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Code Issues Projects Releases Packages Wiki Activity Actions 36

update sambanova models and parameters (#10900)

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* add sambanova to completion input params table

* update sambanova supported args

* update sambanova supported models

* minor changes

* fix sambanova model list

* update sambanova models

* update sambanova models

* update sambanova docs

* minor chnage sambanova url

* update type to match OpenAIGPTConfig

* minor change
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Jorge Piedrahita Ortiz 2025-05-18 22:45:20 -05:00 • committed by GitHub
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1
docs/my-website/docs/completion/input.md
View file

@ -55,6 +55,7 @@ Use `litellm.get_supported_openai_params()` for an updated list of params for ea
|Bedrock| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | ✅ (model dependent) | |
|Sagemaker| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | |
|TogetherAI| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | ✅ | | | ✅ | | ✅ | ✅ | | | |
|Sambanova| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | ✅ | | ✅ | ✅ | | | |
|AlephAlpha| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | |
|NLP Cloud| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | |
|Petals| ✅ | ✅ | | ✅ | ✅ | | | | | |

190
docs/my-website/docs/providers/sambanova.md
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@ -1,8 +1,8 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Sambanova
https://cloud.sambanova.ai/
# SambaNova
[https://cloud.sambanova.ai/](http://cloud.sambanova.ai?utm_source=litellm&utm_medium=external&utm_campaign=cloud_signup)
:::tip
@ -23,20 +23,17 @@ import os
os.environ['SAMBANOVA_API_KEY'] = ""
response = completion(
model="sambanova/Meta-Llama-3.1-8B-Instruct",
model="sambanova/Llama-4-Maverick-17B-128E-Instruct",
messages=[
{
"role": "user",
"content": "What do you know about sambanova.ai. Give your response in json format",
"content": "What do you know about SambaNova Systems",
}
],
max_tokens=10,
response_format={ "type": "json_object" },
stop=["\n\n"],
stop=[],
temperature=0.2,
top_p=0.9,
tool_choice="auto",
tools=[],
user="user",
)
print(response)
@ -49,17 +46,17 @@ import os
os.environ['SAMBANOVA_API_KEY'] = ""
response = completion(
model="sambanova/Meta-Llama-3.1-8B-Instruct",
model="sambanova/Llama-4-Maverick-17B-128E-Instruct",
messages=[
{
"role": "user",
"content": "What do you know about sambanova.ai. Give your response in json format",
"content": "What do you know about SambaNova Systems",
}
],
stream=True,
max_tokens=10,
response_format={ "type": "json_object" },
stop=["\n\n"],
stop=[],
temperature=0.2,
top_p=0.9,
tool_choice="auto",
@ -139,3 +136,174 @@ Here's how to call a Sambanova model with the LiteLLM Proxy Server
</TabItem>
</Tabs>
## SambaNova - Tool Calling
```python
import litellm
# Example dummy function
def get_current_weather(location, unit="fahrenheit"):
if unit == "fahrenheit"
return{"location": location, "temperature": "72", "unit": "fahrenheit"}
else:
return{"location": location, "temperature": "22", "unit": "celsius"}
messages = [{"role": "user", "content": "What's the weather like in San Francisco"}]
tools = [
{
"type": "function",
"function": {
"name": "import litellm",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
},
}
]
response = litellm.completion(
model="sambanova/Meta-Llama-3.3-70B-Instruct",
messages=messages,
tools=tools,
tool_choice="auto", # auto is default, but we'll be explicit
)
print("\nFirst LLM Response:\n", response)
response_message = response.choices[0].message
tool_calls = response_message.tool_calls
if tool_calls:
# Step 2: check if the model wanted to call a function
if tool_calls:
# Step 3: call the function
# Note: the JSON response may not always be valid; be sure to handle errors
available_functions = {
"get_current_weather": get_current_weather,
}
messages.append(
response_message
) # extend conversation with assistant's reply
print("Response message\n", response_message)
# Step 4: send the info for each function call and function response to the model
for tool_call in tool_calls:
function_name = tool_call.function.name
function_to_call = available_functions[function_name]
function_args = json.loads(tool_call.function.arguments)
function_response = function_to_call(
location=function_args.get("location"),
unit=function_args.get("unit"),
)
messages.append(
{
"tool_call_id": tool_call.id,
"role": "tool",
"name": function_name,
"content": function_response,
}
) # extend conversation with function response
print(f"messages: {messages}")
second_response = litellm.completion(
model="sambanova/Meta-Llama-3.3-70B-Instruct", messages=messages
) # get a new response from the model where it can see the function response
print("second response\n", second_response)
```
## SambaNova - Vision Example
```python
import litellm
# Auxiliary function to get b64 images
def data_url_from_image(file_path):
mime_type, _ = mimetypes.guess_type(file_path)
if mime_type is None:
raise ValueError("Could not determine MIME type of the file")
with open(file_path, "rb") as image_file:
encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
data_url = f"data:{mime_type};base64,{encoded_string}"
return data_url
response = litellm.completion(
model = "sambanova/Llama-4-Maverick-17B-128E-Instruct",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": "What's in this image?"
},
{
"type": "image_url",
"image_url": {
"url": data_url_from_image("your_image_path"),
"format": "image/jpeg"
}
}
]
}
],
stream=False
)
print(response.choices[0].message.content)
```
## SambaNova - Structured Output
```python
import litellm
response = litellm.completion(
model="sambanova/Meta-Llama-3.3-70B-Instruct",
messages=[
{
"role": "system",
"content": "You are an expert at structured data extraction. You will be given unstructured text should convert it into the given structure."
},
{
"role": "user",
"content": "the section 24 has appliances, and videogames"
},
],
response_format={
"type": "json_schema",
"json_schema": {
"title": "data",
"name": "data_extraction",
"schema": {
"type": "object",
"properties": {
"section": {
"type": "string" },
"products": {
"type": "array",
"items": { "type": "string" }
}
},
"required": ["section", "products"],
"additionalProperties": False
},
"strict": False
}
},
stream=False
)
print(response.choices[0].message.content))
```

2
litellm/__init__.py
View file

@ -599,7 +599,7 @@ def add_known_models():
cerebras_models.append(key)
elif value.get("litellm_provider") == "galadriel":
galadriel_models.append(key)
elif value.get("litellm_provider") == "sambanova_models":
elif value.get("litellm_provider") == "sambanova":
sambanova_models.append(key)
elif value.get("litellm_provider") == "novita":
novita_models.append(key)

43
litellm/llms/sambanova/chat.py
View file

@ -4,7 +4,7 @@ Sambanova Chat Completions API
this is OpenAI compatible - no translation needed / occurs
"""
from typing import Optional
from typing import Optional, Union
from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
@ -17,26 +17,28 @@ class SambanovaConfig(OpenAIGPTConfig):
"""
max_tokens: Optional[int] = None
response_format: Optional[dict] = None
seed: Optional[int] = None
stream: Optional[bool] = None
temperature: Optional[int] = None
top_p: Optional[int] = None
top_k: Optional[int] = None
stop: Optional[Union[str, list]] = None
stream: Optional[bool] = None
stream_options: Optional[dict] = None
tool_choice: Optional[str] = None
response_format: Optional[dict] = None
tools: Optional[list] = None
user: Optional[str] = None
def __init__(
self,
max_tokens: Optional[int] = None,
response_format: Optional[dict] = None,
seed: Optional[int] = None,
stop: Optional[str] = None,
stream: Optional[bool] = None,
stream_options: Optional[dict] = None,
temperature: Optional[float] = None,
top_p: Optional[int] = None,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
tool_choice: Optional[str] = None,
tools: Optional[list] = None,
user: Optional[str] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
@ -56,12 +58,31 @@ class SambanovaConfig(OpenAIGPTConfig):
return [
"max_tokens",
"response_format",
"seed",
"stop",
"stream",
"stream_options",
"temperature",
"top_p",
"top_k",
"tool_choice",
"tools",
"user",
"parallel_tool_calls"
]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
"""
map max_completion_tokens param to max_tokens
"""
supported_openai_params = self.get_supported_openai_params(model=model)
for param, value in non_default_params.items():
if param == "max_completion_tokens":
optional_params["max_tokens"] = value
elif param in supported_openai_params:
optional_params[param] = value
return optional_params

184
litellm/model_prices_and_context_window_backup.json
View file

@ -12232,81 +12232,169 @@
"metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
},
"sambanova/Meta-Llama-3.1-8B-Instruct": {
"max_tokens": 16000,
"max_input_tokens": 16000,
"max_output_tokens": 16000,
"max_tokens": 16384,
"max_input_tokens": 16384,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000001,
"output_cost_per_token": 0.0000002,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"mode": "chat",
"supports_tool_choice": true
},
"sambanova/Meta-Llama-3.1-70B-Instruct": {
"max_tokens": 128000,
"max_input_tokens": 128000,
"max_output_tokens": 128000,
"input_cost_per_token": 0.0000006,
"output_cost_per_token": 0.0000012,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"mode": "chat",
"supports_tool_choice": true
"supports_tool_choice": true,
"supports_response_schema": true,
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Meta-Llama-3.1-405B-Instruct": {
"max_tokens": 16000,
"max_input_tokens": 16000,
"max_output_tokens": 16000,
"max_tokens": 16384,
"max_input_tokens": 16384,
"max_output_tokens": 16384,
"input_cost_per_token": 0.000005,
"output_cost_per_token": 0.000010,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"mode": "chat",
"supports_tool_choice": true
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_response_schema": true,
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Meta-Llama-3.2-1B-Instruct": {
"max_tokens": 16000,
"max_input_tokens": 16000,
"max_output_tokens": 16000,
"max_tokens": 16384,
"max_input_tokens": 16384,
"max_output_tokens": 16384,
"input_cost_per_token": 0.00000004,
"output_cost_per_token": 0.00000008,
"litellm_provider": "sambanova",
"mode": "chat",
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Meta-Llama-3.2-3B-Instruct": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000008,
"output_cost_per_token": 0.00000016,
"litellm_provider": "sambanova",
"mode": "chat",
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Llama-4-Maverick-17B-128E-Instruct": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 0.00000063,
"output_cost_per_token": 0.0000018,
"litellm_provider": "sambanova",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_response_schema": true,
"supports_vision": true,
"source": "https://cloud.sambanova.ai/plans/pricing",
"metadata": {"notes": "For vision models, images are converted to 6432 input tokens and are billed at that amount"}
},
"sambanova/Llama-4-Scout-17B-16E-Instruct": {
"max_tokens": 8192,
"max_input_tokens": 8192,
"max_output_tokens": 8192,
"input_cost_per_token": 0.0000004,
"output_cost_per_token": 0.0000007,
"litellm_provider": "sambanova",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_response_schema": true,
"source": "https://cloud.sambanova.ai/plans/pricing",
"metadata": {"notes": "For vision models, images are converted to 6432 input tokens and are billed at that amount"}
},
"sambanova/Meta-Llama-3.3-70B-Instruct": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 0.0000006,
"output_cost_per_token": 0.0000012,
"litellm_provider": "sambanova",
"mode": "chat",
"supports_function_calling": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Meta-Llama-Guard-3-8B": {
"max_tokens": 16384,
"max_input_tokens": 16384,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000003,
"output_cost_per_token": 0.0000003,
"litellm_provider": "sambanova",
"mode": "chat",
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Qwen3-32B": {
"max_tokens": 8192,
"max_input_tokens": 8192,
"max_output_tokens": 8192,
"input_cost_per_token": 0.0000004,
"output_cost_per_token": 0.0000008,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_reasoning": true,
"mode": "chat",
"supports_tool_choice": true
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Meta-Llama-3.2-3B-Instruct": {
"max_tokens": 4000,
"max_input_tokens": 4000,
"max_output_tokens": 4000,
"input_cost_per_token": 0.0000008,
"output_cost_per_token": 0.0000016,
"sambanova/QwQ-32B": {
"max_tokens": 16384,
"max_input_tokens": 16384,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000005,
"output_cost_per_token": 0.0000010,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"mode": "chat",
"supports_tool_choice": true
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Qwen2.5-Coder-32B-Instruct": {
"max_tokens": 8000,
"max_input_tokens": 8000,
"max_output_tokens": 8000,
"input_cost_per_token": 0.0000015,
"output_cost_per_token": 0.000003,
"sambanova/Qwen2-Audio-7B-Instruct": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.0000005,
"output_cost_per_token": 0.0001,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"mode": "chat",
"supports_tool_choice": true
"supports_audio_input": true,
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Qwen2.5-72B-Instruct": {
"max_tokens": 8000,
"max_input_tokens": 8000,
"max_output_tokens": 8000,
"input_cost_per_token": 0.000002,
"output_cost_per_token": 0.000004,
"sambanova/DeepSeek-R1-Distill-Llama-70B": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 0.0000007,
"output_cost_per_token": 0.0000014,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"mode": "chat",
"supports_tool_choice": true
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/DeepSeek-R1": {
"max_tokens": 32768,
"max_input_tokens": 32768,
"max_output_tokens": 32768,
"input_cost_per_token": 0.000005,
"output_cost_per_token": 0.000007,
"litellm_provider": "sambanova",
"mode": "chat",
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/DeepSeek-V3-0324": {
"max_tokens": 32768,
"max_input_tokens": 32768,
"max_output_tokens": 32768,
"input_cost_per_token": 0.0000030,
"output_cost_per_token": 0.0000045,
"litellm_provider": "sambanova",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_reasoning": true,
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"assemblyai/nano": {
"mode": "audio_transcription",

184
model_prices_and_context_window.json
View file

@ -12232,81 +12232,169 @@
"metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
},
"sambanova/Meta-Llama-3.1-8B-Instruct": {
"max_tokens": 16000,
"max_input_tokens": 16000,
"max_output_tokens": 16000,
"max_tokens": 16384,
"max_input_tokens": 16384,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000001,
"output_cost_per_token": 0.0000002,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"mode": "chat",
"supports_tool_choice": true
},
"sambanova/Meta-Llama-3.1-70B-Instruct": {
"max_tokens": 128000,
"max_input_tokens": 128000,
"max_output_tokens": 128000,
"input_cost_per_token": 0.0000006,
"output_cost_per_token": 0.0000012,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"mode": "chat",
"supports_tool_choice": true
"supports_tool_choice": true,
"supports_response_schema": true,
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Meta-Llama-3.1-405B-Instruct": {
"max_tokens": 16000,
"max_input_tokens": 16000,
"max_output_tokens": 16000,
"max_tokens": 16384,
"max_input_tokens": 16384,
"max_output_tokens": 16384,
"input_cost_per_token": 0.000005,
"output_cost_per_token": 0.000010,
"litellm_provider": "sambanova",
"supports_function_calling": true,
"mode": "chat",
"supports_tool_choice": true
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_response_schema": true,
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Meta-Llama-3.2-1B-Instruct": {
"max_tokens": 16000,
"max_input_tokens": 16000,
"max_output_tokens": 16000,
"max_tokens": 16384,
"max_input_tokens": 16384,
"max_output_tokens": 16384,
"input_cost_per_token": 0.00000004,
"output_cost_per_token": 0.00000008,
"litellm_provider": "sambanova",
"mode": "chat",
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Meta-Llama-3.2-3B-Instruct": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000008,
"output_cost_per_token": 0.00000016,
"litellm_provider": "sambanova",
"mode": "chat",
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"sambanova/Llama-4-Maverick-17B-128E-Instruct": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 0.00000063,
"output_cost_per_token": 0.0000018,
"litellm_provider": "sambanova",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_response_schema": true,
"supports_vision": true,
"source": "https://cloud.sambanova.ai/plans/pricing",
"metadata": {"notes": "For vision models, images are converted to 6432 input tokens and are billed at that amount"}
},
"sambanova/Llama-4-Scout-17B-16E-Instruct": {
"max_tokens": 8192,
"max_input_tokens": 8192,
"max_output_tokens": 8192,
"input_cost_per_token": 0.0000004,
"output_cost_per_token": 0.0000007,
"litellm_provider": "sambanova",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_response_schema": true,
"source": "https://cloud.sambanova.ai/plans/pricing",
"metadata": {"notes": "For vision models, images are converted to 6432 input tokens and are billed at that amount"}
},
"sambanova/Meta-Llama-3.3-70B-Instruct": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 0.0000006,
"output_cost_per_token": 0.0000012,
"litellm_provider": "sambanova",
"mode": "chat",
"supports_function_calling": true,
"supports_response_schema": true,
"supports_tool_choice": true,
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