llmpm

v3.1.4

Published

9 hours ago

LLM Package Manager — download, run, and share AI models from the command line

llmpm — LLM Package Manager

Command-line package manager for open-sourced large language models. Download and run 10,000+ models, and share LLMs with a single command.

llmpm is a CLI package manager for large language models, inspired by pip and npm. Your command line hub for open-source LLMs. We’ve done the heavy lifting so you can discover, install, and run models instantly.

Models are sourced from HuggingFace Hub, Ollama & Mistral AI.

Explore a Suite of Models at llmpm.co →

Supports:

Text generation (GGUF via llama.cpp and Transformer checkpoints)
Image generation (Diffusion models)
Vision models
Speech-to-text (ASR)
Text-to-speech (TTS)

Installation

via pip (recommended)

pip install llmpm

The pip install is intentionally lightweight — it only installs the CLI tools needed to bootstrap. On first run, llmpm automatically creates an isolated environment at ~/.llmpm/venv and installs all ML backends into it, keeping your system Python untouched.

via npm

npm install -g llmpm

The npm package finds Python on your PATH, creates ~/.llmpm/venv, and installs all backends into it during postinstall.

via Homebrew

brew tap llmpm/llmpm
brew install llmpm

Environment isolation

All llmpm commands always run inside ~/.llmpm/venv. Set LLPM_NO_VENV=1 to bypass this (useful in CI or Docker where isolation is already provided).

Quick start

# Install a model
llmpm install meta-llama/Llama-3.2-3B-Instruct

# Run it
llmpm run meta-llama/Llama-3.2-3B-Instruct
llmpm serve meta-llama/Llama-3.2-3B-Instruct

llmpm demo

Commands

| Command | Description | | ------------------------------- | --------------------------------------------------------------- | | llmpm init | Initialise a llmpm.json in the current directory | | llmpm install | Install all models listed in llmpm.json | | llmpm install <repo> | Download and install a model from HuggingFace, Ollama & Mistral | | llmpm run <repo> | Run an installed model (interactive chat) | | llmpm serve [repo] [repo] ... | Serve one or more models as an OpenAI-compatible API | | llmpm serve | Serve every installed model on a single HTTP server | | llmpm benchmark <repo> | Run evaluation benchmarks against an installed model | | llmpm push <repo> | Upload a model to HuggingFace Hub | | llmpm search <query> | Search HuggingFace Hub for models | | llmpm trending | Show top trending models by likes (text-gen & text-to-image) | | llmpm list | Show all installed models | | llmpm info <repo> | Show details about a model | | llmpm uninstall <repo> | Uninstall a model | | llmpm clean | Remove the managed environment (~/.llmpm/venv) | | llmpm clean --all | Remove environment + all downloaded models and registry |

Local vs global mode

llmpm works in two modes depending on whether a llmpm.json file is present.

Global mode (default)

All models are stored in ~/.llmpm/models/ and the registry lives at ~/.llmpm/registry.json. This is the default when no llmpm.json is found.

Local mode

When a llmpm.json exists in the current directory (or any parent), llmpm switches to local mode: models are stored in .llmpm/models/ next to the manifest file. This keeps project models isolated from your global environment.

my-project/
├── llmpm.json        ← manifest
└── .llmpm/           ← local model store (auto-created)
    ├── registry.json
    └── models/

All commands (install, run, serve, list, info, uninstall) automatically detect the mode and operate on the correct store — no flags required.

`llmpm init`

Initialise a new project manifest in the current directory.

llmpm init              # interactive prompts for name & description
llmpm init --yes        # skip prompts, use directory name as package name

This creates a llmpm.json:

{
  "name": "my-project",
  "description": "",
  "dependencies": {}
}

Models are listed under dependencies without version pins — llmpm models don't use semver. The value is always "*".

`llmpm install`

# Install a Transformer model
llmpm install meta-llama/Llama-3.2-3B-Instruct

# Install a GGUF model (interactive quantisation picker)
llmpm install unsloth/Llama-3.2-3B-Instruct-GGUF

# Install a specific GGUF quantisation
llmpm install unsloth/Llama-3.2-3B-Instruct-GGUF --quant Q4_K_M

# Install a single specific file
llmpm install unsloth/Llama-3.2-3B-Instruct-GGUF --file Llama-3.2-3B-Instruct-Q4_K_M.gguf

# Skip prompts (pick best default)
llmpm install meta-llama/Llama-3.2-3B-Instruct --no-interactive

# Install and record in llmpm.json (local projects)
llmpm install meta-llama/Llama-3.2-3B-Instruct --save

# Install all models listed in llmpm.json (like npm install)
llmpm install

In global mode models are stored in ~/.llmpm/models/. In local mode (when llmpm.json is present) they go into .llmpm/models/.

Gated models

Some models (e.g. google/gemma-2-2b-it, meta-llama/Llama-3.2-3B-Instruct) require you to accept a licence on HuggingFace before downloading. If you try to install one without a token you will see:

  error  Download failed: access to google/gemma-2-2b-it is restricted.

  This is a gated model — you need to:
    1. Accept the licence at https://huggingface.co/google/gemma-2-2b-it
    2. Re-run with your HF token:

         HF_TOKEN=<your_token> llmpm install google/gemma-2-2b-it

Get a token at https://huggingface.co/settings/tokens, accept the model licence on its HuggingFace page, then:

# Inline
HF_TOKEN=hf_your_token llmpm install google/gemma-2-2b-it

# Or export for the session
export HF_TOKEN=hf_your_token
llmpm install google/gemma-2-2b-it

`llmpm install` options

| Option | Description | | ------------------ | -------------------------------------------------------------- | | --quant / -q | GGUF quantisation to download (e.g. Q4_K_M) | | --file / -f | Download a specific file from the repo | | --no-interactive | Never prompt; pick the best default quantisation automatically | | --save | Add the model to llmpm.json dependencies after installing |

`llmpm run`

llmpm run auto-detects the model type and launches the appropriate interactive session. It supports text generation, image generation, vision, speech-to-text (ASR), and text-to-speech (TTS) models.

llmpm run

Text generation (GGUF & Transformers)

# Interactive chat
llmpm run meta-llama/Llama-3.2-3B-Instruct

# Single-turn inference
llmpm run meta-llama/Llama-3.2-3B-Instruct --prompt "Explain quantum computing"

# With a system prompt
llmpm run meta-llama/Llama-3.2-3B-Instruct --system "You are a helpful pirate."

# Limit response length
llmpm run meta-llama/Llama-3.2-3B-Instruct --max-tokens 512

# GGUF model — tune context window and GPU layers
llmpm run unsloth/Llama-3.2-3B-Instruct-GGUF --ctx 8192 --gpu-layers 32

Image generation (Diffusion)

Generates an image from a text prompt and saves it as a PNG on your Desktop.

# Single prompt → saves llmpm_<timestamp>.png to ~/Desktop
llmpm run amused/amused-256 --prompt "a cyberpunk city at sunset"

# Interactive session (type a prompt, get an image each time)
llmpm run amused/amused-256

In interactive mode type your prompt and press Enter. The output path is printed after each generation. Type /exit to quit.

Requires: pip install diffusers torch accelerate

Vision (image-to-text)

Describe or answer questions about an image. Pass the image file path via --prompt.

# Single image description
llmpm run Salesforce/blip-image-captioning-base --prompt /path/to/photo.jpg

# Interactive session: type an image path at each prompt
llmpm run Salesforce/blip-image-captioning-base

Requires: pip install transformers torch Pillow

Speech-to-text / ASR

Transcribe an audio file. Pass the audio file path via --prompt.

# Transcribe a single file
llmpm run openai/whisper-base --prompt recording.wav

# Interactive: enter an audio file path at each prompt
llmpm run openai/whisper-base

Supported formats depend on your installed audio libraries (wav, flac, mp3, …).

Requires: pip install transformers torch

Text-to-speech / TTS

Convert text to speech. The output WAV file is saved to your Desktop.

# Single utterance → saves llmpm_<timestamp>.wav to ~/Desktop
llmpm run suno/bark-small --prompt "Hello, how are you today?"

# Interactive session
llmpm run suno/bark-small

Requires: pip install transformers torch

Running a model from a local path

Use --path to run a model that was not installed via llmpm install — for example, a model you downloaded manually or trained yourself.

# Run a GGUF file directly
llmpm run --path ~/Downloads/mistral-7b-q4.gguf

# Run a HuggingFace-style model directory
llmpm run --path ~/models/whisper-base --prompt recording.wav

# Optional: give the model a display label
llmpm run my-llama --path /data/models/llama-3

--path accepts either a .gguf file or a directory. The model type is auto-detected (GGUF if the path contains .gguf files, otherwise the transformers/diffusion/audio backend is chosen from config.json).

`llmpm run` options

| Option | Default | Description | | ----------------- | -------- | ------------------------------------------------------- | | --prompt / -p | — | Single-turn prompt or input file path (non-interactive) | | --system / -s | — | System prompt (text generation only) | | --max-tokens | 128000 | Maximum tokens to generate per response | | --ctx | 128000 | Context window size (GGUF only) | | --gpu-layers | -1 | GPU layers to offload, -1 = all (GGUF only) | | --verbose | off | Show model loading output | | --path | — | Path to a local model dir or .gguf file (bypasses registry) |

Interactive session commands

These commands work in any interactive session:

| Command | Action | | ---------------- | ------------------------------------------ | | /exit | End the session | | /clear | Clear conversation history (text gen only) | | /system <text> | Update the system prompt (text gen only) |

Model type detection

llmpm run reads config.json / model_index.json from the installed model to determine the pipeline type before loading any weights. The detected type is printed at startup:

  Detected: Image Generation (Diffusion)
  Loading model…  ✓

If detection is ambiguous the model falls back to the text-generation backend.

`llmpm serve`

Start a single local HTTP server exposing one or more models as an OpenAI-compatible REST API. A browser-based chat UI is available at /chat.

llmpm serve

# Serve a single model on the default port (8080)
llmpm serve meta-llama/Llama-3.2-3B-Instruct

# Serve multiple models on one server
llmpm serve meta-llama/Llama-3.2-3B-Instruct amused/amused-256

# Serve ALL installed models automatically
llmpm serve

# Custom port and host
llmpm serve meta-llama/Llama-3.2-3B-Instruct --port 9000 --host 0.0.0.0

# Set the default max tokens (clients may override per-request)
llmpm serve meta-llama/Llama-3.2-3B-Instruct --max-tokens 2048

# GGUF model — tune context window and GPU layers
llmpm serve unsloth/Llama-3.2-3B-Instruct-GGUF --ctx 8192 --gpu-layers 32

# Serve a model from a local path (bypasses registry)
llmpm serve --path ~/models/mistral-7b-q4.gguf
llmpm serve --path ~/models/llama-3

# Mix registry models and local paths
llmpm serve meta-llama/Llama-3.2-3B-Instruct --path ~/models/custom-model

# Serve multiple local-path models
llmpm serve --path ~/models/llama --path ~/models/whisper

Fuzzy model-name matching is applied to each argument — if multiple installed models match you will be prompted to pick one.

`llmpm serve` options

| Option | Default | Description | | --------------- | ----------- | --------------------------------------------------------- | | --port / -p | 8080 | Port to listen on (auto-increments if busy) | | --host / -H | localhost | Host/address to bind to | | --max-tokens | 128000 | Default max tokens per response (overridable per-request) | | --ctx | 128000 | Context window size (GGUF only) | | --gpu-layers | -1 | GPU layers to offload, -1 = all (GGUF only) | | --path | — | Path to a local model dir or .gguf file (repeatable, bypasses registry) |

Multi-model routing

When multiple models are loaded, POST endpoints accept an optional "model" field in the JSON body. If omitted, the first loaded model is used.

# Target a specific model when multiple are loaded
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Llama-3.2-3B-Instruct",
       "messages": [{"role": "user", "content": "Hello!"}]}'

The chat UI at /chat shows a model dropdown when more than one model is loaded. Switching models resets the conversation and adapts the UI to the new model's category.

Endpoints

| Method | Path | Description | | ------ | -------------------------- | -------------------------------------------------------------------- | | GET | /chat | Browser chat / image-gen UI (model dropdown for multi-model serving) | | GET | /health | {"status":"ok","models":["id1","id2",…]} | | GET | /v1/models | List all loaded models with id, category, created | | GET | /v1/models/<id> | Info for a specific loaded model | | POST | /v1/chat/completions | OpenAI-compatible chat inference (SSE streaming supported) | | POST | /v1/completions | Legacy text completion | | POST | /v1/embeddings | Text embeddings | | POST | /v1/images/generations | Text-to-image; pass "image" (base64) for image-to-image | | POST | /v1/audio/transcriptions | Speech-to-text | | POST | /v1/audio/speech | Text-to-speech |

All POST endpoints accept "model": "<id>" to target a specific loaded model.

Example API calls

# Text generation (streaming)
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Hello!"}],
       "max_tokens": 256, "stream": true}'

# Target a specific model when multiple are loaded
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Llama-3.2-1B-Instruct",
       "messages": [{"role": "user", "content": "Hello!"}]}'

# List all loaded models
curl http://localhost:8080/v1/models

# Text-to-image
curl -X POST http://localhost:8080/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{"prompt": "a cat in a forest", "n": 1}'

# Image-to-image (include the source image as base64 in the same endpoint)
IMAGE_B64=$(base64 -i input.png)
curl -X POST http://localhost:8080/v1/images/generations \
  -H "Content-Type: application/json" \
  -d "{\"prompt\": \"turn it into a painting\", \"image\": \"$IMAGE_B64\"}"

# Speech-to-text
curl -X POST http://localhost:8080/v1/audio/transcriptions \
  -H "Content-Type: application/octet-stream" \
  --data-binary @recording.wav

# Text-to-speech
curl -X POST http://localhost:8080/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{"input": "Hello world"}' \
  --output speech.wav

Response shape for chat completions (non-streaming):

{
  "object": "chat.completion",
  "model": "<model-id>",
  "choices": [
    {
      "index": 0,
      "message": { "role": "assistant", "content": "<text>" },
      "finish_reason": "stop"
    }
  ],
  "usage": { "prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0 }
}

Response shape for chat completions (streaming SSE):

Each chunk:

{
  "object": "chat.completion.chunk",
  "model": "<model-id>",
  "choices": [
    {
      "index": 0,
      "delta": { "content": "<token>" },
      "finish_reason": null
    }
  ]
}

Followed by a final data: [DONE] sentinel.

Response shape for image generation:

{
  "created": 1234567890,
  "data": [{ "b64_json": "<base64-png>" }]
}

`llmpm search`

Search HuggingFace Hub for models matching a keyword or phrase.

llmpm search <query>

Results are displayed in a table with model ID, task, download count, like count, and tags.

# Basic search
llmpm search llama

# Filter by pipeline task
llmpm search mistral --task text-generation

# Filter by library
llmpm search whisper --library transformers

# Sort results (downloads, likes, lastModified, trending)
llmpm search stable-diffusion --sort likes

# Limit number of results (default: 20)
llmpm search gemma --limit 10

# View detailed info for a result interactively
llmpm search llama --info

`llmpm search` options

| Option | Default | Description | | ------------------ | ----------- | ------------------------------------------------------------------------------------------------- | | --task / -t | — | Filter by pipeline task (e.g. text-generation, text-to-image, automatic-speech-recognition) | | --library / -l | — | Filter by library (e.g. transformers, gguf, diffusers) | | --sort / -s | downloads | Sort by downloads, likes, lastModified, or trending | | --limit / -n | 20 | Maximum number of results to display | | --info | off | Immediately prompt to view detailed info for a result |

After results are shown you will be asked if you want to view details for a specific model. Selecting one fetches the full model card from HuggingFace and shows author, task, license, languages, file list, tags, and a link to llmpm.co.

`llmpm trending`

Show the top trending models by likes & downloads, grouped by category.

llmpm trending

Displays two sections — Text Generation and Text to Image — each listing the top 5 models with download counts, like counts, and a link to the model page on llmpm.co.

Trending Models

`llmpm benchmark`

Run standard evaluation benchmarks against an installed mode.

Installation

The benchmark backend is an optional dependency — install it separately to keep the base llmpm footprint small:

pip install llmpm[benchmark]

Usage

# Run a single benchmark
llmpm benchmark meta-llama/Llama-3.2-3B-Instruct --tasks ifeval

# Run multiple benchmarks in one pass
llmpm benchmark meta-llama/Llama-3.2-3B-Instruct --tasks ifeval,hellaswag,mmlu

# Run the full Open LLM Leaderboard v1 suite
llmpm benchmark meta-llama/Llama-3.2-3B-Instruct --tasks openllm

# Run the full Open LLM Leaderboard v2 (open datasets, no gated data)
llmpm benchmark meta-llama/Llama-3.2-3B-Instruct --tasks leaderboard

# Benchmark GPT-2 on HellaSwag (no install needed — pulled directly from HuggingFace)
llmpm benchmark pretrained=gpt2 --tasks hellaswag

# With 10-shot prompting
llmpm benchmark pretrained=gpt2 --tasks hellaswag --num-fewshot 10

# Limit to 200 examples for a quick test
llmpm benchmark pretrained=gpt2 --tasks hellaswag --limit 200

# Save a full HTML report to ./results/
llmpm benchmark pretrained=gpt2 --tasks hellaswag --output ./results/

# Limit examples for a quick smoke test
llmpm benchmark meta-llama/Llama-3.2-3B-Instruct --tasks mmlu --limit 100

# Save results + HTML report to a directory
llmpm benchmark meta-llama/Llama-3.2-3B-Instruct --tasks ifeval --output ./results/
# → writes ./results/report.html with a full breakdown of metrics and run config

# Run Humanity's Last Exam (bundled open dataset)
llmpm benchmark meta-llama/Llama-3.2-3B-Instruct --tasks hle

# List all supported benchmarks
llmpm benchmark --list-tasks

`llmpm benchmark` options

| Option | Default | Description | | ---------------------- | ------------ | ------------------------------------------------------------- | | --tasks / -t | required | Comma-separated task or group names (e.g. ifeval,hellaswag) | | --num-fewshot / -n | task default | Override few-shot count for all tasks | | --limit / -l | — | Limit examples per task (integer = count, <1.0 = fraction) | | --batch-size / -b | auto | Inference batch size | | --device | auto | PyTorch device override (e.g. cpu, cuda:0) | | --output / -o | — | Directory to write results; generates report.html inside it | | --list-tasks | — | Print all supported benchmark tasks and exit |

Supported benchmarks

| Category | Tasks | | ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Leaderboard suites | openllm (ARC · HellaSwag · TruthfulQA · MMLU · Winogrande · GSM8K), leaderboard (IFEval · BBH · MATH Hard · GPQA · MuSR · MMLU-Pro — all open datasets), tinyBenchmarks, metabench | | Commonsense | hellaswag, winogrande, wsc273, piqa, siqa, openbookqa, commonsense_qa, logiqa, logiqa2, babi, swag | | Knowledge | mmlu, mmlu_pro, truthfulqa, triviaqa, nq_open, webqs, sciq, agieval, ceval, cmmlu, kmmlu | | Reading comprehension | race, squadv2, drop, coqa, super_glue, glue | | Math & reasoning | arc_easy, arc_challenge, gsm8k, gsm_plus, hendrycks_math, minerva_math, mathqa, arithmetic, asdiv, bbh, bigbench, anli | | Code | humaneval, mbpp | | Instruction following | ifeval | | Long context | longbench | | Language modeling | wikitext, lambada | | Medical & science | medqa, medmcqa, pubmedqa | | Safety & bias | wmdp, bbq, crows_pairs, hendrycks_ethics, realtoxicityprompts | | Multilingual | xnli, xcopa, xwinograd, belebele, mgsm, global_mmlu, okapi | | Summarization | cnn_dailymail | | Bundled (custom) | gpqa, hle |

Report: After every successful run, llmpm benchmark writes a report.html to the --output directory (or the current directory if omitted). The report includes a results table with per-metric scores and ± stderr, plus the full run configuration.

Run llmpm benchmark --list-tasks for the full list with descriptions.

`llmpm push`

# Push an already-installed model
llmpm push my-org/my-fine-tune

# Push a local directory
llmpm push my-org/my-fine-tune --path ./my-model-dir

# Push as private repository
llmpm push my-org/my-fine-tune --private

# Custom commit message
llmpm push my-org/my-fine-tune -m "Add Q4_K_M quantisation"

Requires a HuggingFace token (run huggingface-cli login or set HF_TOKEN).

Backends

All backends (torch, transformers, diffusers, llama-cpp-python, …) are included in pip install llmpm by default and are installed into the managed ~/.llmpm/venv.

| Model type | Pipeline | Backend | | ----------------------- | ---------------- | ------------------------------ | | .gguf files | Text generation | llama.cpp via llama-cpp-python | | .safetensors / .bin | Text generation | HuggingFace Transformers | | Diffusion models | Image generation | HuggingFace Diffusers | | Vision models | Image-to-text | HuggingFace Transformers | | Whisper / ASR models | Speech-to-text | HuggingFace Transformers | | TTS models | Text-to-speech | HuggingFace Transformers |

Selective backend install

If you only need one backend (e.g. on a headless server), install without defaults and add just what you need:

pip install llmpm --no-deps              # CLI only (no ML backends)
pip install llmpm[gguf]                  # + GGUF / llama.cpp
pip install llmpm[transformers]          # + text generation
pip install llmpm[diffusion]             # + image generation
pip install llmpm[vision]                # + vision / image-to-text
pip install llmpm[audio]                 # + ASR + TTS

Configuration

| Variable | Default | Description | | -------------- | ---------- | ------------------------------------------------------------ | | LLMPM_HOME | ~/.llmpm | Root directory for models and registry | | HF_TOKEN | — | HuggingFace API token for gated models | | LLPM_PYTHON | python3 | Python binary used by the npm shim (fallback only) | | LLPM_NO_VENV | — | Set to 1 to skip venv isolation (CI / Docker / containers) |

Configuration examples

Use a HuggingFace token for gated models:

HF_TOKEN=hf_your_token llmpm install meta-llama/Llama-3.2-3B-Instruct
# or export for the session
export HF_TOKEN=hf_your_token
llmpm install meta-llama/Llama-3.2-3B-Instruct

Skip venv isolation (CI / Docker):

# Inline — single command
LLPM_NO_VENV=1 llmpm serve meta-llama/Llama-3.2-3B-Instruct

# Exported — all subsequent commands skip the venv
export LLPM_NO_VENV=1
llmpm install meta-llama/Llama-3.2-3B-Instruct
llmpm serve meta-llama/Llama-3.2-3B-Instruct

When using LLPM_NO_VENV=1, install all backends first: pip install llmpm[all]

Custom model storage location:

LLMPM_HOME=/mnt/models llmpm install meta-llama/Llama-3.2-3B-Instruct
LLMPM_HOME=/mnt/models llmpm serve meta-llama/Llama-3.2-3B-Instruct

Use a specific Python binary (npm installs):

LLPM_PYTHON=/usr/bin/python3.11 llmpm run meta-llama/Llama-3.2-3B-Instruct

Combining variables:

HF_TOKEN=hf_your_token LLMPM_HOME=/data/models LLPM_NO_VENV=1 \
  llmpm install meta-llama/Llama-3.2-3B-Instruct

Docker / CI example:

ENV LLPM_NO_VENV=1
ENV HF_TOKEN=hf_your_token
RUN pip install llmpm[all]
RUN llmpm install meta-llama/Llama-3.2-3B-Instruct
CMD ["llmpm", "serve", "meta-llama/Llama-3.2-3B-Instruct", "--host", "0.0.0.0"]

License

MIT

Published

Vulnerabilities

Links

Maintainers

Keywords

Readme

llmpm — LLM Package Manager

Installation

via pip (recommended)

via npm

via Homebrew

Environment isolation

Quick start

Commands

Local vs global mode

Global mode (default)

Local mode

llmpm init

llmpm install

Gated models

llmpm install options

llmpm run

Text generation (GGUF & Transformers)

Image generation (Diffusion)

Vision (image-to-text)

Speech-to-text / ASR

Text-to-speech / TTS

Running a model from a local path

llmpm run options

Interactive session commands

Model type detection

llmpm serve

llmpm serve options

Multi-model routing

Endpoints

Example API calls

llmpm search

llmpm search options

llmpm trending

llmpm benchmark

Installation

Usage

llmpm benchmark options

Supported benchmarks

llmpm push

Backends

Selective backend install

Configuration

Configuration examples

License

`llmpm init`

`llmpm install`

`llmpm install` options

`llmpm run`

`llmpm run` options

`llmpm serve`

`llmpm serve` options

`llmpm search`

`llmpm search` options

`llmpm trending`

`llmpm benchmark`

`llmpm benchmark` options

`llmpm push`