ik_llama.cpp/examples/spec-bench

ik_llama.cpp/examples/spec-bench

llama-spec-bench is a direct C++ speculative benchmark for prompt-driven tasks. It reuses the normal llama-common startup and speculative lifecycle instead of benchmarking through llama-server.

Scope

• built-in canonical tasks: code, extract, story
• all three canonical built-in workloads by default, or one plain custom prompt via -p / -f
• optional strict JSONL prompt-file override for structured multi-prompt workloads
• baseline and speculative runs use the same binary and normal model/sampler args
• Markdown report by default; compact JSONL is available with --output-format jsonl
• per-stage drafted and accepted counts by speculative position

Benchmark-specific flags

• --prompts <path>: replace the built-in tasks with a strict JSONL prompt file
• -p, --prompt <text>: run one inline custom prompt
• -f, --file <path>: run one plain-text custom prompt file; the file is one prompt, not one task per line
• --task <name[,name...]>: select built-in tasks
• --repeat <n>: repeat each task n times
• --retry <n>: retry transient task failures up to n times
• --output-format jsonl: select the common JSONL output convention; output is written to stdout
• --output-details: print prompts and responses first, followed by normal and detailed Markdown metrics; JSONL includes complete details
• --predict <n> / -n <n>: command-level generation budget for every task without a row override

Input modes

Choose exactly one mode: built-ins (optionally narrowed with --task), one -p prompt, one -f file, or one --prompts JSONL dataset. Examples:

./build/bin/llama-spec-bench -m model.gguf -n 4 -p "Write a merge sort in C++."
./build/bin/llama-spec-bench -m model.gguf -n 4 -f examples/spec-bench/prompts/code.txt

Dataset format

Each JSONL line must be an object containing a non-empty prompt string. Optional fields are id, name, category, and positive integer max_tokens:

{"id":"task-1","name":"math","category":"reasoning","prompt":"Solve 12*17.","max_tokens":64}

IDs default to the one-based input line number and must be unique. Unknown fields, duplicate IDs, empty prompts, malformed JSON, and invalid max_tokens values are rejected. The file replaces the built-in task set for that invocation.

The canonical prompts are embedded into the executable at configure time from prompts/code.txt, prompts/extract.txt, and prompts/story.txt; no source-tree or network access is needed at runtime.

Compact JSONL includes raw drafted_by_position and accepted_by_position arrays for every stage. Detailed JSONL additionally includes the derived acceptance_rate_by_position and conditional_acceptance_rate arrays. Array element zero is speculative position one; the first conditional rate is null because it has no preceding position.

Acceptance length is defined consistently as 1 + accepted_tokens / num_drafts in detailed JSON, compact JSON, Markdown, and repeat summaries.

Repeated attempts are executed in one process. Stateful drafting stages, including adaptive n-gram stages and lookup caches, may therefore carry learned state from an earlier task or repeat; use --repeat 1 and separate invocations when independent samples are required. Pin the chat-template mode (--jinja or --no-jinja) when comparing runs because it changes the effective prompt. Speculative verification can also diverge from a baseline after a near-tie because batched evaluation changes floating-point reduction order, so this tool is a performance and acceptance benchmark, not a bit-identical output checker.

Example

./build/bin/llama-spec-bench \
  -m model.gguf \
  --seed 123 \
  --temp 0 \
  --predict 256 \
  --output-format jsonl \
  --task code,extract,story > results.jsonl