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Explore the answer-space for any prompt and any MLX-supported model.

Project description

llmwalk

Explore the answer-space for any prompt and any MLX-supported model. See https://huggingface.co/mlx-community/models for supported models.

Usage example gif

Instead of sampling from the possible tokens each step, llmwalk branches out and completes all of the branches the sampler would consider based on --top-k, --top-p and --temperature, ranking the results by probability as it goes.

The tree is walked prioritising the most likely branches, until it finds -n branches and then it stops. It doesn't enumerate all possibilities, just enough to know for sure it has found the -n most likely branches.

Usage

  • uvx llmwalk -p "In what year was Barack Obama born?"
  • uvx llmwalk -p "Write a haiku about compilers" -n 5
  • uvx llmwalk -p "Give me one word: " --top-k 200 --temperature 0.7

Options

  • -p, --prompt TEXT: Prompt to score (wrapped with the model’s chat template).
  • -m, --model MODEL: MLX-LM model identifier or path (default: mlx-community/Llama-3.2-1B-Instruct-4bit), supported models can be found at https://huggingface.co/mlx-community/models
  • -n N: Number of answers to show. The search stops once it has N finished answers and no unfinished branch can beat the worst of those N.
  • --min-probability FLOAT: Any branch whose cumulative probability falls below this is marked finished (low_probability) and not expanded further.
  • --top-k INT: At each step, expand at most k next tokens (highest probability).
  • --top-p FLOAT: Nucleus cutoff applied within the top-k tokens at each step (keep adding tokens until cumulative probability ≥ p).
  • --temperature FLOAT: Softmax temperature applied when computing per-step probabilities (1.0 is the model distribution; must be > 0).
  • --stats-interval SECONDS: How often to refresh the live view (<= 0 disables periodic refresh; still renders at start/end).

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