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Litellm dev 12 30 2024 p2 (#7495)

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* test(azure_openai_o1.py): initial commit with testing for azure openai o1 preview model

* fix(base_llm_unit_tests.py): handle azure o1 preview response format tests

skip as o1 on azure doesn't support tool calling yet

* fix: initial commit of azure o1 handler using openai caller

simplifies calling + allows fake streaming logic alr. implemented for openai to just work

* feat(azure/o1_handler.py): fake o1 streaming for azure o1 models

azure does not currently support streaming for o1

* feat(o1_transformation.py): support overriding 'should_fake_stream' on azure/o1 via 'supports_native_streaming' param on model info

enables user to toggle on when azure allows o1 streaming without needing to bump versions

* style(router.py): remove 'give feedback/get help' messaging when router is used

Prevents noisy messaging

Closes https://github.com/BerriAI/litellm/issues/5942

* fix(types/utils.py): handle none logprobs

Fixes https://github.com/BerriAI/litellm/issues/328

* fix(exception_mapping_utils.py): fix error str unbound error

* refactor(azure_ai/): move to openai_like chat completion handler

allows for easy swapping of api base url's (e.g. ai.services.com)

Fixes https://github.com/BerriAI/litellm/issues/7275

* refactor(azure_ai/): move to base llm http handler

* fix(azure_ai/): handle differing api endpoints

* fix(azure_ai/): make sure all unit tests are passing

* fix: fix linting errors

* fix: fix linting errors

* fix: fix linting error

* fix: fix linting errors

* fix(azure_ai/transformation.py): handle extra body param

* fix(azure_ai/transformation.py): fix max retries param handling

* fix: fix test

* test(test_azure_o1.py): fix test

* fix(llm_http_handler.py): support handling azure ai unprocessable entity error

* fix(llm_http_handler.py): handle sync invalid param error for azure ai

* fix(azure_ai/): streaming support with base_llm_http_handler

* fix(llm_http_handler.py): working sync stream calls with unprocessable entity handling for azure ai

* fix: fix linting errors

* fix(llm_http_handler.py): fix linting error

* fix(azure_ai/): handle cohere tool call invalid index param error
This commit is contained in:
Krish Dholakia 2025-01-01 18:57:29 -08:00 • committed by GitHub
parent 0f1b298fe0
commit 0120176541
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42 changed files with 638 additions and 192 deletions
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18
litellm/llms/huggingface/chat/handler.py
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@ -724,12 +724,14 @@ class Huggingface(BaseLLM):
token_logprob = token["logprob"]
# Add the token information to the 'token_info' list
_logprob.tokens.append(token_text)
_logprob.token_logprobs.append(token_logprob)
cast(List[str], _logprob.tokens).append(token_text)
cast(List[float], _logprob.token_logprobs).append(token_logprob)
# stub this to work with llm eval harness
top_alt_tokens = {"": -1.0, "": -2.0, "": -3.0} # noqa: F601
_logprob.top_logprobs.append(top_alt_tokens)
cast(List[Dict[str, float]], _logprob.top_logprobs).append(
top_alt_tokens
)
# For each element in the 'tokens' list, extract the relevant information
for i, token in enumerate(response_details["tokens"]):
@ -751,13 +753,15 @@ class Huggingface(BaseLLM):
top_alt_tokens[text] = logprob
# Add the token information to the 'token_info' list
_logprob.tokens.append(token_text)
_logprob.token_logprobs.append(token_logprob)
_logprob.top_logprobs.append(top_alt_tokens)
cast(List[str], _logprob.tokens).append(token_text)
cast(List[float], _logprob.token_logprobs).append(token_logprob)
cast(List[Dict[str, float]], _logprob.top_logprobs).append(
top_alt_tokens
)
# Add the text offset of the token
# This is computed as the sum of the lengths of all previous tokens
_logprob.text_offset.append(
cast(List[int], _logprob.text_offset).append(
sum(len(t["text"]) for t in response_details["tokens"][:i])
)