Hugging Face Streaming Inference

2025-01-13 12:38 UTC gpt-4o Open in ChatGPT ↗

You

hello I am trying to use huggingface (i have a pro account) to infer the llama model and I am trying to use the streaming option so that I don’t wait idly for the whole response. Can you help me? here is my code so far

import requests

Define the model name (Llama-3.3-70B-Instruct) and your Hugging Face API token

model_name = “meta-llama/Llama-3.3-70B-Instruct” api_token = “token”

Hugging Face Inference API URL

inference_url = f”https://api-inference.huggingface.co/models/{model_name}“

Set headers for the request

headers = { “Authorization”: f”Bearer {api_token}” }

Input text for inference

input_text = “What are the benefits of artificial intelligence?”

Make the request to Hugging Face Inference API with streaming enabled

response = requests.post( inference_url, headers=headers, json={“inputs”: input_text, “options”: {“wait_for_model”: True}}, stream=True # Enable streaming )

Check if the request was successful

if response.status_code == 200: # Read the stream line by line and print each part as it arrives for line in response.iter_lines(): if line: decoded_line = line.decode(“utf-8”) # You may need to decode the JSON and extract the text: try: generated_data = eval(decoded_line) # Convert to dict print(generated_data[‘generated_text’], end=”, flush=True) except: pass else: print(f”Error {response.status_code}: {response.text}”)

ChatGPT

Your code is well-structured for using the Hugging Face Inference API with streaming enabled. However, there are a few improvements and adjustments to ensure it works correctly, especially for the streaming use case:

Key Changes:

1. Stream Decoding:
   • Streaming API responses from Hugging Face are typically in chunks of JSON. Using eval is risky and not recommended; it’s better to use json.loads for decoding JSON safely.
2. Proper Error Handling:
   • In case of malformed JSON, catching specific exceptions is better.
3. Extracting generated_text:
   • Each line in the stream may represent a JSON object, so you need to parse and handle it appropriately.

Here’s the updated and optimized code:

import requests
import json

# Define the model name and your Hugging Face API token
model_name = "meta-llama/Llama-3.3-70B-Instruct"
api_token = "your_huggingface_api_token"

# Hugging Face Inference API URL
inference_url = f"https://api-inference.huggingface.co/models/{model_name}"

# Set headers for the request
headers = {
    "Authorization": f"Bearer {api_token}"
}

# Input text for inference
input_text = "What are the benefits of artificial intelligence?"

# Make the request to Hugging Face Inference API with streaming enabled
response = requests.post(
    inference_url,
    headers=headers,
    json={"inputs": input_text, "options": {"wait_for_model": True}},
    stream=True  # Enable streaming
)

# Check if the request was successful
if response.status_code == 200:
    # Read the stream line by line and print each part as it arrives
    print("Response:")
    for line in response.iter_lines():
        if line:
            try:
                # Decode the line and parse JSON
                decoded_line = line.decode("utf-8")
                chunk = json.loads(decoded_line)

                # Extract and print the generated text if available
                if 'generated_text' in chunk:
                    print(chunk['generated_text'], end='', flush=True)
            except json.JSONDecodeError:
                # Handle any JSON decode errors
                print("\n[Warning] Malformed JSON chunk received:", line)
else:
    print(f"Error {response.status_code}: {response.text}")

Explanation of Changes:

1. Replaced eval with json.loads:
   • Safely parses JSON and avoids potential security vulnerabilities with eval.
2. Improved Error Handling:
   • Catches json.JSONDecodeError for malformed JSON chunks.
3. Printing Streamed Output:
   • Continuously appends and prints the generated_text as chunks arrive.

Notes:

• Model Availability: Ensure the meta-llama/Llama-3.3-70B-Instruct model is hosted and supports streaming on the Hugging Face platform.
• Wait for Model: Setting wait_for_model: True will queue the request if the model isn’t loaded yet, but it might increase latency.
• Streaming Behavior: The API might send partial or full responses over multiple chunks. You need to handle reassembling the response if needed.

If you encounter any issues with the response format, refer to the Hugging Face API documentation for details on the expected output format.

You

is it possible that i need this? from huggingface_hub import AsyncInferenceClient

ChatGPT

Yes, using the huggingface_hub library and its AsyncInferenceClient is an excellent approach if you want to interact with Hugging Face’s Inference API more conveniently. This library simplifies many aspects of handling inference requests, including streaming, authentication, and error handling.

Here’s how you can modify your code to use AsyncInferenceClient for streaming:

Updated Code Using AsyncInferenceClient:

import asyncio
from huggingface_hub import AsyncInferenceClient

# Define your Hugging Face API token and model name
api_token = "your_huggingface_api_token"
model_name = "meta-llama/Llama-3.3-70B-Instruct"

# Initialize the asynchronous inference client
client = AsyncInferenceClient(model=model_name, token=api_token)

async def main():
    # Input text for inference
    input_text = "What are the benefits of artificial intelligence?"

    # Make the request with streaming enabled
    async for response in client.stream({"inputs": input_text}):
        # Response is typically a dictionary with generated text
        if "generated_text" in response:
            print(response["generated_text"], end='', flush=True)

# Run the asynchronous function
asyncio.run(main())

Why Use AsyncInferenceClient?

1. Simplified Streaming:
   • The stream method handles the streaming process and returns parsed chunks, making it easier to process.
2. Authentication Handling:
   • The token parameter takes care of adding the API key to requests.
3. Asynchronous:
   • By leveraging asyncio, you can handle responses efficiently without blocking the main thread.
4. Error Handling:
   • Built-in mechanisms in huggingface_hub simplify error management.

Key Features of This Approach:

• Stream API: The stream method directly streams responses as they are generated by the model.
• Automatic JSON Decoding: No need to manually parse or decode the JSON; the library does it for you.
• Ease of Use: This is more Pythonic and avoids boilerplate code for handling requests.

Requirements:

Ensure you have the huggingface_hub library installed. If not, install it with:

pip install huggingface_hub

This method is more user-friendly and concise compared to manually handling requests with requests. It’s especially helpful if you plan to use Hugging Face’s inference services extensively.

You

I am getting this AttributeError: ‘AsyncInferenceClient’ object has no attribute ‘stream’

ChatGPT

The error indicates that the AsyncInferenceClient class in the huggingface_hub library doesn’t have a stream method. This could happen if you’re using an older version of the library or if the functionality isn’t available.

To resolve this, here’s what you can do:

Verify the huggingface_hub Version

Ensure you’re using the latest version of the library. You can update it by running:

pip install --upgrade huggingface_hub

After updating, check the version:

import huggingface_hub
print(huggingface_hub.__version__)

Ensure it matches or exceeds the version required for streaming (consult the Hugging Face documentation for version requirements).

Alternative: Using requests for Streaming

If AsyncInferenceClient still doesn’t support streaming in your setup, revert to using requests for streaming, as in the example shared earlier.

Check the Documentation

The huggingface_hub library’s API evolves, and some features may be introduced or renamed. Consult the official documentation or use the following commands to explore the capabilities of AsyncInferenceClient in your installed version:

import inspect
from huggingface_hub import AsyncInferenceClient

print(inspect.getmembers(AsyncInferenceClient, predicate=inspect.isfunction))

Manually Handling Streaming with AsyncInferenceClient

If streaming is not directly supported, you can manually implement it using the aiohttp library to send asynchronous streaming requests:

import aiohttp
import asyncio

# Define your Hugging Face API token and model name
api_token = "your_huggingface_api_token"
model_name = "meta-llama/Llama-3.3-70B-Instruct"
inference_url = f"https://api-inference.huggingface.co/models/{model_name}"

async def stream_inference(input_text):
    headers = {
        "Authorization": f"Bearer {api_token}"
    }
    payload = {
        "inputs": input_text,
        "options": {"wait_for_model": True}
    }

    async with aiohttp.ClientSession() as session:
        async with session.post(inference_url, headers=headers, json=payload) as response:
            if response.status == 200:
                async for line in response.content:
                    if line:
                        try:
                            chunk = json.loads(line.decode("utf-8"))
                            if "generated_text" in chunk:
                                print(chunk["generated_text"], end='', flush=True)
                        except Exception as e:
                            print(f"Error parsing line: {e}")
            else:
                print(f"Error {response.status}: {await response.text()}")

# Input text for inference
input_text = "What are the benefits of artificial intelligence?"

# Run the asynchronous streaming function
asyncio.run(stream_inference(input_text))

This uses aiohttp for asynchronous streaming and mirrors the functionality expected from AsyncInferenceClient.stream.

Let me know if you need more help troubleshooting or implementing this!

You

this is the example from huggingface’s website from huggingface_hub import InferenceClient

client = InferenceClient(base_url=“http://127.0.0.1:8080”) output = client.chat.completions.create( messages=[ {“role”: “system”, “content”: “You are a helpful assistant.”}, {“role”: “user”, “content”: “Count to 10”}, ], stream=True, max_tokens=1024, )

ChatGPT

The code example you provided uses the InferenceClient class from the huggingface_hub library, which supports a chat.completions.create method with streaming capabilities. This feature is part of the new design for handling chat-based models, likely inspired by OpenAI’s API.

Here’s how you can adapt and use this example:

Full Example with the Correct Setup

from huggingface_hub import InferenceClient

# Initialize the Inference Client with your Hugging Face Inference API URL
client = InferenceClient(
    model="meta-llama/Llama-3.3-70B-Instruct",  # Replace with your model
    token="your_huggingface_api_token",         # Your API token
)

# Chat-based messages input
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Count to 10"},
]

# Call the chat completion API with streaming enabled
output = client.chat.completions.create(
    messages=messages,
    stream=True,  # Enable streaming
    max_tokens=1024,
)

# Read and print the streamed responses
for chunk in output:
    if "generated_text" in chunk:
        print(chunk["generated_text"], end='', flush=True)

Key Features in the Example

1. InferenceClient:
   • Connects to the Hugging Face Inference API, handling token-based authentication and model endpoints.
2. chat.completions.create:
   • Allows you to send chat-style input (like system/user messages) for models that support chat interfaces.
3. Streaming:
   • By setting stream=True, the output is received incrementally, reducing wait time for long responses.

Key Parameters

• base_url: If running a local Hugging Face server, you can specify a local endpoint like http://127.0.0.1:8080. For the Hugging Face cloud, set model="your_model_name" and omit base_url.
• stream=True: Enables streaming output.
• max_tokens: Specifies the maximum number of tokens to generate.

Required Library Version

Ensure you have the latest version of huggingface_hub, as the chat.completions.create method and its streaming functionality might not be available in older versions. Update it with:

pip install --upgrade huggingface_hub

Compatibility

• The chat.completions.create method is typically for chat-based LLMs. Ensure the model you are using (meta-llama/Llama-3.3-70B-Instruct) supports this type of interaction.
• If you’re running the API on a local server, verify the base URL and ensure the server is properly configured for streaming.

Let me know if you face any specific issues with this approach!

You

can i do this with c# instead?

ChatGPT

Yes, you can perform Hugging Face inference, including streaming, using C#. You can use the HttpClient class in .NET to send HTTP requests and process the streamed response.

Here’s how you can implement the functionality in C#:

---

Example: Streaming Hugging Face Inference in C#

using System;
using System.IO;
using System.Net.Http;
using System.Text;
using System.Text.Json;
using System.Threading.Tasks;

class Program
{
    static async Task Main(string[] args)
    {
        // Define Hugging Face API details
        string apiUrl = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.3-70B-Instruct";
        string apiToken = "your_huggingface_api_token";

        // Input messages for chat
        var requestBody = new
        {
            inputs = new[]
            {
                new { role = "system", content = "You are a helpful assistant." },
                new { role = "user", content = "Count to 10" }
            },
            options = new
            {
                wait_for_model = true
            }
        };

        // Serialize the request body to JSON
        string jsonRequestBody = JsonSerializer.Serialize(requestBody);

        // Create HttpClient with Authorization header
        using var httpClient = new HttpClient();
        httpClient.DefaultRequestHeaders.Add("Authorization", $"Bearer {apiToken}");

        // Set up the HTTP request
        var request = new HttpRequestMessage(HttpMethod.Post, apiUrl)
        {
            Content = new StringContent(jsonRequestBody, Encoding.UTF8, "application/json")
        };

        // Enable streaming
        request.Headers.TransferEncodingChunked = true;

        // Send the request and process the response
        try
        {
            using var response = await httpClient.SendAsync(request, HttpCompletionOption.ResponseHeadersRead);
            if (response.IsSuccessStatusCode)
            {
                using var stream = await response.Content.ReadAsStreamAsync();
                using var reader = new StreamReader(stream);

                Console.WriteLine("Response:");
                while (!reader.EndOfStream)
                {
                    var line = await reader.ReadLineAsync();
                    if (!string.IsNullOrWhiteSpace(line))
                    {
                        try
                        {
                            // Parse the JSON response and extract the text
                            var chunk = JsonSerializer.Deserialize<JsonElement>(line);
                            if (chunk.TryGetProperty("generated_text", out var text))
                            {
                                Console.Write(text.GetString());
                            }
                        }
                        catch (Exception ex)
                        {
                            Console.WriteLine($"Error parsing line: {ex.Message}");
                        }
                    }
                }
            }
            else
            {
                Console.WriteLine($"Error: {response.StatusCode} - {await response.Content.ReadAsStringAsync()}");
            }
        }
        catch (Exception ex)
        {
            Console.WriteLine($"Request failed: {ex.Message}");
        }
    }
}

---

Key Features of the Code:

1. Streaming Response:
   • The HttpCompletionOption.ResponseHeadersRead ensures that the response is processed as soon as headers are received.
   • The StreamReader reads chunks of the response incrementally.
2. Deserialization:
   • Each line from the stream is deserialized using JsonSerializer to extract generated_text.
3. Error Handling:
   • Handles malformed JSON or unexpected response structures.

---

Dependencies:

1. .NET Core:
   • Ensure you are using .NET Core 3.1 or later to utilize asynchronous HTTP handling effectively.
2. System.Text.Json:
   • Use System.Text.Json for lightweight JSON serialization and deserialization.
   • For older versions of .NET, you can use Newtonsoft.Json instead:

     var chunk = JsonConvert.DeserializeObject<dynamic>(line);
     Console.Write(chunk?.generated_text);

---

Run the Program:

1. Replace your_huggingface_api_token with your Hugging Face API token.
2. Make sure the meta-llama/Llama-3.3-70B-Instruct model supports the chat completion API.
3. Compile and run the C# program.

---

Notes:

• If running a local Hugging Face server, update the apiUrl to your server’s endpoint (e.g., http://127.0.0.1:8080).
• For better performance, consider using HttpClient as a singleton in a real application.

This approach ensures you can perform streaming inference from Hugging Face efficiently in C#. Let me know if you encounter any issues or need further clarification!