diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
index f1de9719..fa9ce254 100644
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -17,8 +17,6 @@
     title: Consuming TGI
   - local: basic_tutorials/request_parameters
     title: Request Parameters
-  - local: basic_tutorials/stream
-    title: Stream Responses
   - local: basic_tutorials/preparing_model
     title: Preparing Model for Serving
   - local: basic_tutorials/gated_model_access
diff --git a/docs/source/basic_tutorials/stream.md b/docs/source/basic_tutorials/stream.md
deleted file mode 100644
index cc2f1a5e..00000000
--- a/docs/source/basic_tutorials/stream.md
+++ /dev/null
@@ -1,92 +0,0 @@
-# Stream responses in Javascript and Python
-
-Requesting and generating text with LLMs can be a time-consuming and iterative process. A great way to improve the user experience is streaming tokens to the user as they are generated. Below are two examples of how to stream tokens using Python and JavaScript. For Python, we are going to use the **[huggingface_hub library](https://huggingface.co/docs/huggingface_hub/index), and for JavaScript, the [HuggingFace.js library](https://huggingface.co/docs/huggingface.js/main/en/index)
-
-## Streaming requests with Python
-
-First, you need to install the `huggingface_hub` library:
-
-`pip install -U huggingface_hub`
-
-We can create a `InferenceClient` providing our endpoint URL and credential alongside the hyperparameters we want to use
-
-```python
-from huggingface_hub import InferenceClient
-
-# HF Inference Endpoints parameter
-endpoint_url = "https://YOUR_ENDPOINT.endpoints.huggingface.cloud"
-hf_token = "hf_YOUR_TOKEN"
-
-# Streaming Client
-client = InferenceClient(endpoint_url, token=hf_token)
-
-# generation parameter
-gen_kwargs = dict(
-    max_new_tokens=512,
-    top_k=30,
-    top_p=0.9,
-    temperature=0.2,
-    repetition_penalty=1.02,
-    stop_sequences=["\nUser:", "<|endoftext|>", "</s>"],
-)
-# prompt
-prompt = "What can you do in Nuremberg, Germany? Give me 3 Tips"
-
-stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs)
-
-# yield each generated token
-for r in stream:
-    # skip special tokens
-    if r.token.special:
-        continue
-    # stop if we encounter a stop sequence
-    if r.token.text in gen_kwargs["stop_sequences"]:
-        break
-    # yield the generated token
-    print(r.token.text, end = "")
-    # yield r.token.text
-```
-
-Replace the `print` command with the `yield` or with a function you want to stream the tokens to.
-
-## Streaming requests with JavaScript
-
-First, you need to install the `@huggingface/inference` library.
-
-`npm install @huggingface/inference`
-
-We can create a `HfInferenceEndpoint` providing our endpoint URL and credential alongside the hyperparameter we want to use.
-
-```js
-import { HfInferenceEndpoint } from '@huggingface/inference'
-
-const hf = new HfInferenceEndpoint('https://YOUR_ENDPOINT.endpoints.huggingface.cloud', 'hf_YOUR_TOKEN')
-
-//generation parameter
-const gen_kwargs = {
-  max_new_tokens: 512,
-  top_k: 30,
-  top_p: 0.9,
-  temperature: 0.2,
-  repetition_penalty: 1.02,
-  stop_sequences: ['\nUser:', '<|endoftext|>', '</s>'],
-}
-// prompt
-const prompt = 'What can you do in Nuremberg, Germany? Give me 3 Tips'
-
-const stream = hf.textGenerationStream({ inputs: prompt, parameters: gen_kwargs })
-for await (const r of stream) {
-  // # skip special tokens
-  if (r.token.special) {
-    continue
-  }
-  // stop if we encounter a stop sequence
-  if (gen_kwargs['stop_sequences'].includes(r.token.text)) {
-    break
-  }
-  // yield the generated token
-  process.stdout.write(r.token.text)
-}
-```
-
-Replace the `process.stdout` call with the `yield` or with a function you want to stream the tokens to.
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