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refactor(provider_specific_params.md): create separate doc for provider-specific param

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Make it easier for people to know, how litellm handles provider-specific params.
This commit is contained in:
Krrish Dholakia 2024-07-09 12:23:42 -07:00
parent 6bce7e73a3
commit ba334ff8b9
3 changed files with 437 additions and 396 deletions
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396
docs/my-website/docs/completion/input.md
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@ -229,399 +229,3 @@ def completion(
- `hf_model_name`: *string (optional)* - [Sagemaker Only] The corresponding huggingface name of the model, used to pull the right chat template for the model. - `hf_model_name`: *string (optional)* - [Sagemaker Only] The corresponding huggingface name of the model, used to pull the right chat template for the model.
## Provider-specific Params
Providers might offer params not supported by OpenAI (e.g. top_k). You can pass those in 2 ways:
- via completion(): We'll pass the non-openai param, straight to the provider as part of the request body.
- e.g. `completion(model="claude-instant-1", top_k=3)`
- via provider-specific config variable (e.g. `litellm.OpenAIConfig()`).
<Tabs>
<TabItem value="openai" label="OpenAI">
```python
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)
```
</TabItem>
<TabItem value="openai-text" label="OpenAI Text Completion">
```python
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)
```
</TabItem>
<TabItem value="azure-openai" label="Azure OpenAI">
```python
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)
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
```python
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)
```
</TabItem>
<TabItem value="huggingface" label="Huggingface">
```python
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)
```
</TabItem>
<TabItem value="together_ai" label="TogetherAI">
```python
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)
```
</TabItem>
<TabItem value="ollama" label="Ollama">
```python
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)
```
</TabItem>
<TabItem value="replicate" label="Replicate">
```python
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)
```
</TabItem>
<TabItem value="petals" label="Petals">
```python
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)
```
</TabItem>
<TabItem value="palm" label="Palm">
```python
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)
```
</TabItem>
<TabItem value="ai21" label="AI21">
```python
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)
```
</TabItem>
<TabItem value="cohere" label="Cohere">
```python
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)
```
</TabItem>
</Tabs>
[**Check out the tutorial!**](../tutorials/provider_specific_params.md)

436
docs/my-website/docs/completion/provider_specific_params.md Normal file
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@ -0,0 +1,436 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Provider-specific Params
Providers might offer params not supported by OpenAI (e.g. top_k). LiteLLM treats any non-openai param, as a provider-specific param, and passes it to the provider in the request body, as a kwarg. [**See Reserved Params**](https://github.com/BerriAI/litellm/blob/aa2fd29e48245f360e771a8810a69376464b195e/litellm/main.py#L700)
You can pass those in 2 ways:
- via completion(): We'll pass the non-openai param, straight to the provider as part of the request body.
- e.g. `completion(model="claude-instant-1", top_k=3)`
- via provider-specific config variable (e.g. `litellm.OpenAIConfig()`).
## SDK Usage
<Tabs>
<TabItem value="openai" label="OpenAI">
```python
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)
```
</TabItem>
<TabItem value="openai-text" label="OpenAI Text Completion">
```python
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)
```
</TabItem>
<TabItem value="azure-openai" label="Azure OpenAI">
```python
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)
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
```python
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)
```
</TabItem>
<TabItem value="huggingface" label="Huggingface">
```python
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)
```
</TabItem>
<TabItem value="together_ai" label="TogetherAI">
```python
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)
```
</TabItem>
<TabItem value="ollama" label="Ollama">
```python
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)
```
</TabItem>
<TabItem value="replicate" label="Replicate">
```python
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)
```
</TabItem>
<TabItem value="petals" label="Petals">
```python
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)
```
</TabItem>
<TabItem value="palm" label="Palm">
```python
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)
```
</TabItem>
<TabItem value="ai21" label="AI21">
```python
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)
```
</TabItem>
<TabItem value="cohere" label="Cohere">
```python
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)
```
</TabItem>
</Tabs>
[**Check out the tutorial!**](../tutorials/provider_specific_params.md)
## Proxy Usage
**via Config**
```yaml
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
```
**via Request**
```bash
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
}'
```

1
docs/my-website/sidebars.js
View file

@ -90,6 +90,7 @@ const sidebars = {
}, },
items: [ items: [
"completion/input", "completion/input",
"completion/provider_specific_params",
"completion/drop_params", "completion/drop_params",
"completion/prompt_formatting", "completion/prompt_formatting",
"completion/output", "completion/output",