HuggingFaceTextGenInference Streaming does not output 
Labels: name: auto:bug, name: area: models

### System Info

langchain==0.0.354
text_generation==0.6.1

python:3.10-slim



### Who can help?

@agola11 @hwaking 

### Information

- [ ] 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
- [X] Callbacks/Tracing
- [X] Async

### Reproduction

Model: TheBloke/Llama-2-7B-Chat-GPTQ, but I've also tried TheBloke/Mistral-7B-OpenOrca-GPTQ
FastAPI example with HuggingFaceTextGenInference streaming:
```python

from fastapi import FastAPI

import langchain
from langchain.llms import HuggingFaceTextGenInference
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from fastapi.responses import StreamingResponse
from pydantic import BaseModel

import os
from fastapi.middleware.cors import CORSMiddleware

app = FastAPI()
langchain.debug = True

# Enable CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # You can specify the list of allowed origins or use "*" for any origin
    allow_credentials=True,
    allow_methods=["*"],  # You can specify the HTTP methods that are allowed
    allow_headers=["*"],  # You can specify the HTTP headers that are allowed
)

# Configuration for local LLM
ai_url = "http://tgi-ai-server:" + str(os.getenv("AI_PORT", 80)) + "/generate"

# Configure the LLM
llm = HuggingFaceTextGenInference(
    inference_server_url=ai_url,
    max_new_tokens=20,
    streaming=True,
)

template = """

 

You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.


{prompt}

"""
prompt_template = PromptTemplate(
    template=template, 
    input_variables= ["prompt"]
)

# Initialize the LLM Chain
llm_chain = LLMChain(llm=llm, prompt=prompt_template)

@app.get("/chat")
async def chat():

    prompt = {"prompt":"What is the Nickelodeon channel?"}
    # Generate the response using the LLM Chain and stream the output
    async def generate():
        for text in llm_chain.run(prompt):
            yield text
            

    return StreamingResponse(generate(), media_type="text/plain")

# Run the server (if running this script directly)
# Use the command: uvicorn script_name:app --reload
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
```

Output:
```
[chain/start] [1:chain:LLMChain] Entering Chain run with input:
{
  "prompt": "What is the Nickelodeon channel?"
}
[llm/start] [1:chain:LLMChain > 2:llm:HuggingFaceTextGenInference] Entering LLM run with input:
{
  "prompts": [
    "
 
\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n
\nWhat is the Nickelodeon channel?
"
  ]
}
[llm/end] [1:chain:LLMChain > 2:llm:HuggingFaceTextGenInference] [765ms] Exiting LLM run with output:
{
  "generations": [
    [
      {
        "text": "",
        "generation_info": null,
        "type": "Generation"
      }
    ]
  ],
  "llm_output": null,
  "run": null
}
[chain/end] [1:chain:LLMChain] [765ms] Exiting Chain run with output:
{
  "text": ""
}
```

### Expected behavior

HuggingFaceTextGenInference does not return any streaming data. Works fine when streaming=False in parameters.