bartowski / Cloudflare_clef-GGUF

<|im_start|>system
Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.

# Tools

You have access to the following functions:

<tools>
{"type": "function", "function": {"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>

If you choose to call a function ONLY reply in the following format with NO suffix:

<tool_call>
<function=example_function_name>
<parameter=example_parameter_1>
value_1
</parameter>
<parameter=example_parameter_2>
This is the value for the second parameter
that can span
multiple lines
</parameter>
</function>
</tool_call>

<IMPORTANT>
Reminder:
- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags
- Required parameters MUST be specified
- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after
- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls
</IMPORTANT>

{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>

First, make sure you have the Hugging Face CLI installed:

pip install -U "huggingface_hub[cli]"

Download a specific file:

hf download bartowski/Cloudflare_clef-GGUF --include "Cloudflare_clef-Q4_K_M.gguf" --local-dir ./

The files marked true in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

hf download bartowski/Cloudflare_clef-GGUF --include "Cloudflare_clef-bf16/*" --local-dir ./

You can either specify a new local-dir (Cloudflare_clef-bf16) or download them all in place (./)

Files built from a computed layout:

Checked on bartowski/Altworld_Hemmingway-1-GGUF , which shares this model's architecture and tensor shapes: 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.

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.

{
  "recipe": "calibration-v6",
  "encoder": "chat_template",
  "library_versions": {
    "transformers": "5.9.0",
    "tokenizers": "0.22.2",
    "tiktoken": "0.14.0",
    "blobfile": "3.3.0",
    "huggingface_hub": "1.31.0"
  },
  "chunk_size": 512,
  "prose_chunks": 214,
  "tool_chunks": 369,
  "total_chunks": 583,
  "n_conversations": 137,
  "conversation_token_lengths": [
    604,
    1667,
    1297,
    1559,
    1185,
    1404,
    3220,
    866,
    1267,
    1462,
    1107,
    2136,
    921,
    1293,
    2829,
    1313,
    1160,
    1026,
    777,
    758,
    1402,
    1098,
    1447,
    1242,
    1912,
    1538,
    1694,
    948,
    1455,
    1686,
    1609,
    1260,
    1308,
    1077,
    1060,
    1752,
    1691,
    1215,
    517,
    1950,
    1451,
    1166,
    1438,
    2042,
    2130,
    1355,
    1649,
    947,
    2996,
    1142,
    2908,
    827,
    1064,
    1010,
    947,
    738,
    2530,
    955,
    1188,
    1127,
    1271,
    1211,
    966,
    1259,
    1223,
    1628,
    953,
    1592,
    2166,
    881,
    365,
    1162,
    3375,
    2876,
    749,
    982,
    1072,
    1113,
    1338,
    1122,
    1191,
    835,
    1250,
    1111,
    1331,
    1586,
    1443,
    2106,
    913,
    698,
    2806,
    682,
    1443,
    1715,
    1987,
    1243,
    683,
    1396,
    1168,
    1769,
    1884,
    1755,
    857,
    1055,
    1077,
    2896,
    780,
    774,
    803,
    1452,
    1092,
    1617,
    808,
    392,
    361,
    2666,
    1058,
    1179,
    1870,
    2079,
    2735,
    2717,
    856,
    1020,
    880,
    999,
    1279,
    1039,
    882,
    1397,
    870,
    750,
    1783,
    1067,
    957,
    1356,
    1519
  ]
}

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.