L are the small, medium and large sizes of Q6_K, with Q6_K_L about halfway to Q8_0. In earlier releases an _L name meant the embedding and output weights were kept at Q8_0; in these files it means the larger size of the base type. There is no size target, so each file's bits per weight is reported rather than promised.
The layout each of these files was built with is published in the layouts/ folder: <file>.tensor-types.txt is the exact --tensor-type-file given to llama-quantize, and <file>.layout.json records how it was computed, including the generator version, the llama.cpp release and the commit, so any of them can be rebuilt. The code that computed them is public at quantization-config; its tag key-3bf8b43e20a68f0b is the exact snapshot these files record. The method is described in this write-up.
Checked on this model before any of these files were released: Q4_K_M reached 0.87×, Q3_K_M 0.88× and IQ2_XXS 1.01× the KL divergence of the standard layout at the same file size.
Layout details
Files built from a computed layout:
Quant
Size
Body bits/weight
File bits/weight
Body kept at base type
Q6_K_L
36.95GB
7.40
7.43
50 %
Q6_K
35.20GB
7.04
7.08
70 %
Q6_K_S
33.57GB
6.71
6.75
91 %
Q5_K_M
30.81GB
6.14
6.20
70 %
Q5_K_S
28.60GB
5.69
5.75
90 %
Q4_K_L
27.29GB
5.42
5.49
50 %
Q4_K_M
25.35GB
5.03
5.10
70 %
IQ4_NL
25.32GB
5.02
5.09
70 %
Q4_K_S
23.83GB
4.71
4.79
90 %
IQ4_XS
22.45GB
4.43
4.51
90 %
IQ3_M
21.05GB
4.15
4.23
50 %
Q3_K_L
20.59GB
4.05
4.14
50 %
Q3_K_M
19.53GB
3.84
3.93
70 %
Q3_K_S
18.44GB
3.61
3.71
90 %
IQ3_XS
18.32GB
3.59
3.68
90 %
IQ3_XXS
17.49GB
3.42
3.52
70 %
Q2_K
15.48GB
3.01
3.11
70 %
IQ2_M
14.88GB
2.89
2.99
70 %
IQ2_S
13.39GB
2.58
2.69
70 %
IQ2_XS
12.74GB
2.45
2.56
90 %
IQ2_XXS
12.03GB
2.30
2.42
70 %
Checked on this model: the computed layout against the standard one, measured by KL divergence against the unquantized model. The ratio compares each computed file with the standard ladder read at that file's own size, so it can differ from the two KLD columns when the two files differ in size.
Quant
Computed layout KLD
Standard layout KLD
Ratio at equal size
Size vs standard file
Q4_K_M
0.1496 ± 0.0042
0.1919 ± 0.0046
0.87×
+3.8 %
Q3_K_M
0.3019 ± 0.0055
0.3913 ± 0.0066
0.88×
+5.7 %
IQ2_XXS
0.9915 ± 0.0101
1.1292 ± 0.0109
1.01×
+8.6 %
How it works: the base type is a floor for every body tensor and a fixed share of the body bytes stays at it (90 % for S, 70 % for M, 50 % for L); the remaining bytes go where a cross-model sensitivity prior, measured by KL divergence against the unquantized model, says they buy the most quality. The embedding and output tensors are sized by their share of the file: a small table is kept at Q8_0, a large one follows the file's bitrate. A K-quant and the IQ quant with the same base bitrate (Q3_K_S and IQ3_XS, Q3_K_M and IQ3_S, Q3_K_L and IQ3_M) come out at about the same size; the IQ file is the GPU-oriented twin.
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: RecursiveSI_flint-calibration-v6.txt. The imatrix is available here: RecursiveSI_flint-imatrix.gguf.
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.
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:
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.
