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🍎 feat: Apple MLX as Known Endpoint (#2580)
* add integration with Apple MLX * fix: apple icon + image mkd link --------- Co-authored-by: “Extremys” <“Extremys@email.com”> Co-authored-by: Danny Avila <danny@librechat.ai>
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@ -271,6 +271,40 @@ Some of the endpoints are marked as **Known,** which means they might have speci
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## Apple MLX
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> MLX API key: ignored - [MLX OpenAI Compatibility](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/SERVER.md)
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**Notes:**
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- **Known:** icon provided.
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- API is mostly strict with unrecognized parameters.
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- Support only one model at a time, otherwise you'll need to run a different endpoint with a different `baseURL`.
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```yaml
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- name: "MLX"
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apiKey: "mlx"
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baseURL: "http://localhost:8080/v1/"
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models:
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default: [
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"Meta-Llama-3-8B-Instruct-4bit"
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]
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fetch: false # fetching list of models is not supported
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titleConvo: true
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titleModel: "current_model"
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summarize: false
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summaryModel: "current_model"
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forcePrompt: false
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modelDisplayLabel: "Apple MLX"
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addParams:
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max_tokens: 2000
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"stop": [
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"<|eot_id|>"
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]
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```
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## Ollama
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> Ollama API key: Required but ignored - [Ollama OpenAI Compatibility](https://github.com/ollama/ollama/blob/main/docs/openai.md)
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@ -1213,6 +1213,7 @@ Each endpoint in the `custom` array should have the following structure:
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- "Perplexity"
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- "together.ai"
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- "Ollama"
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- "MLX"
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### **models**
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30
docs/install/configuration/mlx.md
Normal file
30
docs/install/configuration/mlx.md
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@ -0,0 +1,30 @@
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---
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title: Apple MLX
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description: Using LibreChat with Apple MLX
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weight: -6
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---
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## MLX
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Use [MLX](https://ml-explore.github.io/mlx/build/html/index.html) for
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* Running large language models on local Apple Silicon hardware (M1, M2, M3) ARM with unified CPU/GPU memory)
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### 1. Install MLX on MacOS
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#### Mac MX series only
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MLX supports GPU acceleration on Apple Metal backend via `mlx-lm` Python package. Follow Instructions at [Install `mlx-lm` package](https://github.com/ml-explore/mlx-examples/tree/main/llms)
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### 2. Load Models with MLX
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MLX supports common HuggingFace models directly, but it's recommended to use converted and tested quantized models (depending on your hardware capability) provided by the [mlx-community](https://huggingface.co/mlx-community).
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Follow Instructions at [Install `mlx-lm` package](https://github.com/ml-explore/mlx-examples/tree/main/llms)
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1. Browse the available models [HuggingFace](https://huggingface.co/models?search=mlx-community)
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2. Copy the text from the model page `<author>/<model_id>` (ex: `mlx-community/Meta-Llama-3-8B-Instruct-4bit`)
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3. Check model size. Models that can run in CPU/GPU unified memory perform the best.
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4. Follow the instructions to launch the model server [Run OpenAI Compatible Server Locally](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/SERVER.md)
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```mlx_lm.server --model <author>/<model_id>```
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### 3. Configure LibreChat
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Use `librechat.yaml` [Configuration file (guide here)](./ai_endpoints.md) to add MLX as a separate endpoint, an example with Llama-3 is provided.
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@ -24,6 +24,7 @@ weight: 1
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* 🤖 [AI Setup](./configuration/ai_setup.md)
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* 🚅 [LiteLLM](./configuration/litellm.md)
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* 🦙 [Ollama](./configuration/ollama.md)
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* 🍎 [Apple MLX](./configuration/mlx.md)
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* 💸 [Free AI APIs](./configuration/free_ai_apis.md)
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* 🛂 [Authentication System](./configuration/user_auth_system.md)
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* 🍃 [Online MongoDB](./configuration/mongodb.md)
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