Summarization: documentation example not replicable. Invalid response object from API  together/or w/ reponse time error
Labels: name: auto:bug, name: auto:documentation, name: area: models

### System Info

Trying to replicate the basic langchain summarization type = Stuff usage as on [website documentation](https://python.langchain.com/docs/use_cases/summarization), but it is not able to process long doc.

Test model used:  meta-llama/Llama-2-7b-chat-hf

Test result: tried its original demo code, it did not work, and gives two types of error alternatively after dozens of trials: **Error 1:** timeout error (I've referred to the [related langchain timeout discussion](https://github.com/langchain-ai/langchain/issues/3005) here to increase request_timeout=600/3600 secs, but still get the timeout error. checked my host is on)

**Error 2:** Invalid response object from API Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.._completion_with_retry in 4.0 seconds as it raised APIError: Invalid response object from API: '{"object":"error","message":"Expecting value: line 1 column 1 (char 0)","code":50001}' (HTTP response code was 400).


### Who can help?

Hi @hwchase17 , include this issue with you, as I am using llm **chat wrappers** like `from langchain.llms.openai import OpenAI`, `from langchain.chains.combine_documents.stuff import StuffDocumentsChain` and `from langchain.chains.summarize import load_summarize_chain` etc and error comes from there
Hi @agola11 , include you w/ this issue re **callbacks**, as in the errors mentioned above, the Error2: goes to  -> 
--> stuff_chainself.combine_docs(docs, callbacks=_run_manager.get_child(), **other_keys)   
--> 171 return self.llm_chain.predict(callbacks=callbacks, **inputs)
--> 257     return self(kwargs, callbacks=callbacks)[self.output_key]
--> 310     raise e
--> 304         self._call(inputs, run_manager=run_manager)
---> 93     response = self.generate([inputs], run_manager=run_manager)


### Information

- [X] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async

### Reproduction

To get something out from langchain summarization,  I downsized the model from llama2-7b to "Sheared-LLaMA-1.3B" and replaced the original example's doc with a shorter doc "". I can see from langchain w/ verbose started w/ > Entering new StuffDocumentsChain chain..., > Entering new LLMChain chain... w/ print out the original full doc, but it returns'> Finished chain. returns "Got any creative ideas for a 10 year old’s birthday?"'

```
from langchain.chat_models import ChatOpenAI
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chains.combine_documents.stuff import StuffDocumentsChain


prompt_template = """Write a concise summary of the following:
"{text}"
CONCISE SUMMARY:"""
prompt = PromptTemplate.from_template(prompt_template)

llm = ChatOpenAI(verbose = True
                 , temperature=0.1                  
                 , model_name=model_name
                 , openai_api_key = "EMPTY"
                 , openai_api_base = "http://localhost:8000/v1"
                 , request_timeout=600
                 , max_retries=1)

llm_chain = LLMChain(llm=llm, prompt=prompt, verbose = True)

stuff_chain = StuffDocumentsChain(
    llm_chain=llm_chain, document_variable_name="text"
)

loader = WebBaseLoader("https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B")
docs = loader.load()
print(stuff_chain.run(docs ))
```
But I got  strange result as below:
<img width="692" alt="image" src="https://github.com/langchain-ai/langchain/assets/66184093/ef38f029-9d0c-401e-8b66-7312e44d75b8">
(my local host heart beats well)

Try the same code with a small piece of business-related conversation doc(~ 1k words) w/ StuffDocumentsChain returns something relevant, but load_summarize_chain only gives 
<img width="713" alt="image" src="https://github.com/langchain-ai/langchain/assets/66184093/0d776be5-1288-49c7-8f6f-ec41171ce2c1">

More problem comes, when I double the size on my test doc(4k words, definitely have to be>4096 tokens, that's the point of using summarization), it returns Error 1 and Error 2 mentioned at the beginning, same as attempting replication langchain documentation, so get the trouble it is not able to process long doc gain.




### Expected behavior

returns some relavent result. though understand it does not have to be good, as I am using toy model 1.3Brelevant