README.md

# 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](https://huggingface.co), [Ollama](https://ollama.com/search) & [Mistral AI](https://docs.mistral.ai/getting-started/models).

**Explore a Suite of Models at [llmpm.co](https://llmpm.co/models) →**

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)

```sh
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

```sh
npm install -g llmpm
```

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

### 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

```sh
# 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](https://res.cloudinary.com/dehc0rbua/image/upload/v1772781378/LLMPMDemo_fuckwk.gif)

---

## 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 push <repo>`             | Upload a model to HuggingFace Hub                               |
| `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.

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

This creates a `llmpm.json`:

```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`

```sh
# 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/`.

### `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](https://res.cloudinary.com/dehc0rbua/image/upload/v1772781378/LLMPMrunprompt_vc72qd.gif)

### Text generation (GGUF & Transformers)

```sh
# 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.

```sh
# 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`.

```sh
# 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`.

```sh
# 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.

```sh
# 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`

### `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                               |

### 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](https://res.cloudinary.com/dehc0rbua/image/upload/v1772781377/LLMPMservemultimodels_m5ahlv.gif)

```sh
# 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
```

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)             |

### 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.

```sh
# 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

```sh
# 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):

```json
{
  "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:
```json
{
  "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:

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

---

## `llmpm push`

```sh
# 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:

```sh
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:**

```sh
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):**

```sh
# 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:**

```sh
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):**

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

**Combining variables:**

```sh
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:**

```dockerfile
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