diff --git a/docs/my-website/docs/simple_proxy.md b/docs/my-website/docs/simple_proxy.md
index a0889ba2a3f..0e7d5c35504 100644
--- a/docs/my-website/docs/simple_proxy.md
+++ b/docs/my-website/docs/simple_proxy.md
@@ -549,7 +549,7 @@ general_settings:
   master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234)
 ```
 
-### Quick Start - Config 
+### Multiple Models - Quick Start
 
 Here's how you can use multiple llms with one proxy `config.yaml`. 
 
@@ -601,6 +601,8 @@ curl --location 'http://0.0.0.0:8000/chat/completions' \
 ```
 
 
+
+
 ### Save Model-specific params (API Base, API Keys, Temperature, Headers etc.)
 You can use the config to save model-specific information like api_base, api_key, temperature, max_tokens, etc. 
 
@@ -654,6 +656,31 @@ model_list:
         model: ollama/llama2
 ```
 
+### Multiple Instances of 1 model
+
+If you have multiple instances of the same model,
+
+in the `config.yaml` just add all of them with the same 'model_name', and the proxy will handle routing requests (using LiteLLM's Router). 
+
+In the config below requests with `model=zephyr-beta` will be routed across multiple instances of `HuggingFaceH4/zephyr-7b-beta`
+
+```yaml
+model_list:
+  - model_name: zephyr-beta
+    litellm_params:
+        model: huggingface/HuggingFaceH4/zephyr-7b-beta
+        api_base: http://0.0.0.0:8001
+  - model_name: zephyr-beta
+    litellm_params:
+        model: huggingface/HuggingFaceH4/zephyr-7b-beta
+        api_base: http://0.0.0.0:8002
+  - model_name: zephyr-beta
+    litellm_params:
+        model: huggingface/HuggingFaceH4/zephyr-7b-beta
+        api_base: http://0.0.0.0:8003
+```
+
+
 ### Set Custom Prompt Templates
 
 LiteLLM by default checks if a model has a [prompt template and applies it](./completion/prompt_formatting.md) (e.g. if a huggingface model has a saved chat template in it's tokenizer_config.json). However, you can also set a custom prompt template on your proxy in the `config.yaml`: 
@@ -867,222 +894,3 @@ Expected output on Langfuse
      ```
 
 
-
-<!-- 
-## Tutorials (Chat-UI, NeMO-Guardrails, PromptTools, Phoenix ArizeAI, Langchain, ragas, LlamaIndex, etc.)
-
-**Start server:**
-```shell
-`docker run -e PORT=8000 -p 8000:8000 ghcr.io/berriai/litellm:latest`
-```
-The server is now live on http://0.0.0.0:8000
-
-<Tabs>
-<TabItem value="chat-ui" label="Chat UI">
-
-Here's the `docker-compose.yml` for running LiteLLM Server with Mckay Wrigley's Chat-UI: 
-```yaml
-version: '3'
-services:
-  container1:
-    image: ghcr.io/berriai/litellm:latest
-    ports:
-      - '8000:8000'
-    environment:
-      - PORT=8000
-      - OPENAI_API_KEY=<your-openai-key>
-
-  container2:
-    image: ghcr.io/mckaywrigley/chatbot-ui:main
-    ports:
-      - '3000:3000'
-    environment:
-      - OPENAI_API_KEY=my-fake-key
-      - OPENAI_API_HOST=http://container1:8000
-```
-
-Run this via: 
-```shell
-docker-compose up
-```
-</TabItem>
-<TabItem value="nemo-guardrails" label="NeMO-Guardrails">
-
-#### Adding NeMO-Guardrails to Bedrock 
-
-1. Start server
-```shell
-`docker run -e PORT=8000 -e AWS_ACCESS_KEY_ID=<your-aws-access-key> -e AWS_SECRET_ACCESS_KEY=<your-aws-secret-key> -p 8000:8000 ghcr.io/berriai/litellm:latest`
-```
-
-2. Install dependencies
-```shell
-pip install nemoguardrails langchain
-```
-
-3. Run script
-```python
-import openai
-from langchain.chat_models import ChatOpenAI
-
-llm = ChatOpenAI(model_name="bedrock/anthropic.claude-v2", openai_api_base="http://0.0.0.0:8000", openai_api_key="my-fake-key")
-
-from nemoguardrails import LLMRails, RailsConfig
-
-config = RailsConfig.from_path("./config.yml")
-app = LLMRails(config, llm=llm)
-
-new_message = app.generate(messages=[{
-    "role": "user",
-    "content": "Hello! What can you do for me?"
-}])
-``` 
-</TabItem>
-<TabItem value="prompttools" label="PromptTools">
-
-Use [PromptTools](https://github.com/hegelai/prompttools) for evaluating different LLMs
-
-1. Start server
-```shell
-`docker run -e PORT=8000 -p 8000:8000 ghcr.io/berriai/litellm:latest`
-```
-
-2. Install dependencies 
-```python 
-pip install prompttools
-```
-
-3. Run script 
-```python 
-import os
-os.environ['DEBUG']=""  # Set this to "" to call OpenAI's API
-os.environ['AZURE_OPENAI_KEY'] = "my-api-key"  # Insert your key here
-
-from typing import Dict, List
-from prompttools.experiment import OpenAIChatExperiment
-
-models = ["gpt-3.5-turbo", "gpt-3.5-turbo-0613"]
-messages = [
-    [
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "Who was the first president?"},
-    ]
-]
-temperatures = [0.0, 1.0]
-# You can add more parameters that you'd like to test here.
-
-experiment = OpenAIChatExperiment(models, messages, temperature=temperatures, azure_openai_service_configs={"AZURE_OPENAI_ENDPOINT": "http://0.0.0.0:8000", "API_TYPE": "azure", "API_VERSION": "2023-05-15"})
-```
-</TabItem>
-<TabItem value="phoenix-arizeai" label="ArizeAI">
-
-Use [Arize AI's LLM Evals](https://github.com/Arize-ai/phoenix#llm-evals) to evaluate different LLMs
-
-1. Start server
-```shell
-`docker run -e PORT=8000 -p 8000:8000 ghcr.io/berriai/litellm:latest`
-```
-
-2. Use this LLM Evals Quickstart colab
-[![Open in Colab](https://img.shields.io/static/v1?message=Open%20in%20Colab&logo=googlecolab&labelColor=grey&color=blue&logoColor=orange&label=%20)](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/evals/evaluate_relevance_classifications.ipynb)
-
-3. Call the model
-```python
-import openai 
-
-## SET API BASE + PROVIDER KEY
-openai.api_base = "http://0.0.0.0:8000
-openai.api_key = "my-anthropic-key"
-
-## CALL MODEL 
-model = OpenAIModel(
-    model_name="claude-2",
-    temperature=0.0,
-)
-```
-</TabItem>
-<TabItem value="langchain" label="Langchain">
-
-```python
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    AIMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-from langchain.schema import AIMessage, HumanMessage, SystemMessage
-
-chat = ChatOpenAI(model_name="claude-instant-1", openai_api_key="my-anthropic-key", openai_api_base="http://0.0.0.0:8000")
-
-messages = [
-    SystemMessage(
-        content="You are a helpful assistant that translates English to French."
-    ),
-    HumanMessage(
-        content="Translate this sentence from English to French. I love programming."
-    ),
-]
-chat(messages)
-```
-</TabItem>
-<TabItem value="ragas" label="ragas">
-
-#### Evaluating with Open-Source LLMs 
-
-Use [Ragas](https://github.com/explodinggradients/ragas/blob/7b123533df80d0ada33a2cb2dd2fdedf36807f33/docs/howtos/customisations/llms.ipynb#L247) to evaluate LLMs for RAG-scenarios.
-```python
-from langchain.chat_models import ChatOpenAI
-
-inference_server_url = "http://localhost:8080/v1"
-
-chat = ChatOpenAI(
-    model="bedrock/anthropic.claude-v2",
-    openai_api_key="no-key",
-    openai_api_base=inference_server_url,
-    max_tokens=5,
-    temperature=0,
-)
-
-from ragas.metrics import (
-    context_precision,
-    answer_relevancy,
-    faithfulness,
-    context_recall,
-)
-from ragas.metrics.critique import harmfulness
-
-# change the LLM
-
-faithfulness.llm.langchain_llm = chat
-answer_relevancy.llm.langchain_llm = chat
-context_precision.llm.langchain_llm = chat
-context_recall.llm.langchain_llm = chat
-harmfulness.llm.langchain_llm = chat
-
-
-# evaluate
-from ragas import evaluate
-
-result = evaluate(
-    fiqa_eval["baseline"].select(range(5)),  # showing only 5 for demonstration
-    metrics=[faithfulness],
-)
-
-result
-```
-</TabItem>
-<TabItem value="llama_index" label="Llama Index">
-
-```python
-!pip install llama-index
-```
-```python
-from llama_index.llms import OpenAI
-
-response = OpenAI(model="claude-2", api_key="your-anthropic-key",api_base="http://0.0.0.0:8000").complete('Paul Graham is ')
-print(response)
-```
-</TabItem>
-</Tabs> -->
-
diff --git a/litellm/.env.template b/litellm/.env.template
deleted file mode 100644
index 93495decbb9..00000000000
--- a/litellm/.env.template
+++ /dev/null
@@ -1,19 +0,0 @@
-### KEYS ###
-# HUGGINGFACE_API_KEY="" # Uncomment to save your Hugging Face API key
-# OPENAI_API_KEY="" # Uncomment to save your OpenAI API Key
-# TOGETHERAI_API_KEY="" # Uncomment to save your TogetherAI API key
-# NLP_CLOUD_API_KEY="" # Uncomment to save your NLP Cloud API key
-# ANTHROPIC_API_KEY="" # Uncomment to save your Anthropic API key
-
-### MODEL CUSTOM PROMPT TEMPLATE ###
-# MODEL_SYSTEM_MESSAGE_START_TOKEN = "<|prompter|>" # This does not need to be a token, can be any string
-# MODEL_SYSTEM_MESSAGE_END_TOKEN = "<|endoftext|>" # This does not need to be a token, can be any string
-
-# MODEL_USER_MESSAGE_START_TOKEN = "<|prompter|>" # This does not need to be a token, can be any string
-# MODEL_USER_MESSAGE_END_TOKEN = "<|endoftext|>" # Applies only to user messages. Can be any string.
-
-# MODEL_ASSISTANT_MESSAGE_START_TOKEN = "<|prompter|>" # Applies only to assistant messages. Can be any string.
-# MODEL_ASSISTANT_MESSAGE_END_TOKEN = "<|endoftext|>" # Applies only to system messages. Can be any string.
-
-# MODEL_PRE_PROMPT = "You are a good bot" # Applied at the start of the prompt
-# MODEL_POST_PROMPT = "Now answer as best as you can" # Applied at the end of the prompt
\ No newline at end of file