Provider-spezifische Parameter

Anbieter bieten möglicherweise Parameter an, die von OpenAI nicht unterstützt werden (z. B. top_k). LiteLLM behandelt alle nicht-OpenAI-Parameter als anbieterspezifische Parameter und übergibt sie als Keyword-Argumente an den Anbieter in der Anfrage. Reservierte Parameter anzeigen

Sie können diese auf 2 Arten übergeben

• Über completion(): Wir übergeben den nicht-OpenAI-Parameter direkt an den Anbieter als Teil des Anfragekörpers.
  • z. B. completion(model="claude-instant-1", top_k=3)
• Über anbieterspezifische Konfigurationsvariable (z. B. litellm.OpenAIConfig()).

SDK-Nutzung

OpenAI

import litellm, os

# set env variables
os.environ["OPENAI_API_KEY"] = "your-openai-key"

## SET MAX TOKENS - via completion() 
response_1 = litellm.completion(
            model="gpt-3.5-turbo",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.OpenAIConfig(max_tokens=10)

response_2 = litellm.completion(
            model="gpt-3.5-turbo",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

OpenAI Text Completion

import litellm, os

# set env variables
os.environ["OPENAI_API_KEY"] = "your-openai-key"


## SET MAX TOKENS - via completion() 
response_1 = litellm.completion(
            model="text-davinci-003",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.OpenAITextCompletionConfig(max_tokens=10)
response_2 = litellm.completion(
            model="text-davinci-003",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

Azure OpenAI

import litellm, os

# set env variables
os.environ["AZURE_API_BASE"] = "your-azure-api-base"
os.environ["AZURE_API_TYPE"] = "azure" # [OPTIONAL] 
os.environ["AZURE_API_VERSION"] = "2023-07-01-preview" # [OPTIONAL]

## SET MAX TOKENS - via completion() 
response_1 = litellm.completion(
            model="azure/chatgpt-v-2",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.AzureOpenAIConfig(max_tokens=10)
response_2 = litellm.completion(
            model="azure/chatgpt-v-2",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

Anthropic

import litellm, os 

# set env variables
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"

## SET MAX TOKENS - via completion()
response_1 = litellm.completion(
            model="claude-instant-1",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.AnthropicConfig(max_tokens_to_sample=200)
response_2 = litellm.completion(
            model="claude-instant-1",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

Huggingface

import litellm, os 

# set env variables
os.environ["HUGGINGFACE_API_KEY"] = "your-huggingface-key" #[OPTIONAL]

## SET MAX TOKENS - via completion()
response_1 = litellm.completion(
            model="huggingface/mistralai/Mistral-7B-Instruct-v0.1",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            api_base="https://your-huggingface-api-endpoint",
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.HuggingfaceConfig(max_new_tokens=200)
response_2 = litellm.completion(
            model="huggingface/mistralai/Mistral-7B-Instruct-v0.1",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            api_base="https://your-huggingface-api-endpoint"
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

TogetherAI

import litellm, os 

# set env variables
os.environ["TOGETHERAI_API_KEY"] = "your-togetherai-key" 

## SET MAX TOKENS - via completion()
response_1 = litellm.completion(
            model="together_ai/togethercomputer/llama-2-70b-chat",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.TogetherAIConfig(max_tokens_to_sample=200)
response_2 = litellm.completion(
            model="together_ai/togethercomputer/llama-2-70b-chat",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

Ollama

import litellm, os 

## SET MAX TOKENS - via completion()
response_1 = litellm.completion(
            model="ollama/llama2",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.OllamConfig(num_predict=200)
response_2 = litellm.completion(
            model="ollama/llama2",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

Replicate

import litellm, os 

# set env variables
os.environ["REPLICATE_API_KEY"] = "your-replicate-key" 

## SET MAX TOKENS - via completion()
response_1 = litellm.completion(
            model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.ReplicateConfig(max_new_tokens=200)
response_2 = litellm.completion(
            model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

Petals

import litellm

## SET MAX TOKENS - via completion()
response_1 = litellm.completion(
            model="petals/petals-team/StableBeluga2",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            api_base="https://chat.petals.dev/api/v1/generate",
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.PetalsConfig(max_new_tokens=10)
response_2 = litellm.completion(
            model="petals/petals-team/StableBeluga2",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            api_base="https://chat.petals.dev/api/v1/generate",
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

Palm

import litellm, os 

# set env variables
os.environ["PALM_API_KEY"] = "your-palm-key"  

## SET MAX TOKENS - via completion()
response_1 = litellm.completion(
            model="palm/chat-bison",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.PalmConfig(maxOutputTokens=10)
response_2 = litellm.completion(
            model="palm/chat-bison",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

AI21

import litellm, os 

# set env variables
os.environ["AI21_API_KEY"] = "your-ai21-key"  

## SET MAX TOKENS - via completion()
response_1 = litellm.completion(
            model="j2-mid",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.AI21Config(maxOutputTokens=10)
response_2 = litellm.completion(
            model="j2-mid",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

Cohere

import litellm, os 

# set env variables
os.environ["COHERE_API_KEY"] = "your-cohere-key"   

## SET MAX TOKENS - via completion()
response_1 = litellm.completion(
            model="command-nightly",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            max_tokens=10
        )

response_1_text = response_1.choices[0].message.content

## SET MAX TOKENS - via config
litellm.CohereConfig(max_tokens=200)
response_2 = litellm.completion(
            model="command-nightly",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
        )

response_2_text = response_2.choices[0].message.content

## TEST OUTPUT
assert len(response_2_text) > len(response_1_text)

Schauen Sie sich das Tutorial an!

Proxy-Nutzung

Über Konfiguration

model_list:
    - model_name: llama-3-8b-instruct
      litellm_params:
        model: predibase/llama-3-8b-instruct
        api_key: os.environ/PREDIBASE_API_KEY
        tenant_id: os.environ/PREDIBASE_TENANT_ID
        max_tokens: 256
        adapter_base: <my-special_base> # 👈 PROVIDER-SPECIFIC PARAM

Über Anfrage

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-D '{
  "model": "llama-3-8b-instruct",
  "messages": [
    {
      "role": "user",
      "content": "What'\''s the weather like in Boston today?"
    }
  ],
  "adapater_id": "my-special-adapter-id" # 👈 PROVIDER-SPECIFIC PARAM
  }'