--- title: "Replicate" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/replicate source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:37:38.713Z license: CC-BY-4.0 attribution: "Replicate — Claudary (https://claudary.paisolsolutions.com/skills/replicate)" --- # Replicate import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Replicate LiteLLM supports all models on Replicate ## Usage ### API KEYS ```python import os os.environ["REPLICATE_API_KEY"] = "" ``` ### Example Call ```python from litellm import completion import os ## set ENV variables os.environ["REPLICATE_API_KEY"] = "replicate key" # replicate llama-3 call response = completion( model="replicate/meta/meta-llama-3-8b-instruct", messages = [{ "content": "Hello, how are you?","role": "user"}] ) ``` 1. Add models to your config.yaml ```yaml model_list: - model_name: llama-3 litellm_params: model: replicate/meta/meta-llama-3-8b-instruct api_key: os.environ/REPLICATE_API_KEY ``` 2. Start the proxy ```bash $ litellm --config /path/to/config.yaml --debug ``` 3. Send Request to LiteLLM Proxy Server ```python import openai client = openai.OpenAI( api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys base_url="http://0.0.0.0:4000" # litellm-proxy-base url ) response = client.chat.completions.create( model="llama-3", messages = [ { "role": "system", "content": "Be a good human!" }, { "role": "user", "content": "What do you know about earth?" } ] ) print(response) ``` ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Authorization: Bearer sk-1234' \\ --header 'Content-Type: application/json' \\ --data '{ "model": "llama-3", "messages": [ { "role": "system", "content": "Be a good human!" }, { "role": "user", "content": "What do you know about earth?" } ], }' ``` ### Expected Replicate Call This is the call litellm will make to replicate, from the above example: ```bash POST Request Sent from LiteLLM: curl -X POST \\ https://api.replicate.com/v1/models/meta/meta-llama-3-8b-instruct \\ -H 'Authorization: Token your-api-key' -H 'Content-Type: application/json' \\ -d '{'version': 'meta/meta-llama-3-8b-instruct', 'input': {'prompt': '<|start_header_id|>system<|end_header_id|>\\n\\nBe a good human!<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nWhat do you know about earth?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n'}}' ``` ## Advanced Usage - Prompt Formatting LiteLLM has prompt template mappings for all `meta-llama` llama3 instruct models. [**See Code**](https://github.com/BerriAI/litellm/blob/4f46b4c3975cd0f72b8c5acb2cb429d23580c18a/litellm/llms/prompt_templates/factory.py#L1360) To apply a custom prompt template: ```python import litellm import os os.environ["REPLICATE_API_KEY"] = "" # Create your own custom prompt template litellm.register_prompt_template( model="togethercomputer/LLaMA-2-7B-32K", initial_prompt_value="You are a good assistant" # [OPTIONAL] roles={ "system": { "pre_message": "[INST] <>\\n", # [OPTIONAL] "post_message": "\\n<>\\n [/INST]\\n" # [OPTIONAL] }, "user": { "pre_message": "[INST] ", # [OPTIONAL] "post_message": " [/INST]" # [OPTIONAL] }, "assistant": { "pre_message": "\\n" # [OPTIONAL] "post_message": "\\n" # [OPTIONAL] } } final_prompt_value="Now answer as best you can:" # [OPTIONAL] ) def test_replicate_custom_model(): model = "replicate/togethercomputer/LLaMA-2-7B-32K" response = completion(model=model, messages=messages) print(response['choices'][0]['message']['content']) return response test_replicate_custom_model() ``` ```yaml # Model-specific parameters model_list: - model_name: mistral-7b # model alias litellm_params: # actual params for litellm.completion() model: "replicate/mistralai/Mistral-7B-Instruct-v0.1" api_key: os.environ/REPLICATE_API_KEY initial_prompt_value: "\\n" roles: {"system":{"pre_message":"<|im_start|>system\\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\\n","post_message":"<|im_end|>"}} final_prompt_value: "\\n" bos_token: "" eos_token: "" max_tokens: 4096 ``` ## Advanced Usage - Calling Replicate Deployments Calling a [deployed replicate LLM](https://replicate.com/deployments) Add the `replicate/deployments/` prefix to your model, so litellm will call the `deployments` endpoint. This will call `ishaan-jaff/ishaan-mistral` deployment on replicate ```python response = completion( model="replicate/deployments/ishaan-jaff/ishaan-mistral", messages= [{ "content": "Hello, how are you?","role": "user"}] ) ``` :::warning Replicate Cold Boots Replicate responses can take 3-5 mins due to replicate cold boots, if you're trying to debug try making the request with `litellm.set_verbose=True`. [More info on replicate cold boots](https://replicate.com/docs/how-does-replicate-work#cold-boots) ::: ## Replicate Models liteLLM supports all replicate LLMs For replicate models ensure to add a `replicate/` prefix to the `model` arg. liteLLM detects it using this arg. Below are examples on how to call replicate LLMs using liteLLM Model Name | Function Call | Required OS Variables | -----------------------------|----------------------------------------------------------------|--------------------------------------| replicate/llama-2-70b-chat | `completion(model='replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf', messages)` | `os.environ['REPLICATE_API_KEY']` | a16z-infra/llama-2-13b-chat| `completion(model='replicate/a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52', messages)`| `os.environ['REPLICATE_API_KEY']` | replicate/vicuna-13b | `completion(model='replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b', messages)` | `os.environ['REPLICATE_API_KEY']` | daanelson/flan-t5-large | `completion(model='replicate/daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f', messages)` | `os.environ['REPLICATE_API_KEY']` | custom-llm | `completion(model='replicate/custom-llm-version-id', messages)` | `os.environ['REPLICATE_API_KEY']` | replicate deployment | `completion(model='replicate/deployments/ishaan-jaff/ishaan-mistral', messages)` | `os.environ['REPLICATE_API_KEY']` | ## Passing additional params - max_tokens, temperature See all litellm.completion supported params [here](https://docs.litellm.ai/docs/completion/input) ```python # !uv add litellm from litellm import completion import os ## set ENV variables os.environ["REPLICATE_API_KEY"] = "replicate key" # replicate llama-2 call response = completion( model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", messages = [{ "content": "Hello, how are you?","role": "user"}], max_tokens=20, temperature=0.5 ) ``` **proxy** ```yaml model_list: - model_name: llama-3 litellm_params: model: replicate/meta/meta-llama-3-8b-instruct api_key: os.environ/REPLICATE_API_KEY max_tokens: 20 temperature: 0.5 ``` ## Passings Replicate specific params Send params [not supported by `litellm.completion()`](https://docs.litellm.ai/docs/completion/input) but supported by Replicate by passing them to `litellm.completion` Example `seed`, `min_tokens` are Replicate specific param ```python # !uv add litellm from litellm import completion import os ## set ENV variables os.environ["REPLICATE_API_KEY"] = "replicate key" # replicate llama-2 call response = completion( model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", messages = [{ "content": "Hello, how are you?","role": "user"}], seed=-1, min_tokens=2, top_k=20, ) ``` **proxy** ```yaml model_list: - model_name: llama-3 litellm_params: model: replicate/meta/meta-llama-3-8b-instruct api_key: os.environ/REPLICATE_API_KEY min_tokens: 2 top_k: 20 ``` --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/replicate) · https://claudary.paisolsolutions.com