Warning: model not found. Using cl100k_base encoding. with  Azure Openai and  load_summarize_chain when I am trying to implement map_reduce
Labels: name: auto:bug, name: auto:question, name: area: models

### System Info

Hi:  I have tried several strategies to implement  map reduce summarization using Azure OpenAi and Langchain . My model is "gpt-35-turbo-16k".


I have tried several experiments but always get to the same warning: 

from langchain.schema.document import Document
from langchain.chains.mapreduce import MapReduceChain
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import TextLoader
llm_summary = AzureChatOpenAI(
            openai_api_base= azure_api_base,
            openai_api_version=azure_openai_api_version,
            deployment_name=azure_deployment_name,
            openai_api_key=azure_openai_api_key,
            openai_api_type= azure_api_type,
            model_name=azure_model_name,
            temperature=azure_model_temperature
            
    )

text="""The ReduceDocumentsChain handles taking the document mapping results and reducing them into a single output.\
        It wraps a generic CombineDocumentsChain (like StuffDocumentsChain) but adds the ability to collapse documents before passing it 
        to the CombineDocumentsChain if their cumulative size exceeds token_max. In this example, we can actually re-use our chain for combining
        our docs to also collapse our docs."""
        
text1=""" You can continue with your English studies and never use Inversion in sentences. That’s perfectly okay. However, if you are preparing for a Cambridge or IELTS exam or other exams or situations where you need to demonstrate an extensive use of English, you will be expected to know about Inversion.

Let’s start with why and when. After all, if you don’t know why we use Inversion, you won’t know when to use it.

WHY & WHEN do we use INVERSION?
Inversion is mainly used for EMPHASIS. The expressions used (never, rarely, no sooner, only then, etc.) have much more impact when used at the beginning of a sentence than the more common pronoun subject, especially as most of  them are negative.
Negatives are more dramatic. Consider negative contractions: don’t, won’t, can’t, haven’t, etc. They usually have strong stress in English whilst positive contractions: I’m, he’ll, she’s, we’ve, I’d, etc. usually have weak stress. 
"""




doc= [Document(page_content=text1)]


chain = load_summarize_chain(llm_summary, chain_type="map_reduce") #chain_type="map_reduce"

chain.run(doc)`




and Strategy 2 with text_splitter: 

`from langchain import PromptTemplate
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter


text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=50)
chunks = text_splitter.create_documents([text1])

chain = load_summarize_chain(
    llm_summary,
    chain_type='map_reduce',
    verbose=False
)
summary = chain.run(chunks)
summary

I always get the same output:

<img width="1473" alt="image" src="https://github.com/langchain-ai/langchain/assets/7675634/fc871a72-4f5f-43ff-9725-52a718ebaeac">


I have some questions:

1) How to fix this warning?
2) Can I trust in the output when the model is not found? 




### Who can help?

@agola11

### Information

- [X] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

run this chunks of code in any notebook

### Expected behavior

I want to fix this warning helping to langchain to find the model.