add --no-mmproj to disable vision support if not needed (uses less memory)

Used MTP test script from the PR itself, so just 192 tokens/request.

Will test with longer prompts and larger context, thank you for the idea. By the way, combination of draft model and ngram is working and even for 192 tokens, check second and third run tps:

$ bash mtp-bench.sh 
--------------- spec-draft-ngram-q3.5-0.8b-q4-0-16-default: starting 3 runs -------------------
Running: ./bin/llama-server -np 1 --model ./models/Qwen3.6-27B-Q4_K_M.gguf --ctx-size 16384 --cache-type-k q8_0 --cache-type-v q8_0 --spec-type ngram-mod,draft-simple --spec-draft-model ./models/Qwen_Qwen3.5-0.8B-Q4_K_M.gguf --spec-draft-n-min 0 --spec-draft-n-max 16
nohup: appending output to 'nohup.out'
--------------- spec-draft-ngram-q3.5-0.8b-q4-0-16-default: run 1/3 -------------------
  code_python        pred= 192 draft= 163 acc= 128 rate=0.785 tok/s=37.8
  code_cpp           pred= 192 draft= 160 acc= 124 rate=0.775 tok/s=38.0
  explain_concept    pred= 192 draft= 135 acc=  92 rate=0.681 tok/s=31.7
  summarize          pred= 192 draft= 218 acc= 140 rate=0.642 tok/s=34.3
  qa_factual         pred= 192 draft= 212 acc= 109 rate=0.514 tok/s=34.4
  translation        pred= 192 draft= 195 acc= 118 rate=0.605 tok/s=37.9
  creative_short     pred= 192 draft= 123 acc= 107 rate=0.870 tok/s=39.0
  stepwise_math      pred= 192 draft= 156 acc= 130 rate=0.833 tok/s=39.8
  long_code_review   pred= 192 draft= 134 acc=  96 rate=0.716 tok/s=33.9

Aggregate: {
  "n_requests": 9,
  "total_predicted": 1728,
  "total_draft": 1496,
  "total_draft_accepted": 1044,
  "aggregate_accept_rate": 0.6979,
  "wall_s_total": 51.22
}
--------------- spec-draft-ngram-q3.5-0.8b-q4-0-16-default: run 2/3 -------------------
  code_python        pred= 192 draft= 171 acc= 150 rate=0.877 tok/s=73.7
  code_cpp           pred= 192 draft= 285 acc= 144 rate=0.505 tok/s=51.1
  explain_concept    pred= 192 draft= 135 acc=  92 rate=0.681 tok/s=32.5
  summarize          pred= 192 draft= 218 acc= 140 rate=0.642 tok/s=34.5
  qa_factual         pred= 192 draft= 212 acc= 109 rate=0.514 tok/s=34.7
  translation        pred= 192 draft= 195 acc= 118 rate=0.605 tok/s=38.0
  creative_short     pred= 192 draft= 123 acc= 107 rate=0.870 tok/s=40.5
  stepwise_math      pred= 192 draft= 156 acc= 130 rate=0.833 tok/s=40.4
  long_code_review   pred= 192 draft= 134 acc=  96 rate=0.716 tok/s=34.1

Aggregate: {
  "n_requests": 9,
  "total_predicted": 1728,
  "total_draft": 1629,
  "total_draft_accepted": 1086,
  "aggregate_accept_rate": 0.6667,
  "wall_s_total": 45.89
}
--------------- spec-draft-ngram-q3.5-0.8b-q4-0-16-default: run 3/3 -------------------
  code_python        pred= 192 draft= 163 acc= 128 rate=0.785 tok/s=37.9
  code_cpp           pred= 192 draft= 165 acc= 146 rate=0.885 tok/s=64.6
  explain_concept    pred= 192 draft= 204 acc= 109 rate=0.534 tok/s=38.6
  summarize          pred= 192 draft= 166 acc= 165 rate=0.994 tok/s=105.6
  qa_factual         pred= 192 draft= 191 acc= 129 rate=0.675 tok/s=42.3
  translation        pred= 192 draft= 220 acc= 175 rate=0.795 tok/s=115.6
  creative_short     pred= 192 draft= 276 acc= 162 rate=0.587 tok/s=53.3
  stepwise_math      pred= 192 draft= 172 acc= 140 rate=0.814 tok/s=49.0
  long_code_review   pred= 192 draft= 222 acc= 148 rate=0.667 tok/s=58.1

Aggregate: {
  "n_requests": 9,
  "total_predicted": 1728,
  "total_draft": 1779,
  "total_draft_accepted": 1302,
  "aggregate_accept_rate": 0.7319,
  "wall_s_total": 34.4
}