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📖 docs: Update litellm.md to add Ollama (#1616)
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---
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title: 🚅 LiteLLM
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title: 🚅 LiteLLM and Ollama
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description: Using LibreChat with LiteLLM Proxy
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weight: -7
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---
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@ -102,3 +102,57 @@ Key components and features include:
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- **Proxy CLI Arguments**: A wide range of command-line arguments for customization.
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Overall, LiteLLM Server offers a comprehensive suite of tools for managing, deploying, and interacting with a variety of LLMs, making it a versatile choice for large-scale AI applications.
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## Ollama
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Use [Ollama](https://ollama.ai/) for
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* Run large language models on local hardware
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* Host multiple models
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* Dynamically load the model upon request
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### docker-compose.yaml with GPU
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```yaml
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version: "3.8"
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services:
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litellm:
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image: ghcr.io/berriai/litellm:main-v1.18.8
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volumes:
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- ./litellm/litellm-config.yaml:/app/config.yaml
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command: [ "--config", "/app/config.yaml", "--port", "8000", "--num_workers", "8" ]
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ollama:
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image: ollama/ollama
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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capabilities: [compute, utility]
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ports:
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- "11434:11434"
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volumes:
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- ./ollama:/root/.ollama
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```
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### Loading Models in Ollama
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1. Browse the available models at [Ollama Library](https://ollama.ai/library)
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2. Run ```docker exec -it ollama /bin/bash```
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3. Copy the text from the Tags tab from the library website. It should begin with 'ollama run'
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4. Check model size. Models that can run in GPU memory perform the best.
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5. Use /bye to exit the terminal
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### Litellm Ollama Configuration
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Add the below lines to the config to access the Ollama models
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```yaml
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- model_name: mixtral
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litellm_params:
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model: ollama/mixtral:8x7b-instruct-v0.1-q5_K_M
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api_base: http://ollama:11434
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stream: True
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- model_name: mistral
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litellm_params:
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model: ollama/mistral
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api_base: http://ollama:11434
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stream: True
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```
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