You can use any LLM just like JEV

You can simply run any GGUF with llama.cpp with n\_predict=1 and n\_probs=10, disable reasoning, and prompt it such as "If the following email is spam, respond with 1, if not spam, respond with 0. Do not respond with anything other than 1 or 0. Email: ...."

And that is it! It returns confidence percentages such as:

1 = 94.9%
0 = 5.08%

Example:

llama-server -m "C:\\Users\\MyUserName\\llama.cpp\\models\\Spark-X2.5-4B-Q4\_K\_M.gguf" -c 4096 -ngl all -fit off -fa on -b 2048 -ub 512 -np 1 --cache-ram 0 --reasoning off --no-reasoning-preserve --perf

Then:

curl.exe -s -X POST http://localhost:8080/v1/chat/completions \-H "Content-Type: application/json" -d "{\\"messages\\":\[{\\"role\\":\\"system\\",\\"content\\":\\"Classify spam. Reply only 1=spam or 0=not spam.\\"},{\\"role\\":\\"user\\",\\"content\\":\\"CONGRATULATIONS!!! You have won $5,000,000! Click here immediately to claim your prize!\\"}\],\\"max\_tokens\\":1,\\"logprobs\\":true,\\"top\_logprobs\\":10,\\"temperature\\":1.0,\\"top\_p\\":1.0}"

Result:

{"choices":\[{"finish\_reason":"length","index":0,"message":{"role":"assistant","content":"1"},"logprobs":{"content":\[{"id":30,"token":"1","bytes":\[49\],"logprob":-0.00456317700445652,"top\_logprobs":\[{"id":30,"token":"1","bytes":\[49\],"logprob":-0.00456317700445652},{"id":29,"token":"0","bytes":\[48\],"logprob":-5.395024299621582},{"id":1033,"token":"\*\*","bytes":\[42,42\],"logprob":-12.013711929321289},{"id":1046,"token":"The","bytes":\[84,104,101\],"logprob":-13.005236625671387},{"id":198,"token":"\\n","bytes":\[10\],"logprob":-13.100714683532715},{"id":54,"token":"I","bytes":\[73\],"logprob":-14.624603271484375},{"id":3640,"token":"This","bytes":\[84,104,105,115\],"logprob":-14.800630569458008},{"id":130977,"token":"<tool\_call>","bytes":\[60,116,111,111,108,95,99,97,108,108,62\],"logprob":-14.971238136291504},{"id":6908,"token":"Class","bytes":\[67,108,97,115,115\],"logprob":-15.373867988586426},{"id":3923,"token":"class","bytes":\[99,108,97,115,115\],"logprob":-15.442902565002441}\]}\]}}\],"created":1789950066,"model":"C:\\\\Users\\\\MyUserName\\\\llama.cpp\\\\models\\\\Spark-X2.5-4B-Q4\_K\_M.gguf","system\_fingerprint":"b11026-b49650adb","object":"chat.completion","usage":{"completion\_tokens":1,"prompt\_tokens":64,"total\_tokens":65,"prompt\_tokens\_details":{"cached\_tokens":59}},"id":"chatcmpl-x2WrCObzFNYjKVkwDmcL8FLquwfZ0NEa","timings":{"cache\_n":59,"prompt\_n":5,"prompt\_ms":634.566,"prompt\_per\_token\_ms":126.9132,"prompt\_per\_second":7.879401039450585,"predicted\_n":1,"predicted\_ms":0.001,"predicted\_per\_token\_ms":0.0,"predicted\_per\_second":0.0}}

Convert to probability:

probability = e\^(logprob)
1 = e\^(-0.00456317700445652) = \~99.5%
0 = e\^(-5.395024299621582) = \~0.5%

Speed:

On my 170gb/s bandwidth 4gb vram GPU, I got 634ms! On a H200, I would probably get 30-75ms.

Multiple Questions at Once:
In theory you can ask multiple questions at once. You just gotta be clever with the math. For example:

Q1: Is it spam?
Q2: Is it phishing?
Q3: Is it urgent?
Q4: Is it malicious?

A = 0000, B = 0001, C = 0010, D = 0011, .... O = 1110, P = 1111 where each bit corresponds to a yes no answer. Let's say LLM answers with:

A 0.2% B 0.1% C 0.2% D 0.2% E 0.5% F 0.5% G 0.5% H 1.0% I 1.0% J 1.5% K 2.0% L 3.0% M 5.0% N 10.0% O 20.0% P 54.3%

These add up to 100%. To learn possibility of "Is it spam?", just sum tokens where first bit was 1 such as:

I + J + K + L + M + N + O + P = %96.8

Repeating the same logic, you could get:

Spam: 96.8%
Phishing: 91.8%
Urgent: 81.2%
Malicious: 70.6%