mirror of
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58141df65d
* fix(azure/chat/gpt_transformation.py): add 'prediction' as a support azure param Closes https://github.com/BerriAI/litellm/issues/8500 * build(model_prices_and_context_window.json): add new 'gemini-2.0-pro-exp-02-05' model * style: cleanup invalid json trailing commma * feat(utils.py): support passing 'tokenizer_config' to register_prompt_template enables passing complete tokenizer config of model to litellm Allows calling deepseek on bedrock with the correct prompt template * fix(utils.py): fix register_prompt_template for custom model names * test(test_prompt_factory.py): fix test * test(test_completion.py): add e2e test for bedrock invoke deepseek ft model * feat(base_invoke_transformation.py): support hf_model_name param for bedrock invoke calls enables proxy admin to set base model for ft bedrock deepseek model * feat(bedrock/invoke): support deepseek_r1 route for bedrock makes it easy to apply the right chat template to that call * feat(constants.py): store deepseek r1 chat template - allow user to get correct response from deepseek r1 without extra work * test(test_completion.py): add e2e mock test for bedrock deepseek * docs(bedrock.md): document new deepseek_r1 route for bedrock allows us to use the right config * fix(exception_mapping_utils.py): catch read operation timeout
427 lines
16 KiB
Python
427 lines
16 KiB
Python
from typing import List
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ROUTER_MAX_FALLBACKS = 5
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DEFAULT_BATCH_SIZE = 512
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DEFAULT_FLUSH_INTERVAL_SECONDS = 5
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DEFAULT_MAX_RETRIES = 2
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DEFAULT_FAILURE_THRESHOLD_PERCENT = (
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0.5 # default cooldown a deployment if 50% of requests fail in a given minute
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)
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DEFAULT_COOLDOWN_TIME_SECONDS = 5
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DEFAULT_REPLICATE_POLLING_RETRIES = 5
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DEFAULT_REPLICATE_POLLING_DELAY_SECONDS = 1
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DEFAULT_IMAGE_TOKEN_COUNT = 250
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DEFAULT_IMAGE_WIDTH = 300
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DEFAULT_IMAGE_HEIGHT = 300
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SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD = 1000 # Minimum number of requests to consider "reasonable traffic". Used for single-deployment cooldown logic.
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#### RELIABILITY ####
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REPEATED_STREAMING_CHUNK_LIMIT = 100 # catch if model starts looping the same chunk while streaming. Uses high default to prevent false positives.
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#### Networking settings ####
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request_timeout: float = 6000 # time in seconds
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LITELLM_CHAT_PROVIDERS = [
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"openai",
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"openai_like",
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"xai",
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"custom_openai",
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"text-completion-openai",
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"cohere",
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"cohere_chat",
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"clarifai",
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"anthropic",
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"anthropic_text",
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"replicate",
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"huggingface",
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"together_ai",
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"openrouter",
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"vertex_ai",
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"vertex_ai_beta",
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"gemini",
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"ai21",
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"baseten",
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"azure",
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"azure_text",
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"azure_ai",
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"sagemaker",
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"sagemaker_chat",
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"bedrock",
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"vllm",
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"nlp_cloud",
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"petals",
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"oobabooga",
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"ollama",
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"ollama_chat",
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"deepinfra",
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"perplexity",
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"mistral",
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"groq",
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"nvidia_nim",
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"cerebras",
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"ai21_chat",
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"volcengine",
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"codestral",
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"text-completion-codestral",
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"deepseek",
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"sambanova",
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"maritalk",
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"cloudflare",
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"fireworks_ai",
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"friendliai",
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"watsonx",
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"watsonx_text",
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"triton",
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"predibase",
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"databricks",
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"empower",
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"github",
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"custom",
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"litellm_proxy",
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"hosted_vllm",
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"lm_studio",
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"galadriel",
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]
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OPENAI_CHAT_COMPLETION_PARAMS = [
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"functions",
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"function_call",
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"temperature",
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"temperature",
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"top_p",
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"n",
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"stream",
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"stream_options",
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"stop",
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"max_completion_tokens",
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"modalities",
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"prediction",
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"audio",
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"max_tokens",
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"presence_penalty",
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"frequency_penalty",
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"logit_bias",
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"user",
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"request_timeout",
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"api_base",
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"api_version",
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"api_key",
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"deployment_id",
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"organization",
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"base_url",
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"default_headers",
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"timeout",
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"response_format",
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"seed",
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"tools",
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"tool_choice",
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"max_retries",
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"parallel_tool_calls",
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"logprobs",
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"top_logprobs",
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"reasoning_effort",
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"extra_headers",
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]
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openai_compatible_endpoints: List = [
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"api.perplexity.ai",
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"api.endpoints.anyscale.com/v1",
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"api.deepinfra.com/v1/openai",
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"api.mistral.ai/v1",
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"codestral.mistral.ai/v1/chat/completions",
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"codestral.mistral.ai/v1/fim/completions",
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"api.groq.com/openai/v1",
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"https://integrate.api.nvidia.com/v1",
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"api.deepseek.com/v1",
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"api.together.xyz/v1",
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"app.empower.dev/api/v1",
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"https://api.friendli.ai/serverless/v1",
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"api.sambanova.ai/v1",
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"api.x.ai/v1",
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"api.galadriel.ai/v1",
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]
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openai_compatible_providers: List = [
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"anyscale",
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"mistral",
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"groq",
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"nvidia_nim",
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"cerebras",
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"sambanova",
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"ai21_chat",
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"ai21",
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"volcengine",
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"codestral",
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"deepseek",
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"deepinfra",
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"perplexity",
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"xinference",
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"xai",
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"together_ai",
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"fireworks_ai",
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"empower",
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"friendliai",
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"azure_ai",
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"github",
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"litellm_proxy",
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"hosted_vllm",
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"lm_studio",
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"galadriel",
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]
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openai_text_completion_compatible_providers: List = (
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[ # providers that support `/v1/completions`
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"together_ai",
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"fireworks_ai",
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"hosted_vllm",
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]
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)
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_openai_like_providers: List = [
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"predibase",
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"databricks",
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"watsonx",
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] # private helper. similar to openai but require some custom auth / endpoint handling, so can't use the openai sdk
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# well supported replicate llms
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replicate_models: List = [
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# llama replicate supported LLMs
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"replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
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"a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52",
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"meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db",
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# Vicuna
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"replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b",
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"joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe",
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# Flan T-5
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"daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f",
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# Others
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"replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5",
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"replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad",
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]
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clarifai_models: List = [
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"clarifai/meta.Llama-3.Llama-3-8B-Instruct",
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"clarifai/gcp.generate.gemma-1_1-7b-it",
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"clarifai/mistralai.completion.mixtral-8x22B",
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"clarifai/cohere.generate.command-r-plus",
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"clarifai/databricks.drbx.dbrx-instruct",
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"clarifai/mistralai.completion.mistral-large",
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"clarifai/mistralai.completion.mistral-medium",
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"clarifai/mistralai.completion.mistral-small",
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"clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1",
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"clarifai/gcp.generate.gemma-2b-it",
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"clarifai/gcp.generate.gemma-7b-it",
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"clarifai/deci.decilm.deciLM-7B-instruct",
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"clarifai/mistralai.completion.mistral-7B-Instruct",
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"clarifai/gcp.generate.gemini-pro",
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"clarifai/anthropic.completion.claude-v1",
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"clarifai/anthropic.completion.claude-instant-1_2",
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"clarifai/anthropic.completion.claude-instant",
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"clarifai/anthropic.completion.claude-v2",
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"clarifai/anthropic.completion.claude-2_1",
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"clarifai/meta.Llama-2.codeLlama-70b-Python",
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"clarifai/meta.Llama-2.codeLlama-70b-Instruct",
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"clarifai/openai.completion.gpt-3_5-turbo-instruct",
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"clarifai/meta.Llama-2.llama2-7b-chat",
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"clarifai/meta.Llama-2.llama2-13b-chat",
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"clarifai/meta.Llama-2.llama2-70b-chat",
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"clarifai/openai.chat-completion.gpt-4-turbo",
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"clarifai/microsoft.text-generation.phi-2",
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"clarifai/meta.Llama-2.llama2-7b-chat-vllm",
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"clarifai/upstage.solar.solar-10_7b-instruct",
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"clarifai/openchat.openchat.openchat-3_5-1210",
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"clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B",
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"clarifai/gcp.generate.text-bison",
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"clarifai/meta.Llama-2.llamaGuard-7b",
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"clarifai/fblgit.una-cybertron.una-cybertron-7b-v2",
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"clarifai/openai.chat-completion.GPT-4",
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"clarifai/openai.chat-completion.GPT-3_5-turbo",
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"clarifai/ai21.complete.Jurassic2-Grande",
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"clarifai/ai21.complete.Jurassic2-Grande-Instruct",
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"clarifai/ai21.complete.Jurassic2-Jumbo-Instruct",
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"clarifai/ai21.complete.Jurassic2-Jumbo",
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"clarifai/ai21.complete.Jurassic2-Large",
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"clarifai/cohere.generate.cohere-generate-command",
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"clarifai/wizardlm.generate.wizardCoder-Python-34B",
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"clarifai/wizardlm.generate.wizardLM-70B",
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"clarifai/tiiuae.falcon.falcon-40b-instruct",
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"clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat",
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"clarifai/gcp.generate.code-gecko",
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"clarifai/gcp.generate.code-bison",
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"clarifai/mistralai.completion.mistral-7B-OpenOrca",
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"clarifai/mistralai.completion.openHermes-2-mistral-7B",
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"clarifai/wizardlm.generate.wizardLM-13B",
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"clarifai/huggingface-research.zephyr.zephyr-7B-alpha",
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"clarifai/wizardlm.generate.wizardCoder-15B",
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"clarifai/microsoft.text-generation.phi-1_5",
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"clarifai/databricks.Dolly-v2.dolly-v2-12b",
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"clarifai/bigcode.code.StarCoder",
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"clarifai/salesforce.xgen.xgen-7b-8k-instruct",
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"clarifai/mosaicml.mpt.mpt-7b-instruct",
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"clarifai/anthropic.completion.claude-3-opus",
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"clarifai/anthropic.completion.claude-3-sonnet",
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"clarifai/gcp.generate.gemini-1_5-pro",
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"clarifai/gcp.generate.imagen-2",
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"clarifai/salesforce.blip.general-english-image-caption-blip-2",
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]
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huggingface_models: List = [
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"meta-llama/Llama-2-7b-hf",
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"meta-llama/Llama-2-7b-chat-hf",
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"meta-llama/Llama-2-13b-hf",
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"meta-llama/Llama-2-13b-chat-hf",
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"meta-llama/Llama-2-70b-hf",
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"meta-llama/Llama-2-70b-chat-hf",
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"meta-llama/Llama-2-7b",
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"meta-llama/Llama-2-7b-chat",
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"meta-llama/Llama-2-13b",
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"meta-llama/Llama-2-13b-chat",
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"meta-llama/Llama-2-70b",
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"meta-llama/Llama-2-70b-chat",
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] # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers
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empower_models = [
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"empower/empower-functions",
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"empower/empower-functions-small",
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]
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together_ai_models: List = [
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# llama llms - chat
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"togethercomputer/llama-2-70b-chat",
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# llama llms - language / instruct
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"togethercomputer/llama-2-70b",
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"togethercomputer/LLaMA-2-7B-32K",
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"togethercomputer/Llama-2-7B-32K-Instruct",
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"togethercomputer/llama-2-7b",
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# falcon llms
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"togethercomputer/falcon-40b-instruct",
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"togethercomputer/falcon-7b-instruct",
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# alpaca
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"togethercomputer/alpaca-7b",
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# chat llms
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"HuggingFaceH4/starchat-alpha",
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# code llms
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"togethercomputer/CodeLlama-34b",
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"togethercomputer/CodeLlama-34b-Instruct",
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"togethercomputer/CodeLlama-34b-Python",
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"defog/sqlcoder",
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"NumbersStation/nsql-llama-2-7B",
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"WizardLM/WizardCoder-15B-V1.0",
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"WizardLM/WizardCoder-Python-34B-V1.0",
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# language llms
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"NousResearch/Nous-Hermes-Llama2-13b",
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"Austism/chronos-hermes-13b",
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"upstage/SOLAR-0-70b-16bit",
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"WizardLM/WizardLM-70B-V1.0",
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] # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...)
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baseten_models: List = [
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"qvv0xeq",
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"q841o8w",
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"31dxrj3",
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] # FALCON 7B # WizardLM # Mosaic ML
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open_ai_embedding_models: List = ["text-embedding-ada-002"]
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cohere_embedding_models: List = [
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"embed-english-v3.0",
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"embed-english-light-v3.0",
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"embed-multilingual-v3.0",
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"embed-english-v2.0",
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"embed-english-light-v2.0",
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"embed-multilingual-v2.0",
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]
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bedrock_embedding_models: List = [
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"amazon.titan-embed-text-v1",
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"cohere.embed-english-v3",
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"cohere.embed-multilingual-v3",
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]
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known_tokenizer_config = {
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"mistralai/Mistral-7B-Instruct-v0.1": {
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"tokenizer": {
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"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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"status": "success",
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},
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"meta-llama/Meta-Llama-3-8B-Instruct": {
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"tokenizer": {
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"chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
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"bos_token": "<|begin_of_text|>",
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"eos_token": "",
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},
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"status": "success",
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},
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"deepseek-r1/deepseek-r1-7b-instruct": {
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"tokenizer": {
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"add_bos_token": True,
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"add_eos_token": False,
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"bos_token": {
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"__type": "AddedToken",
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"content": "<｜begin▁of▁sentence｜>",
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"lstrip": False,
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"normalized": True,
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"rstrip": False,
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"single_word": False,
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},
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"clean_up_tokenization_spaces": False,
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"eos_token": {
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"__type": "AddedToken",
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"content": "<｜end▁of▁sentence｜>",
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"lstrip": False,
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"normalized": True,
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"rstrip": False,
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"single_word": False,
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},
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"legacy": True,
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"model_max_length": 16384,
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"pad_token": {
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"__type": "AddedToken",
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"content": "<｜end▁of▁sentence｜>",
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"lstrip": False,
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"normalized": True,
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"rstrip": False,
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"single_word": False,
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},
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"sp_model_kwargs": {},
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"unk_token": None,
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"tokenizer_class": "LlamaTokenizerFast",
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"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\\n'}}{% endif %}",
|
||
},
|
||
"status": "success",
|
||
},
|
||
}
|
||
|
||
|
||
OPENAI_FINISH_REASONS = ["stop", "length", "function_call", "content_filter", "null"]
|
||
HUMANLOOP_PROMPT_CACHE_TTL_SECONDS = 60 # 1 minute
|
||
RESPONSE_FORMAT_TOOL_NAME = "json_tool_call" # default tool name used when converting response format to tool call
|
||
|
||
########################### Logging Callback Constants ###########################
|
||
AZURE_STORAGE_MSFT_VERSION = "2019-07-07"
|
||
|
||
########################### LiteLLM Proxy Specific Constants ###########################
|
||
########################################################################################
|
||
MAX_SPENDLOG_ROWS_TO_QUERY = (
|
||
1_000_000 # if spendLogs has more than 1M rows, do not query the DB
|
||
)
|
||
# makes it clear this is a rate limit error for a litellm virtual key
|
||
RATE_LIMIT_ERROR_MESSAGE_FOR_VIRTUAL_KEY = "LiteLLM Virtual Key user_api_key_hash"
|
||
|
||
# pass through route constansts
|
||
BEDROCK_AGENT_RUNTIME_PASS_THROUGH_ROUTES = [
|
||
"agents/",
|
||
"knowledgebases/",
|
||
"flows/",
|
||
"retrieveAndGenerate/",
|
||
"rerank/",
|
||
"generateQuery/",
|
||
"optimize-prompt/",
|
||
]
|
||
|
||
BATCH_STATUS_POLL_INTERVAL_SECONDS = 3600 # 1 hour
|
||
BATCH_STATUS_POLL_MAX_ATTEMPTS = 24 # for 24 hours
|
||
|
||
HEALTH_CHECK_TIMEOUT_SECONDS = 60 # 60 seconds
|
||
|
||
UI_SESSION_TOKEN_TEAM_ID = "litellm-dashboard"
|